1
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Sabatier P, Wack M, Pouchot J, Danchin N, Jannot AS. A data-driven pipeline to extract potential adverse drug reactions through prescription, procedures and medical diagnoses analysis: application to a cohort study of 2,010 patients taking hydroxychloroquine with an 11-year follow-up. BMC Med Res Methodol 2022; 22:166. [PMID: 35676635 PMCID: PMC9175346 DOI: 10.1186/s12874-022-01628-3] [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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 05/06/2022] [Indexed: 12/05/2022] Open
Abstract
Context Real-life data consist of exhaustive data which are not subject to selection bias. These data enable to study drug-safety profiles but are underused because of their temporality, necessitating complex models (i.e., safety depends on the dose, timing, and duration of treatment). We aimed to create a data-driven pipeline strategy that manages the complex temporality of real-life data to highlight the safety profile of a given drug. Methods We proposed to apply the weighted cumulative exposure (WCE) statistical model to all health events occurring after a drug introduction (in this paper HCQ) and performed bootstrap to select relevant diagnoses, drugs and interventions which could reflect an adverse drug reactions (ADRs). We applied this data-driven pipeline on a French national medico-administrative database to extract the safety profile of hydroxychloroquine (HCQ) from a cohort of 2,010 patients. Results The proposed method selected eight drugs (metopimazine, anethole trithione, tropicamide, alendronic acid & colecalciferol, hydrocortisone, chlormadinone, valsartan and tixocortol), twelve procedures (six ophthalmic procedures, two dental procedures, two skin lesions procedures and osteodensitometry procedure) and two medical diagnoses (systemic lupus erythematous, unspecified and discoid lupus erythematous) to be significantly associated with HCQ exposure. Conclusion We provide a method extracting the broad spectrum of diagnoses, drugs and interventions associated to any given drug, potentially highlighting ADRs. Applied to hydroxychloroquine, this method extracted among others already known ADRs. Supplementary Information The online version contains supplementary material available at 10.1186/s12874-022-01628-3. • The challenge of drug-safety signal detection methods is to handle four types of difficulties: ○ The data source, the study of long-term adverse drug reactions or effects not suspected by healthcare professionals, requires the use of a real-life data source. ○ The consideration of a broad spectrum of potential adverse drug reactions (ADRs), and not only candidate ADRs. ○ The temporal impact (meaning that safety depends on the dose, date and duration of treatment). ○ The difference between true ADRs and disease natural course. • We aimed to create a data-driven pipeline strategy, without any assumption of any ADRs, which take into account the complex temporality of real-life data to provide the safety profile of a given drug. • Our pipeline used three sources of real-life data to establish a safety profile of a given drug: drug prescriptions, procedures and medical diagnoses. • We successfully applied our data-driven pipeline strategy to hydroxychloroquine (HCQ). Our pipeline enabled us to find diagnoses, drugs and interventions related to HCQ and which could reflect an ADR due to HCQ or the disease itself. • This data-driven pipeline strategy may be of interest to other experts involved in the pharmacovigilance discipline.
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Affiliation(s)
- P Sabatier
- Inria, HeKA, PariSantéCampus, 10 Rue d'Oradour-sur-Glane, 75015, Paris, France. .,Inserm, Centre de Recherche des Cordeliers, Sorbonne Université, Université de Paris Cité, 75006, Paris, France. .,AP-HP: Medical Informatics Department, Georges Pompidou European Hospital, 20 Rue Leblanc, 75015, Paris, France.
| | - M Wack
- Inria, HeKA, PariSantéCampus, 10 Rue d'Oradour-sur-Glane, 75015, Paris, France.,Inserm, Centre de Recherche des Cordeliers, Sorbonne Université, Université de Paris Cité, 75006, Paris, France.,AP-HP: Medical Informatics Department, Georges Pompidou European Hospital, 20 Rue Leblanc, 75015, Paris, France
| | - J Pouchot
- AP-HP: Department of Cardiology, Georges Pompidou European Hospital, 75015, Paris, France
| | - N Danchin
- AP-HP: Department of Internal Medicine, Georges Pompidou European Hospital, 75015, Paris, France
| | - A S Jannot
- Inria, HeKA, PariSantéCampus, 10 Rue d'Oradour-sur-Glane, 75015, Paris, France.,Inserm, Centre de Recherche des Cordeliers, Sorbonne Université, Université de Paris Cité, 75006, Paris, France.,AP-HP: Medical Informatics Department, Georges Pompidou European Hospital, 20 Rue Leblanc, 75015, Paris, France
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2
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Dragon-Durey MA, Chen X, Kirilovsky A, Ben Hamouda N, El Sissy C, Russick J, Charpentier E, Binois Y, Marliot F, Meylan M, Granier C, Pere H, Saldmann A, Rance B, Jannot AS, Baron S, Chebbi M, Fayol A, Josseaume N, Rives-Lange C, Tharaux PL, Cholley B, Diehl JL, Arlet JB, Azizi M, Karras A, Czernichow S, Smadja DM, Hulot JS, Cremer I, Tartour E, Mousseaux E, Pagès F. Differential association between inflammatory cytokines and multiorgan dysfunction in COVID-19 patients with obesity. PLoS One 2021; 16:e0252026. [PMID: 34038475 PMCID: PMC8153504 DOI: 10.1371/journal.pone.0252026] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 05/08/2021] [Indexed: 01/08/2023] Open
Abstract
To investigate the mechanisms underlying the SARS-CoV-2 infection severity observed in patients with obesity, we performed a prospective study of 51 patients evaluating the impact of multiple immune parameters during 2 weeks after admission, on vital organs' functions according to body mass index (BMI) categories. High-dimensional flow cytometric characterization of immune cell subsets was performed at admission, 30 systemic cytokines/chemokines levels were sequentially measured, thirteen endothelial markers were determined at admission and at the zenith of the cytokines. Computed tomography scans on admission were quantified for lung damage and hepatic steatosis (n = 23). Abnormal BMI (> 25) observed in 72.6% of patients, was associated with a higher rate of intensive care unit hospitalization (p = 0.044). SARS-CoV-2 RNAaemia, peripheral immune cell subsets and cytokines/chemokines were similar among BMI groups. A significant association between inflammatory cytokines and liver, renal, and endothelial dysfunctions was observed only in patients with obesity (BMI > 30). In contrast, early signs of lung damage (ground-glass opacity) correlated with Th1/M1/inflammatory cytokines only in normal weight patients. Later lesions of pulmonary consolidation correlated with BMI but were independent of cytokine levels. Our study reveals distinct physiopathological mechanisms associated with SARS-CoV-2 infection in patients with obesity that may have important clinical implications.
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Affiliation(s)
- Marie-Agnès Dragon-Durey
- Laboratory of Immunology
- Hôpital Européen Georges Pompidou, AP-HP, Paris, France
- Université de Paris, Paris, France
- INSERM UMRS 1138, Cordeliers Research Center, Team Inflammation, Complement, and Cancer, Paris, France
- Sorbonne Université, Cordeliers Research Center, Paris, France
| | - Xiaoyi Chen
- Sorbonne Université, Cordeliers Research Center, Paris, France
- INSERM UMRS 1138, Cordeliers Research Center, Team Information Sciences to Support Personalized Medicine, Paris, France
- Laboratory of Information Sciences to support Personalized Medicine, Paris, France
| | - Amos Kirilovsky
- Sorbonne Université, Cordeliers Research Center, Paris, France
- INSERM UMRS 1138, Cordeliers Research Center, Team Integrative Cancer Immunology, Paris, France
| | - Nadine Ben Hamouda
- Laboratory of Immunology
- Hôpital Européen Georges Pompidou, AP-HP, Paris, France
| | - Carine El Sissy
- Laboratory of Immunology
- Hôpital Européen Georges Pompidou, AP-HP, Paris, France
- Université de Paris, Paris, France
- Sorbonne Université, Cordeliers Research Center, Paris, France
- INSERM UMRS 1138, Cordeliers Research Center, Team Integrative Cancer Immunology, Paris, France
| | - Jules Russick
- INSERM UMRS 1138, Cordeliers Research Center, Team Inflammation, Complement, and Cancer, Paris, France
- Sorbonne Université, Cordeliers Research Center, Paris, France
| | - Etienne Charpentier
- Hôpital Européen Georges Pompidou, AP-HP, Paris, France
- Department of Radiology
| | - Yannick Binois
- Hôpital Européen Georges Pompidou, AP-HP, Paris, France
- Department of Nephrology
| | - Florence Marliot
- Laboratory of Immunology
- Hôpital Européen Georges Pompidou, AP-HP, Paris, France
- Université de Paris, Paris, France
- Sorbonne Université, Cordeliers Research Center, Paris, France
- INSERM UMRS 1138, Cordeliers Research Center, Team Integrative Cancer Immunology, Paris, France
| | - Maxime Meylan
- INSERM UMRS 1138, Cordeliers Research Center, Team Inflammation, Complement, and Cancer, Paris, France
- Sorbonne Université, Cordeliers Research Center, Paris, France
| | - Clémence Granier
- Laboratory of Immunology
- Hôpital Européen Georges Pompidou, AP-HP, Paris, France
- Université de Paris, Paris, France
- INSERM, Paris Cardiovascular Center / PARCC, UMR 970, Paris, France
| | - Hélène Pere
- Hôpital Européen Georges Pompidou, AP-HP, Paris, France
- Université de Paris, Paris, France
- INSERM, Paris Cardiovascular Center / PARCC, UMR 970, Paris, France
- Laboratory of Virology
| | - Antonin Saldmann
- Laboratory of Immunology
- Hôpital Européen Georges Pompidou, AP-HP, Paris, France
- Université de Paris, Paris, France
- INSERM, Paris Cardiovascular Center / PARCC, UMR 970, Paris, France
| | - Bastien Rance
- Hôpital Européen Georges Pompidou, AP-HP, Paris, France
- Université de Paris, Paris, France
- INSERM UMRS 1138, Cordeliers Research Center, Team Information Sciences to Support Personalized Medicine, Paris, France
- Laboratory of Information Sciences to support Personalized Medicine, Paris, France
- Biostatistics and Public Health Department
| | - Anne Sophie Jannot
- Hôpital Européen Georges Pompidou, AP-HP, Paris, France
- Université de Paris, Paris, France
- INSERM UMRS 1138, Cordeliers Research Center, Team Information Sciences to Support Personalized Medicine, Paris, France
- Laboratory of Information Sciences to support Personalized Medicine, Paris, France
- Biostatistics and Public Health Department
| | - Stéphanie Baron
- Hôpital Européen Georges Pompidou, AP-HP, Paris, France
- Université de Paris, Paris, France
- Department of Physiology
| | - Mouna Chebbi
- Hôpital Européen Georges Pompidou, AP-HP, Paris, France
- Université de Paris, Paris, France
- Department of Physiology
| | - Antoine Fayol
- Hôpital Européen Georges Pompidou, AP-HP, Paris, France
- Université de Paris, Paris, France
- Clinic Investigation Center 1418
| | - Nathalie Josseaume
- INSERM UMRS 1138, Cordeliers Research Center, Team Inflammation, Complement, and Cancer, Paris, France
- Sorbonne Université, Cordeliers Research Center, Paris, France
| | - Claire Rives-Lange
- Hôpital Européen Georges Pompidou, AP-HP, Paris, France
- Université de Paris, Paris, France
- Department of Nutrition
| | - Pierre-Louis Tharaux
- Hôpital Européen Georges Pompidou, AP-HP, Paris, France
- Université de Paris, Paris, France
- INSERM, Paris Cardiovascular Center / PARCC, UMR 970, Paris, France
| | - Bernard Cholley
- Hôpital Européen Georges Pompidou, AP-HP, Paris, France
- Université de Paris, Paris, France
- Department of Intensive Medicine, Reanimation
| | - Jean-Luc Diehl
- Hôpital Européen Georges Pompidou, AP-HP, Paris, France
- Université de Paris, Paris, France
- Department of Intensive Medicine, Reanimation
- INSERM UMR-S1140, Team Innovative Therapies in Haemostasis, Paris, France
| | - Jean-Benoît Arlet
- Hôpital Européen Georges Pompidou, AP-HP, Paris, France
- Université de Paris, Paris, France
- Department of Internal Medicine
| | - Michel Azizi
- Hôpital Européen Georges Pompidou, AP-HP, Paris, France
- Université de Paris, Paris, France
- Department of Vascular Medicine
| | - Alexandre Karras
- Hôpital Européen Georges Pompidou, AP-HP, Paris, France
- Université de Paris, Paris, France
- Department of Nephrology
| | - Sébastien Czernichow
- Hôpital Européen Georges Pompidou, AP-HP, Paris, France
- Université de Paris, Paris, France
- Department of Nutrition
| | - David M. Smadja
- Hôpital Européen Georges Pompidou, AP-HP, Paris, France
- Université de Paris, Paris, France
- INSERM UMR-S1140, Team Innovative Therapies in Haemostasis, Paris, France
- Department of Hematology
| | - Jean-Sébastien Hulot
- Hôpital Européen Georges Pompidou, AP-HP, Paris, France
- Université de Paris, Paris, France
- INSERM, Paris Cardiovascular Center / PARCC, UMR 970, Paris, France
- Clinic Investigation Center 1418
| | - Isabelle Cremer
- INSERM UMRS 1138, Cordeliers Research Center, Team Inflammation, Complement, and Cancer, Paris, France
- Sorbonne Université, Cordeliers Research Center, Paris, France
| | - Eric Tartour
- Laboratory of Immunology
- Hôpital Européen Georges Pompidou, AP-HP, Paris, France
- Université de Paris, Paris, France
- INSERM, Paris Cardiovascular Center / PARCC, UMR 970, Paris, France
| | - Elie Mousseaux
- Hôpital Européen Georges Pompidou, AP-HP, Paris, France
- Université de Paris, Paris, France
- Department of Radiology
| | - Franck Pagès
- Laboratory of Immunology
- Hôpital Européen Georges Pompidou, AP-HP, Paris, France
- Université de Paris, Paris, France
- Sorbonne Université, Cordeliers Research Center, Paris, France
- INSERM UMRS 1138, Cordeliers Research Center, Team Integrative Cancer Immunology, Paris, France
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3
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Brat GA, Weber GM, Gehlenborg N, Avillach P, Palmer NP, Chiovato L, Cimino J, Waitman LR, Omenn GS, Malovini A, Moore JH, Beaulieu-Jones BK, Tibollo V, Murphy SN, Yi SL, Keller MS, Bellazzi R, Hanauer DA, Serret-Larmande A, Gutierrez-Sacristan A, Holmes JJ, Bell DS, Mandl KD, Follett RW, Klann JG, Murad DA, Scudeller L, Bucalo M, Kirchoff K, Craig J, Obeid J, Jouhet V, Griffier R, Cossin S, Moal B, Patel LP, Bellasi A, Prokosch HU, Kraska D, Sliz P, Tan ALM, Ngiam KY, Zambelli A, Mowery DL, Schiver E, Devkota B, Bradford RL, Daniar M, Daniel C, Benoit V, Bey R, Paris N, Serre P, Orlova N, Dubiel J, Hilka M, Jannot AS, Breant S, Leblanc J, Griffon N, Burgun A, Bernaux M, Sandrin A, Salamanca E, Cormont S, Ganslandt T, Gradinger T, Champ J, Boeker M, Martel P, Esteve L, Gramfort A, Grisel O, Leprovost D, Moreau T, Varoquaux G, Vie JJ, Wassermann D, Mensch A, Caucheteux C, Haverkamp C, Lemaitre G, Bosari S, Krantz ID, South A, Cai T, Kohane IS. International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium. NPJ Digit Med 2020. [PMID: 32864472 DOI: 10.1101/2020.04.13.20059691v5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions.
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Affiliation(s)
- Gabriel A Brat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Griffin M Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Nathan P Palmer
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Luca Chiovato
- IRCCS ICS Maugeri, Pavia, Italy.,Department of Internal Medicine and Medical Therapy, University of Pavia, Pavia, Italy
| | | | - Lemuel R Waitman
- Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, KS USA
| | - Gilbert S Omenn
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI USA
| | | | - Jason H Moore
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA USA.,Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA USA
| | | | | | - Shawn N Murphy
- Department of Neurology, Massachusetts General Hospital, Boston, MA USA
| | - Sehi L' Yi
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Mark S Keller
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Riccardo Bellazzi
- IRCCS ICS Maugeri, Pavia, Italy.,Department of Electrical Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - David A Hanauer
- Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI USA
| | | | | | - John J Holmes
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA USA.,Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI USA
| | - Douglas S Bell
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA USA
| | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA USA
| | - Robert W Follett
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA USA
| | - Jeffrey G Klann
- Department of Medicine, Massachusetts General Hospital, Boston, MA USA
| | - Douglas A Murad
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA USA
| | - Luigia Scudeller
- Scientific Direction, IRCCS Ca' Granda Ospedale Maggiore Policlinico di Milano, Milano, Italy
| | - Mauro Bucalo
- BIOMERIS (BIOMedical Research Informatics Solutions), Pavia, Italy
| | - Katie Kirchoff
- Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC USA
| | - Jean Craig
- Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC USA
| | - Jihad Obeid
- Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC USA
| | | | | | | | | | - Lav P Patel
- Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, KS USA
| | - Antonio Bellasi
- UOC Ricerca, Innovazione e Brand Reputation, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - Hans U Prokosch
- Department of Medical Informatics, University of Erlangen-Nürnberg, Erlangen, Germany
| | - Detlef Kraska
- Center for Medical Information and Communication Technology, University Hospital Erlangen, Erlangen, Germany
| | - Piotr Sliz
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA USA
| | - Amelia L M Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Kee Yuan Ngiam
- National University Health Systems, Singapore, Singapore
| | - Alberto Zambelli
- Department of Oncology, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - Danielle L Mowery
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA USA.,Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI USA
| | - Emily Schiver
- Penn Medicine, Data Analytics Center, Philadelphia, PA USA
| | - Batsal Devkota
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA USA
| | - Robert L Bradford
- North Carolina Translational and Clinical Sciences (NC TraCS) Institute, UNC Chapel Hill, Chapel Hill, NC USA
| | - Mohamad Daniar
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA USA
| | - Christel Daniel
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Vincent Benoit
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Romain Bey
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Nicolas Paris
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Patricia Serre
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Nina Orlova
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Julien Dubiel
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Martin Hilka
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Anne Sophie Jannot
- Department of Biomedical Informatics, HEGP, APHP Greater Paris University Hospital, Paris, France
| | - Stephane Breant
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Judith Leblanc
- Clinical Research Unit, Saint Antoine Hospital, APHP Greater Paris University Hospital, Paris, France
| | - Nicolas Griffon
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Anita Burgun
- Department of Biomedical Informatics, HEGP, APHP Greater Paris University Hospital, Paris, France
| | - Melodie Bernaux
- Strategy and Transformation Department, APHP Greater Paris University Hospital, Paris, France
| | - Arnaud Sandrin
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Elisa Salamanca
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Sylvie Cormont
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Thomas Ganslandt
- Heinrich-Lanz-Center for Digital Health, University Medicine Mannheim, Heidelberg University, Mannheim, Germany
| | - Tobias Gradinger
- Heinrich-Lanz-Center for Digital Health, University Medicine Mannheim, Heidelberg University, Mannheim, Germany
| | - Julien Champ
- INRIA Sophia-Antipolis-ZENITH Team, LIRMM, Montpellier, France
| | - Martin Boeker
- Institute of Medical Biometry and Statistics, Medical Center, University of Freiburg, Freiburg im Breisgau, Germany
| | - Patricia Martel
- Clinical Research Unit, Paris Saclay, APHP Greater Paris University Hospital, Paris, France
| | - Loic Esteve
- SED/SIERRA, Inria Centre de Paris, Paris, France
| | | | | | | | | | | | | | | | | | | | - Christian Haverkamp
- Institute of Digitalization in Medicine, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg im Breisgau, Germany
| | | | - Silvano Bosari
- IRCCS Ca' Granda Ospedale Maggiore Policlinico di Milano, Milano, Italy
| | - Ian D Krantz
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA USA
| | - Andrew South
- Brenner Children's Hospital, Wake Forest School of Medicine, Winston-Salem, NC USA
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
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4
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Brat GA, Weber GM, Gehlenborg N, Avillach P, Palmer NP, Chiovato L, Cimino J, Waitman LR, Omenn GS, Malovini A, Moore JH, Beaulieu-Jones BK, Tibollo V, Murphy SN, Yi SL, Keller MS, Bellazzi R, Hanauer DA, Serret-Larmande A, Gutierrez-Sacristan A, Holmes JJ, Bell DS, Mandl KD, Follett RW, Klann JG, Murad DA, Scudeller L, Bucalo M, Kirchoff K, Craig J, Obeid J, Jouhet V, Griffier R, Cossin S, Moal B, Patel LP, Bellasi A, Prokosch HU, Kraska D, Sliz P, Tan ALM, Ngiam KY, Zambelli A, Mowery DL, Schiver E, Devkota B, Bradford RL, Daniar M, Daniel C, Benoit V, Bey R, Paris N, Serre P, Orlova N, Dubiel J, Hilka M, Jannot AS, Breant S, Leblanc J, Griffon N, Burgun A, Bernaux M, Sandrin A, Salamanca E, Cormont S, Ganslandt T, Gradinger T, Champ J, Boeker M, Martel P, Esteve L, Gramfort A, Grisel O, Leprovost D, Moreau T, Varoquaux G, Vie JJ, Wassermann D, Mensch A, Caucheteux C, Haverkamp C, Lemaitre G, Bosari S, Krantz ID, South A, Cai T, Kohane IS. International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium. NPJ Digit Med 2020; 3:109. [PMID: 32864472 PMCID: PMC7438496 DOI: 10.1038/s41746-020-00308-0] [Citation(s) in RCA: 102] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 06/16/2020] [Indexed: 12/18/2022] Open
Abstract
We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions.
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Affiliation(s)
- Gabriel A. Brat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Griffin M. Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Nathan P. Palmer
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Luca Chiovato
- IRCCS ICS Maugeri, Pavia, Italy
- Department of Internal Medicine and Medical Therapy, University of Pavia, Pavia, Italy
| | | | - Lemuel R. Waitman
- Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, KS USA
| | - Gilbert S. Omenn
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI USA
| | | | - Jason H. Moore
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA USA
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA USA
| | | | | | - Shawn N. Murphy
- Department of Neurology, Massachusetts General Hospital, Boston, MA USA
| | - Sehi L’ Yi
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Mark S. Keller
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Riccardo Bellazzi
- IRCCS ICS Maugeri, Pavia, Italy
- Department of Electrical Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - David A. Hanauer
- Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI USA
| | | | | | - John J. Holmes
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA USA
- Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI USA
| | - Douglas S. Bell
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA USA
| | - Kenneth D. Mandl
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA USA
| | - Robert W. Follett
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA USA
| | - Jeffrey G. Klann
- Department of Medicine, Massachusetts General Hospital, Boston, MA USA
| | - Douglas A. Murad
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA USA
| | - Luigia Scudeller
- Scientific Direction, IRCCS Ca’ Granda Ospedale Maggiore Policlinico di Milano, Milano, Italy
| | - Mauro Bucalo
- BIOMERIS (BIOMedical Research Informatics Solutions), Pavia, Italy
| | - Katie Kirchoff
- Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC USA
| | - Jean Craig
- Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC USA
| | - Jihad Obeid
- Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC USA
| | | | | | | | | | - Lav P. Patel
- Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, KS USA
| | - Antonio Bellasi
- UOC Ricerca, Innovazione e Brand Reputation, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - Hans U. Prokosch
- Department of Medical Informatics, University of Erlangen-Nürnberg, Erlangen, Germany
| | - Detlef Kraska
- Center for Medical Information and Communication Technology, University Hospital Erlangen, Erlangen, Germany
| | - Piotr Sliz
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA USA
| | - Amelia L. M. Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Kee Yuan Ngiam
- National University Health Systems, Singapore, Singapore
| | - Alberto Zambelli
- Department of Oncology, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - Danielle L. Mowery
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA USA
- Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI USA
| | - Emily Schiver
- Penn Medicine, Data Analytics Center, Philadelphia, PA USA
| | - Batsal Devkota
- Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA USA
| | - Robert L. Bradford
- North Carolina Translational and Clinical Sciences (NC TraCS) Institute, UNC Chapel Hill, Chapel Hill, NC USA
| | - Mohamad Daniar
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA USA
| | - Christel Daniel
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Vincent Benoit
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Romain Bey
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Nicolas Paris
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Patricia Serre
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Nina Orlova
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Julien Dubiel
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Martin Hilka
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Anne Sophie Jannot
- Department of Biomedical Informatics, HEGP, APHP Greater Paris University Hospital, Paris, France
| | - Stephane Breant
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Judith Leblanc
- Clinical Research Unit, Saint Antoine Hospital, APHP Greater Paris University Hospital, Paris, France
| | - Nicolas Griffon
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Anita Burgun
- Department of Biomedical Informatics, HEGP, APHP Greater Paris University Hospital, Paris, France
| | - Melodie Bernaux
- Strategy and Transformation Department, APHP Greater Paris University Hospital, Paris, France
| | - Arnaud Sandrin
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Elisa Salamanca
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Sylvie Cormont
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Thomas Ganslandt
- Heinrich-Lanz-Center for Digital Health, University Medicine Mannheim, Heidelberg University, Mannheim, Germany
| | - Tobias Gradinger
- Heinrich-Lanz-Center for Digital Health, University Medicine Mannheim, Heidelberg University, Mannheim, Germany
| | - Julien Champ
- INRIA Sophia-Antipolis—ZENITH Team, LIRMM, Montpellier, France
| | - Martin Boeker
- Institute of Medical Biometry and Statistics, Medical Center, University of Freiburg, Freiburg im Breisgau, Germany
| | - Patricia Martel
- Clinical Research Unit, Paris Saclay, APHP Greater Paris University Hospital, Paris, France
| | - Loic Esteve
- SED/SIERRA, Inria Centre de Paris, Paris, France
| | | | | | | | | | | | | | | | | | | | - Christian Haverkamp
- Institute of Digitalization in Medicine, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg im Breisgau, Germany
| | | | - Silvano Bosari
- IRCCS Ca’ Granda Ospedale Maggiore Policlinico di Milano, Milano, Italy
| | - Ian D. Krantz
- Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA USA
| | - Andrew South
- Brenner Children’s Hospital, Wake Forest School of Medicine, Winston-Salem, NC USA
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Isaac S. Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
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Dohan A, Hoeffel C, Soyer P, Jannot AS, Valette PJ, Thivolet A, Passot G, Glehen O, Rousset P. Evaluation of the peritoneal carcinomatosis index with CT and MRI. Br J Surg 2017; 104:1244-1249. [PMID: 28376270 DOI: 10.1002/bjs.10527] [Citation(s) in RCA: 79] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2016] [Revised: 02/03/2017] [Accepted: 02/07/2017] [Indexed: 12/17/2022]
Abstract
BACKGROUND The aim was to determine the incremental value of MRI compared with CT in the preoperative estimation of the peritoneal carcinomatosis index (PCI). METHODS CT and MRI examinations of patients with peritoneal carcinomatosis were evaluated. CT images were first analysed by two observers who determined a first PCI (PCICT ). Then, the two observers reviewed MRI examinations in combination with CT and determined a second PCI (PCICT+MRI ). The sensitivity and negative predictive value of the two imaging sets were determined using surgery as a reference standard (PCIRef ). RESULTS CT plus MRI was more accurate in predicting the surgical PCI than CT alone. The absolute difference between PCICT+MRI and PCIRef was lower than that between PCICT and PCIRef (mean(s.d.) 3·96(4·10) versus 4·89(4·73); P = 0·010). The number of true-positive findings increased from 106 to 125 for reader 1 and from 117 to 132 for reader 2 with the adjunct of MRI. For both readers, an increased sensitivity was obtained when both MRI and CT were used (from 63 to 81 per cent for reader 1; from 44 to 81 per cent for reader 2). The increase in sensitivity was greater for patients with a moderate volume of disease. CONCLUSION The combination of CT and MRI improved the preoperative estimation of PCI compared with CT alone.
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Affiliation(s)
- A Dohan
- Department of Body and Interventional Imaging, Hôpital Lariboisière, Assistance Publique - Hôpitaux de Paris (AP-HP), Université Diderot-Paris 7 and Institut National de la Santé et de la Recherche Médicale (INSERM) U965, AP-HP, Paris, France.,Department of Radiology, McGill University Health Centre, Montreal, Quebec, Canada
| | - C Hoeffel
- Department of Radiology, Hôpital Robert-Debré, Reims, France
| | - P Soyer
- Department of Body and Interventional Imaging, Hôpital Lariboisière, Assistance Publique - Hôpitaux de Paris (AP-HP), Université Diderot-Paris 7 and Institut National de la Santé et de la Recherche Médicale (INSERM) U965, AP-HP, Paris, France
| | - A S Jannot
- INSERM-Unité Mixte de Recherche en Santé 1138 Team 22, Cordeliers Research Centre, Paris Descartes University, Department of Medical Informatics and Public Health, European George Pompidou Hospital, AP-HP, Paris, France
| | - P-J Valette
- Department of Radiology, Centre Hospitalier Lyon Sud - Hospices Civils de Lyon, Lyon 1 University, Equipe Mixte de Recherche 3738, Lyon, France
| | - A Thivolet
- Department of Radiology, Centre Hospitalier Lyon Sud - Hospices Civils de Lyon, Lyon 1 University, Equipe Mixte de Recherche 3738, Lyon, France
| | - G Passot
- Department of Digestive and Oncological Surgery, Centre Hospitalier Lyon Sud - Hospices Civils de Lyon, Lyon 1 University, Equipe Mixte de Recherche 3738, Lyon, France
| | - O Glehen
- Department of Digestive and Oncological Surgery, Centre Hospitalier Lyon Sud - Hospices Civils de Lyon, Lyon 1 University, Equipe Mixte de Recherche 3738, Lyon, France
| | - P Rousset
- Department of Radiology, Centre Hospitalier Lyon Sud - Hospices Civils de Lyon, Lyon 1 University, Equipe Mixte de Recherche 3738, Lyon, France
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6
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Barat M, Fohlen A, Cassinotto C, Jannot AS, Dautry R, Pelage JP, Boudiaf M, Pocard M, Eveno C, Taouli B, Soyer P, Dohan A. One-month apparent diffusion coefficient correlates with response to radiofrequency ablation of hepatocellular carcinoma. J Magn Reson Imaging 2016; 45:1648-1658. [PMID: 27766709 DOI: 10.1002/jmri.25521] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [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: 07/21/2016] [Accepted: 10/05/2016] [Indexed: 12/13/2022] Open
Abstract
PURPOSE To assess whether apparent diffusion coefficient (ADC) values at 1 and 3 months after radiofrequency ablation (RFA) may be associated with a favorable response to therapy for hepatocellular carcinoma (HCC) and liver metastases. MATERIALS AND METHODS Fifty-nine patients with HCC (n = 35) or liver metastases (n = 24) who underwent 1.5T diffusion-weighted magnetic resonance imaging (DWMRI) at 1 and 3 months post-RFA were included. ADC values of patients with local tumor recurrence were compared to those without local recurrence. A subgroup analysis was performed for HCC and metastases. RESULTS Thirty-eight HCC and 27 metastases were evaluated. The ADC value of HCC at 1 month after RFA was lower in recurrent tumors (0.957 ± 0.229 [SD] × 10-3 mm2 ) compared to tumors with complete response (1.414 ± 0.322 [SD] × 10-3 mm2 /s, P = 0.006). At multivariate analysis, ADC at 1 month was the single independent variable associated with recurrence for HCC (area under the receiver operating characteristic curve = 0.860). No significant association was observed for liver metastases (P = 0.089). CONCLUSION A low ADC value at 1 month after RFA is associated with an early local recurrence of HCC. This study does not confirm that such association exists for hepatic metastases. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;45:1648-1658.
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Affiliation(s)
- Maxime Barat
- Department of Body and Interventional Imaging, Hôpital Lariboisière, Paris, France
| | - Audrey Fohlen
- CNRS, UMR 6301-ISTCT, CERVOxy, GIP CYCERON, Caen, France.,CEA, DSV/I2BM, UMR6301-ISTCT, Caen, France.,Normandie Université, France.,UNICAEN, UMR6301-ISTCT, Caen, France.,CHU de CAEN, Service d'Imagerie Diagnostique et de Radiologie Thérapeutique, Caen, France
| | - Christophe Cassinotto
- Department of Diagnostic and Interventional Imaging, Hôpîtal Haut-Lévêque, Centre Hospitalier Universitaire de Bordeaux, Pessac, France.,INSERM U1053, Université Bordeaux, Bordeaux, France
| | - Anne Sophie Jannot
- INSERM-UMRS 1138 Team 22, Cordeliers Research Center, Paris, France.,Paris Descartes University, Paris, France.,Department of Medical Informatics and Public Health, European George Pompidou Hospital, Paris, France
| | - Raphael Dautry
- Department of Body and Interventional Imaging, Hôpital Lariboisière, Paris, France
| | - Jean-Pierre Pelage
- CNRS, UMR 6301-ISTCT, CERVOxy, GIP CYCERON, Caen, France.,CEA, DSV/I2BM, UMR6301-ISTCT, Caen, France.,Normandie Université, France.,UNICAEN, UMR6301-ISTCT, Caen, France.,CHU de CAEN, Service d'Imagerie Diagnostique et de Radiologie Thérapeutique, Caen, France
| | - Mourad Boudiaf
- Department of Body and Interventional Imaging, Hôpital Lariboisière, Paris, France
| | - Marc Pocard
- Université Paris-Diderot, Sorbonne-Paris Cité, Paris, France.,UMR CART - INSERM 965, Hôpital Lariboisière, Paris, France.,Department of Digestive Surgery, Hôpital Lariboisière, Paris, France
| | - Clarisse Eveno
- Université Paris-Diderot, Sorbonne-Paris Cité, Paris, France.,UMR CART - INSERM 965, Hôpital Lariboisière, Paris, France.,Department of Digestive Surgery, Hôpital Lariboisière, Paris, France
| | - Bachir Taouli
- Department of Radiology and Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Philippe Soyer
- Department of Body and Interventional Imaging, Hôpital Lariboisière, Paris, France.,Université Paris-Diderot, Sorbonne-Paris Cité, Paris, France.,UMR CART - INSERM 965, Hôpital Lariboisière, Paris, France
| | - Anthony Dohan
- Department of Body and Interventional Imaging, Hôpital Lariboisière, Paris, France.,Université Paris-Diderot, Sorbonne-Paris Cité, Paris, France.,UMR CART - INSERM 965, Hôpital Lariboisière, Paris, France.,McGill University Health Center, Department of Radiology, McGill University Health Center, Montreal, QC, Canada
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7
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Jannot AS, Perneger T. Perceived usefulness of nine quality improvement tools among Swiss physicians. Qual Prim Care 2014; 22:278-281. [PMID: 25887653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
BACKGROUND Doctors' opinions about quality improvement tools likely influence their uptake and eventual impact on patient care. Little is known about physicians' perception of the comparative utility of various quality improvement tools. METHODS We conducted a mail survey of doctors in Geneva, Switzerland (2745 physicians, of whom 56% participated), to measure the perceived usefulness of 9 quality improvement tools. RESULTS In decreasing order of perceived utility these tools were regular continuous education (rated as very or extremely useful by 75% of respondents), mortality and morbidity conferences (65%), quality circles (60%), patient satisfaction measurement (42%), assessment of the fulfillment of therapeutic objectives (41%), assessment of compliance with guidelines (36%), periodic evaluation of doctors' skills (14%), onsite visits with peer-review of medical records (11%), and certification of office practices (8%). CONCLUSION Quality improvement tools seen as most useful by physicians are traditional methods such as continuous education and mortality and morbidity conferences. Methods that rely on the measurement of indicators or that have a judgmental component received less support.
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Affiliation(s)
- A S Jannot
- Division of clinical epidemiology, University Hospitals of Geneva, Geneva, Switzerland; Service d'épidémiologie clinique, Hôpitaux Universitaires de Genève, Rue Gabrielle Perret-Gentil 6, 1211 GENEVE 14, Switzerland.
| | - T Perneger
- Division of clinical epidemiology, University Hospitals of Geneva, Geneva, Switzerland
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9
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Demongeot J, Hansen O, Hessami H, Jannot AS, Mintsa J, Rachdi M, Taramasco C. Random modelling of contagious diseases. Acta Biotheor 2013; 61:141-72. [PMID: 23525763 DOI: 10.1007/s10441-013-9176-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2012] [Accepted: 01/11/2013] [Indexed: 01/01/2023]
Abstract
Modelling contagious diseases needs to include a mechanistic knowledge about contacts between hosts and pathogens as specific as possible, e.g., by incorporating in the model information about social networks through which the disease spreads. The unknown part concerning the contact mechanism can be modelled using a stochastic approach. For that purpose, we revisit SIR models by introducing first a microscopic stochastic version of the contacts between individuals of different populations (namely Susceptible, Infective and Recovering), then by adding a random perturbation in the vicinity of the endemic fixed point of the SIR model and eventually by introducing the definition of various types of random social networks. We propose as example of application to contagious diseases the HIV, and we show that a micro-simulation of individual based modelling (IBM) type can reproduce the current stable incidence of the HIV epidemic in a population of HIV-positive men having sex with men (MSM).
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Affiliation(s)
- J Demongeot
- AGIM, FRE, CNRS 3405, Faculty of Medicine of Grenoble, University J. Fourier, 38700 La Tronche, France.
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