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Clarke R, Halsey J, Emberson J, Collins R, Leon DA, Kivimäki M, Shipley MJ. Lifetime and 10-year risks of cardiovascular mortality in relation to risk factors in middle and old age: 50-year follow-up of the Whitehall study of London Civil Servants. Public Health 2024; 230:73-80. [PMID: 38513300 DOI: 10.1016/j.puhe.2024.02.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 01/26/2024] [Accepted: 02/20/2024] [Indexed: 03/23/2024]
Abstract
BACKGROUND Cardiovascular disease (CVD)-related mortality has declined substantially in the United Kingdom (UK) in recent decades, but the continued relevance of conventional risk factors for prediction of CVD mortality throughout the life-course is uncertain. We compared the 10-year risks and lifetime risks of CVD mortality associated with conventional risk factors recorded in middle and old age. METHODS The Whitehall study was a prospective study of 19,019 male London civil servants (mean age 52 years) when enrolled in 1967-1970 and followed-up for 50 years for cause-specific mortality. In 1997, 7044 (83%) survivors (mean age 77 years) were re-surveyed. The 10-year and lifetime risks of CVD mortality were estimated by levels of CVD risk factors recorded in middle-age and old-age, respectively. RESULTS By July 2020, 97% had died (22%, 51% and 80% before age 70, 80 and 90 years, respectively) and 7944 of 17,673 deaths (45%) were from CVD. The 10-year and lifetime risks of CVD death increased linearly with higher levels of CVD risk factors recorded in middle-age and in old-age. Individuals in the top versus bottom 5% of CVD risk scores in middle age had a 10.3% (95% CI:7.2-13.4) vs 0.6% (0.1-1.2) 10-year risk of CVD mortality, a 61.4% (59.4-65.3) vs 31.3% (24.1-34.5) lifetime risk of CVD mortality and a 12-year difference in life expectancy from age 50 years. The corresponding differences using a CVD risk score in old-age were 11.0% (4.4-17.5) vs 0.8% (0.0-2.2) for 10-year risk and 42.1% (28.2-50.0) vs 30.3% (6.0-38.0) for lifetime risk of CVD mortality and a 6-year difference in life expectancy from age 70 years. CONCLUSIONS Conventional risk factors remained highly predictive of CVD mortality and life expectancy through the life-course. The findings highlight the relevance of estimation of both lifetime risks of CVD and 10-year risks of CVD for primary prevention of CVD.
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Affiliation(s)
- R Clarke
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
| | - J Halsey
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - J Emberson
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK; MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - R Collins
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - D A Leon
- London School of Hygiene and Tropical Medicine, London, UK
| | - M Kivimäki
- University College London Brain Sciences, University College London, London, UK
| | - M J Shipley
- Department of Epidemiology and Public Health, University College London Medical School, London, UK
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2
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Harbison J, Collins R, McCormack J, Brych O, Fallon C, Cassidy T. Response to: Impact of prehospital care and door-to-computed tomography scan time on stroke outcomes. QJM 2024; 117:309-310. [PMID: 38229247 DOI: 10.1093/qjmed/hcae003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Indexed: 01/18/2024] Open
Affiliation(s)
- J Harbison
- Irish National Audit of Stroke, National Office of Clinical Audit, Dublin 2, Ireland
- Department of Medical Gerontology, Trinity College Dublin, Dublin 2, Ireland
| | - R Collins
- Irish National Audit of Stroke, National Office of Clinical Audit, Dublin 2, Ireland
- Department of Medical Gerontology, Trinity College Dublin, Dublin 2, Ireland
- National Clinical Programme for Stroke, Health Service Executive, Dublin 8, Ireland
| | - J McCormack
- Irish National Audit of Stroke, National Office of Clinical Audit, Dublin 2, Ireland
| | - O Brych
- Irish National Audit of Stroke, National Office of Clinical Audit, Dublin 2, Ireland
| | - C Fallon
- Irish National Audit of Stroke, National Office of Clinical Audit, Dublin 2, Ireland
- Department of Geriatric Medicine, Midland Regional Hospital, Mullingar, County Westmeath, Ireland
| | - T Cassidy
- Irish National Audit of Stroke, National Office of Clinical Audit, Dublin 2, Ireland
- Department of Geriatric Medicine, St Vincent's University Hospital, Dublin 4, Ireland
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3
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Rosenberg E, Andersen TI, Samajdar R, Petukhov A, Hoke JC, Abanin D, Bengtsson A, Drozdov IK, Erickson C, Klimov PV, Mi X, Morvan A, Neeley M, Neill C, Acharya R, Allen R, Anderson K, Ansmann M, Arute F, Arya K, Asfaw A, Atalaya J, Bardin JC, Bilmes A, Bortoli G, Bourassa A, Bovaird J, Brill L, Broughton M, Buckley BB, Buell DA, Burger T, Burkett B, Bushnell N, Campero J, Chang HS, Chen Z, Chiaro B, Chik D, Cogan J, Collins R, Conner P, Courtney W, Crook AL, Curtin B, Debroy DM, Barba ADT, Demura S, Di Paolo A, Dunsworth A, Earle C, Faoro L, Farhi E, Fatemi R, Ferreira VS, Burgos LF, Forati E, Fowler AG, Foxen B, Garcia G, Genois É, Giang W, Gidney C, Gilboa D, Giustina M, Gosula R, Dau AG, Gross JA, Habegger S, Hamilton MC, Hansen M, Harrigan MP, Harrington SD, Heu P, Hill G, Hoffmann MR, Hong S, Huang T, Huff A, Huggins WJ, Ioffe LB, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Juhas P, Kafri D, Khattar T, Khezri M, Kieferová M, Kim S, Kitaev A, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Laws L, Lee J, Lee KW, Lensky YD, Lester BJ, Lill AT, Liu W, Locharla A, Mandrà S, Martin O, Martin S, McClean JR, McEwen M, Meeks S, Miao KC, Mieszala A, Montazeri S, Movassagh R, Mruczkiewicz W, Nersisyan A, Newman M, Ng JH, Nguyen A, Nguyen M, Niu MY, O'Brien TE, Omonije S, Opremcak A, Potter R, Pryadko LP, Quintana C, Rhodes DM, Rocque C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schurkus HF, Schuster C, Shearn MJ, Shorter A, Shutty N, Shvarts V, Sivak V, Skruzny J, Smith WC, Somma RD, Sterling G, Strain D, Szalay M, Thor D, Torres A, Vidal G, Villalonga B, Heidweiller CV, White T, Woo BWK, Xing C, Yao ZJ, Yeh P, Yoo J, Young G, Zalcman A, Zhang Y, Zhu N, Zobrist N, Neven H, Babbush R, Bacon D, Boixo S, Hilton J, Lucero E, Megrant A, Kelly J, Chen Y, Smelyanskiy V, Khemani V, Gopalakrishnan S, Prosen T, Roushan P. Dynamics of magnetization at infinite temperature in a Heisenberg spin chain. Science 2024; 384:48-53. [PMID: 38574139 DOI: 10.1126/science.adi7877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 03/01/2024] [Indexed: 04/06/2024]
Abstract
Understanding universal aspects of quantum dynamics is an unresolved problem in statistical mechanics. In particular, the spin dynamics of the one-dimensional Heisenberg model were conjectured as to belong to the Kardar-Parisi-Zhang (KPZ) universality class based on the scaling of the infinite-temperature spin-spin correlation function. In a chain of 46 superconducting qubits, we studied the probability distribution of the magnetization transferred across the chain's center, [Formula: see text]. The first two moments of [Formula: see text] show superdiffusive behavior, a hallmark of KPZ universality. However, the third and fourth moments ruled out the KPZ conjecture and allow for evaluating other theories. Our results highlight the importance of studying higher moments in determining dynamic universality classes and provide insights into universal behavior in quantum systems.
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Affiliation(s)
- E Rosenberg
- Google Research, Mountain View, CA, USA
- Department of Physics, Cornell University, Ithaca, NY, USA
| | | | - R Samajdar
- Department of Physics, Princeton University, Princeton, NJ, USA
- Princeton Center for Theoretical Science, Princeton University, Princeton, NJ, USA
| | | | - J C Hoke
- Department of Physics, Stanford University, Stanford, CA, USA
| | - D Abanin
- Google Research, Mountain View, CA, USA
| | | | - I K Drozdov
- Google Research, Mountain View, CA, USA
- Department of Physics, University of Connecticut, Storrs, CT, USA
| | | | | | - X Mi
- Google Research, Mountain View, CA, USA
| | - A Morvan
- Google Research, Mountain View, CA, USA
| | - M Neeley
- Google Research, Mountain View, CA, USA
| | - C Neill
- Google Research, Mountain View, CA, USA
| | - R Acharya
- Google Research, Mountain View, CA, USA
| | - R Allen
- Google Research, Mountain View, CA, USA
| | | | - M Ansmann
- Google Research, Mountain View, CA, USA
| | - F Arute
- Google Research, Mountain View, CA, USA
| | - K Arya
- Google Research, Mountain View, CA, USA
| | - A Asfaw
- Google Research, Mountain View, CA, USA
| | - J Atalaya
- Google Research, Mountain View, CA, USA
| | - J C Bardin
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | - A Bilmes
- Google Research, Mountain View, CA, USA
| | - G Bortoli
- Google Research, Mountain View, CA, USA
| | | | - J Bovaird
- Google Research, Mountain View, CA, USA
| | - L Brill
- Google Research, Mountain View, CA, USA
| | | | | | - D A Buell
- Google Research, Mountain View, CA, USA
| | - T Burger
- Google Research, Mountain View, CA, USA
| | - B Burkett
- Google Research, Mountain View, CA, USA
| | | | - J Campero
- Google Research, Mountain View, CA, USA
| | - H-S Chang
- Google Research, Mountain View, CA, USA
| | - Z Chen
- Google Research, Mountain View, CA, USA
| | - B Chiaro
- Google Research, Mountain View, CA, USA
| | - D Chik
- Google Research, Mountain View, CA, USA
| | - J Cogan
- Google Research, Mountain View, CA, USA
| | - R Collins
- Google Research, Mountain View, CA, USA
| | - P Conner
- Google Research, Mountain View, CA, USA
| | | | - A L Crook
- Google Research, Mountain View, CA, USA
| | - B Curtin
- Google Research, Mountain View, CA, USA
| | | | | | - S Demura
- Google Research, Mountain View, CA, USA
| | | | | | - C Earle
- Google Research, Mountain View, CA, USA
| | - L Faoro
- Google Research, Mountain View, CA, USA
| | - E Farhi
- Google Research, Mountain View, CA, USA
| | - R Fatemi
- Google Research, Mountain View, CA, USA
| | | | | | - E Forati
- Google Research, Mountain View, CA, USA
| | | | - B Foxen
- Google Research, Mountain View, CA, USA
| | - G Garcia
- Google Research, Mountain View, CA, USA
| | - É Genois
- Google Research, Mountain View, CA, USA
| | - W Giang
- Google Research, Mountain View, CA, USA
| | - C Gidney
- Google Research, Mountain View, CA, USA
| | - D Gilboa
- Google Research, Mountain View, CA, USA
| | | | - R Gosula
- Google Research, Mountain View, CA, USA
| | | | - J A Gross
- Google Research, Mountain View, CA, USA
| | | | - M C Hamilton
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
| | - M Hansen
- Google Research, Mountain View, CA, USA
| | | | | | - P Heu
- Google Research, Mountain View, CA, USA
| | - G Hill
- Google Research, Mountain View, CA, USA
| | | | - S Hong
- Google Research, Mountain View, CA, USA
| | - T Huang
- Google Research, Mountain View, CA, USA
| | - A Huff
- Google Research, Mountain View, CA, USA
| | | | - L B Ioffe
- Google Research, Mountain View, CA, USA
| | | | - J Iveland
- Google Research, Mountain View, CA, USA
| | - E Jeffrey
- Google Research, Mountain View, CA, USA
| | - Z Jiang
- Google Research, Mountain View, CA, USA
| | - C Jones
- Google Research, Mountain View, CA, USA
| | - P Juhas
- Google Research, Mountain View, CA, USA
| | - D Kafri
- Google Research, Mountain View, CA, USA
| | - T Khattar
- Google Research, Mountain View, CA, USA
| | - M Khezri
- Google Research, Mountain View, CA, USA
| | - M Kieferová
- Google Research, Mountain View, CA, USA
- QSI, Faculty of Engineering & Information Technology, University of Technology Sydney, Ultimo, NSW, Australia
| | - S Kim
- Google Research, Mountain View, CA, USA
| | - A Kitaev
- Google Research, Mountain View, CA, USA
| | - A R Klots
- Google Research, Mountain View, CA, USA
| | - A N Korotkov
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA
| | | | | | | | - P Laptev
- Google Research, Mountain View, CA, USA
| | - K-M Lau
- Google Research, Mountain View, CA, USA
| | - L Laws
- Google Research, Mountain View, CA, USA
| | - J Lee
- Google Research, Mountain View, CA, USA
- Department of Chemistry, Columbia University, New York, NY, USA
| | - K W Lee
- Google Research, Mountain View, CA, USA
| | | | | | - A T Lill
- Google Research, Mountain View, CA, USA
| | - W Liu
- Google Research, Mountain View, CA, USA
| | | | - S Mandrà
- Google Research, Mountain View, CA, USA
| | - O Martin
- Google Research, Mountain View, CA, USA
| | - S Martin
- Google Research, Mountain View, CA, USA
| | | | - M McEwen
- Google Research, Mountain View, CA, USA
| | - S Meeks
- Google Research, Mountain View, CA, USA
| | - K C Miao
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - M Newman
- Google Research, Mountain View, CA, USA
| | - J H Ng
- Google Research, Mountain View, CA, USA
| | - A Nguyen
- Google Research, Mountain View, CA, USA
| | - M Nguyen
- Google Research, Mountain View, CA, USA
| | - M Y Niu
- Google Research, Mountain View, CA, USA
| | | | - S Omonije
- Google Research, Mountain View, CA, USA
| | | | - R Potter
- Google Research, Mountain View, CA, USA
| | - L P Pryadko
- Department of Physics and Astronomy, University of California, Riverside, CA, USA
| | | | | | - C Rocque
- Google Research, Mountain View, CA, USA
| | - N C Rubin
- Google Research, Mountain View, CA, USA
| | - N Saei
- Google Research, Mountain View, CA, USA
| | - D Sank
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - A Shorter
- Google Research, Mountain View, CA, USA
| | - N Shutty
- Google Research, Mountain View, CA, USA
| | - V Shvarts
- Google Research, Mountain View, CA, USA
| | - V Sivak
- Google Research, Mountain View, CA, USA
| | - J Skruzny
- Google Research, Mountain View, CA, USA
| | | | - R D Somma
- Google Research, Mountain View, CA, USA
| | | | - D Strain
- Google Research, Mountain View, CA, USA
| | - M Szalay
- Google Research, Mountain View, CA, USA
| | - D Thor
- Google Research, Mountain View, CA, USA
| | - A Torres
- Google Research, Mountain View, CA, USA
| | - G Vidal
- Google Research, Mountain View, CA, USA
| | | | | | - T White
- Google Research, Mountain View, CA, USA
| | - B W K Woo
- Google Research, Mountain View, CA, USA
| | - C Xing
- Google Research, Mountain View, CA, USA
| | | | - P Yeh
- Google Research, Mountain View, CA, USA
| | - J Yoo
- Google Research, Mountain View, CA, USA
| | - G Young
- Google Research, Mountain View, CA, USA
| | - A Zalcman
- Google Research, Mountain View, CA, USA
| | - Y Zhang
- Google Research, Mountain View, CA, USA
| | - N Zhu
- Google Research, Mountain View, CA, USA
| | - N Zobrist
- Google Research, Mountain View, CA, USA
| | - H Neven
- Google Research, Mountain View, CA, USA
| | - R Babbush
- Google Research, Mountain View, CA, USA
| | - D Bacon
- Google Research, Mountain View, CA, USA
| | - S Boixo
- Google Research, Mountain View, CA, USA
| | - J Hilton
- Google Research, Mountain View, CA, USA
| | - E Lucero
- Google Research, Mountain View, CA, USA
| | - A Megrant
- Google Research, Mountain View, CA, USA
| | - J Kelly
- Google Research, Mountain View, CA, USA
| | - Y Chen
- Google Research, Mountain View, CA, USA
| | | | - V Khemani
- Department of Physics, Stanford University, Stanford, CA, USA
| | | | - T Prosen
- Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia
| | - P Roushan
- Google Research, Mountain View, CA, USA
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4
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Mi X, Michailidis AA, Shabani S, Miao KC, Klimov PV, Lloyd J, Rosenberg E, Acharya R, Aleiner I, Andersen TI, Ansmann M, Arute F, Arya K, Asfaw A, Atalaya J, Bardin JC, Bengtsson A, Bortoli G, Bourassa A, Bovaird J, Brill L, Broughton M, Buckley BB, Buell DA, Burger T, Burkett B, Bushnell N, Chen Z, Chiaro B, Chik D, Chou C, Cogan J, Collins R, Conner P, Courtney W, Crook AL, Curtin B, Dau AG, Debroy DM, Del Toro Barba A, Demura S, Di Paolo A, Drozdov IK, Dunsworth A, Erickson C, Faoro L, Farhi E, Fatemi R, Ferreira VS, Burgos LF, Forati E, Fowler AG, Foxen B, Genois É, Giang W, Gidney C, Gilboa D, Giustina M, Gosula R, Gross JA, Habegger S, Hamilton MC, Hansen M, Harrigan MP, Harrington SD, Heu P, Hoffmann MR, Hong S, Huang T, Huff A, Huggins WJ, Ioffe LB, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Juhas P, Kafri D, Kechedzhi K, Khattar T, Khezri M, Kieferová M, Kim S, Kitaev A, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Laws L, Lee J, Lee KW, Lensky YD, Lester BJ, Lill AT, Liu W, Locharla A, Malone FD, Martin O, McClean JR, McEwen M, Mieszala A, Montazeri S, Morvan A, Movassagh R, Mruczkiewicz W, Neeley M, Neill C, Nersisyan A, Newman M, Ng JH, Nguyen A, Nguyen M, Niu MY, O'Brien TE, Opremcak A, Petukhov A, Potter R, Pryadko LP, Quintana C, Rocque C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schurkus HF, Schuster C, Shearn MJ, Shorter A, Shutty N, Shvarts V, Skruzny J, Smith WC, Somma R, Sterling G, Strain D, Szalay M, Torres A, Vidal G, Villalonga B, Heidweiller CV, White T, Woo BWK, Xing C, Yao ZJ, Yeh P, Yoo J, Young G, Zalcman A, Zhang Y, Zhu N, Zobrist N, Neven H, Babbush R, Bacon D, Boixo S, Hilton J, Lucero E, Megrant A, Kelly J, Chen Y, Roushan P, Smelyanskiy V, Abanin DA. Stable quantum-correlated many-body states through engineered dissipation. Science 2024; 383:1332-1337. [PMID: 38513021 DOI: 10.1126/science.adh9932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 02/13/2024] [Indexed: 03/23/2024]
Abstract
Engineered dissipative reservoirs have the potential to steer many-body quantum systems toward correlated steady states useful for quantum simulation of high-temperature superconductivity or quantum magnetism. Using up to 49 superconducting qubits, we prepared low-energy states of the transverse-field Ising model through coupling to dissipative auxiliary qubits. In one dimension, we observed long-range quantum correlations and a ground-state fidelity of 0.86 for 18 qubits at the critical point. In two dimensions, we found mutual information that extends beyond nearest neighbors. Lastly, by coupling the system to auxiliaries emulating reservoirs with different chemical potentials, we explored transport in the quantum Heisenberg model. Our results establish engineered dissipation as a scalable alternative to unitary evolution for preparing entangled many-body states on noisy quantum processors.
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Affiliation(s)
- X Mi
- Google Research, Mountain View, CA, USA
| | - A A Michailidis
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
| | - S Shabani
- Google Research, Mountain View, CA, USA
| | - K C Miao
- Google Research, Mountain View, CA, USA
| | | | - J Lloyd
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
| | | | - R Acharya
- Google Research, Mountain View, CA, USA
| | - I Aleiner
- Google Research, Mountain View, CA, USA
| | | | - M Ansmann
- Google Research, Mountain View, CA, USA
| | - F Arute
- Google Research, Mountain View, CA, USA
| | - K Arya
- Google Research, Mountain View, CA, USA
| | - A Asfaw
- Google Research, Mountain View, CA, USA
| | - J Atalaya
- Google Research, Mountain View, CA, USA
| | - J C Bardin
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | | | - G Bortoli
- Google Research, Mountain View, CA, USA
| | | | - J Bovaird
- Google Research, Mountain View, CA, USA
| | - L Brill
- Google Research, Mountain View, CA, USA
| | | | | | - D A Buell
- Google Research, Mountain View, CA, USA
| | - T Burger
- Google Research, Mountain View, CA, USA
| | - B Burkett
- Google Research, Mountain View, CA, USA
| | | | - Z Chen
- Google Research, Mountain View, CA, USA
| | - B Chiaro
- Google Research, Mountain View, CA, USA
| | - D Chik
- Google Research, Mountain View, CA, USA
| | - C Chou
- Google Research, Mountain View, CA, USA
| | - J Cogan
- Google Research, Mountain View, CA, USA
| | - R Collins
- Google Research, Mountain View, CA, USA
| | - P Conner
- Google Research, Mountain View, CA, USA
| | | | - A L Crook
- Google Research, Mountain View, CA, USA
| | - B Curtin
- Google Research, Mountain View, CA, USA
| | - A G Dau
- Google Research, Mountain View, CA, USA
| | | | | | - S Demura
- Google Research, Mountain View, CA, USA
| | | | | | | | | | - L Faoro
- Google Research, Mountain View, CA, USA
| | - E Farhi
- Google Research, Mountain View, CA, USA
| | - R Fatemi
- Google Research, Mountain View, CA, USA
| | | | | | - E Forati
- Google Research, Mountain View, CA, USA
| | | | - B Foxen
- Google Research, Mountain View, CA, USA
| | - É Genois
- Google Research, Mountain View, CA, USA
| | - W Giang
- Google Research, Mountain View, CA, USA
| | - C Gidney
- Google Research, Mountain View, CA, USA
| | - D Gilboa
- Google Research, Mountain View, CA, USA
| | | | - R Gosula
- Google Research, Mountain View, CA, USA
| | - J A Gross
- Google Research, Mountain View, CA, USA
| | | | - M C Hamilton
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
| | - M Hansen
- Google Research, Mountain View, CA, USA
| | | | | | - P Heu
- Google Research, Mountain View, CA, USA
| | | | - S Hong
- Google Research, Mountain View, CA, USA
| | - T Huang
- Google Research, Mountain View, CA, USA
| | - A Huff
- Google Research, Mountain View, CA, USA
| | | | - L B Ioffe
- Google Research, Mountain View, CA, USA
| | | | - J Iveland
- Google Research, Mountain View, CA, USA
| | - E Jeffrey
- Google Research, Mountain View, CA, USA
| | - Z Jiang
- Google Research, Mountain View, CA, USA
| | - C Jones
- Google Research, Mountain View, CA, USA
| | - P Juhas
- Google Research, Mountain View, CA, USA
| | - D Kafri
- Google Research, Mountain View, CA, USA
| | | | - T Khattar
- Google Research, Mountain View, CA, USA
| | - M Khezri
- Google Research, Mountain View, CA, USA
| | - M Kieferová
- Google Research, Mountain View, CA, USA
- Centre for Quantum Software and Information (QSI), Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia
| | - S Kim
- Google Research, Mountain View, CA, USA
| | - A Kitaev
- Google Research, Mountain View, CA, USA
| | - A R Klots
- Google Research, Mountain View, CA, USA
| | - A N Korotkov
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA
| | | | | | | | - P Laptev
- Google Research, Mountain View, CA, USA
| | - K-M Lau
- Google Research, Mountain View, CA, USA
| | - L Laws
- Google Research, Mountain View, CA, USA
| | - J Lee
- Google Research, Mountain View, CA, USA
- Department of Chemistry, Columbia University, New York, NY, USA
| | - K W Lee
- Google Research, Mountain View, CA, USA
| | | | | | - A T Lill
- Google Research, Mountain View, CA, USA
| | - W Liu
- Google Research, Mountain View, CA, USA
| | | | | | - O Martin
- Google Research, Mountain View, CA, USA
| | | | - M McEwen
- Google Research, Mountain View, CA, USA
| | | | | | - A Morvan
- Google Research, Mountain View, CA, USA
| | | | | | - M Neeley
- Google Research, Mountain View, CA, USA
| | - C Neill
- Google Research, Mountain View, CA, USA
| | | | - M Newman
- Google Research, Mountain View, CA, USA
| | - J H Ng
- Google Research, Mountain View, CA, USA
| | - A Nguyen
- Google Research, Mountain View, CA, USA
| | - M Nguyen
- Google Research, Mountain View, CA, USA
| | - M Y Niu
- Google Research, Mountain View, CA, USA
| | | | | | | | - R Potter
- Google Research, Mountain View, CA, USA
| | - L P Pryadko
- Google Research, Mountain View, CA, USA
- Department of Physics and Astronomy, University of California, Riverside, CA, USA
| | | | - C Rocque
- Google Research, Mountain View, CA, USA
| | - N C Rubin
- Google Research, Mountain View, CA, USA
| | - N Saei
- Google Research, Mountain View, CA, USA
| | - D Sank
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - A Shorter
- Google Research, Mountain View, CA, USA
| | - N Shutty
- Google Research, Mountain View, CA, USA
| | - V Shvarts
- Google Research, Mountain View, CA, USA
| | - J Skruzny
- Google Research, Mountain View, CA, USA
| | - W C Smith
- Google Research, Mountain View, CA, USA
| | - R Somma
- Google Research, Mountain View, CA, USA
| | | | - D Strain
- Google Research, Mountain View, CA, USA
| | - M Szalay
- Google Research, Mountain View, CA, USA
| | - A Torres
- Google Research, Mountain View, CA, USA
| | - G Vidal
- Google Research, Mountain View, CA, USA
| | | | | | - T White
- Google Research, Mountain View, CA, USA
| | - B W K Woo
- Google Research, Mountain View, CA, USA
| | - C Xing
- Google Research, Mountain View, CA, USA
| | - Z J Yao
- Google Research, Mountain View, CA, USA
| | - P Yeh
- Google Research, Mountain View, CA, USA
| | - J Yoo
- Google Research, Mountain View, CA, USA
| | - G Young
- Google Research, Mountain View, CA, USA
| | - A Zalcman
- Google Research, Mountain View, CA, USA
| | - Y Zhang
- Google Research, Mountain View, CA, USA
| | - N Zhu
- Google Research, Mountain View, CA, USA
| | - N Zobrist
- Google Research, Mountain View, CA, USA
| | - H Neven
- Google Research, Mountain View, CA, USA
| | - R Babbush
- Google Research, Mountain View, CA, USA
| | - D Bacon
- Google Research, Mountain View, CA, USA
| | - S Boixo
- Google Research, Mountain View, CA, USA
| | - J Hilton
- Google Research, Mountain View, CA, USA
| | - E Lucero
- Google Research, Mountain View, CA, USA
| | - A Megrant
- Google Research, Mountain View, CA, USA
| | - J Kelly
- Google Research, Mountain View, CA, USA
| | - Y Chen
- Google Research, Mountain View, CA, USA
| | - P Roushan
- Google Research, Mountain View, CA, USA
| | | | - D A Abanin
- Google Research, Mountain View, CA, USA
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
- Department of Physics, Princeton University, Princeton, NJ, USA
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5
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Harbison J, Collins R, McCormack J, Brych O, Fallon C, Cassidy T. Response to: Relationship between hospital size, remoteness and stroke outcome. QJM 2023; 116:820-821. [PMID: 37498588 DOI: 10.1093/qjmed/hcad182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Indexed: 07/28/2023] Open
Affiliation(s)
- J Harbison
- Irish National Audit of Stroke, National Office of Clinical Audit, 111 St Stephens Green, Dublin 2, Ireland
- Department of Medical Gerontology, Trinity College Dublin, Dublin 2, Ireland
| | - R Collins
- Irish National Audit of Stroke, National Office of Clinical Audit, 111 St Stephens Green, Dublin 2, Ireland
- Department of Medical Gerontology, Trinity College Dublin, Dublin 2, Ireland
- National Clinical Programme for Stroke, Health Service Executive, Dublin 8, Ireland
| | - J McCormack
- Irish National Audit of Stroke, National Office of Clinical Audit, 111 St Stephens Green, Dublin 2, Ireland
| | - O Brych
- Irish National Audit of Stroke, National Office of Clinical Audit, 111 St Stephens Green, Dublin 2, Ireland
| | - C Fallon
- Irish National Audit of Stroke, National Office of Clinical Audit, 111 St Stephens Green, Dublin 2, Ireland
- Department of Geriatrics, Midland Regional Hospital, Longford Road, Mullingar, Ireland
| | - T Cassidy
- Irish National Audit of Stroke, National Office of Clinical Audit, 111 St Stephens Green, Dublin 2, Ireland
- Department of Geriatrics, St Vincent's University Hospital, Elm Park, Ireland
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6
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Hoke JC, Ippoliti M, Rosenberg E, Abanin D, Acharya R, Andersen TI, Ansmann M, Arute F, Arya K, Asfaw A, Atalaya J, Bardin JC, Bengtsson A, Bortoli G, Bourassa A, Bovaird J, Brill L, Broughton M, Buckley BB, Buell DA, Burger T, Burkett B, Bushnell N, Chen Z, Chiaro B, Chik D, Cogan J, Collins R, Conner P, Courtney W, Crook AL, Curtin B, Dau AG, Debroy DM, Del Toro Barba A, Demura S, Di Paolo A, Drozdov IK, Dunsworth A, Eppens D, Erickson C, Farhi E, Fatemi R, Ferreira VS, Burgos LF, Forati E, Fowler AG, Foxen B, Giang W, Gidney C, Gilboa D, Giustina M, Gosula R, Gross JA, Habegger S, Hamilton MC, Hansen M, Harrigan MP, Harrington SD, Heu P, Hoffmann MR, Hong S, Huang T, Huff A, Huggins WJ, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Juhas P, Kafri D, Kechedzhi K, Khattar T, Khezri M, Kieferová M, Kim S, Kitaev A, Klimov PV, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Laws L, Lee J, Lee KW, Lensky YD, Lester BJ, Lill AT, Liu W, Locharla A, Martin O, McClean JR, McEwen M, Miao KC, Mieszala A, Montazeri S, Morvan A, Movassagh R, Mruczkiewicz W, Neeley M, Neill C, Nersisyan A, Newman M, Ng JH, Nguyen A, Nguyen M, Niu MY, O’Brien TE, Omonije S, Opremcak A, Petukhov A, Potter R, Pryadko LP, Quintana C, Rocque C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schurkus HF, Schuster C, Shearn MJ, Shorter A, Shutty N, Shvarts V, Skruzny J, Smith WC, Somma R, Sterling G, Strain D, Szalay M, Torres A, Vidal G, Villalonga B, Heidweiller CV, White T, Woo BWK, Xing C, Yao ZJ, Yeh P, Yoo J, Young G, Zalcman A, Zhang Y, Zhu N, Zobrist N, Neven H, Babbush R, Bacon D, Boixo S, Hilton J, Lucero E, Megrant A, Kelly J, Chen Y, Smelyanskiy V, Mi X, Khemani V, Roushan P. Measurement-induced entanglement and teleportation on a noisy quantum processor. Nature 2023; 622:481-486. [PMID: 37853150 PMCID: PMC10584681 DOI: 10.1038/s41586-023-06505-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 08/01/2023] [Indexed: 10/20/2023]
Abstract
Measurement has a special role in quantum theory1: by collapsing the wavefunction, it can enable phenomena such as teleportation2 and thereby alter the 'arrow of time' that constrains unitary evolution. When integrated in many-body dynamics, measurements can lead to emergent patterns of quantum information in space-time3-10 that go beyond the established paradigms for characterizing phases, either in or out of equilibrium11-13. For present-day noisy intermediate-scale quantum (NISQ) processors14, the experimental realization of such physics can be problematic because of hardware limitations and the stochastic nature of quantum measurement. Here we address these experimental challenges and study measurement-induced quantum information phases on up to 70 superconducting qubits. By leveraging the interchangeability of space and time, we use a duality mapping9,15-17 to avoid mid-circuit measurement and access different manifestations of the underlying phases, from entanglement scaling3,4 to measurement-induced teleportation18. We obtain finite-sized signatures of a phase transition with a decoding protocol that correlates the experimental measurement with classical simulation data. The phases display remarkably different sensitivity to noise, and we use this disparity to turn an inherent hardware limitation into a useful diagnostic. Our work demonstrates an approach to realizing measurement-induced physics at scales that are at the limits of current NISQ processors.
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7
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Andersen TI, Lensky YD, Kechedzhi K, Drozdov IK, Bengtsson A, Hong S, Morvan A, Mi X, Opremcak A, Acharya R, Allen R, Ansmann M, Arute F, Arya K, Asfaw A, Atalaya J, Babbush R, Bacon D, Bardin JC, Bortoli G, Bourassa A, Bovaird J, Brill L, Broughton M, Buckley BB, Buell DA, Burger T, Burkett B, Bushnell N, Chen Z, Chiaro B, Chik D, Chou C, Cogan J, Collins R, Conner P, Courtney W, Crook AL, Curtin B, Debroy DM, Del Toro Barba A, Demura S, Dunsworth A, Eppens D, Erickson C, Faoro L, Farhi E, Fatemi R, Ferreira VS, Burgos LF, Forati E, Fowler AG, Foxen B, Giang W, Gidney C, Gilboa D, Giustina M, Gosula R, Dau AG, Gross JA, Habegger S, Hamilton MC, Hansen M, Harrigan MP, Harrington SD, Heu P, Hilton J, Hoffmann MR, Huang T, Huff A, Huggins WJ, Ioffe LB, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Juhas P, Kafri D, Khattar T, Khezri M, Kieferová M, Kim S, Kitaev A, Klimov PV, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Laws L, Lee J, Lee KW, Lester BJ, Lill AT, Liu W, Locharla A, Lucero E, Malone FD, Martin O, McClean JR, McCourt T, McEwen M, Miao KC, Mieszala A, Mohseni M, Montazeri S, Mount E, Movassagh R, Mruczkiewicz W, Naaman O, Neeley M, Neill C, Nersisyan A, Newman M, Ng JH, Nguyen A, Nguyen M, Niu MY, O’Brien TE, Omonije S, Petukhov A, Potter R, Pryadko LP, Quintana C, Rocque C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schurkus HF, Schuster C, Shearn MJ, Shorter A, Shutty N, Shvarts V, Skruzny J, Smith WC, Somma R, Sterling G, Strain D, Szalay M, Torres A, Vidal G, Villalonga B, Heidweiller CV, White T, Woo BWK, Xing C, Yao ZJ, Yeh P, Yoo J, Young G, Zalcman A, Zhang Y, Zhu N, Zobrist N, Neven H, Boixo S, Megrant A, Kelly J, Chen Y, Smelyanskiy V, Kim EA, Aleiner I, Roushan P. Non-Abelian braiding of graph vertices in a superconducting processor. Nature 2023; 618:264-269. [PMID: 37169834 DOI: 10.1038/s41586-023-05954-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 03/14/2023] [Indexed: 06/09/2023]
Abstract
Indistinguishability of particles is a fundamental principle of quantum mechanics1. For all elementary and quasiparticles observed to date-including fermions, bosons and Abelian anyons-this principle guarantees that the braiding of identical particles leaves the system unchanged2,3. However, in two spatial dimensions, an intriguing possibility exists: braiding of non-Abelian anyons causes rotations in a space of topologically degenerate wavefunctions4-8. Hence, it can change the observables of the system without violating the principle of indistinguishability. Despite the well-developed mathematical description of non-Abelian anyons and numerous theoretical proposals9-22, the experimental observation of their exchange statistics has remained elusive for decades. Controllable many-body quantum states generated on quantum processors offer another path for exploring these fundamental phenomena. Whereas efforts on conventional solid-state platforms typically involve Hamiltonian dynamics of quasiparticles, superconducting quantum processors allow for directly manipulating the many-body wavefunction by means of unitary gates. Building on predictions that stabilizer codes can host projective non-Abelian Ising anyons9,10, we implement a generalized stabilizer code and unitary protocol23 to create and braid them. This allows us to experimentally verify the fusion rules of the anyons and braid them to realize their statistics. We then study the prospect of using the anyons for quantum computation and use braiding to create an entangled state of anyons encoding three logical qubits. Our work provides new insights about non-Abelian braiding and, through the future inclusion of error correction to achieve topological protection, could open a path towards fault-tolerant quantum computing.
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8
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Morvan A, Andersen TI, Mi X, Neill C, Petukhov A, Kechedzhi K, Abanin DA, Michailidis A, Acharya R, Arute F, Arya K, Asfaw A, Atalaya J, Bardin JC, Basso J, Bengtsson A, Bortoli G, Bourassa A, Bovaird J, Brill L, Broughton M, Buckley BB, Buell DA, Burger T, Burkett B, Bushnell N, Chen Z, Chiaro B, Collins R, Conner P, Courtney W, Crook AL, Curtin B, Debroy DM, Del Toro Barba A, Demura S, Dunsworth A, Eppens D, Erickson C, Faoro L, Farhi E, Fatemi R, Flores Burgos L, Forati E, Fowler AG, Foxen B, Giang W, Gidney C, Gilboa D, Giustina M, Grajales Dau A, Gross JA, Habegger S, Hamilton MC, Harrigan MP, Harrington SD, Hoffmann M, Hong S, Huang T, Huff A, Huggins WJ, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Juhas P, Kafri D, Khattar T, Khezri M, Kieferová M, Kim S, Kitaev AY, Klimov PV, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Laws L, Lee J, Lee KW, Lester BJ, Lill AT, Liu W, Locharla A, Malone F, Martin O, McClean JR, McEwen M, Meurer Costa B, Miao KC, Mohseni M, Montazeri S, Mount E, Mruczkiewicz W, Naaman O, Neeley M, Nersisyan A, Newman M, Nguyen A, Nguyen M, Niu MY, O'Brien TE, Olenewa R, Opremcak A, Potter R, Quintana C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schurkus HF, Schuster C, Shearn MJ, Shorter A, Shvarts V, Skruzny J, Smith WC, Strain D, Sterling G, Su Y, Szalay M, Torres A, Vidal G, Villalonga B, Vollgraff-Heidweiller C, White T, Xing C, Yao Z, Yeh P, Yoo J, Zalcman A, Zhang Y, Zhu N, Neven H, Bacon D, Hilton J, Lucero E, Babbush R, Boixo S, Megrant A, Kelly J, Chen Y, Smelyanskiy V, Aleiner I, Ioffe LB, Roushan P. Formation of robust bound states of interacting microwave photons. Nature 2022; 612:240-245. [PMID: 36477133 PMCID: PMC9729104 DOI: 10.1038/s41586-022-05348-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 09/14/2022] [Indexed: 12/12/2022]
Abstract
Systems of correlated particles appear in many fields of modern science and represent some of the most intractable computational problems in nature. The computational challenge in these systems arises when interactions become comparable to other energy scales, which makes the state of each particle depend on all other particles1. The lack of general solutions for the three-body problem and acceptable theory for strongly correlated electrons shows that our understanding of correlated systems fades when the particle number or the interaction strength increases. One of the hallmarks of interacting systems is the formation of multiparticle bound states2-9. Here we develop a high-fidelity parameterizable fSim gate and implement the periodic quantum circuit of the spin-½ XXZ model in a ring of 24 superconducting qubits. We study the propagation of these excitations and observe their bound nature for up to five photons. We devise a phase-sensitive method for constructing the few-body spectrum of the bound states and extract their pseudo-charge by introducing a synthetic flux. By introducing interactions between the ring and additional qubits, we observe an unexpected resilience of the bound states to integrability breaking. This finding goes against the idea that bound states in non-integrable systems are unstable when their energies overlap with the continuum spectrum. Our work provides experimental evidence for bound states of interacting photons and discovers their stability beyond the integrability limit.
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Affiliation(s)
- A Morvan
- Google Research, Mountain View, CA, USA
| | | | - X Mi
- Google Research, Mountain View, CA, USA
| | - C Neill
- Google Research, Mountain View, CA, USA
| | | | | | - D A Abanin
- Google Research, Mountain View, CA, USA
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
| | - A Michailidis
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
| | - R Acharya
- Google Research, Mountain View, CA, USA
| | - F Arute
- Google Research, Mountain View, CA, USA
| | - K Arya
- Google Research, Mountain View, CA, USA
| | - A Asfaw
- Google Research, Mountain View, CA, USA
| | - J Atalaya
- Google Research, Mountain View, CA, USA
| | - J C Bardin
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | - J Basso
- Google Research, Mountain View, CA, USA
| | | | - G Bortoli
- Google Research, Mountain View, CA, USA
| | | | - J Bovaird
- Google Research, Mountain View, CA, USA
| | - L Brill
- Google Research, Mountain View, CA, USA
| | | | | | - D A Buell
- Google Research, Mountain View, CA, USA
| | - T Burger
- Google Research, Mountain View, CA, USA
| | - B Burkett
- Google Research, Mountain View, CA, USA
| | | | - Z Chen
- Google Research, Mountain View, CA, USA
| | - B Chiaro
- Google Research, Mountain View, CA, USA
| | - R Collins
- Google Research, Mountain View, CA, USA
| | - P Conner
- Google Research, Mountain View, CA, USA
| | | | - A L Crook
- Google Research, Mountain View, CA, USA
| | - B Curtin
- Google Research, Mountain View, CA, USA
| | | | | | - S Demura
- Google Research, Mountain View, CA, USA
| | | | - D Eppens
- Google Research, Mountain View, CA, USA
| | | | - L Faoro
- Google Research, Mountain View, CA, USA
| | - E Farhi
- Google Research, Mountain View, CA, USA
| | - R Fatemi
- Google Research, Mountain View, CA, USA
| | | | - E Forati
- Google Research, Mountain View, CA, USA
| | | | - B Foxen
- Google Research, Mountain View, CA, USA
| | - W Giang
- Google Research, Mountain View, CA, USA
| | - C Gidney
- Google Research, Mountain View, CA, USA
| | - D Gilboa
- Google Research, Mountain View, CA, USA
| | | | | | - J A Gross
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - S Hong
- Google Research, Mountain View, CA, USA
| | - T Huang
- Google Research, Mountain View, CA, USA
| | - A Huff
- Google Research, Mountain View, CA, USA
| | | | | | - J Iveland
- Google Research, Mountain View, CA, USA
| | - E Jeffrey
- Google Research, Mountain View, CA, USA
| | - Z Jiang
- Google Research, Mountain View, CA, USA
| | - C Jones
- Google Research, Mountain View, CA, USA
| | - P Juhas
- Google Research, Mountain View, CA, USA
| | - D Kafri
- Google Research, Mountain View, CA, USA
| | - T Khattar
- Google Research, Mountain View, CA, USA
| | - M Khezri
- Google Research, Mountain View, CA, USA
| | - M Kieferová
- Google Research, Mountain View, CA, USA
- Centre for Quantum Computation and Communication Technology, Centre for Quantum Software and Information, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, New South Wales, Australia
| | - S Kim
- Google Research, Mountain View, CA, USA
| | - A Y Kitaev
- Google Research, Mountain View, CA, USA
- Institute for Quantum Information and Matter, California Institute of Technology, Pasadena, CA, USA
| | | | - A R Klots
- Google Research, Mountain View, CA, USA
| | - A N Korotkov
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA
| | | | | | | | - P Laptev
- Google Research, Mountain View, CA, USA
| | - K-M Lau
- Google Research, Mountain View, CA, USA
| | - L Laws
- Google Research, Mountain View, CA, USA
| | - J Lee
- Google Research, Mountain View, CA, USA
| | - K W Lee
- Google Research, Mountain View, CA, USA
| | | | - A T Lill
- Google Research, Mountain View, CA, USA
| | - W Liu
- Google Research, Mountain View, CA, USA
| | | | - F Malone
- Google Research, Mountain View, CA, USA
| | - O Martin
- Google Research, Mountain View, CA, USA
| | | | - M McEwen
- Google Research, Mountain View, CA, USA
- Department of Physics, University of California, Santa Barbara, CA, USA
| | | | - K C Miao
- Google Research, Mountain View, CA, USA
| | - M Mohseni
- Google Research, Mountain View, CA, USA
| | | | - E Mount
- Google Research, Mountain View, CA, USA
| | | | - O Naaman
- Google Research, Mountain View, CA, USA
| | - M Neeley
- Google Research, Mountain View, CA, USA
| | | | - M Newman
- Google Research, Mountain View, CA, USA
| | - A Nguyen
- Google Research, Mountain View, CA, USA
| | - M Nguyen
- Google Research, Mountain View, CA, USA
| | - M Y Niu
- Google Research, Mountain View, CA, USA
| | | | - R Olenewa
- Google Research, Mountain View, CA, USA
| | | | - R Potter
- Google Research, Mountain View, CA, USA
| | | | - N C Rubin
- Google Research, Mountain View, CA, USA
| | - N Saei
- Google Research, Mountain View, CA, USA
| | - D Sank
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - A Shorter
- Google Research, Mountain View, CA, USA
| | - V Shvarts
- Google Research, Mountain View, CA, USA
| | - J Skruzny
- Google Research, Mountain View, CA, USA
| | - W C Smith
- Google Research, Mountain View, CA, USA
| | - D Strain
- Google Research, Mountain View, CA, USA
| | | | - Y Su
- Google Research, Mountain View, CA, USA
| | - M Szalay
- Google Research, Mountain View, CA, USA
| | - A Torres
- Google Research, Mountain View, CA, USA
| | - G Vidal
- Google Research, Mountain View, CA, USA
| | | | | | - T White
- Google Research, Mountain View, CA, USA
| | - C Xing
- Google Research, Mountain View, CA, USA
| | - Z Yao
- Google Research, Mountain View, CA, USA
| | - P Yeh
- Google Research, Mountain View, CA, USA
| | - J Yoo
- Google Research, Mountain View, CA, USA
| | - A Zalcman
- Google Research, Mountain View, CA, USA
| | - Y Zhang
- Google Research, Mountain View, CA, USA
| | - N Zhu
- Google Research, Mountain View, CA, USA
| | - H Neven
- Google Research, Mountain View, CA, USA
| | - D Bacon
- Google Research, Mountain View, CA, USA
| | - J Hilton
- Google Research, Mountain View, CA, USA
| | - E Lucero
- Google Research, Mountain View, CA, USA
| | - R Babbush
- Google Research, Mountain View, CA, USA
| | - S Boixo
- Google Research, Mountain View, CA, USA
| | - A Megrant
- Google Research, Mountain View, CA, USA
| | - J Kelly
- Google Research, Mountain View, CA, USA
| | - Y Chen
- Google Research, Mountain View, CA, USA
| | | | - I Aleiner
- Google Research, Mountain View, CA, USA.
| | - L B Ioffe
- Google Research, Mountain View, CA, USA.
| | - P Roushan
- Google Research, Mountain View, CA, USA.
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9
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Mi X, Sonner M, Niu MY, Lee KW, Foxen B, Acharya R, Aleiner I, Andersen TI, Arute F, Arya K, Asfaw A, Atalaya J, Bardin JC, Basso J, Bengtsson A, Bortoli G, Bourassa A, Brill L, Broughton M, Buckley BB, Buell DA, Burkett B, Bushnell N, Chen Z, Chiaro B, Collins R, Conner P, Courtney W, Crook AL, Debroy DM, Demura S, Dunsworth A, Eppens D, Erickson C, Faoro L, Farhi E, Fatemi R, Flores L, Forati E, Fowler AG, Giang W, Gidney C, Gilboa D, Giustina M, Dau AG, Gross JA, Habegger S, Harrigan MP, Hoffmann M, Hong S, Huang T, Huff A, Huggins WJ, Ioffe LB, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Kafri D, Kechedzhi K, Khattar T, Kim S, Kitaev AY, Klimov PV, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Lee J, Laws L, Liu W, Locharla A, Martin O, McClean JR, McEwen M, Meurer Costa B, Miao KC, Mohseni M, Montazeri S, Morvan A, Mount E, Mruczkiewicz W, Naaman O, Neeley M, Neill C, Newman M, O’Brien TE, Opremcak A, Petukhov A, Potter R, Quintana C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schuster C, Shearn MJ, Shvarts V, Strain D, Su Y, Szalay M, Vidal G, Villalonga B, Vollgraff-Heidweiller C, White T, Yao Z, Yeh P, Yoo J, Zalcman A, Zhang Y, Zhu N, Neven H, Bacon D, Hilton J, Lucero E, Babbush R, Boixo S, Megrant A, Chen Y, Kelly J, Smelyanskiy V, Abanin DA, Roushan P. Noise-resilient edge modes on a chain of superconducting qubits. Science 2022; 378:785-790. [DOI: 10.1126/science.abq5769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Inherent symmetry of a quantum system may protect its otherwise fragile states. Leveraging such protection requires testing its robustness against uncontrolled environmental interactions. Using 47 superconducting qubits, we implement the one-dimensional kicked Ising model, which exhibits nonlocal Majorana edge modes (MEMs) with
ℤ
2
parity symmetry. We find that any multiqubit Pauli operator overlapping with the MEMs exhibits a uniform late-time decay rate comparable to single-qubit relaxation rates, irrespective of its size or composition. This characteristic allows us to accurately reconstruct the exponentially localized spatial profiles of the MEMs. Furthermore, the MEMs are found to be resilient against certain symmetry-breaking noise owing to a prethermalization mechanism. Our work elucidates the complex interplay between noise and symmetry-protected edge modes in a solid-state environment.
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Affiliation(s)
- X. Mi
- Google Research, Mountain View, CA, USA
| | - M. Sonner
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
| | - M. Y. Niu
- Google Research, Mountain View, CA, USA
| | - K. W. Lee
- Google Research, Mountain View, CA, USA
| | - B. Foxen
- Google Research, Mountain View, CA, USA
| | | | | | | | - F. Arute
- Google Research, Mountain View, CA, USA
| | - K. Arya
- Google Research, Mountain View, CA, USA
| | - A. Asfaw
- Google Research, Mountain View, CA, USA
| | | | - J. C. Bardin
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | - J. Basso
- Google Research, Mountain View, CA, USA
| | | | | | | | - L. Brill
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - Z. Chen
- Google Research, Mountain View, CA, USA
| | - B. Chiaro
- Google Research, Mountain View, CA, USA
| | | | - P. Conner
- Google Research, Mountain View, CA, USA
| | | | | | | | - S. Demura
- Google Research, Mountain View, CA, USA
| | | | - D. Eppens
- Google Research, Mountain View, CA, USA
| | | | - L. Faoro
- Google Research, Mountain View, CA, USA
| | - E. Farhi
- Google Research, Mountain View, CA, USA
| | - R. Fatemi
- Google Research, Mountain View, CA, USA
| | - L. Flores
- Google Research, Mountain View, CA, USA
| | - E. Forati
- Google Research, Mountain View, CA, USA
| | | | - W. Giang
- Google Research, Mountain View, CA, USA
| | - C. Gidney
- Google Research, Mountain View, CA, USA
| | - D. Gilboa
- Google Research, Mountain View, CA, USA
| | | | - A. G. Dau
- Google Research, Mountain View, CA, USA
| | | | | | | | | | - S. Hong
- Google Research, Mountain View, CA, USA
| | - T. Huang
- Google Research, Mountain View, CA, USA
| | - A. Huff
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - Z. Jiang
- Google Research, Mountain View, CA, USA
| | - C. Jones
- Google Research, Mountain View, CA, USA
| | - D. Kafri
- Google Research, Mountain View, CA, USA
| | | | | | - S. Kim
- Google Research, Mountain View, CA, USA
| | - A. Y. Kitaev
- Google Research, Mountain View, CA, USA
- Institute for Quantum Information and Matter, California Institute of Technology, Pasadena, CA, USA
| | | | | | - A. N. Korotkov
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA
| | | | | | | | - P. Laptev
- Google Research, Mountain View, CA, USA
| | - K.-M. Lau
- Google Research, Mountain View, CA, USA
| | - J. Lee
- Google Research, Mountain View, CA, USA
| | - L. Laws
- Google Research, Mountain View, CA, USA
| | - W. Liu
- Google Research, Mountain View, CA, USA
| | | | - O. Martin
- Google Research, Mountain View, CA, USA
| | | | - M. McEwen
- Google Research, Mountain View, CA, USA
- Department of Physics, University of California, Santa Barbara, CA, USA
| | | | | | | | | | - A. Morvan
- Google Research, Mountain View, CA, USA
| | - E. Mount
- Google Research, Mountain View, CA, USA
| | | | - O. Naaman
- Google Research, Mountain View, CA, USA
| | - M. Neeley
- Google Research, Mountain View, CA, USA
| | - C. Neill
- Google Research, Mountain View, CA, USA
| | - M. Newman
- Google Research, Mountain View, CA, USA
| | | | | | | | - R. Potter
- Google Research, Mountain View, CA, USA
| | | | | | - N. Saei
- Google Research, Mountain View, CA, USA
| | - D. Sank
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - D. Strain
- Google Research, Mountain View, CA, USA
| | - Y. Su
- Google Research, Mountain View, CA, USA
| | - M. Szalay
- Google Research, Mountain View, CA, USA
| | - G. Vidal
- Google Research, Mountain View, CA, USA
| | | | | | - T. White
- Google Research, Mountain View, CA, USA
| | - Z. Yao
- Google Research, Mountain View, CA, USA
| | - P. Yeh
- Google Research, Mountain View, CA, USA
| | - J. Yoo
- Google Research, Mountain View, CA, USA
| | | | - Y. Zhang
- Google Research, Mountain View, CA, USA
| | - N. Zhu
- Google Research, Mountain View, CA, USA
| | - H. Neven
- Google Research, Mountain View, CA, USA
| | - D. Bacon
- Google Research, Mountain View, CA, USA
| | - J. Hilton
- Google Research, Mountain View, CA, USA
| | - E. Lucero
- Google Research, Mountain View, CA, USA
| | | | - S. Boixo
- Google Research, Mountain View, CA, USA
| | | | - Y. Chen
- Google Research, Mountain View, CA, USA
| | - J. Kelly
- Google Research, Mountain View, CA, USA
| | | | - D. A. Abanin
- Google Research, Mountain View, CA, USA
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
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10
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Collins R. Working together to ensure the realisation of ‘life to years’. Neth Heart J 2022; 30:557-558. [DOI: 10.1007/s12471-022-01739-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/02/2022] [Indexed: 11/16/2022] Open
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11
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Harte G, Keane J, Ryan D, Collins R, Garcia C, Howlin R, Ryan S, Connolly A, Leitch E, Moynan W, Healy S, Keenan M. 321 UNMET NEEDS AFTER STROKE. Age Ageing 2022. [DOI: 10.1093/ageing/afac218.282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Background
Unmet needs, such as lack of access to rehabilitation, support and information, are experienced by a high proportion of stroke survivors after discharge from acute and rehabilitation services. The UK guidelines on stroke (2016) recommend that all patients should be offered a structured health and social care review at 6 months post-stroke. However, no such clinics exist to date in Ireland. We aimed to explore the extent of unmet needs in the catchment area of a large teaching hospital.
Methods
A random selection of patients 6-months post-stroke were contacted by telephone and unmet needs were assessed using a validated tool, Post-Stroke Checklist. Following analysis of data, the need for a pilot clinic was identified. A separate random selection of patients discharged from acute services 6 months previously were invited to attend a review clinic. Patients were assessed using an adapted version of the Greater Manchester Stroke Assessment Tool. Assessments were conducted jointly by a physiotherapist and a speech and language therapist, and appropriate onward referrals were made.
Results
Telephone clinic: 51 patients completed the checklist. The most prevalent symptoms reported were fatigue (75%), reduced participation in hobbies and activities, decline in cognition (61%) and mobility problems (59%). Review clinic: 21 patients attended. The most prevalent symptoms reported were reduced memory/concentration (71%), low mood (71%), unintentional weight loss/gain (62%), and reduced mobility (43%). Onward referrals were made for in 16/21 (76%) cases; physiotherapy (n=12), occupational therapy (n=7), speech and language therapy (n=7), clinical nutrition (n=5), social work (n=7), psychology (n=5).
Conclusion
Data from this exploratory study supports previous research indicating a high number of stroke survivors with unmet needs. This highlights the importance of establishing post-acute stroke review clinics and pathways in the Irish setting.
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Affiliation(s)
- G Harte
- Tallaght University Hospital , Dublin, Ireland
| | - J Keane
- Tallaght University Hospital , Dublin, Ireland
| | - D Ryan
- Tallaght University Hospital , Dublin, Ireland
| | - R Collins
- Tallaght University Hospital , Dublin, Ireland
| | - C Garcia
- Tallaght University Hospital , Dublin, Ireland
| | - R Howlin
- Tallaght University Hospital , Dublin, Ireland
| | - S Ryan
- Tallaght University Hospital , Dublin, Ireland
| | - A Connolly
- Tallaght University Hospital , Dublin, Ireland
| | - E Leitch
- Tallaght University Hospital , Dublin, Ireland
| | - W Moynan
- Tallaght University Hospital , Dublin, Ireland
| | - S Healy
- Tallaght University Hospital , Dublin, Ireland
| | - M Keenan
- Tallaght University Hospital , Dublin, Ireland
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12
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Breen C, Collins R, Coleman S. 288 EARLY SUPPORTED DISCHARGE AFTER STROKE IN THE COVID-19 PANDEMIC: A CONTAGIOUS SERVICE-DELIVERY MODEL. Age Ageing 2022. [DOI: 10.1093/ageing/afac218.255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Background
Early Supported Discharge (ESD) after stroke is an internationally recognised model of best practice for patients with mild to moderate functional deficits after stroke. By end of 2021 there were nine stroke-specific ESD teams in operation in Ireland. This paper outlines the analysis of annual reports submitted by each site to the National Stroke Programme.
Methods
We completed data analysis of the 2020 and 2021 clinical activity of ESD teams, the functional outcomes achieved using the Functional Independence Measure/Functional Assessment Measure (FIM/FAM), and qualitative reporting from patients. Teams were also asked to report on team composition and service developments.
Results
Team composition and size varied, but all had some combination of Occupational Therapy, Physiotherapy, and Speech and Language Therapy. 764 patients received ESD after acute stroke in 2021, representing 24.7% of all patients discharged with stroke in participating sites. Activity increased by 39.9% from 2020 (n = 546), which was in turn an increase of 48% on the 2019 figure (n = 370). Although new sites were added in each year, the increased capacity alone does not account for the growth. All sites reported patient improvement on the extended FIM/FAM. Patient feedback was positive, with many reporting particularly on the benefits of receiving intervention in the home at a time when hospital visiting was restricted. Many sites also reported introducing telehealth to support delivery of ESD.
Conclusion
ESD after stroke continues to grow at pace in Ireland, proving resilient to the pressures of operating in a pandemic. It remains a highly acceptable model to patients, and growth during the pandemic may have been supported by increased capacity, new ways of working, and patients' desire for earlier discharge. Although all teams reported functional improvement among their patients, some sites are achieving greater gains, suggesting that further research into patient selection, treatment intensity and further embedding of telehealth is warranted.
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Affiliation(s)
- C Breen
- Galway University Hospitals Department of Occupational Therapy, , Galway, Ireland
| | - R Collins
- Tallaght University Hospital Department of Geriatric & Stroke Medicine, , Dublin, Ireland
- HSE National Clinical Programme for Stroke , Dublin, Ireland
| | - S Coleman
- HSE National Clinical Programme for Stroke , Dublin, Ireland
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13
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Connor AO, Hobson H, Ryan D, Collins R. 238 THE IMPACT OF COVID-19 ON ACUTE STROKE TREATMENT. Age Ageing 2022. [PMCID: PMC9620361 DOI: 10.1093/ageing/afac218.207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Background Acute stroke management is time critical. Treatment options are dictated by time of symptom onset. 2019 Irish National Audit of Stroke (INAS) showed less than 50% stroke cases arrived in hospital within three hours of symptoms and national average rate of thrombolysis was 11%. The median time Door-To-Needle time (DTN) was 56 minutes, and to thrombectomy 93 and 240 minutes ‘direct to mothership’ / ‘drip and ship’ transfer to thrombectomy centre respectively. Methods Retrospective review of all stroke FAST calls on our acute stroke database in TUH from July 2019 -2021. Data was analysed pre (up until March 2020) and during COVID-19 pandemic. Results FAST protocol imaging was obtained in 78% of cases (464/594). Documented time of symptom onset to CT was 1:50:00 pre-Covid-19 and 2:26:00 during Covid (p<0.001). ED registration to CT was 28mins pre-Covid-19 and 30mins during Covid (p<0.001). The median DTN time was 41mins pre-Covid-19 (n=21), and 54mins during Covid-19 (n=37). Conclusion In contrast to INAS COVID-19 report, median DTN times and thrombolysis rates dis-improved at TUH. Trends observed probably reflect multiple factors; patient hesitation to attend ED; patient isolation from family; busier ambulance service; Infection control protocols. We observed small but significant change in door-to-CT time but a more significant increase in DTN. This may reflect time taken donning Personal Protective Equipment (PPE) or general increased ED activity. Understanding the impact of COVID-19 on acute stroke treatment metrics in addition to differences at sites may identify targets for national quality improvement in service delivery.
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Affiliation(s)
- AO Connor
- Tallaght University Hospital , Dublin, Ireland
| | - H Hobson
- Tallaght University Hospital , Dublin, Ireland
| | - D Ryan
- Tallaght University Hospital , Dublin, Ireland
| | - R Collins
- Tallaght University Hospital , Dublin, Ireland
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14
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Hopper L, Collins R, Forristal K, Riello M, Conotter V, Meyer G, Vugt M. 141 THE INTENSE PROJECT: IMPROVING DEMENTIA CARE THROUGH SIMULATION OF SELF-EXPERIENCE. Age Ageing 2022. [DOI: 10.1093/ageing/afac218.119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
Background
The EC is concerned about the growing shortage of experienced health and social care professionals to care for people with dementia. Although specialized dementia education programs have begun to appear in recent years, a lack of consistency and adequate level of knowledge of dementia to anticipate the required treatment remains across Europe. The Improving demeNtia care Through Self-Experience (INTENSE) project supports the creation of skills improvement paths for professionals across Europe that incorporate and promote successful self-experience methodologies and practices to increase knowledge and understanding of the lived experience of dementia (e.g., Virtual Dementia Tours, role plays and practices of theatre workshops).
Methods
A systematic review of self-experience tools, training and interventions was conducted. Participatory stakeholder workshops were then held in Ireland, Italy, Germany and the Netherlands to explore ways in which self-experience could be used to better understand the experience of the person with dementia and how the simulation of self-experience could be incorporated into existing (or new) training programmes. Workshop discussions were transcribed and thematically analysed along with workshop outputs to develop the INTenSE toolkit and supporting platform.
Results
An INTenSE toolkit was developed containing interactive no-tech, low-tech and high-tech approaches to simulating self-experience. Training scenarios, facilitation approaches and sustainability plans were designed and a prototype of the INTenSE Platform (website to house and support use of the toolkit) was developed. Beta versions of each will be presented.
Conclusion
INTenSE has illustrated how participatory research can strengthen cooperation and exchange of experiences between organizations working in the field of dementia care; improve social awareness of the ability of self-experience to improve dementia care; promote the integration of self-experience practices in the training of health and social care professional; and highlight the potential for self-experience simulation to be incorporated into carer training and dementia awareness education, subject to the provision of appropriate supports.
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Affiliation(s)
- L Hopper
- Dublin City University , Dublin, Ireland
| | - R Collins
- Dublin City University , Dublin, Ireland
| | | | | | | | - G Meyer
- Martin Luther University , Halle-Wittenberg, Germany
| | - M Vugt
- Maastricht University , Maastricht, Netherlands
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15
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Collins R, Forristal K, Hopper L, Riello M, Conotter V, Meyer G, Vugt MD. 189 IMPROVING DEMENTIA CARE THROUGH SELF-EXPERIENCE; CO-DESIGNING A SIMULATION-BASED DEMENTIA EDUCATION TOOLKIT. Age Ageing 2022. [DOI: 10.1093/ageing/afac218.162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Background
Although specialised dementia education programmes have begun to appear in recent years, they lack consistency. Knowledge gaps persist, which results in a professional workforce who can struggle to anticipate the care needs of people with dementia. Self-experience or simulation practices (e.g., roleplays, virtual reality, sensory tools) are established teaching methods that can provide learning opportunities to experience aspects of illness. These innovative learning approaches can positively impact empathy, understanding, and quality of care; however, they are rarely implemented in specialised care for people with dementia. The Improving demeNtia care Through Self-Experience (INTENSE) project has co-designed a dementia simulation toolkit to educate, equip, and train professionals to better support people with dementia.
Methods
As part of the Erasmus+ INTenSE project, a series of three participatory, online workshops were conducted with people with dementia, informal caregivers, and dementia-care professionals in Ireland, Italy, Germany, and the Netherlands. The first explored the co-design of training scenarios using simulation tools to demonstrate the lived experience of dementia. The second involved the co-design of an online training platform. The third examined the sustainable implementation of INTenSE into practice. All workshops were inductively coded and analysed thematically.
Results
Recommended training scenarios demonstrate daily activities and environments that impact people with dementia using a combination of existing and newly developed simulation tools. Four themes arose from workshop discussions: 1) representation of dementia, 2) daily challenges that impact people with dementia, 3) applying a person-centred approach, and 4) implementing self-experience training. Together these were used to develop facilitation guides to support the implementation of the INTenSE simulation toolkit into practice.
Conclusion
These findings demonstrate the potential of simulating self-experience across a range of everyday scenarios as a means to increase professionals' empathy and understanding of the lived experience of dementia. Recommendations have been developed that support the use of self-experience tools in practice.
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Affiliation(s)
- R Collins
- Dublin City University , Dublin, Ireland
| | | | - L Hopper
- Dublin City University , Dublin, Ireland
| | | | | | - G Meyer
- Martin Luther University , Halle-Wittenberg, Germany
| | - MD Vugt
- Maastricht University , Maastricht, Netherlands
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16
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Moran CN, Jeffares I, Merriman NA, McCormack J, Harbison J, Sexton E, Williams D, Kelly PJ, Horgan F, Collins R, Bhreacáin MN, Byrne E, Thornton J, Tully C, Hickey A. 119 ENHANCING THE QUALITY OF STROKE CARE IN IRELAND - DEVELOPMENT OF AN IRISH NATIONAL STROKE AUDIT. Age Ageing 2022. [DOI: 10.1093/ageing/afac218.098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Background
Population ageing, stroke treatment advances, changing models of care, and between-hospital heterogeneity in stroke outcomes demonstrate the necessity of continual audit of stroke care to support quality improvement at local and national levels, and to enhance patient recovery and wellbeing. This project aims to identify the core minimum datasets for acute and non-acute stroke care, and Patient-Reported Outcome Measures (PROMs), for integration in to the newly-developed Irish National Audit of Stroke (INAS), in addition to identifying resourcing needs and implementation procedures.
Methods
In Phase 1, a minimum dataset for acute stroke care was identified based on a scoping review of international practice and available guidelines. Phase 2 (ongoing) involves identifying datasets for non-acute rehabilitative and follow-up care based on a scoping review of international practice, iterative cycles of qualitative stakeholder engagement, and systematic review of PROMs. In Phase 3, a review of resourcing and data collection procedures used in stroke audits internationally will be used to produce an implementation strategy for data collection, contextualised to the Irish healthcare system.
Results
Twenty-one eligible international stroke registries were identified from the scoping review. Within Phase 1, core clinical and thrombectomy items in the Irish registry were benchmarked against internationally-collected items to identify common items and to generate an inventory of items that other registries collect that Ireland does not. Based on consensus agreement on the most frequently-occurring international items, as reviewed by key stakeholders, a core minimum dataset for audit of acute stroke care was delivered.
Conclusion
These minimum datasets shall act as the “gold standard” for evaluating stroke care in Ireland, by not only incorporating structure, process, and care quality outcome indicators, but also PROMs. The resultant datasets may inform policy and quality improvement initiatives, and shape health service delivery across the trajectory of stroke care, from hyper-acute care, to rehabilitation, and return to the community.
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Affiliation(s)
- CN Moran
- RCSI Dept. of Health Psychology, , Dublin, Ireland
| | - I Jeffares
- RCSI Dept. of Health Psychology, , Dublin, Ireland
| | - NA Merriman
- RCSI Dept. of Health Psychology, , Dublin, Ireland
| | - J McCormack
- National Office of Clinical Audit , Dublin, Ireland
| | - J Harbison
- National Office of Clinical Audit , Dublin, Ireland
| | - E Sexton
- RCSI Dept. of Health Psychology, , Dublin, Ireland
| | - D Williams
- Beaumont Hospital Dept. of Geriatric and Stroke Medicine, , Dublin, Ireland
- RCSI Dept. of Geriatric and Stroke Medicine, , Dublin, Ireland
| | - PJ Kelly
- Mater Misericordiae University Hospital Dept. of Neurology, , Dublin, Ireland
- University College Dublin Neurovascular Clinical Science Unit, , Dublin, Ireland
| | - F Horgan
- School of Physiotherapy, RCSI , Dublin, Ireland
| | - R Collins
- Tallaght University Hospital Dept. of Geriatric and Stroke Medicine, , Dublin, Ireland
| | | | - E Byrne
- Institute of Leadership, RCSI , Dublin, Ireland
| | - J Thornton
- Beaumont Hospital Dept. of Radiology, , Dublin, Ireland
| | - C Tully
- National Office of Clinical Audit , Dublin, Ireland
| | - A Hickey
- RCSI Dept. of Health Psychology, , Dublin, Ireland
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17
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Wijesurendra R, Sardell R, Hill M, Jayaram R, Samuel N, Staplin N, Emberson J, Collins R, Zheng Z, Haynes R, Casadei B. Perioperative rosuvastatin therapy increases creatine kinase and the risk of acute kidney injury in patients undergoing cardiac surgery. Eur Heart J 2022. [DOI: 10.1093/eurheartj/ehac544.2675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Introduction
In patients undergoing cardiac surgery, perioperative statin therapy does not prevent atrial fibrillation or myocardial injury, but results in increased creatinine levels after surgery. Here we investigated the incidence of acute kidney injury (AKI) in 1922 patients scheduled for elective cardiac surgery who were randomized to perioperative rosuvastatin (20 mg once daily) or placebo in the Statin Therapy In Cardiac Surgery (STICS) trial.
Methods
AKI post-surgery was defined according to international guidelines using plasma creatinine. Biomarkers related to kidney function, muscle injury and inflammation were investigated, including cystatin C, total creatine kinase (CK), troponin I, growth differentiation factor 15 (GDF-15), interleukin-6 (IL-6), procalcitonin, and placental growth factor (PGF).
Results
At 48 hours post-surgery, AKI was significantly more common in patients allocated to rosuvastatin compared to placebo when defined by creatinine (24.7% vs 19.3%; OR 1.37 [95% CI 1.10–1.70]; p=0.005; Figure 1A) or by cystatin C (9.2% vs 5.1%; OR 1.86 [95% CI 1.29–2.67]; p<0.001; Figure 1B). Elevations in CK to >10x and >40x baseline level were also more frequent in rosuvastatin-allocated patients compared to placebo (30.9% vs 26.5%, p=0.02, and 2.1% vs 0.7%, p=0.02, respectively; Figure 1C). Post-operative concentrations of troponin I, GDF-15, IL-6, procalcitonin, and PGF were similar between the groups (Table 1).
Conclusions
Perioperative rosuvastatin initiation increased the absolute risk of AKI after cardiac surgery by 4–5%. Rosuvastatin also led to greater elevations in post-operative creatine kinase, but did not affect other biomarkers of tissue injury, inflammation, and myocardial injury. Further research is needed to delineate the underlying mechanism of AKI with perioperative rosuvastatin.
Funding Acknowledgement
Type of funding sources: Foundation. Main funding source(s): British Heart Foundation
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Affiliation(s)
- R Wijesurendra
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - R Sardell
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - M Hill
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - R Jayaram
- University of Oxford, Division of Cardiovascular Medicine, Radcliffe Department of Medicine , Oxford , United Kingdom
| | - N Samuel
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - N Staplin
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - J Emberson
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - R Collins
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - Z Zheng
- Fuwai Hospital , Beijing , China
| | - R Haynes
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - B Casadei
- University of Oxford, Division of Cardiovascular Medicine, Radcliffe Department of Medicine , Oxford , United Kingdom
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18
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Ameri P, Alings M, Colivicchi F, Collins R, De Luca L, Di Nisio M, Fabbri G, Gabrielli D, Janssens S, Maggioni AP, Parrini I, Pinto FJ, Turazza FM, Zamorano JL, Gulizia MM. Baseline characteristics of patients with atrial fibrillation and cancer enrolled in the BLITZ-AF Cancer registry. Eur Heart J 2022. [DOI: 10.1093/eurheartj/ehac544.525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Background
Evidences on atrial fibrillation (AF) in patients with cancer are limited, specifically with respect to antithrombotic therapy.
Methods
BLITZ-AF Cancer is a prospective, non-interventional study of the epidemiology and management of AF in patients with cancer. Patients were included from 112 cardiology units in Italy, Belgium, Netherlands, Spain, Portugal, and Ireland, based on the following criteria: age ≥18 years; documented cancer other than basal-cell or squamous-cell carcinoma of the skin diagnosed within 3 years; electrocardiographically confirmed AF within 1 year; no concomitant interventional study. Follow-up is ongoing.
Results
From June 26th, 2019 to Sep. 30th, 2021, 1,514 subjects were enrolled.
The most frequent cancer locations were lung (14.9%), colorectal (14.1%), breast (13.9%), prostate (8.8%), and non-Hodgkin lymphoma (8.1%); 463 (30.6%) of participants had metastases.
AF was first-detected in 323 (21.3%), paroxysmal in 460 (30.4%), persistent in 192 (12.7%), long-standing persistent in 33 (2.2%), and permanent in 506 (33.4%); 590 (39.0%) patients had symptoms attributable to AF.
Baseline characteristics are presented in Table 1. Males were more than women and almost half of the subjects was >75 years-old. Cardiovascular risk factors were common and approximately 31% had heart failure or coronary artery disease. Previous thromboembolic and haemorrhagic events had occurred in 14% and 10% of subjects, respectively. The median CHA2DS2VASc score was 3.
As shown in Figure 1, the prescription of oral anticoagulants, especially direct-acting ones (DOACs), rose after the cardiology assessment, while the percentage of participants without any antithrombotic therapy declined.
Among 1,427 patients with non-valvular AF (i.e., no mitral stenosis or prosthetic mechanical valve), 997 (69.9%) were prescribed on DOACs at discharge/after consultation. At multivariable logistic regression analysis, variables associated with DOAC use were female sex (OR 1.58, 95% CI 1.22–2.05), age (OR 2.00, 95% CI 1.39–2.88 and OR 2.63, 95% CI 1.84–3.76, respectively, for 65–74 years and ≥75 years vs <65 years), hypertension (OR 1.43, 95% CI 1.10–1.87), long-standing persistent or permanent AF (OR 1.36, 95% CI 1.05–1.78). Haemoglobin <12 g/dL (OR 0.57, 95% CI 0.45–0.73), and planned cancer treatment (OR 0.72, 95% CI 0.57–0.92) were independently associated with a lower prescription of DOACs.
Conclusions
BLITZ-AF Cancer provides extensive information on a large, contemporary cohort of individuals with AF and cancer. This baseline snapshot indicates that cardiologists pursue the implementation of DOACs in these patients, although residual use of other antithrombotic therapies or lack of any thrombo-prophylaxis remains substantial.
Funding Acknowledgement
Type of funding sources: Private company. Main funding source(s): This study was supported by an unrestricted grant from Daiichi Sankyo.
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Affiliation(s)
- P Ameri
- IRCCS Ospedale Policlinico San Martino, Department of Internal Medicine, University of Genova , Genova , Italy
| | - M Alings
- Amphia Hospital , Breda , The Netherlands
| | - F Colivicchi
- San Filippo Neri Hospital, ASL Rome 1, Clinical and Rehabilitation Unit , Rome , Italy
| | - R Collins
- Tallaght University Hospital, Age-Related Health Care Department , Dublin , Ireland
| | - L De Luca
- San Camillo Forlanini Hospital, Division of Cardiology, Department of Cardiosciences , Rome , Italy
| | - M Di Nisio
- University G. D'Annunzio, Department of Medicine and Ageing Sciences , Chieti , Italy
| | - G Fabbri
- ANMCO Research Center of the Heart Care Foundation , Florence , Italy
| | - D Gabrielli
- San Camillo Forlanini Hospital, Division of Cardiology, Department of Cardiosciences , Rome , Italy
| | - S Janssens
- University Hospitals Leuven, Department of Cardiology , Leuven , Belgium
| | - A P Maggioni
- ANMCO Research Center of the Heart Care Foundation , Florence , Italy
| | - I Parrini
- Mauriziano Umberto I Hospital, Cardiology Department , Turin , Italy
| | - F J Pinto
- Centro Hospitalar Universitário Lisboa Norte, Cardiology Department , Lisbon , Portugal
| | - F M Turazza
- IRCCS Fondazione Istituto Nazionale dei Tumori, Cardiology Department , Milan , Italy
| | - J L Zamorano
- University Hospital Ramόn y Cajal, Centro de Investigaciόn Biomédica en Red en Enfermedades Cardiovasculares (CIBERCV) , Madrid , Spain
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19
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Zhou J, Wu R, Williams C, Emberson J, Reith C, Keech A, Robson J, Wilkinson K, Armitage J, Collins R, Gray A, Simes J, Baigent C, Mihaylova B. Impact of cardiovascular events on primary and hospital care costs: findings from UK Biobank study. Eur Heart J 2022. [DOI: 10.1093/eurheartj/ehac544.2852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
Background
Need for primary and secondary healthcare increases following cardiovascular disease (CVD) events but there is no data on comparative increases in costs.
Purpose
To estimate annual primary care and hospital inpatient costs associated with key CVD and other adverse events using the UK Biobank (UKB) individual participant data.
Methods
UKB participants with linked primary care data (192,983 participants) or hospital inpatient episodes data (all 501,807 participants) contributed data to this study. The three categories of primary care services (patient consultations, diagnostic and monitoring tests, prescription medications), and hospital episodes were costed (2020 UK£) using the NHS England reference costs. Annual primary care costs and, separately, annual hospital inpatient costs were modelled as functions of participant characteristics at entry (socio-demographic, clinical, prior diseases) and time-updated first occurrences of myocardial infarction, stroke, coronary revascularization, incident cancer, incident diabetes, vascular death and non-vascular death during follow-up (p-value <0.01 in stepwise covariate selection). One-part generalized linear regression model (GLM) with Poisson distribution and identity link function was used for primary care costs, and two-part model was used for inpatient costs (part 1: logistic regression models probability of incurring costs; part 2: GLM with Poisson distribution and identity link function models costs conditional on incurring any). Separate models were fitted among participants with and without previous CVD at entry into UKB.
Results
Most adverse events were associated with excess primary care and hospital inpatient costs. Compared to people without previous CVD, people with previous CVD had on average larger excess primary care and hospital inpatient costs in years with myocardial infarction, stroke and vascular death; but similar excess costs in years with other events. Among both people without and with previous CVD, the excess annual primary care costs were less than 7% of the excess annual hospital inpatient costs for vascular events (Table). However, following diabetes diagnosis the excess annual primary care costs were higher than the excess annual hospital inpatient costs (Table).
Conclusions
These excess primary and hospital care costs associated with CVD events could inform assessments of interventions and policies to reduce CVD risks in UK.
Funding Acknowledgement
Type of funding sources: Public grant(s) – National budget only. Main funding source(s): UK National Institute for Health Research (NIHR) Health Technology Assessment (HTA) Programme, UK Medical Research Council (MRC), British Heart Foundation
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Affiliation(s)
- J Zhou
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - R Wu
- Queen Mary University of London, Wolfson Institute of Population Health , London , United Kingdom
| | - C Williams
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - J Emberson
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - C Reith
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - A Keech
- University of Sydney, NHMRC Clinical Trials Centre , Sydney , Australia
| | - J Robson
- Queen Mary University of London, Wolfson Institute of Population Health , London , United Kingdom
| | - K Wilkinson
- Public Representative , Oxford , United Kingdom
| | - J Armitage
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - R Collins
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - A Gray
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - J Simes
- University of Sydney, NHMRC Clinical Trials Centre , Sydney , Australia
| | - C Baigent
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - B Mihaylova
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
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20
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Mihaylova B, Wu R, Williams C, Zhou J, Schlackow I, Emberson J, Reith C, Keech A, Robson J, Wilkinson K, Armitage J, Collins R, Gray A, Simes J, Baigent C. Cost-effectiveness of statin therapy in categories of patients in the UK. Eur Heart J 2022. [DOI: 10.1093/eurheartj/ehac544.2841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Background
Cardiovascular disease (CVD) mortality has declined steadily over the last few decades across Europe and North America.
Purpose
To provide contemporary estimates of long-term effectiveness and cost-effectiveness of statin therapy in different categories of patients in UK.
Methods
The CTT-UKB micro-simulation model, developed using the Cholesterol Treatment Trialists' Collaboration data (CTT: 118,000 participants; 5 years follow-up), and calibrated in the UK Biobank cohort (UKB: 502,000 participants; 9 years follow-up). The model integrates parametric risk equations for incident myocardial infarction, stroke, coronary revascularization, diabetes, cancer and vascular and nonvascular death, and projects annually these endpoints and survival using patient characteristics at entry. UKB data and linked primary and hospital care data informed healthcare costs in the model (2020 UK£); 2021 UK NHS Drug Tariff informed statin costs (atorvastatin 40mg at £1.22 and 80mg at £1.68 per 28 tablets); and Health Survey for England data informed health-related quality of life in the model. Previous CTT meta-analysis, atorvastatin dose-response randomized trials, and further meta-analyses of statin trials and cohort studies informed effects of 40mg/80mg atorvastatin therapy daily on rates of incident myocardial infarction, stroke, coronary revascularization, vascular death, diabetes, myopathy and rhabdomyolysis.
The model was used to project gains in quality-adjusted life years (QALYs) and additional cost per QALY with lifetime use of atorvastatin 40mg or 80mg daily in categories of UKB participants by sex, age at statin initiation (40–49; 50–59 and 60–70 years), and 10-year CVD risk (QRISK3 risk (%): <5; 5–10, 10–15, 15–20, ≥20). Further scenarios explored effects of 5-year delay of statin initiation in people under 45 years of age or stopping statin therapy at 80 years of age.
Results
Across men and women in categories by age and CVD risk, lifetime use of atorvastatin 40mg daily was associated with increases in survival by 0.44–1.69 years (0.28–1.02 QALYs), and atorvastatin 80mg daily with increases in survival of 0.45–1.87 years (0.32–1.13 QALYs; Figure 1) with gains larger among participants at higher CVD risk. Both atorvastatin 40mg and 80mg doses were in the range of cost-effective treatments with incremental cost per QALY gained with atorvastatin 40mg daily versus no statin therapy below £7200/QALY and with atorvastatin 80mg vs 40mg daily below £16000/QALY (Figure 2) across all patient categories studied. Compared to lifetime statin therapy, stopping therapy at 80 years of age substantially reduced benefits and was not cost-effective in any patient category studied. Similarly, compared to immediate initiation, 5-year delay of statin therapy in 40–45 years old patients was not a cost-effective.
Conclusions
In the UK, statin therapy remains highly cost-effective across men and women 40–70 years old, including those at 10-year CVD risk <5%.
Funding Acknowledgement
Type of funding sources: Public grant(s) – National budget only. Main funding source(s): UK National Institute for Health Research (NIHR) Health Technology Assessment (HTA) Programme, UK National Institute for Health Research (NIHR) Health Technology Assessment (HTA) Programme, UK Medical Research Council (MRC), British Heart Foundation
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Affiliation(s)
- B Mihaylova
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - R Wu
- Queen Mary University of London, Wolfson Institute of Population Health , London , United Kingdom
| | - C Williams
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - J Zhou
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - I Schlackow
- University of Oxford , Oxford , United Kingdom
| | - J Emberson
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - C Reith
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - A Keech
- University of Sydney, NHMRC Clinical Trials Centre , Sydney , Australia
| | - J Robson
- Queen Mary University of London, Wolfson Institute of Population Health , London , United Kingdom
| | - K Wilkinson
- Public Representative , Oxford , United Kingdom
| | - J Armitage
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - R Collins
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - A Gray
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - J Simes
- University of Sydney, NHMRC Clinical Trials Centre , Sydney , Australia
| | - C Baigent
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
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21
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Wu R, Williams C, Zhou J, Schlackow I, Emberson J, Reith C, Keech A, Robson J, Wilkinson K, Armitage J, Collins R, Gray A, Simes J, Baigent C, Mihaylova B. Benefit accrual with cardiovascular disease prevention and effects of discontinuation: a modelling study. Eur Heart J 2022. [DOI: 10.1093/eurheartj/ehac544.2850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Background
Statin therapy reduces rates of heart attacks and strokes and improves survival in people at increased cardiovascular disease (CVD) risk. However, there is some uncertainty when to start and how long to persist with statin therapy so as to optimise benefits.
Purpose
To project the accrual of benefit with statin therapy in population groups by age at therapy initiation using a newly developed micro-simulation model.
Methods
Participants without previous CVD (N=44,412) and with previous CVD (N=13,061) at entry were randomly selected from the UK Biobank cohort, ensuring sufficient representation in respective categories by age, LDL cholesterol, diabetes and 10-year CVD risk categories (QRISK3 score, for those without previous CVD only). The CTT-UKB model, a CVD micro-simulation model [1], was used to predict subsequent survival and quality-adjusted life years (QALYs) of the participants using their characteristics at entry. Treatment with atorvastatin 40mg daily was used as an example to illustrate the effect of the therapy compared to no such therapy. Scenarios include: (1) lifelong preventive therapy, (2) preventive therapy stopped at 80 years of age, and (3) delayed initiation of preventive therapy by 5 years in participants under 45 years of age.
Results
Statin treatment benefits, measured in QALYs gained, accrue over lifetime. The majority of benefits accrue later in life. Men accumulate larger benefits and earlier than women (Figure 1A). The pattern of benefits accrual is similar for participants with and without previous CVD (data not shown). The higher the participants' CVD risk, the larger and earlier the benefits, with younger participants accruing larger benefits (Figure 1B). Compared with lifelong prevention, stopping treatment at 80 years of age leads to large reductions in overall benefits, especially in women and those at lower CVD risk. For example, compared to lifelong therapy, people without previous CVD who initiate therapy in their 50s, would lose 47% of QALYs benefit (if men), 66% (if women), 73% (if with CVD risk <5%), and 35% (if with CVD risk ≥20%), respectively, if they stop treatment when they reach 80 years of age. Five-year delay of statin therapy initiation in people under 45 years of age reduces their benefits by about 4% on average, though the loss is somewhat larger in people at higher CVD risk (Figure 2).
Conclusion
Benefits from lifelong cardiovascular prevention accrue over peoples' lifespan with large share of benefits accruing at older age. Stopping treatment earlier substantially reduces benefits.
Funding Acknowledgement
Type of funding sources: Public grant(s) – National budget only. Main funding source(s): UK NationalInstitute for Health Research (NIHR) Health Technology Assessment (HTA) Programme, UK Medical Research Council (MRC), and British Heart Foundation
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Affiliation(s)
- R Wu
- Queen Mary University of London, Wolfson Institute of Population Health , London , United Kingdom
| | - C Williams
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - J Zhou
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - I Schlackow
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - J Emberson
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - C Reith
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - A Keech
- University of Sydney, NHMRC Clinical Trials Centre , Sydney , Australia
| | - J Robson
- Queen Mary University of London, Wolfson Institute of Population Health , London , United Kingdom
| | - K Wilkinson
- Public Representative , Oxford , United Kingdom
| | - J Armitage
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - R Collins
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - A Gray
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - J Simes
- University of Sydney, NHMRC Clinical Trials Centre , Sydney , Australia
| | - C Baigent
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - B Mihaylova
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
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22
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Wijesurendra R, Sardell R, Hill M, Jayaram R, Staplin N, Collins R, Chen Z, Emberson J, Haynes R, Casadei B. Determinants of post-operative atrial fibrillation in 1613 patients undergoing coronary artery bypass grafting in the Statin Therapy In Cardiac Surgery (STICS) trial. Eur Heart J 2022. [DOI: 10.1093/eurheartj/ehac544.639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Introduction
Post-operative atrial fibrillation (POAF) occurs in 20–40% of patients in the first week after cardiac surgery, and is associated with longer hospital stay, higher stroke risk, and worse overall prognosis. The surgery-related inflammatory response has been strongly implicated in POAF pathogenesis; however, lower CRP levels resulting from perioperative rosuvastatin therapy in the Statin Therapy In Cardiac Surgery (STICS) randomized trial were not associated with a reduced incidence of POAF. Furthermore, POAF independently predicts subsequent clinical AF and as such may reflect the presence of a subclinical cardiomyopathic substrate. We tested this hypothesis by investigating determinants of POAF in 1613 patients who underwent isolated coronary artery bypass grafting in China in the STICS trial.
Methods
Clinical data included age, sex, body mass index, medical history, medications, and type of surgery (on-pump vs off-pump). Blood taken prior to surgery was assayed for troponin I, N-terminal pro–brain natriuretic peptide (NT-proBNP), creatinine, low-density lipoprotein (LDL) cholesterol, and serum CD40 ligand. The biomarkers growth differentiation factor 15, interleukin-6, procalcitonin, and placental growth factor were measured at baseline and at 6 hours after surgery. Echocardiography evaluated left ventricular ejection fraction (LVEF) and left atrial (LA) size. POAF was detected by continuous Holter electrocardiographic monitoring for 5 days after surgery.
Results
POAF occurred in 314 of 1613 patients (19%). As expected, age was the single strongest predictor of POAF (C-statistic 0.66 [95% CI 0.62–0.70]). After adjustment for age, NT-proBNP, LA size, Troponin, LVEF, sex, calcium-channel blocker use, and prior myocardial infarction were all significantly associated with POAF when assessed individually (all P<0.05). In multivariate analysis, a basic model incorporating only age, NT-proBNP, and LA size had a C-statistic of 0.69 (95% CI 0.66–0.73). This performance was not significantly different to that of models including all available variables, irrespective of whether baseline or post-surgery biomarker results were used (all C-statistics 0.71 [95% CI 0.68–0.75]; Table 1). The basic model numerically outperformed more complex risk prediction scores including CHARGE-AF (0.66, 95% CI 0.63–0.70; Figure 1), POAF score (0.64, 95% CI 0.61–0.68), CHA2DS2-VASc (0.60, 95% CI 0.57–0.63), and AF risk index (0.57, 95% CI 0.54–0.60).
Conclusions
A basic model requiring only age, NT-proBNP, and LA size has good predictive value for POAF in this population, comparing well to more complex risk prediction scores. More broadly, these results suggest that systemic inflammation and perioperative myocardial injury may be less relevant to the pathogenesis of POAF than the effects of aging and cardiac structural and functional changes.
Funding Acknowledgement
Type of funding sources: Foundation. Main funding source(s): British Heart Foundation
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Affiliation(s)
- R Wijesurendra
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - R Sardell
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - M Hill
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - R Jayaram
- University of Oxford, Division of Cardiovascular Medicine, Radcliffe Department of Medicine , Oxford , United Kingdom
| | - N Staplin
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - R Collins
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - Z Chen
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - J Emberson
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - R Haynes
- University of Oxford, Nuffield Department of Population Health , Oxford , United Kingdom
| | - B Casadei
- University of Oxford, Division of Cardiovascular Medicine, Radcliffe Department of Medicine , Oxford , United Kingdom
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23
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O’Connor A, Hobson H, Collins R. 1083 A REVIEW OF FAST DATA IN TALLAGHT UNIVERSITY HOSPITAL, AND THE IMPACT OF COVID ON TREATMENT. Age Ageing 2022. [DOI: 10.1093/ageing/afac126.088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Introduction
Stroke is a leading cause of morbidity. Current guidelines advise maximum of 4.5 hours post symptom onset for thrombolysis, and 24 hours for thrombectomy. (1) Delay between symptom onset and treatment is associated with an inferior outcome. Thrombolysis is available in 27 Irish centers. Average rate of thrombolysis is 11%. (3) Fewer than half of stroke patients arrived in hospital within three hours of symptom onset in 2019. (4) Median door-to-needle time is 48 minutes.
Method
FAST calls in Tallaght University Hospital, from 2/7/19–1/7/21, were included in this analysis (n = 594).
Results
160 FAST calls took place pre-Covid (20/month), and 434 post-Covid (27.12/month). Time of symptom onset was recorded in 390 cases. Time patients last seen well was known in 185 cases, unknown time of onset in 19. After review by the stroke clinician, FAST imaging was obtained in 78% of cases (n = 464). Of these, 34 cases of FAST imaging were performed for inpatients. Average time from registration to CT was 35:24 minutes pre covid, and 45:52 minutes post. 9.7% of patients were thrombolysed. The median door-to-needle time was 41 minutes pre-Covid(n = 21, 2.625/month), and 54 minutes after (n = 37, 2.3/month). Thrombectomy was performed in 46 cases. 222 patients were diagnosed with an ischaemic stroke, 50 had TIA and 48 had haemorrhagic strokes. Other diagnoses included migraine (6.7%, n = 40), seizures (6.7%, n = 40) and Bells Palsy (3.7%, n = 22). 55% (n = 330) of cases were registered to ED with FAST call between the hours of 9 am-5 pm. 27% (n = 161) of cases occurred during the night shift.
Conclusion
The median door-to-needle times were below national median pre-Covid, and longer post-pandemic, with an increase in the rate of presentation in the same time-frame. This report highlights the effect of the pandemic on time-critical patient interventions in stroke and the need to stratify services to respond to structural challenges.
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Sammons E, Hopewell JC, Chen F, Stevens W, Wallendszus K, Valdes-Marquez E, Dayanandan R, Knott C, Murphy K, Wincott E, Baxter A, Goodenough R, Lay M, Hill M, Macdonnell S, Fabbri G, Lucci D, Fajardo-Moser M, Brenner S, Hao D, Zhang H, Liu J, Wuhan B, Mosegaard S, Herrington W, Wanner C, Angermann C, Ertl G, Maggioni A, Barter P, Mihaylova B, Mitchel Y, Blaustein R, Goto S, Tobert J, DeLucca P, Chen Y, Chen Z, Gray A, Haynes R, Armitage J, Baigent C, Wiviott S, Cannon C, Braunwald E, Collins R, Bowman L, Landray M. Long-term safety and efficacy of anacetrapib in patients with atherosclerotic vascular disease. Eur Heart J 2022; 43:1416-1424. [PMID: 34910136 PMCID: PMC8986460 DOI: 10.1093/eurheartj/ehab863] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 09/30/2021] [Accepted: 12/02/2021] [Indexed: 01/04/2023] Open
Abstract
AIMS REVEAL was the first randomized controlled trial to demonstrate that adding cholesteryl ester transfer protein inhibitor therapy to intensive statin therapy reduced the risk of major coronary events. We now report results from extended follow-up beyond the scheduled study treatment period. METHODS AND RESULTS A total of 30 449 adults with prior atherosclerotic vascular disease were randomly allocated to anacetrapib 100 mg daily or matching placebo, in addition to open-label atorvastatin therapy. After stopping the randomly allocated treatment, 26 129 survivors entered a post-trial follow-up period, blind to their original treatment allocation. The primary outcome was first post-randomization major coronary event (i.e. coronary death, myocardial infarction, or coronary revascularization) during the in-trial and post-trial treatment periods, with analysis by intention-to-treat. Allocation to anacetrapib conferred a 9% [95% confidence interval (CI) 3-15%; P = 0.004] proportional reduction in the incidence of major coronary events during the study treatment period (median 4.1 years). During extended follow-up (median 2.2 years), there was a further 20% (95% CI 10-29%; P < 0.001) reduction. Overall, there was a 12% (95% CI 7-17%, P < 0.001) proportional reduction in major coronary events during the overall follow-up period (median 6.3 years), corresponding to a 1.8% (95% CI 1.0-2.6%) absolute reduction. There were no significant effects on non-vascular mortality, site-specific cancer, or other serious adverse events. Morbidity follow-up was obtained for 25 784 (99%) participants. CONCLUSION The beneficial effects of anacetrapib on major coronary events increased with longer follow-up, and no adverse effects emerged on non-vascular mortality or morbidity. These findings illustrate the importance of sufficiently long treatment and follow-up duration in randomized trials of lipid-modifying agents to assess their full benefits and potential harms. TRIAL REGISTRATION International Standard Randomized Controlled Trial Number (ISRCTN) 48678192; ClinicalTrials.gov No. NCT01252953; EudraCT No. 2010-023467-18.
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Affiliation(s)
- E Sammons
- REVEAL Central Coordinating Office, Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
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25
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Collins R, Lafford G, Meghji S, Burrows S. 96 Adult Nasal Chondromesenchymal Hamartoma: A Rare and Benign Tumour with Aggressive Malignant Transformation. Br J Surg 2022. [DOI: 10.1093/bjs/znac039.050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Abstract
Nasal chondromesenchymal hamartoma (NCMH) is an extremely rare benign tumour of the nasal cavity predominantly described in infants. To date, a total of 59 cases have been described. We report a case involving a 48-year-old female who had been diagnosed with NCMH a year earlier and now represented with a short history of progressive nasal blockage, recurrent epistaxis, and orbital apex syndrome.
Histopathology was suggestive of malignant transformation into sinonasal sarcoma. However, following multi-disciplinary team (MDT) discussions including a second and third opinion from external departments, the histological diagnosis was revised to ‘NCMH with bizarre stromal cells. Despite this, clinically the lesion demonstrated malignant features with rapid, invasive growth and was treated with palliative radiotherapy. The patient later developed radiological signs of lung and liver metastases and subsequent pulmonary emboli. Shortly after this she passed away. This case is unique in its diagnostic challenge, with ambiguous histopathological findings, and highlights the importance of an MDT approach when managing complex sinonasal tumours.
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Affiliation(s)
- R. Collins
- Norfolk and Norwich University Hospital, Norwich, United Kingdom
| | - G. Lafford
- Norfolk and Norwich University Hospital, Norwich, United Kingdom
| | - S. Meghji
- Norfolk and Norwich University Hospital, Norwich, United Kingdom
| | - S. Burrows
- Norfolk and Norwich University Hospital, Norwich, United Kingdom
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26
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Collins R, Lafford G. 97 Paralysis From an Ear Infection: A Severe Case of Otitis Externa Leading to Acute Complete Cervical Cord Syndrome. Br J Surg 2022. [DOI: 10.1093/bjs/znac039.051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Abstract
We report a case of a generally fit and well 54-year-old gentleman who presented with a two-day history of worsening left sided ottorhea, headache, neck stiffness, vomiting and pyrexia on the background of a seven-week history of OE. His condition progressed dramatically as he developed symptoms consistent with acute complete cervical cord syndrome with radiological evidence of skull base osteomyelitis, parapharyngeal, retropharyngeal and paravertebral abscesses and sigmoid sinus thrombus. Ultimately, he made a significant, although not complete, recovery.
This case is unique in demonstrating how OE can develop into a potentially life-threatening condition. It emphasises the importance of early diagnosis and treatment of OE, the recognition of ‘red flag’ symptoms and highlights the importance of a multi-disciplinary team (MDT) approach when managing complex complications of OE.
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Affiliation(s)
- R. Collins
- Norfolk and Norwich University Hospital, Norwich, United Kingdom
| | - G. Lafford
- Norfolk and Norwich University Hospital, Norwich, United Kingdom
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27
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Finnegan J, Mello S, Cogan N, Greene S, Ryan D, Collins R. Stroke Risk Factors, Subtype, and Outcomes in a Multi-Ethnic Stroke Population. Ir Med J 2022; 115:520. [PMID: 35279054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Aim We aim to describe differences in stroke risk factors, subtypes and outcomes in a multi-ethnic Irish Stroke population. Gaining an insight into prevalent risk factors and subtypes in ethnic groups may help target prevention efforts. Methods We retrospectively identified patients originally not of Irish ethnicity (ONIE) admitted to the acute stroke unit between 2016 and 2018 through surname recognition (N=44). Country of origin was confirmed on chart review. The presumed native Irish (PNI) patients admitted over the same time frame were used as a comparison group (N=437). Data was collected on stroke subtype, comorbidities, outcomes and socioeconomic factors. Results Patients ONIE made up 9.1% of all stroke unit admissions. Male gender was more common accounting for 33 of 44 (75%) patients ONIE and 251 of 437 (57.4%) PNI (p = 0.02). Overall ONIE were younger than PNI patients (mean age 57.5 [SD 13.0] vs 69.6yr [SD 13.2], p <0.001). Patients ONIE also recorded higher rates of intracranial haemorrhage(ICH) (N = 15 [34.1%] vs N=51 [11.7%], p <0.01). Conclusion Our study demonstrates that stroke patients ONIE have a different stroke subtype and demographic profile compared to Irish patients. Patients ONIE are more likely to be young, male with higher rates of ICH.
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Affiliation(s)
- J Finnegan
- Tallaght University Hospital, Dublin, Ireland
| | - S Mello
- Department of Age-Related Healthcare and Stroke Medicine, Tallaght University Hospital, Dublin, Ireland
| | - N Cogan
- Department of Age-Related Healthcare and Stroke Medicine, Tallaght University Hospital, Dublin, Ireland
| | - S Greene
- Department of Age-Related Healthcare and Stroke Medicine, Tallaght University Hospital, Dublin, Ireland
| | - D Ryan
- Department of Age-Related Healthcare and Stroke Medicine, Tallaght University Hospital, Dublin, Ireland
| | - R Collins
- Department of Age-Related Healthcare and Stroke Medicine, Tallaght University Hospital, Dublin, Ireland
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28
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Satzinger KJ, Liu YJ, Smith A, Knapp C, Newman M, Jones C, Chen Z, Quintana C, Mi X, Dunsworth A, Gidney C, Aleiner I, Arute F, Arya K, Atalaya J, Babbush R, Bardin JC, Barends R, Basso J, Bengtsson A, Bilmes A, Broughton M, Buckley BB, Buell DA, Burkett B, Bushnell N, Chiaro B, Collins R, Courtney W, Demura S, Derk AR, Eppens D, Erickson C, Faoro L, Farhi E, Fowler AG, Foxen B, Giustina M, Greene A, Gross JA, Harrigan MP, Harrington SD, Hilton J, Hong S, Huang T, Huggins WJ, Ioffe LB, Isakov SV, Jeffrey E, Jiang Z, Kafri D, Kechedzhi K, Khattar T, Kim S, Klimov PV, Korotkov AN, Kostritsa F, Landhuis D, Laptev P, Locharla A, Lucero E, Martin O, McClean JR, McEwen M, Miao KC, Mohseni M, Montazeri S, Mruczkiewicz W, Mutus J, Naaman O, Neeley M, Neill C, Niu MY, O'Brien TE, Opremcak A, Pató B, Petukhov A, Rubin NC, Sank D, Shvarts V, Strain D, Szalay M, Villalonga B, White TC, Yao Z, Yeh P, Yoo J, Zalcman A, Neven H, Boixo S, Megrant A, Chen Y, Kelly J, Smelyanskiy V, Kitaev A, Knap M, Pollmann F, Roushan P. Realizing topologically ordered states on a quantum processor. Science 2021; 374:1237-1241. [PMID: 34855491 DOI: 10.1126/science.abi8378] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
[Figure: see text].
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Affiliation(s)
| | - Y-J Liu
- Department of Physics, Technical University of Munich, 85748 Garching, Germany.,Munich Center for Quantum Science and Technology (MCQST), Schellingstraße 4, 80799 München, Germany
| | - A Smith
- Department of Physics, Technical University of Munich, 85748 Garching, Germany.,School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD, UK.,Centre for the Mathematics and Theoretical Physics of Quantum Non-Equilibrium Systems, University of Nottingham, Nottingham NG7 2RD, UK
| | - C Knapp
- Department of Physics and Institute for Quantum Information and Matter, California Institute of Technology, Pasadena, CA, USA.,Walter Burke Institute for Theoretical Physics, California Institute of Technology, Pasadena, CA, USA
| | - M Newman
- Google Quantum AI, Mountain View, CA, USA
| | - C Jones
- Google Quantum AI, Mountain View, CA, USA
| | - Z Chen
- Google Quantum AI, Mountain View, CA, USA
| | - C Quintana
- Google Quantum AI, Mountain View, CA, USA
| | - X Mi
- Google Quantum AI, Mountain View, CA, USA
| | | | - C Gidney
- Google Quantum AI, Mountain View, CA, USA
| | - I Aleiner
- Google Quantum AI, Mountain View, CA, USA
| | - F Arute
- Google Quantum AI, Mountain View, CA, USA
| | - K Arya
- Google Quantum AI, Mountain View, CA, USA
| | - J Atalaya
- Google Quantum AI, Mountain View, CA, USA
| | - R Babbush
- Google Quantum AI, Mountain View, CA, USA
| | - J C Bardin
- Google Quantum AI, Mountain View, CA, USA.,Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | - R Barends
- Google Quantum AI, Mountain View, CA, USA
| | - J Basso
- Google Quantum AI, Mountain View, CA, USA
| | | | - A Bilmes
- Google Quantum AI, Mountain View, CA, USA
| | | | | | - D A Buell
- Google Quantum AI, Mountain View, CA, USA
| | - B Burkett
- Google Quantum AI, Mountain View, CA, USA
| | - N Bushnell
- Google Quantum AI, Mountain View, CA, USA
| | - B Chiaro
- Google Quantum AI, Mountain View, CA, USA
| | - R Collins
- Google Quantum AI, Mountain View, CA, USA
| | - W Courtney
- Google Quantum AI, Mountain View, CA, USA
| | - S Demura
- Google Quantum AI, Mountain View, CA, USA
| | - A R Derk
- Google Quantum AI, Mountain View, CA, USA
| | - D Eppens
- Google Quantum AI, Mountain View, CA, USA
| | - C Erickson
- Google Quantum AI, Mountain View, CA, USA
| | - L Faoro
- Laboratoire de Physique Theorique et Hautes Energies, Sorbonne Université, 75005 Paris, France
| | - E Farhi
- Google Quantum AI, Mountain View, CA, USA
| | - A G Fowler
- Google Quantum AI, Mountain View, CA, USA
| | - B Foxen
- Google Quantum AI, Mountain View, CA, USA
| | - M Giustina
- Google Quantum AI, Mountain View, CA, USA
| | - A Greene
- Google Quantum AI, Mountain View, CA, USA.,Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - J A Gross
- Google Quantum AI, Mountain View, CA, USA
| | | | | | - J Hilton
- Google Quantum AI, Mountain View, CA, USA
| | - S Hong
- Google Quantum AI, Mountain View, CA, USA
| | - T Huang
- Google Quantum AI, Mountain View, CA, USA
| | | | - L B Ioffe
- Google Quantum AI, Mountain View, CA, USA
| | - S V Isakov
- Google Quantum AI, Mountain View, CA, USA
| | - E Jeffrey
- Google Quantum AI, Mountain View, CA, USA
| | - Z Jiang
- Google Quantum AI, Mountain View, CA, USA
| | - D Kafri
- Google Quantum AI, Mountain View, CA, USA
| | | | - T Khattar
- Google Quantum AI, Mountain View, CA, USA
| | - S Kim
- Google Quantum AI, Mountain View, CA, USA
| | - P V Klimov
- Google Quantum AI, Mountain View, CA, USA
| | - A N Korotkov
- Google Quantum AI, Mountain View, CA, USA.,Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA
| | | | - D Landhuis
- Google Quantum AI, Mountain View, CA, USA
| | - P Laptev
- Google Quantum AI, Mountain View, CA, USA
| | - A Locharla
- Google Quantum AI, Mountain View, CA, USA
| | - E Lucero
- Google Quantum AI, Mountain View, CA, USA
| | - O Martin
- Google Quantum AI, Mountain View, CA, USA
| | | | - M McEwen
- Google Quantum AI, Mountain View, CA, USA.,Department of Physics, University of California, Santa Barbara, CA, USA
| | - K C Miao
- Google Quantum AI, Mountain View, CA, USA
| | - M Mohseni
- Google Quantum AI, Mountain View, CA, USA
| | | | | | - J Mutus
- Google Quantum AI, Mountain View, CA, USA
| | - O Naaman
- Google Quantum AI, Mountain View, CA, USA
| | - M Neeley
- Google Quantum AI, Mountain View, CA, USA
| | - C Neill
- Google Quantum AI, Mountain View, CA, USA
| | - M Y Niu
- Google Quantum AI, Mountain View, CA, USA
| | | | - A Opremcak
- Google Quantum AI, Mountain View, CA, USA
| | - B Pató
- Google Quantum AI, Mountain View, CA, USA
| | - A Petukhov
- Google Quantum AI, Mountain View, CA, USA
| | - N C Rubin
- Google Quantum AI, Mountain View, CA, USA
| | - D Sank
- Google Quantum AI, Mountain View, CA, USA
| | - V Shvarts
- Google Quantum AI, Mountain View, CA, USA
| | - D Strain
- Google Quantum AI, Mountain View, CA, USA
| | - M Szalay
- Google Quantum AI, Mountain View, CA, USA
| | | | - T C White
- Google Quantum AI, Mountain View, CA, USA
| | - Z Yao
- Google Quantum AI, Mountain View, CA, USA
| | - P Yeh
- Google Quantum AI, Mountain View, CA, USA
| | - J Yoo
- Google Quantum AI, Mountain View, CA, USA
| | - A Zalcman
- Google Quantum AI, Mountain View, CA, USA
| | - H Neven
- Google Quantum AI, Mountain View, CA, USA
| | - S Boixo
- Google Quantum AI, Mountain View, CA, USA
| | - A Megrant
- Google Quantum AI, Mountain View, CA, USA
| | - Y Chen
- Google Quantum AI, Mountain View, CA, USA
| | - J Kelly
- Google Quantum AI, Mountain View, CA, USA
| | | | - A Kitaev
- Google Quantum AI, Mountain View, CA, USA.,Department of Physics and Institute for Quantum Information and Matter, California Institute of Technology, Pasadena, CA, USA.,Walter Burke Institute for Theoretical Physics, California Institute of Technology, Pasadena, CA, USA
| | - M Knap
- Department of Physics, Technical University of Munich, 85748 Garching, Germany.,Munich Center for Quantum Science and Technology (MCQST), Schellingstraße 4, 80799 München, Germany.,Institute for Advanced Study, Technical University of Munich, 85748 Garching, Germany
| | - F Pollmann
- Department of Physics, Technical University of Munich, 85748 Garching, Germany.,Munich Center for Quantum Science and Technology (MCQST), Schellingstraße 4, 80799 München, Germany
| | - P Roushan
- Google Quantum AI, Mountain View, CA, USA
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29
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McGarvey C, Hobson H, Greene S, Cogan N, McCabe D, McCarthy A, Murphy S, O'Dowd S, Walsh R, Coughlan T, O'Neill D, Kennelly S, Mello S, Coveney S, Ryan D, Collins R. 209 NEURO-MEDICAL COMPLICATIONS OF STROKE—TRENDS OVER THE DECADES IN AN ACUTE STROKE UNIT. Age Ageing 2021. [DOI: 10.1093/ageing/afab219.209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023] Open
Abstract
Abstract
Background
Neuro-medical complications post-stroke are common and often serious [1]. We first described complications in our stroke cohort in 1998 and sought to assess whether the severity and the nature of neuro-medical complications may have changed over time due to changes in presentation and the processes of care [2].
Methods
Analysis of stroke service database, which captures all neuro-medical complications as part of its portal for the Irish National Audit of Stroke (INAS), was completed. The frequency of each of the 19 complications was expressed as the percentage of patients that developed each complication over a certain year and over 5 years. Historical comparison was made with dataset from 1998, which captured six complications.
Results
Data on 1,283 patients presenting over 5 years between 2015–2019 was collected. The median age of all patients was 71 years (Range 21–101). In all, 19 different post-stroke complications were recorded; 48% (n = 622) had post-stroke pain, while 23.85% (n = 306) had cognitive decline. Data on 100 patients from 1998 was compared for a number of common metrics including; 21.82% (n = 275) of patients developed an LRTI in the 2015–2019 cohort compared with 14%(n = 14) in the 1998 cohort (p = 0.09) while 16.29% (n = 209) of patients developed a swallow disorder compared to 21% (n = 21) in 1998 (p = 0.22).
Conclusion
There are high levels of neuro-medical complications in stroke patients. Twenty years has seen extensive investment in hyperacute stroke care yet post-acute care complications did not appear to reduce significantly between this time, albeit with low numbers. Direction of future funding may consider the full spectrum of stroke care.
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Affiliation(s)
- C McGarvey
- Department of Age-Related Health Care/Stroke Service and School of Gerontology Trinity College Dublin , Dublin, Ireland
| | - H Hobson
- Department of Age-Related Health Care/Stroke Service and School of Gerontology Trinity College Dublin , Dublin, Ireland
| | - S Greene
- Department of Age-Related Health Care/Stroke Service and School of Gerontology Trinity College Dublin , Dublin, Ireland
| | - N Cogan
- Department of Age-Related Health Care/Stroke Service and School of Gerontology Trinity College Dublin , Dublin, Ireland
| | - D McCabe
- Department of Neurology, Tallaght University Hospital , Dublin, Ireland
| | - A McCarthy
- Department of Neurology, Tallaght University Hospital , Dublin, Ireland
| | - S Murphy
- Department of Neurology, Tallaght University Hospital , Dublin, Ireland
| | - S O'Dowd
- Department of Neurology, Tallaght University Hospital , Dublin, Ireland
| | - R Walsh
- Department of Neurology, Tallaght University Hospital , Dublin, Ireland
| | - T Coughlan
- Department of Age-Related Health Care/Stroke Service and School of Gerontology Trinity College Dublin , Dublin, Ireland
| | - D O'Neill
- Department of Age-Related Health Care/Stroke Service and School of Gerontology Trinity College Dublin , Dublin, Ireland
| | - S Kennelly
- Department of Age-Related Health Care/Stroke Service and School of Gerontology Trinity College Dublin , Dublin, Ireland
| | - S Mello
- Department of Age-Related Health Care/Stroke Service and School of Gerontology Trinity College Dublin , Dublin, Ireland
| | - S Coveney
- Department of Age-Related Health Care/Stroke Service and School of Gerontology Trinity College Dublin , Dublin, Ireland
| | - D Ryan
- Department of Age-Related Health Care/Stroke Service and School of Gerontology Trinity College Dublin , Dublin, Ireland
| | - R Collins
- Department of Age-Related Health Care/Stroke Service and School of Gerontology Trinity College Dublin , Dublin, Ireland
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30
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Wu R, Williams C, Schlackow I, Zhou J, Emberson J, Reith C, Keech A, Robson J, Wilkinson K, Armitage J, Collins R, Gray A, Simes J, Baigent C, Mihaylova B. A model of lifetime health outcomes in cardiovascular disease based on clinical trials and large cohorts. Eur Heart J 2021. [DOI: 10.1093/eurheartj/ehab724.3149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
Background and purpose
Cardiovascular disease (CVD) risk of individuals depends on their socio-demographic characteristics, clinical risk factors, and treatments, and strongly influences their quality of life and survival. Individual-based long-term disease models, which aim to more accurately calculate the lifetime consequences, can help to target treatments, develop disease management programmes, and assess the value of new therapies. We present a new micro-simulation CVD model.
Methods
This micro-simulation model was developed using individual participant data from the Cholesterol Treatment Trialists' collaboration (CTT: 118,000 participants; 15 trials) and calibrated (with added socioeconomic deprivation, ethnicity, physical activity, mental illness, cancer and incident diabetes) in the UK Biobank cohort (UKB: 502,000 participants). Parametric survival models estimated risks of key endpoints (myocardial infarction (MI), stroke, coronary revascularisation (CRV), diabetes, cancer and vascular (VD) and nonvascular death (NVD) using participants' age, sex, ethnicity, physical activity, socioeconomic deprivation, smoking history, lipids, blood pressure, creatinine, previous cardiovascular diseases, diabetes, mental illness and cancer at entry and non-fatal incidents of the key endpoints during follow-up. The model integrates the risk equations and enables annual projection of endpoints and survival over individuals' lifetimes. The model was used to project remaining life expectancy across UK Biobank participants.
Results
Nonfatal cardiovascular events and age were the major determinants of CVD risk and, together with incident diabetes and cancer, of individuals' survival. The cumulative incidence of the key endpoints predicted by the CTT-UKB model corresponded well to their observed incidence in the UK Biobank cohort, overall (Figure 1) and in categories of participants by age, sex, prior CVD and CVD risk. Predicted remaining life expectancy across UK Biobank participants without history of CVD ranged between 22 and 43 years in men and between 24 and 46 years in women, depending on their age and CVD risk (Figure 2). Among UK Biobank participants with history of CVD, depending on their age, predicted remaining life expectancy ranged from 20 to 32 years in men and from 26 to 38 years in women.
Conclusion
This new lifetime CVD model accurately predicts morbidity and mortality in a large UK population cohort. It will be made available to provide individualised projections of expected lifetime health outcomes and benefits of treatments.
Funding Acknowledgement
Type of funding sources: Public grant(s) – National budget only. Main funding source(s): UK National Institute for Health Research (NIHR) Health Technology Assessment (HTA) Programme, UK Medical Research Council (MRC), British Heart Foundation Figure 1. Predicted (in black) versus observed (95% CI; in red) incidence of major clinical outcomes in the UK Biobank.Figure 2. Predicted remaining life expectancy of participants in UK Biobank cohort, by age and CVD risk or previous CVD at entry. QRISK, a 10-year CVD risk scoring algorithm for people without previous CVD, recommended for use in the UK National Health Service.
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Affiliation(s)
- R Wu
- Queen Mary University of London, London, United Kingdom
| | - C Williams
- University of Oxford, Nuffield Department of Population Health, Oxford, United Kingdom
| | - I Schlackow
- University of Oxford, Nuffield Department of Population Health, Oxford, United Kingdom
| | - J Zhou
- University of Oxford, Nuffield Department of Population Health, Oxford, United Kingdom
| | - J Emberson
- University of Oxford, Nuffield Department of Population Health, Oxford, United Kingdom
| | - C Reith
- University of Oxford, Nuffield Department of Population Health, Oxford, United Kingdom
| | - A Keech
- University of Sydney, Clinical Trials Centre, Sydney, Australia
| | - J Robson
- Queen Mary University of London, London, United Kingdom
| | - K Wilkinson
- Public Representative, Oxford, United Kingdom
| | - J Armitage
- University of Oxford, Nuffield Department of Population Health, Oxford, United Kingdom
| | - R Collins
- University of Oxford, Nuffield Department of Population Health, Oxford, United Kingdom
| | - A Gray
- University of Oxford, Nuffield Department of Population Health, Oxford, United Kingdom
| | - J Simes
- University of Sydney, Clinical Trials Centre, Sydney, Australia
| | - C Baigent
- University of Oxford, Nuffield Department of Population Health, Oxford, United Kingdom
| | - B Mihaylova
- University of Oxford, Nuffield Department of Population Health, Oxford, United Kingdom
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31
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Suleiman S, Coughlan J, Waters M, Collins R, Moore D. Prevalence and predictors of inappropriate DOAC prescription on first attendance at a dedicated atrial fibrillation clinic. Eur Heart J 2021. [DOI: 10.1093/eurheartj/ehab724.0570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Introduction
Direct-acting oral anticoagulants (DOACs) are the preferred agents for stroke prevention in patients with non-valvular atrial fibrillation (AF). DOACs may require dose adjustment based on several factors, including: age, renal function and body weight. An inappropriate DOAC prescription is defined as a deviation of the drug specific recommended dose as mentioned in the summary of product characteristics. Inappropriate DOAC prescription may consist of both under- and over-dosing, potentially exposing patients to harm. Therefore, we carried out the current study, with the aim of defining the prevalence and predictors of inappropriate DOAC prescription on first attendance of patients at a specialist AF clinic.
Methods
We performed a retrospective analysis of the clinical database associated with a dedicated AF clinic in a large Irish hospital from August 2015 to March 2020. All new patients who were referred to the clinic and prescribed a DOAC prior to attendance were included. Data collected on patients included demographic and biochemical data in addition to clinical information on medical co-morbidities. In addition, the CHADS2VASc and HASBLED score was calculated for all patients. A multivariable logistic regression model was developed to assess for predictors of inappropriate DOAC dosing. Purposeful variable selection was used with univariate regression performed initially in order to identify predictors to include in the multivariable model.
Results
We included 367 patients in the analysis. An inappropriate DOAC dose was identified in 47 of 367 patients (12.8%). The majority of inappropriate DOAC doses were due to under-dosing (76.6%). Patients prescribed an inappropriate DOAC dose tended to be older (78.9±8.4 vs 69.0±10.5 years, p<.001), with higher creatinine (108.5±4.6 vs 88.9±1.3, p<.001). Patients prescribed an inappropriate DOAC dose also tended to have higher CHADS2VASc (3.8±1.7 vs 3.0±1.5, p=.001) and HASBLED scores (2.0±1.0 vs 1.6±1.0, p=.01) than patients prescribed an appropriate DOAC dose. DOAC choice did not differ between the inappropriate and appropriate DOAC dose groups. On univariate logistic regression analysis, several predictors of inappropriate DOAC prescription were identified, including age, renal function, history of falls, CHADS2VASc score and HASBLED score. However, in the multivariate logistic regression model, only increasing age (p<0.001) and decreasing renal function (p<0.001) remained significant predictors of inappropriate DOAC prescription.
Conclusions
Over one in eight patients (12.8%) are prescribed an inappropriate DOAC dose on first attendance at a dedicated atrial fibrillation clinic. In the majority of cases, the inappropriate DOAC prescription was secondary to under-dosing. In our multivariable, logistic regression model, increasing age and decreasing renal function were significant predictors of inappropriate DOAC prescription.
Funding Acknowledgement
Type of funding sources: None. Table 1
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Affiliation(s)
- S Suleiman
- Tallaght University Hospital, Department of Cardiology, Dublin, Ireland
| | - J.J Coughlan
- German Heart Centre Munich, Department of Cardiology, Munich, Germany
| | - M Waters
- Tallaght University Hospital, Department of Cardiology, Dublin, Ireland
| | - R Collins
- Tallaght University Hospital, Department of Geriatrics and Stroke Medicine, Dublin, Ireland
| | - D Moore
- Tallaght University Hospital, Department of Cardiology, Dublin, Ireland
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32
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Collins R, Addison A, Paul C, Clark A, Philpott C. 918 Nasal Packing Duration in The Management of Epistaxis: A Cohort Study. Br J Surg 2021. [DOI: 10.1093/bjs/znab259.556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Abstract
Aim
Nasal packing is one of the mainstays of inpatient treatment for epistaxis. However, no current guidelines exist on optimal duration of nasal packing. This paper seeks to determine the optimal duration of nasal packing in terms of re-bleeding.
Method
A retrospective cohort study was conducted on patients admitted over a two-year period. Data collected included re-bleeding events following pack removal, age, gender, site of epistaxis, hypertension, trauma and anticoagulation medication.
Results
The rate of re-bleeding increased with length of pack duration; over 12 hours was associated with greater re-bleeding rates (p = <0.001) and continued to be statistically significant when controlling for co-founders (p = 0.01). Those packed over 24 hours were over five times more likely to re-bleed than those packed less than 12 hours (p = 0.01. OR 5.34).
Conclusions
A packing duration of less than 12 hours is associated with lower rates of re-bleeding. Admitting teams should therefore aim to ensure that patient pathways involve early pack removal.
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Affiliation(s)
- R Collins
- University of East Anglia, Norwich, United Kingdom
- Norfolk and Norwich University Hospital, Norwich, United Kingdom
| | - A Addison
- Norfolk and Norwich University Hospital, Norwich, United Kingdom
| | - C Paul
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - A Clark
- University of East Anglia, Norwich, United Kingdom
| | - C Philpott
- University of East Anglia, Norwich, United Kingdom
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33
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Rhind JH, Ramhamadany E, Collins R, Govilkar S, Dass D, Hay S. 42 An Analysis of Virtual Fracture Clinics in Orthopaedic Trauma in the UK During the Coronavirus Crisis. Br J Surg 2021. [DOI: 10.1093/bjs/znab259.434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Abstract
Aim
Virtual Fracture Clinics (VFC) are advocated by new Orthopaedic (British Orthopaedic Association) and National Health Service (NHS) guidelines in the United Kingdom. We discuss benefits and limitations, reviewing the literature. As well as recommendations on introducing a VFC service during the Coronavirus pandemic and into the future.
Method
A narrative review identifying current literature on virtual fracture clinic outcomes when compared to traditional model fracture clinics in the UK. We identify 9 relevant publications related to VFC.
Results
The Glasgow Model initiated in 2011 has become the benchmark. Clinical efficiency can be improved, reducing the number of ED referrals seen in VFC by 15%-28% and face to face consultations by 65%. 33-60% of patients may be discharged after review in the VFC. Some studies have shown no negative impact on the Emergency Department (ED), the time to discharge was not increased. Patients satisfaction ranges from 91%-97% using a VFC service, and there may be cost saving benefits annually from £67,385-£212,705. Non-attendance may be reduced by 75% and there are educational opportunities for trainees. However, evidence is limited, 28% of patients prefer face-to-face consultations and not all have access to internet or email (72%).
Conclusions
We propose a pathway integrating the VFC model, whilst having Senior Orthopaedic decision makers available in ED, during normal working hours, to cope with the pandemic. Beyond the pandemic, evidence suggests the Glasgow model is viable for day-to-day practice.
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Affiliation(s)
- J H Rhind
- Robert Jones Agnes Hunt Hopsital, Oswestry, United Kingdom
| | - E Ramhamadany
- Robert Jones Agnes Hunt Hopsital, Oswestry, United Kingdom
| | - R Collins
- Robert Jones Agnes Hunt Hopsital, Oswestry, United Kingdom
| | - S Govilkar
- Robert Jones Agnes Hunt Hopsital, Oswestry, United Kingdom
| | - D Dass
- Robert Jones Agnes Hunt Hopsital, Oswestry, United Kingdom
| | - S Hay
- Robert Jones Agnes Hunt Hopsital, Oswestry, United Kingdom
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Sexton E, Hickey A, Williams DJ, Horgan F, Byrne E, Macey C, Cuffe P, Timmons S, Collins R, Bennett K. Identifying priority interventions for stroke in Ireland through stakeholder engagement to inform population-based modelling: a mixed methods protocol. HRB Open Res 2021; 4:109. [PMID: 38567097 PMCID: PMC10985459 DOI: 10.12688/hrbopenres.13413.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/09/2021] [Indexed: 04/04/2024] Open
Abstract
Introduction Improvements in stroke survival have resulted in increasing numbers of people living with stroke, and with a rapidly evolving evidence-base for stroke prevention and management, there is a need for robust data and evidence to inform future policy decision-making. Population-based modelling and economic evaluation of alternative policy options is a useful tool to support decision making. However, this process must be aligned to key stakeholder priorities. The aim of the proposed research is to engage with stakeholders in Ireland to identify their priorities for the development of stroke prevention and management strategies and policies. Methods The design is iterative, based on mixed methods. Phase 1 involves a qualitative approach for initial priority gathering, based on an open-ended online survey (target sample: 100-120) and interviews (target sample: 34-40). Stakeholders will include: 1) stroke survivors and family member/main carers, 2) healthcare professionals (HCPs) providing stroke care and 3) people working in stroke research, policy and advocacy. These data will be analysed qualitatively, with the aim of identifying a long-list of specific interventions. Phase 2 involves an interim priority-setting exercise, based on a quantitative online survey. Participants will be asked to rank the interventions on the initial long-list. These rankings will be used to inform a final priority-setting workshop (Phase 3), where a small stakeholder group will decide on the final set of priorities. Discussion The rich and detailed quantitative and qualitative data, based on the views of diverse stakeholders, will be directly relevant to policy makers and service planners involved in developing and improving stroke care in Ireland. The information provided will also be essential to inform the Scenario and Intervention Modelling in Ireland for Stroke (SIMI-Stroke) project, a population-based economic and epidemiological modelling study aimed at identifying cost-effective interventions for stroke across the prevention, acute and post-acute care continuum.
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Affiliation(s)
- Eithne Sexton
- Division of Population Health Sciences, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Anne Hickey
- Division of Population Health Sciences, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - David J. Williams
- Department of Geriatric and Stroke Medicine, RCSI University of Medicine and Health Sciences, Dublin, Ireland
- Department of Geriatric and Stroke Medicine, Beaumont Hospital, Dublin, Ireland
| | - F. Horgan
- School of Physiotherapy, RCSI University of Medicine and Health Sciences, Dublin, Ireland
| | - Elaine Byrne
- Graduate School of Healthcare Management, RCSI University of Medicine and Health Sciences, Dublin, Ireland
| | | | - Padraic Cuffe
- Irish Heart Foundation, Dublin, Ireland
- Patient collaborator, Sligo, Ireland
| | - Suzanne Timmons
- Centre for Gerontology and Rehabilitation, University College Cork, Cork, Ireland
- National Dementia Office, Health Service Executive, Tullamore, Co Offaly, Ireland
| | - Rónán Collins
- National Clinical Programme for Stroke, Health Service Executive, Dublin, Ireland
- Age-Related Health Care and Stroke Service, Tallaght University Hospital, Dublin, Ireland
| | - K. Bennett
- Division of Population Health Sciences, Royal College of Surgeons in Ireland, Dublin, Ireland
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Affiliation(s)
- H K Wong
- Department of Surgery, Queen Mary Hospital, The University of Hong Kong, Pokfulam, Hong Kong
| | - S Law
- Department of Surgery, Queen Mary Hospital, The University of Hong Kong, Pokfulam, Hong Kong
| | - R Collins
- Department of Pathology, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong
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36
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Kennedy C, Gabr A, McCormack J, Collins R, Barry M, Harbison J. The association between increasing oral anticoagulant prescribing and atrial fibrillation related stroke in Ireland. Br J Clin Pharmacol 2021; 88:178-186. [PMID: 34131941 DOI: 10.1111/bcp.14938] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 02/12/2021] [Accepted: 05/24/2021] [Indexed: 11/26/2022] Open
Abstract
AIMS Recent increases in the number of patients with atrial fibrillation (AF) prescribed oral anticoagulants (OAC) are evident in Ireland and internationally, largely due to the availability of direct oral anticoagulants (DOACs). This study aimed to determine the rate of stroke in the context of increasing anticoagulation utilisation, with a focus on AF-related ischaemic stroke (IS). METHODS Dispensing data for OACs were identified for the period 2010-2018 as well as hospital discharges for IS (2005-2018). Irish National Stroke Register data were used to elucidate the characteristics of patients with acute ischaemic stroke. RESULTS The number of patients prescribed OACs increased by 94% from 2010-2018 with a significant change from 2013 (β = 2.57, P = .038), associated with a large increase in the number of patients on DOACs. There was 3.3-fold increase in expenditure on OACs nationally from 2013 to 2018, of which 94% was DOAC related. Using the 2013 timepoint, ischaemic stroke rates until 2018 did not show a significant deviation from the previous trend (β = 0.00, P = .898). The percentage of AF-related ischaemic stroke was stable from 2013 to 2017 with a 4.5% decrease in 2018. The percentage of ischaemic stroke patients with previously diagnosed AF decreased from 2013 to 2018; however, there was an increase in the percentage of ischaemic strokes while on OAC in this cohort. CONCLUSION Large increases in OAC utilisation have not resulted in changes in ischaemic stroke rates at a national level. The percentage of ischaemic strokes with a previous diagnosis of AF has decreased indicating a possible benefit from greater OAC utilisation. However, the percentage presenting with an ischaemic stroke while on OAC treatment is increasing. The increase in patients presenting with stroke while treated with OAC may largely reflect the national increase in patients prescribed DOACs but the findings raise concerns about treatment failures. The real-world effectiveness of DOACs requires further examination.
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Affiliation(s)
- Cormac Kennedy
- Department of Pharmacology and Therapeutics, Health Sciences Centre, Trinity College Dublin, Dublin 8, Ireland.,Department of Pharmacology, St James Hospital, Dublin 8, Ireland
| | - Ahmed Gabr
- Department of Pharmacology and Therapeutics, Health Sciences Centre, Trinity College Dublin, Dublin 8, Ireland
| | - Joan McCormack
- National Office of Clinical Audit, St Stephens Green, Dublin, Ireland
| | - Rónán Collins
- Department Geriatrics and Stroke Medicine, Tallaght University Hospital, Dublin, Ireland
| | - Michael Barry
- Department of Pharmacology and Therapeutics, Health Sciences Centre, Trinity College Dublin, Dublin 8, Ireland.,Department of Pharmacology, St James Hospital, Dublin 8, Ireland
| | - Joe Harbison
- Mercer's Institute for Successful Ageing, St James Hospital, Dublin, Ireland.,Discipline of Medical Gerontology, School of Medicine, Trinity College Dublin, Ireland
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37
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McCabe JJ, O’Reilly E, Coveney S, Harbison J, Collins R, Healy L, McManus J, Mulcahy R, Moynihan B, Cassidy T, Hsu F, Worrall B, Murphy S, O’Donnell M, Kelly PJ. 505 INTERLEUKIN-6, C-REACTIVE PROTEIN, FIBRINOGEN, AND RISK OF RECURRENCE AFTER ISCHEMIC STROKE: SYSTEMATIC REVIEW AND META-ANALYSIS. Age Ageing 2021. [DOI: 10.1093/ageing/afab117.09] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Background
Recent randomised trials showed benefit for anti-inflammatory therapies in coronary disease but excluded stroke. The prognostic value of blood inflammatory markers after stroke is uncertain and guidelines do not recommend their routine measurement for risk stratification.
Methods
We performed a systematic review and meta-analysis of studies investigating the association of C-reactive protein (CRP), interleukin-6 (IL-6) and fibrinogen and risk of recurrent stroke or major vascular events (MVEs). We searched EMBASE and Ovid Medline until 10/1/19. Random-effects meta-analysis was performed for studies reporting comparable effect measures.
Results
Of 2,515 reports identified, 39 met eligibility criteria (IL-6, n = 10; CRP, n = 33; fibrinogen, n = 16). An association with recurrent stroke was reported in 12/26 studies (CRP), 2/11 (fibrinogen) and 3/6 (IL-6). On random-effects meta-analysis of comparable studies, CRP was associated with an increased risk of recurrent stroke [pooled hazard ratio (HR) per 1 standard-deviation (SD) increase in loge-CRP (1.14, 95% CI 1.06-1.22, p < 0.01)] and MVEs (pooled HR 1.21, CI 1.10-1.34, p < 0.01). Fibrinogen was also associated with recurrent stroke (HR 1.26, CI 1.07-1.47, p < 0.01) and MVEs (HR 1.31, 95% CI 1.15-1.49, p < 0.01). Trends were identified for IL-6 for recurrent stroke (HR per 1-SD increase 1.17, CI 0.97-1.41, p = 0.10) and MVEs (HR 1.22, CI 0.96-1.55, p = 0.10).
Conclusion
Despite evidence suggesting an association between inflammatory markers and post-stroke vascular recurrence, substantial methodological heterogeneity was apparent between studies. Individual-patient pooled analysis and standardisation of methods are needed to determine the prognostic role of blood inflammatory markers and to improve patient selection for randomised trials of inflammatory therapies.
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Affiliation(s)
- J J McCabe
- Health Research Board (HRB) Stroke Clinical Trials Network Ireland (SCTNI), Dublin, Ireland
| | - E O’Reilly
- Health Research Board (HRB) Stroke Clinical Trials Network Ireland (SCTNI), Dublin, Ireland
| | - S Coveney
- Health Research Board (HRB) Stroke Clinical Trials Network Ireland (SCTNI), Dublin, Ireland
| | - J Harbison
- Health Research Board (HRB) Stroke Clinical Trials Network Ireland (SCTNI), Dublin, Ireland
| | - R Collins
- Health Research Board (HRB) Stroke Clinical Trials Network Ireland (SCTNI), Dublin, Ireland
| | - L Healy
- Health Research Board (HRB) Stroke Clinical Trials Network Ireland (SCTNI), Dublin, Ireland
| | - J McManus
- Health Research Board (HRB) Stroke Clinical Trials Network Ireland (SCTNI), Dublin, Ireland
| | - R Mulcahy
- Health Research Board (HRB) Stroke Clinical Trials Network Ireland (SCTNI), Dublin, Ireland
| | - B Moynihan
- Health Research Board (HRB) Stroke Clinical Trials Network Ireland (SCTNI), Dublin, Ireland
| | - T Cassidy
- Health Research Board (HRB) Stroke Clinical Trials Network Ireland (SCTNI), Dublin, Ireland
| | - F Hsu
- Health Research Board (HRB) Stroke Clinical Trials Network Ireland (SCTNI), Dublin, Ireland
| | - B Worrall
- Health Research Board (HRB) Stroke Clinical Trials Network Ireland (SCTNI), Dublin, Ireland
| | - S Murphy
- Health Research Board (HRB) Stroke Clinical Trials Network Ireland (SCTNI), Dublin, Ireland
| | - M O’Donnell
- Health Research Board (HRB) Stroke Clinical Trials Network Ireland (SCTNI), Dublin, Ireland
| | - P J Kelly
- Health Research Board (HRB) Stroke Clinical Trials Network Ireland (SCTNI), Dublin, Ireland
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38
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Collins R, Lafford G, Ferris R, Turner J, Tassone P. 37 Improving the Management of Post-Operative Hypocalcaemia in Thyroid Surgery. Br J Surg 2021. [DOI: 10.1093/bjs/znab134.350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Abstract
Introduction
Hypocalcaemia is a frequent, and potentially dangerous, complication of total thyroidectomy [1, 2]. This quality improvement (QI) project was undertaken in a large ENT department in the East of England over a year. The project improved postoperative guideline compliance by optimising the recognition and management of patients at risk of hypocalcaemia. This process focussed on improving parathyroid hormone (PTH) and calcium blood testing, appropriate prescribing and the monitoring and management of hypocalcaemia.
Method
Following a baseline audit the QI process subsequently involved the introduction of a new intraoperative PTH pathway and the amendment of trust guidelines. In addition, there was a focus on improving clinician awareness of guidelines, junior doctor education, communication between operating surgeons and junior doctors and the optimisation of patient handover.
Results
The measurement of PTH at four hours improved from 42.5% to 52.2%. The project saw a significant improvement in the monitoring of hypocalcaemia (from 22.2% to 83.3% for patients with an intermediate risk of hypocalcaemia) and in the prescribing of prophylactic calcium supplements from 7.5% to 43.5%.
Conclusions
By optimising postoperative care this QI project improved patient safety as well as impacting on the duration, and overall cost, of inpatient stay.
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Affiliation(s)
- R Collins
- Norfolk and Norwich University Hospital, Norwich, United Kingdom
| | - G Lafford
- Norfolk and Norwich University Hospital, Norwich, United Kingdom
| | - R Ferris
- Norfolk and Norwich University Hospital, Norwich, United Kingdom
| | - J Turner
- Norfolk and Norwich University Hospital, Norwich, United Kingdom
| | - P Tassone
- Norfolk and Norwich University Hospital, Norwich, United Kingdom
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39
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Bruen C, Merriman NA, Murphy PJ, McCormack J, Sexton E, Harbison J, Williams D, Kelly PJ, Horgan F, Collins R, Ní Bhreacáin M, Byrne E, Thornton J, Tully C, Hickey A. Development of a national stroke audit in Ireland: scoping review protocol. HRB Open Res 2021; 4:31. [PMID: 36330536 PMCID: PMC9607932 DOI: 10.12688/hrbopenres.13244.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/18/2021] [Indexed: 11/20/2022] Open
Abstract
Introduction
Recent advances in stroke management and care have resulted in improved survival and outcomes. However, providing equitable access to acute care, rehabilitation and longer-term stroke care is challenging. Recent Irish evidence indicates variation in stroke outcomes across hospitals, and a need for continuous audit of stroke care to support quality improvement. The aim of this project is to develop a core minimum dataset for use in the new Irish National Audit of Stroke (INAS), which aims to improve the standard of stroke care in Ireland. This paper outlines the protocol for conducting a scoping review of international practice and guidelines in auditing acute and non-acute stroke care.
Objective
Identify data items that are currently collected by stroke audits internationally, and identify audit guidelines that exist for recommending inclusion of content in stroke audit datasets.
Methods and analysis
This scoping review will be conducted in accordance with the Preferred Reporting Items for Systematic Reviews extension for Scoping Reviews (PRISMA-ScR). We will search the following databases: Medline Ovid; Embase; CINAHL EBSCOHost. Grey literature will also be searched for relevant materials, as will relevant websites. Study selection and review will be carried out independently by two researchers, with discrepancies resolved by a third. Data charting and synthesis will involve sub-dividing relevant sources of evidence, and synthesising data into three categories: i) acute stroke care; ii) non-acute stroke care; and iii) audit data collection procedures and resourcing. Data will be charted using a standardised form specific to each category. Consultation with knowledge users will be conducted at all stages of the scoping review.
Discussion
This scoping review will contribute to a larger project aimed at developing an internationally benchmarked stroke audit tool that will be used prospectively to collect data on all stroke admissions in Ireland, encompassing both acute and non-acute data items.
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Affiliation(s)
- Carlos Bruen
- Dept of Health Psychology, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Niamh A. Merriman
- Dept of Health Psychology, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Paul J. Murphy
- Library Services, Royal College of Surgeons in Ireland, Dublin, Ireland
| | | | - Eithne Sexton
- Dept of Health Psychology, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Joseph Harbison
- National Office of Clinical Audit, Dublin, Ireland
- School of Medicine, Trinity College Dublin, Dublin, Ireland
- Dept of Geriatric and Stroke Medicine, St. James' Hospital, Dublin, Ireland
| | - David Williams
- Dept of Geriatric and Stroke Medicine, Beaumont Hospital, Dublin, Ireland
- Dept of Geriatric and Stroke Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Peter J. Kelly
- Dept of Neurology, Mater Misericordiae University Hospital, Dublin, Ireland
- Neurovascular Clinical Science Unit, University College Dublin, Dublin, Ireland
| | - Frances Horgan
- School of Physiotherapy, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Rónán Collins
- Dept of Geriatric and Stroke Medicine, Tallaght University Hospital, Dublin, Ireland
| | | | - Elaine Byrne
- Institute of Leadership, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - John Thornton
- Dept. of Radiology, Beaumont Hospital, Dublin, Ireland
| | | | - Anne Hickey
- Dept of Health Psychology, Royal College of Surgeons in Ireland, Dublin, Ireland
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40
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McEwen M, Kafri D, Chen Z, Atalaya J, Satzinger KJ, Quintana C, Klimov PV, Sank D, Gidney C, Fowler AG, Arute F, Arya K, Buckley B, Burkett B, Bushnell N, Chiaro B, Collins R, Demura S, Dunsworth A, Erickson C, Foxen B, Giustina M, Huang T, Hong S, Jeffrey E, Kim S, Kechedzhi K, Kostritsa F, Laptev P, Megrant A, Mi X, Mutus J, Naaman O, Neeley M, Neill C, Niu M, Paler A, Redd N, Roushan P, White TC, Yao J, Yeh P, Zalcman A, Chen Y, Smelyanskiy VN, Martinis JM, Neven H, Kelly J, Korotkov AN, Petukhov AG, Barends R. Removing leakage-induced correlated errors in superconducting quantum error correction. Nat Commun 2021; 12:1761. [PMID: 33741936 PMCID: PMC7979694 DOI: 10.1038/s41467-021-21982-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 02/23/2021] [Indexed: 11/30/2022] Open
Abstract
Quantum computing can become scalable through error correction, but logical error rates only decrease with system size when physical errors are sufficiently uncorrelated. During computation, unused high energy levels of the qubits can become excited, creating leakage states that are long-lived and mobile. Particularly for superconducting transmon qubits, this leakage opens a path to errors that are correlated in space and time. Here, we report a reset protocol that returns a qubit to the ground state from all relevant higher level states. We test its performance with the bit-flip stabilizer code, a simplified version of the surface code for quantum error correction. We investigate the accumulation and dynamics of leakage during error correction. Using this protocol, we find lower rates of logical errors and an improved scaling and stability of error suppression with increasing qubit number. This demonstration provides a key step on the path towards scalable quantum computing.
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Affiliation(s)
- M McEwen
- Department of Physics, University of California, Santa Barbara, CA, USA
- Google, Santa Barbara, CA, USA
| | | | - Z Chen
- Google, Santa Barbara, CA, USA
| | | | | | | | | | - D Sank
- Google, Santa Barbara, CA, USA
| | | | | | - F Arute
- Google, Santa Barbara, CA, USA
| | - K Arya
- Google, Santa Barbara, CA, USA
| | | | | | | | | | | | | | | | | | - B Foxen
- Google, Santa Barbara, CA, USA
| | | | - T Huang
- Google, Santa Barbara, CA, USA
| | - S Hong
- Google, Santa Barbara, CA, USA
| | | | - S Kim
- Google, Santa Barbara, CA, USA
| | | | | | | | | | - X Mi
- Google, Santa Barbara, CA, USA
| | - J Mutus
- Google, Santa Barbara, CA, USA
| | | | | | - C Neill
- Google, Santa Barbara, CA, USA
| | | | - A Paler
- Johannes Kepler University, Linz, Austria
- University of Texas at Dallas, Richardson, TX, USA
| | - N Redd
- Google, Santa Barbara, CA, USA
| | | | | | - J Yao
- Google, Santa Barbara, CA, USA
| | - P Yeh
- Google, Santa Barbara, CA, USA
| | | | - Yu Chen
- Google, Santa Barbara, CA, USA
| | | | - John M Martinis
- Department of Physics, University of California, Santa Barbara, CA, USA
| | - H Neven
- Google, Santa Barbara, CA, USA
| | - J Kelly
- Google, Santa Barbara, CA, USA
| | - A N Korotkov
- Google, Santa Barbara, CA, USA
- Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA
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41
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Law ZK, Desborough M, Roberts I, Al-Shahi Salman R, England TJ, Werring DJ, Robinson T, Krishnan K, Dineen R, Laska AC, Peters N, Egea-Guerrero JJ, Karlinski M, Christensen H, Roffe C, Bereczki D, Ozturk S, Thanabalan J, Collins R, Beridze M, Bath PM, Sprigg N. Outcomes in Antiplatelet-Associated Intracerebral Hemorrhage in the TICH-2 Randomized Controlled Trial. J Am Heart Assoc 2021; 10:e019130. [PMID: 33586453 PMCID: PMC8174262 DOI: 10.1161/jaha.120.019130] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Background Antiplatelet therapy increases the risk of hematoma expansion in intracerebral hemorrhage (ICH) while the effect on functional outcome is uncertain. Methods and Results This is an exploratory analysis of the TICH‐2 (Tranexamic Acid in Intracerebral Hemorrhage‐2) double‐blind, randomized, placebo‐controlled trial, which studied the efficacy of tranexamic acid in patients with spontaneous ICH within 8 hours of onset. Multivariable logistic regression and ordinal regression were performed to explore the relationship between pre‐ICH antiplatelet therapy, and 24‐hour hematoma expansion and day 90 modified Rankin Scale score, as well as the effect of tranexamic acid. Of 2325 patients, 611 (26.3%) had pre‐ICH antiplatelet therapy. They were older (mean age, 75.7 versus 66.5 years), more likely to have ischemic heart disease (25.4% versus 2.7%), ischemic stroke (36.2% versus 6.3%), intraventricular hemorrhage (40.2% versus 27.5%), and larger baseline hematoma volume (mean, 28.1 versus 22.6 mL) than the no‐antiplatelet group. Pre‐ICH antiplatelet therapy was associated with a significantly increased risk of hematoma expansion (adjusted odds ratio [OR], 1.28; 95% CI, 1.01–1.63), a shift toward unfavorable outcome in modified Rankin Scale (adjusted common OR, 1.58; 95% CI, 1.32–1.91) and a higher risk of death at day 90 (adjusted OR, 1.63; 95% CI, 1.25–2.11). Tranexamic acid reduced the risk of hematoma expansion in the overall patients with ICH (adjusted OR, 0.76; 95% CI, 0.62–0.93) and antiplatelet subgroup (adjusted OR, 0.61; 95% CI, 0.41–0.91) with no significant interaction between pre‐ICH antiplatelet therapy and tranexamic acid (P interaction=0.248). Conclusions Antiplatelet therapy is independently associated with hematoma expansion and unfavorable functional outcome. Tranexamic acid reduced hematoma expansion regardless of prior antiplatelet therapy use. Registration URL: https://www.isrctn.com; Unique identifier: ISRCTN93732214.
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Affiliation(s)
- Zhe Kang Law
- Stroke Trials Unit Division of Clinical Neuroscience University of Nottingham United Kingdom.,Department of Medicine National University of Malaysia Kuala Lumpur Malaysia
| | - Michael Desborough
- Haemophilia and Thrombosis Centre Guy's and St Thomas' NHS Foundation Trust London United Kingdom
| | - Ian Roberts
- Clinical Trials Unit London School of Hygiene & Tropical Medicine London United Kingdom
| | | | - Timothy J England
- Vascular Medicine Division of Medical Sciences & GEM Royal Derby Hospital CentreUniversity of Nottingham United Kingdom
| | - David J Werring
- Stroke Research Centre UCL Queen Square Institute of Neurology London United Kingdom
| | - Thompson Robinson
- Department of Cardiovascular Sciences and National Institute for Health Research Biomedical Research Centre University of Leicester United Kingdom
| | - Kailash Krishnan
- Nottingham University Hospitals NHS Trust Nottingham United Kingdom
| | - Robert Dineen
- Radiological Sciences University of Nottingham United Kingdom.,National Institute for Health Research Nottingham Biomedical Research Centre Nottingham United Kingdom
| | - Ann Charlotte Laska
- Department of Clinical Sciences Karolinska InstitutetDanderyd Hospital Sweden
| | - Nils Peters
- Neurology and Stroke Center Klinik Hirslanden Zürich Switzerland.,Neurology and Neurorehabilitation Unit University Center for Medicine of Aging Felix Platter-Hospital Basel Switzerland.,Department of Neurology and Stroke Center University Hospital Basel and University of Basel Switzerland
| | | | | | - Hanne Christensen
- Department of Neurology Bispebjerg Hospital and University of Copenhagen Denmark
| | - Christine Roffe
- Stroke Research Faculty of Medicine and Health Sciences Keele University Stoke-on-Trent United Kingdom
| | - Daniel Bereczki
- Department of Neurology Semmelweis University Budapest Hungary
| | - Serefnur Ozturk
- Department of Neurology Selcuk University Faculty of Medicine Konya Turkey
| | - Jegan Thanabalan
- Division of Neurosurgery Department of Surgery National University of Malaysia Kuala Lumpur Malaysia
| | - Rónán Collins
- Tallaght University Hospital Dublin Republic of Ireland
| | - Maia Beridze
- The First University Clinic of Tbilisi State Medical University Tbilisi Georgia
| | - Philip M Bath
- Stroke Trials Unit Division of Clinical Neuroscience University of Nottingham United Kingdom.,Nottingham University Hospitals NHS Trust Nottingham United Kingdom
| | - Nikola Sprigg
- Stroke Trials Unit Division of Clinical Neuroscience University of Nottingham United Kingdom.,Nottingham University Hospitals NHS Trust Nottingham United Kingdom
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42
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McCabe JJ, O'Reilly E, Coveney S, Collins R, Healy L, McManus J, Mulcahy R, Moynihan B, Cassidy T, Hsu F, Worrall B, Murphy S, O'Donnell M, Kelly PJ. Interleukin-6, C-reactive protein, fibrinogen, and risk of recurrence after ischaemic stroke: Systematic review and meta-analysis. Eur Stroke J 2021; 6:62-71. [PMID: 33817336 PMCID: PMC7995315 DOI: 10.1177/2396987320984003] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 12/06/2020] [Indexed: 01/02/2023] Open
Abstract
Background Recent randomised trials showed benefit for anti-inflammatory therapies in coronary disease but excluded stroke. The prognostic value of blood inflammatory markers after stroke is uncertain and guidelines do not recommend their routine measurement for risk stratification. Methods We performed a systematic review and meta-analysis of studies investigating the association of C-reactive protein (CRP), interleukin-6 (IL-6) and fibrinogen and risk of recurrent stroke or major vascular events (MVEs). We searched EMBASE and Ovid Medline until 10/1/19. Random-effects meta-analysis was performed for studies reporting comparable effect measures. Results Of 2,515 reports identified, 39 met eligibility criteria (IL-6, n = 10; CRP, n = 33; fibrinogen, n = 16). An association with recurrent stroke was reported in 12/26 studies (CRP), 2/11 (fibrinogen) and 3/6 (IL-6). On random-effects meta-analysis of comparable studies, CRP was associated with an increased risk of recurrent stroke [pooled hazard ratio (HR) per 1 standard-deviation (SD) increase in loge-CRP (1.14, 95% CI 1.06-1.22, p < 0.01)] and MVEs (pooled HR 1.21, CI 1.10-1.34, p < 0.01). Fibrinogen was also associated with recurrent stroke (HR 1.26, CI 1.07-1.47, p < 0.01) and MVEs (HR 1.31, 95% CI 1.15-1.49, p < 0.01). Trends were identified for IL-6 for recurrent stroke (HR per 1-SD increase 1.17, CI 0.97-1.41, p = 0.10) and MVEs (HR 1.22, CI 0.96-1.55, p = 0.10). Conclusion Despite evidence suggesting an association between inflammatory markers and post-stroke vascular recurrence, substantial methodological heterogeneity was apparent between studies. Individual-patient pooled analysis and standardisation of methods are needed to determine the prognostic role of blood inflammatory markers and to improve patient selection for randomised trials of inflammatory therapies.
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Affiliation(s)
- J J McCabe
- Health Research Board (HRB) Stroke Clinical Trials Network Ireland (SCTNI), Dublin, Ireland.,Neurovascular Unit for Applied Translational and Therapeutics Research, Catherine McAuley Centre, Dublin, Ireland.,School of Medicine, University College Dublin, Dublin, Ireland.,Medicine for the Elderly Department/Stroke Medicine, Mater Misericordiae University Hospital, Dublin, Ireland
| | - E O'Reilly
- Health Research Board (HRB) Stroke Clinical Trials Network Ireland (SCTNI), Dublin, Ireland
| | - S Coveney
- Health Research Board (HRB) Stroke Clinical Trials Network Ireland (SCTNI), Dublin, Ireland.,Neurovascular Unit for Applied Translational and Therapeutics Research, Catherine McAuley Centre, Dublin, Ireland.,Department of Geriatric Medicine, Tallaght University Hospital, Dublin, Ireland
| | - R Collins
- Health Research Board (HRB) Stroke Clinical Trials Network Ireland (SCTNI), Dublin, Ireland.,Department of Geriatric Medicine, Tallaght University Hospital, Dublin, Ireland
| | - L Healy
- Health Research Board (HRB) Stroke Clinical Trials Network Ireland (SCTNI), Dublin, Ireland.,Department of Geriatric Medicine, Cork University Hospital, Cork, Ireland
| | - J McManus
- Health Research Board (HRB) Stroke Clinical Trials Network Ireland (SCTNI), Dublin, Ireland.,Department of Geriatric Medicine, University Hospital Limerick, Ireland
| | - R Mulcahy
- Health Research Board (HRB) Stroke Clinical Trials Network Ireland (SCTNI), Dublin, Ireland.,Department of Geriatric Medicine, Waterford University Hospital, Waterford, Ireland
| | - B Moynihan
- Health Research Board (HRB) Stroke Clinical Trials Network Ireland (SCTNI), Dublin, Ireland.,Department of Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - T Cassidy
- School of Medicine, University College Dublin, Dublin, Ireland.,Department of Geriatric and Stroke Medicine, St Vincent's University Hospital, Dublin, Ireland
| | - F Hsu
- The Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - B Worrall
- Departments of Neurology and Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - S Murphy
- Health Research Board (HRB) Stroke Clinical Trials Network Ireland (SCTNI), Dublin, Ireland.,Neurovascular Unit for Applied Translational and Therapeutics Research, Catherine McAuley Centre, Dublin, Ireland.,School of Medicine, University College Dublin, Dublin, Ireland.,Medicine for the Elderly Department/Stroke Medicine, Mater Misericordiae University Hospital, Dublin, Ireland
| | - M O'Donnell
- Health Research Board (HRB) Stroke Clinical Trials Network Ireland (SCTNI), Dublin, Ireland.,Department of Geriatric Medicine, University Hospital Galway, Galway, Ireland.,Department of Translational Medicine, National University of Ireland Galway, Ireland
| | - P J Kelly
- Health Research Board (HRB) Stroke Clinical Trials Network Ireland (SCTNI), Dublin, Ireland.,Neurovascular Unit for Applied Translational and Therapeutics Research, Catherine McAuley Centre, Dublin, Ireland.,Department of Neurology/Stroke Medicine, Mater Misericordiae University Hospital, Dublin, Ireland
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43
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Foxen B, Neill C, Dunsworth A, Roushan P, Chiaro B, Megrant A, Kelly J, Chen Z, Satzinger K, Barends R, Arute F, Arya K, Babbush R, Bacon D, Bardin JC, Boixo S, Buell D, Burkett B, Chen Y, Collins R, Farhi E, Fowler A, Gidney C, Giustina M, Graff R, Harrigan M, Huang T, Isakov SV, Jeffrey E, Jiang Z, Kafri D, Kechedzhi K, Klimov P, Korotkov A, Kostritsa F, Landhuis D, Lucero E, McClean J, McEwen M, Mi X, Mohseni M, Mutus JY, Naaman O, Neeley M, Niu M, Petukhov A, Quintana C, Rubin N, Sank D, Smelyanskiy V, Vainsencher A, White TC, Yao Z, Yeh P, Zalcman A, Neven H, Martinis JM. Demonstrating a Continuous Set of Two-Qubit Gates for Near-Term Quantum Algorithms. Phys Rev Lett 2020; 125:120504. [PMID: 33016760 DOI: 10.1103/physrevlett.125.120504] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Revised: 06/27/2020] [Accepted: 07/22/2020] [Indexed: 06/11/2023]
Abstract
Quantum algorithms offer a dramatic speedup for computational problems in material science and chemistry. However, any near-term realizations of these algorithms will need to be optimized to fit within the finite resources offered by existing noisy hardware. Here, taking advantage of the adjustable coupling of gmon qubits, we demonstrate a continuous two-qubit gate set that can provide a threefold reduction in circuit depth as compared to a standard decomposition. We implement two gate families: an imaginary swap-like (iSWAP-like) gate to attain an arbitrary swap angle, θ, and a controlled-phase gate that generates an arbitrary conditional phase, ϕ. Using one of each of these gates, we can perform an arbitrary two-qubit gate within the excitation-preserving subspace allowing for a complete implementation of the so-called Fermionic simulation (fSim) gate set. We benchmark the fidelity of the iSWAP-like and controlled-phase gate families as well as 525 other fSim gates spread evenly across the entire fSim(θ,ϕ) parameter space, achieving a purity-limited average two-qubit Pauli error of 3.8×10^{-3} per fSim gate.
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Affiliation(s)
- B Foxen
- Department of Physics, University of California, Santa Barbara, California 93106, USA
- Google Research, Santa Barbara, California 93117, USA
| | - C Neill
- Google Research, Santa Barbara, California 93117, USA
| | - A Dunsworth
- Google Research, Santa Barbara, California 93117, USA
| | - P Roushan
- Google Research, Santa Barbara, California 93117, USA
| | - B Chiaro
- Department of Physics, University of California, Santa Barbara, California 93106, USA
| | - A Megrant
- Google Research, Santa Barbara, California 93117, USA
| | - J Kelly
- Google Research, Santa Barbara, California 93117, USA
| | - Zijun Chen
- Google Research, Santa Barbara, California 93117, USA
| | - K Satzinger
- Google Research, Santa Barbara, California 93117, USA
| | - R Barends
- Google Research, Santa Barbara, California 93117, USA
| | - F Arute
- Google Research, Santa Barbara, California 93117, USA
| | - K Arya
- Google Research, Santa Barbara, California 93117, USA
| | - R Babbush
- Google Research, Santa Barbara, California 93117, USA
| | - D Bacon
- Google Research, Santa Barbara, California 93117, USA
| | - J C Bardin
- Google Research, Santa Barbara, California 93117, USA
- Department of Electrical and Computer Engineering, University of Massachusetts-Amherst, Amherst, Massachusetts 01003, USA
| | - S Boixo
- Google Research, Santa Barbara, California 93117, USA
| | - D Buell
- Google Research, Santa Barbara, California 93117, USA
| | - B Burkett
- Google Research, Santa Barbara, California 93117, USA
| | - Yu Chen
- Google Research, Santa Barbara, California 93117, USA
| | - R Collins
- Google Research, Santa Barbara, California 93117, USA
| | - E Farhi
- Google Research, Santa Barbara, California 93117, USA
| | - A Fowler
- Google Research, Santa Barbara, California 93117, USA
| | - C Gidney
- Google Research, Santa Barbara, California 93117, USA
| | - M Giustina
- Google Research, Santa Barbara, California 93117, USA
| | - R Graff
- Google Research, Santa Barbara, California 93117, USA
| | - M Harrigan
- Google Research, Santa Barbara, California 93117, USA
| | - T Huang
- Google Research, Santa Barbara, California 93117, USA
| | - S V Isakov
- Google Research, Santa Barbara, California 93117, USA
| | - E Jeffrey
- Google Research, Santa Barbara, California 93117, USA
| | - Z Jiang
- Google Research, Santa Barbara, California 93117, USA
| | - D Kafri
- Google Research, Santa Barbara, California 93117, USA
| | - K Kechedzhi
- Google Research, Santa Barbara, California 93117, USA
| | - P Klimov
- Google Research, Santa Barbara, California 93117, USA
| | - A Korotkov
- Google Research, Santa Barbara, California 93117, USA
| | - F Kostritsa
- Google Research, Santa Barbara, California 93117, USA
| | - D Landhuis
- Google Research, Santa Barbara, California 93117, USA
| | - E Lucero
- Google Research, Santa Barbara, California 93117, USA
| | - J McClean
- Google Research, Santa Barbara, California 93117, USA
| | - M McEwen
- Department of Physics, University of California, Santa Barbara, California 93106, USA
| | - X Mi
- Google Research, Santa Barbara, California 93117, USA
| | - M Mohseni
- Google Research, Santa Barbara, California 93117, USA
| | - J Y Mutus
- Google Research, Santa Barbara, California 93117, USA
| | - O Naaman
- Google Research, Santa Barbara, California 93117, USA
| | - M Neeley
- Google Research, Santa Barbara, California 93117, USA
| | - M Niu
- Google Research, Santa Barbara, California 93117, USA
| | - A Petukhov
- Google Research, Santa Barbara, California 93117, USA
| | - C Quintana
- Google Research, Santa Barbara, California 93117, USA
| | - N Rubin
- Google Research, Santa Barbara, California 93117, USA
| | - D Sank
- Google Research, Santa Barbara, California 93117, USA
| | - V Smelyanskiy
- Google Research, Santa Barbara, California 93117, USA
| | - A Vainsencher
- Google Research, Santa Barbara, California 93117, USA
| | - T C White
- Google Research, Santa Barbara, California 93117, USA
| | - Z Yao
- Google Research, Santa Barbara, California 93117, USA
| | - P Yeh
- Google Research, Santa Barbara, California 93117, USA
| | - A Zalcman
- Google Research, Santa Barbara, California 93117, USA
| | - H Neven
- Google Research, Santa Barbara, California 93117, USA
| | - J M Martinis
- Department of Physics, University of California, Santa Barbara, California 93106, USA
- Google Research, Santa Barbara, California 93117, USA
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44
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Mills R, Slingsby B, Coleman J, Collins R, Holt G, Metelko C, Schnellbach Y. A simple method for estimating the major nuclide fractional fission rates within light water and advanced gas cooled reactors. Nuclear Engineering and Technology 2020. [DOI: 10.1016/j.net.2020.03.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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45
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Richardson H, Collins R, Williams J. Sport science relevance and integration in horseracing: perceptions of UK racehorse trainers. Comparative Exercise Physiology 2020. [DOI: 10.3920/cep190003] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Whilst equestrian sport science research has expanded over recent years, and technologies to positively impact training and performance have been developed, long-standing traditions and experiential learning in the racing industry still appear to impede the integration of sport science knowledge. This study used semi-structured interviews to investigate the perceptions of eleven national hunt and flat-based racehorse trainers to determine the current status of sport science integration within the racing industry, the perceived barriers to its uptake, and areas where trainers sought further knowledge. Three key higher order themes emerged from the interviews: the current training and monitoring principles for health and fitness of racehorses, trainers’ attitudes toward sport science research, and areas for potential future research and integration of sports science in training. Subjective methods grounded in personal experience were found to form the basis of racehorse training principles, with the application of sport science minimal, namely due to poor integration strategies. Negative connotations arising from a general lack of understanding of the application of knowledge and a scepticism toward adapting already successful principles, as well as pressure from industry stakeholders, appear to create barriers to sport science uptake. Trainers felt a stronger evidence base emphasising performance benefits is needed to overcome these. Where trainers identified areas of research potential, many studies had already been undertaken, highlighting the necessity for effective dissemination strategies to demonstrate how research could apply to industry practice. Increased educational initiatives to showcase technology and improve trainer understanding and application of currently available sport science knowledge is also warranted.
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Affiliation(s)
- H. Richardson
- Hartpury University, Hartpury House, GL19 3BE Gloucester, United Kingdom
| | - R. Collins
- Hartpury University, Hartpury House, GL19 3BE Gloucester, United Kingdom
| | - J.M. Williams
- Hartpury University, Hartpury House, GL19 3BE Gloucester, United Kingdom
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46
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Barends R, Quintana CM, Petukhov AG, Chen Y, Kafri D, Kechedzhi K, Collins R, Naaman O, Boixo S, Arute F, Arya K, Buell D, Burkett B, Chen Z, Chiaro B, Dunsworth A, Foxen B, Fowler A, Gidney C, Giustina M, Graff R, Huang T, Jeffrey E, Kelly J, Klimov PV, Kostritsa F, Landhuis D, Lucero E, McEwen M, Megrant A, Mi X, Mutus J, Neeley M, Neill C, Ostby E, Roushan P, Sank D, Satzinger KJ, Vainsencher A, White T, Yao J, Yeh P, Zalcman A, Neven H, Smelyanskiy VN, Martinis JM. Diabatic Gates for Frequency-Tunable Superconducting Qubits. Phys Rev Lett 2019; 123:210501. [PMID: 31809160 DOI: 10.1103/physrevlett.123.210501] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Indexed: 06/10/2023]
Abstract
We demonstrate diabatic two-qubit gates with Pauli error rates down to 4.3(2)×10^{-3} in as fast as 18 ns using frequency-tunable superconducting qubits. This is achieved by synchronizing the entangling parameters with minima in the leakage channel. The synchronization shows a landscape in gate parameter space that agrees with model predictions and facilitates robust tune-up. We test both iswap-like and cphase gates with cross-entropy benchmarking. The presented approach can be extended to multibody operations as well.
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Affiliation(s)
- R Barends
- Google, Santa Barbara, California 93117, USA
| | | | | | - Yu Chen
- Google, Santa Barbara, California 93117, USA
| | - D Kafri
- Google, Venice, California 90291, USA
| | | | - R Collins
- Google, Santa Barbara, California 93117, USA
| | - O Naaman
- Google, Santa Barbara, California 93117, USA
| | - S Boixo
- Google, Venice, California 90291, USA
| | - F Arute
- Google, Santa Barbara, California 93117, USA
| | - K Arya
- Google, Santa Barbara, California 93117, USA
| | - D Buell
- Google, Santa Barbara, California 93117, USA
| | - B Burkett
- Google, Santa Barbara, California 93117, USA
| | - Z Chen
- Google, Santa Barbara, California 93117, USA
| | - B Chiaro
- Department of Physics, University of California, Santa Barbara, California 93106, USA
| | - A Dunsworth
- Google, Santa Barbara, California 93117, USA
| | - B Foxen
- Department of Physics, University of California, Santa Barbara, California 93106, USA
| | - A Fowler
- Google, Santa Barbara, California 93117, USA
| | - C Gidney
- Google, Santa Barbara, California 93117, USA
| | - M Giustina
- Google, Santa Barbara, California 93117, USA
| | - R Graff
- Google, Santa Barbara, California 93117, USA
| | - T Huang
- Google, Santa Barbara, California 93117, USA
| | - E Jeffrey
- Google, Santa Barbara, California 93117, USA
| | - J Kelly
- Google, Santa Barbara, California 93117, USA
| | - P V Klimov
- Google, Santa Barbara, California 93117, USA
| | - F Kostritsa
- Google, Santa Barbara, California 93117, USA
| | - D Landhuis
- Google, Santa Barbara, California 93117, USA
| | - E Lucero
- Google, Santa Barbara, California 93117, USA
| | - M McEwen
- Department of Physics, University of California, Santa Barbara, California 93106, USA
| | - A Megrant
- Google, Santa Barbara, California 93117, USA
| | - X Mi
- Google, Santa Barbara, California 93117, USA
| | - J Mutus
- Google, Santa Barbara, California 93117, USA
| | - M Neeley
- Google, Santa Barbara, California 93117, USA
| | - C Neill
- Google, Santa Barbara, California 93117, USA
| | - E Ostby
- Google, Venice, California 90291, USA
| | - P Roushan
- Google, Santa Barbara, California 93117, USA
| | - D Sank
- Google, Santa Barbara, California 93117, USA
| | | | | | - T White
- Google, Santa Barbara, California 93117, USA
| | - J Yao
- Google, Santa Barbara, California 93117, USA
| | - P Yeh
- Google, Santa Barbara, California 93117, USA
| | - A Zalcman
- Google, Santa Barbara, California 93117, USA
| | - H Neven
- Google, Venice, California 90291, USA
| | | | - John M Martinis
- Google, Santa Barbara, California 93117, USA
- Department of Physics, University of California, Santa Barbara, California 93106, USA
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47
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Conroy M, Sellors J, Effingham M, Littlejohns TJ, Boultwood C, Gillions L, Sudlow CLM, Collins R, Allen NE. The advantages of UK Biobank's open-access strategy for health research. J Intern Med 2019; 286:389-397. [PMID: 31283063 PMCID: PMC6790705 DOI: 10.1111/joim.12955] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Ready access to health research studies is becoming more important as researchers, and their funders, seek to maximize the opportunities for scientific innovation and health improvements. Large-scale population-based prospective studies are particularly useful for multidisciplinary research into the causes, treatment and prevention of many different diseases. UK Biobank has been established as an open-access resource for public health research, with the intention of making the data as widely available as possible in an equitable and transparent manner. Access to UK Biobank's unique breadth of phenotypic and genetic data has attracted researchers worldwide from across academia and industry. As a consequence, it has enabled scientists to perform world-leading collaborative research. Moreover, open access to an already deeply characterized cohort has encouraged both public and private sector investment in further enhancements to make UK Biobank an unparalleled resource for public health research and an exemplar for the development of open-access approaches for other studies.
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Affiliation(s)
- M Conroy
- UK Biobank, Cheadle, Stockport, UK.,Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | | | - T J Littlejohns
- UK Biobank, Cheadle, Stockport, UK.,Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | | | - C L M Sudlow
- UK Biobank, Cheadle, Stockport, UK.,Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - R Collins
- UK Biobank, Cheadle, Stockport, UK.,Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - N E Allen
- UK Biobank, Cheadle, Stockport, UK.,Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
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48
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Martin E, Mainwaring R, Collins R, Hanley F. SURGICAL REPAIR OF PERIPHERAL PULMONARY ARTERY STENOSIS. Can J Cardiol 2019. [DOI: 10.1016/j.cjca.2019.07.439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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49
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Gnatiuc L, Alegre-Diaz J, Garcilazo-Avila A, Ramirez R, Gonzales-Carballo C, Solano-Sanchez M, Chiquete E, Wade R, Clarke R, Herrington WG, Collins R, Peto R, Tapia-Conyer R, Kuri-Morales P, Emberson J. P3824Body composition and mortality from vascular or metabolic causes among 150,000 participants in the Mexico City Prospective Study. Eur Heart J 2019. [DOI: 10.1093/eurheartj/ehz745.0666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
Background
Higher body-mass index is associated with increased mortality from vascular disease, renal disease and other metabolic causes. However, body mass reflects both fat and lean mass, which may have very different effects on risk. We investigated the individual and joint relevance of fat and lean mass to mortality from these causes, using data from the Mexico City Prospective Study.
Methods
Between 1998 and 2004, 150,000 adults from Mexico City were recruited into a prospective study and tracked for cause-specific mortality for 14 years. Fat and lean mass at recruitment were predicted using Mexican-specific anthropometric equations, validated in a subset of participants with additional bio-impedance measures. Cox regression was used to assess the relevance of fat and lean mass at recruitment to mortality from a vascular, renal, or other metabolic cause at ages 35–74 years. Analyses were adjusted for age at risk, sex, residential district, education, recreational physical activity, smoking and alcohol consumption. To avoid reverse causality, analyses excluded those with diabetes or other chronic diseases at recruitment, and deaths in the first 5 years of follow-up. Mortality rate ratios (RRs) relate to the differences per SD of the usual values of various factors or the differences between the top tenth and bottom fifth of the values.
Results
Among 112,923 participants aged 35–74 years, mean (SD) fat mass in men and women was 22.0 (6.4) kgs and 29.4 (7.8) kgs respectively, while mean (SD) lean mass was 54.9 (7.2) kgs and 39.2 (5.0) kgs respectively. In both men and women, equation-predicted fat and lean mass closely matched the bio-impedance values (all r>0.86). Both fat and lean mass were positively and approximately log-linearly associated with mortality from a vascular or metabolic cause. However, the association of lean mass with mortality was more than accounted for by the correlation of lean with fat mass. Hence, after adjustment for fat mass, lean mass was inversely associated with risk. For a given amount of fat mass, the RR for vascular/metabolic mortality comparing those in the top tenth versus bottom fifth of the predicted lean mass was 0.35 (95% CI 0.24–0.52). Conversely, for a given amount of lean mass, the RR comparing those in the top tenth versus bottom fifth of the predicted fat mass was 4.06 (3.06–5.39). The RRs associated with each SD higher fat mass (1.51, 1.40–1.63) or lean mass (0.79, 0.73–0.86) appeared to be little affected by age, sex, or levels of other confounders, and were broadly similar for the major vascular, renal, and other metabolic mortality. The height-adjusted RRs were 1.41 (1.30–1.53) for fat mass and 0.91 (0.82–1.00) for lean mass.
Conclusions
In this Mexican cohort, predicted fat and lean mass had opposing effects on vascular and other metabolic deaths, with no evidence of any thresholds throughout the ranges studied.
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Affiliation(s)
- L Gnatiuc
- University of Oxford, CTSU, Nuffield Department of Population Health,, Oxford, United Kingdom
| | - J Alegre-Diaz
- National Autonomous University of Mexico, School of Medicine, Mexico City, Mexico
| | - A Garcilazo-Avila
- National Autonomous University of Mexico, School of Medicine, Mexico City, Mexico
| | - R Ramirez
- National Autonomous University of Mexico, School of Medicine, Mexico City, Mexico
| | - C Gonzales-Carballo
- National Autonomous University of Mexico, School of Medicine, Mexico City, Mexico
| | - M Solano-Sanchez
- National Autonomous University of Mexico, School of Medicine, Mexico City, Mexico
| | - E Chiquete
- National Autonomous University of Mexico, School of Medicine, Mexico City, Mexico
| | - R Wade
- University of Oxford, MRC Population Health Research Unit; Nuffield Department of Population Health, Oxford, United Kingdom
| | - R Clarke
- University of Oxford, CTSU, Nuffield Department of Population Health,, Oxford, United Kingdom
| | - W G Herrington
- University of Oxford, MRC Population Health Research Unit; Nuffield Department of Population Health, Oxford, United Kingdom
| | - R Collins
- University of Oxford, CTSU, Nuffield Department of Population Health,, Oxford, United Kingdom
| | - R Peto
- University of Oxford, CTSU, Nuffield Department of Population Health,, Oxford, United Kingdom
| | - R Tapia-Conyer
- National Autonomous University of Mexico, School of Medicine, Mexico City, Mexico
| | - P Kuri-Morales
- National Autonomous University of Mexico, School of Medicine, Mexico City, Mexico
| | - J Emberson
- University of Oxford, MRC Population Health Research Unit; Nuffield Department of Population Health, Oxford, United Kingdom
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50
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Scuron M, Fay B, Parker M, Collins R, Huarte E, Yao W, Smith P. 385 Ruxolitinib cream ameliorates a preclinical model of skin dermatitis via modulation of inflammatory T-cell subsets. J Invest Dermatol 2019. [DOI: 10.1016/j.jid.2019.07.387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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