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Pandit AS, Khan DZ, Hanrahan JG, Dorward NL, Baldeweg SE, Nachev P, Marcus HJ. Historical and future trends in emergency pituitary referrals: a machine learning analysis. Pituitary 2022; 25:927-937. [PMID: 36085340 PMCID: PMC9462621 DOI: 10.1007/s11102-022-01269-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/02/2022] [Indexed: 11/03/2022]
Abstract
PURPOSE Acute pituitary referrals to neurosurgical services frequently necessitate emergency care. Yet, a detailed characterisation of pituitary emergency referral patterns, including how they may change prospectively is lacking. This study aims to evaluate historical and current pituitary referral patterns and utilise state-of-the-art machine learning tools to predict future service use. METHODS A data-driven analysis was performed using all available electronic neurosurgical referrals (2014-2021) to the busiest U.K. pituitary centre. Pituitary referrals were characterised and volumes were predicted using an auto-regressive moving average model with a preceding seasonal and trend decomposition using Loess step (STL-ARIMA), compared against a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) algorithm, Prophet and two standard baseline forecasting models. Median absolute, and median percentage error scoring metrics with cross-validation were employed to evaluate algorithm performance. RESULTS 462 of 36,224 emergency referrals were included (referring centres = 48; mean patient age = 56.7 years, female:male = 0.49:0.51). Emergency medicine and endocrinology accounted for the majority of referrals (67%). The most common presentations were headache (47%) and visual field deficits (32%). Lesions mainly comprised tumours or haemorrhage (85%) and involved the pituitary gland or fossa (70%). The STL-ARIMA pipeline outperformed CNN-LSTM, Prophet and baseline algorithms across scoring metrics, with standard accuracy being achieved for yearly predictions. Referral volumes significantly increased from the start of data collection with future projected increases (p < 0.001) and did not significantly reduce during the COVID-19 pandemic. CONCLUSION This work is the first to employ large-scale data and machine learning to describe and predict acute pituitary referral volumes, estimate future service demands, explore the impact of system stressors (e.g. COVID pandemic), and highlight areas for service improvement.
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Affiliation(s)
- A S Pandit
- High-Dimensional Neurology, Queen Square Institute of Neurology, University College London, London, UK
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
| | - D Z Khan
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - J G Hanrahan
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - N L Dorward
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
| | - S E Baldeweg
- Department of Diabetes and Endocrinology, University College London Hospital, London, UK
- Centre for Obesity & Metabolism, Department of Experimental & Translational Medicine, Division of Medicine, University College London, London, UK
| | - P Nachev
- High-Dimensional Neurology, Queen Square Institute of Neurology, University College London, London, UK
| | - H J Marcus
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK.
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK.
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2
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Solomou G, Gharooni A, Whitehouse K, Poon MTC, Piper RJ, Fountain DM, Khan DZ, Lopez CC, Ooi SZ, Lammy S, Maqsood R, Brochert RJ, Patel W, Baig A, Haq M, O’Donnell A, Joseph G, Kolias AG, Ashkan K, Jenkinson MD, Plaha P, Price SJ, Watts C. OS07.2.A Evaluation of Intraoperative Surgical Adjuncts and Resection of Glioblastoma (ELISAR GB): A UK and Ireland multicentre, prospective observational cohort study. Neuro Oncol 2022. [DOI: 10.1093/neuonc/noac174.047] [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
Despite operative and adjuvant therapies, glioblastoma remains incurable, with the extent of resection being one of few treatments that can improve survival. To improve resection, operative adjuncts are used, with neuronavigation and 5-aminolevulinic acid (5-ALA) recommended as a standard of care in those aimed for maximal safe resection. Despite the standards, meta-analysis concluded that the impact of 5-ALA on the extent of surgical resection is of low quality due to bias in reporting tumour location and additional image guidance used, factors impacting on extent of resection as well as short-term neurological outcomes being uncertain. Therefore we aimed to evaluate the availability and use of 5-ALA and other adjuncts and compare surgical outcomes of 5-ALA-guided versus non-5-ALA-guided resections.
Material and Methods
A multicenter prospective observational cohort study was conducted across 27 out of 31 available centres in the UK and Ireland from 6 January until 19 March 2020. Inclusion criteria included adults with first diagnosis, supratentorial glioblastoma undergoing resection. Primary outcomes included: i) the availability and use of surgical adjuncts and ii) complete resection of enhancing tissue (CRET). Secondary outcomes included adverse events, new onset of postoperative neurological deficit and post-operative neurological function. Descriptive and inferential statistics were used for analysis with a p-value <0.05 deemed significant.
Results
232 consecutive cases were identified. 142/232 cases were aimed for maximal safe resection subsequently divided into 5-ALA-guided (n=92) versus non-5-ALA-guided (n=50) resections. 5-ALA and neuronavigation were available across all centres. Neuronavigation and 5-ALA were used in 91% (n=129/142) and 65% (n=92/142) of cases aimed for maximal safe resection whereas 83% (n=75/90) and 49% (n=44/90) for debulk surgery. 35 unique combinations of surgical adjuncts were used in 232 operations. 5-ALA-guided resection yielded a higher percentage of CRET than without (55% versus 28%, p < 0.01). The two groups showed no difference in adverse events (p=0.98), new onset of neurological deficit (p=0.88) nor neurological function (p=0.7). A logistic regression analysis showed that 5-ALA was an important predictor of CRET regardless of additional adjuncts used (OR 2.4, CI 0.96-5.97, P = 0.05), tumour location and molecular characterisation (OR 3.48, CI 1.61-7.51, P <0.01).
Conclusion
Firstly, we showed that 5-ALA is not always used for glioblastoma aimed for CRET. Secondly, we report a great heterogeneity of adjuncts used for resection, possibly explained by a lack of high-quality evidence and surgeon training. Thirdly we demonstrate that 5-ALA-guided resection leads to higher percentage of CRET regardless of other adjuncts used, tumour location and molecular characterisation.
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Affiliation(s)
- G Solomou
- University of Cambridge , Cambridge , United Kingdom
| | - A Gharooni
- University of Cambridge , Cambridge , United Kingdom
| | - K Whitehouse
- Department of Neurosurgery, University Hospital of Wales, , Cardiff , United Kingdom
| | - M T C Poon
- Usher Institute, The University of Edinburgh , Edinburgh , United Kingdom
| | - R J Piper
- Department of Neurosurgery, John Radcliffe Hospital , Oxford , United Kingdom
| | - D M Fountain
- Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, , Manchester , United Kingdom
| | - D Z Khan
- Welcome/EPSRC Centre for Interventional and Surgical Sciences, National Hospital for Neurology and Neurosurgery , London , United Kingdom
| | - C C Lopez
- Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, , Manchester , United Kingdom
| | - S Z Ooi
- Cardiff University School of Medicine, Cardiff , Cardiff , United Kingdom
| | - S Lammy
- Department of Neurosurgery Institute of Neurological Sciences , Glasgow , United Kingdom
| | - R Maqsood
- University of Glasgow , Glasgow , United Kingdom
| | - R J Brochert
- Neurosurgery Division, Department of Clinical Neurosciences, Cambridge University , Cambridge , United Kingdom
| | - W Patel
- Department of Neurosurgery, John Radcliffe Hospital , Oxford , United Kingdom
| | - A Baig
- Department of Neurosurgery, King’s College Hospital NHS Foundation Trust , London , United Kingdom
| | - M Haq
- GKT School of Medical Education, Guy’s Campus , London , United Kingdom
| | - A O’Donnell
- Royal Sussex County Hospital , Brighton , United Kingdom
| | - G Joseph
- Keele University, Institute of Science and Technology , Keele , United Kingdom
| | - A G Kolias
- Neurosurgery Division, Department of Clinical Neurosciences, Cambridge University , Cambridge , United Kingdom
| | - K Ashkan
- Department of Neurosurgery, King’s College Hospital NHS Foundation Trust, King’s College London, , London , United Kingdom
| | - M D Jenkinson
- Department of Neurosurgery, The Walton Centre NHS Foundation Trust, , Liverpool , United Kingdom
| | - P Plaha
- Department of Neurosurgery, John Radcliffe Hospital , Oxford , United Kingdom
| | - S J Price
- Neurosurgery Division, Department of Clinical Neurosciences, Cambridge University , Cambridge , United Kingdom
| | - C Watts
- Institute of Cancer and Genomic Sciences, University of Birmingham , Birmingham , United Kingdom
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Muirhead WR, Layard Horsfall H, Khan DZ, Koh C, Grover PJ, Toma AK, Castanho P, Stoyanov D, Marcus HJ, Murphy M. Microsurgery for intracranial aneurysms: A qualitative survey on technical challenges and technological solutions. Front Surg 2022; 9:957450. [PMID: 35990100 PMCID: PMC9386123 DOI: 10.3389/fsurg.2022.957450] [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] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 07/06/2022] [Indexed: 11/24/2022] Open
Abstract
Introduction Microsurgery for the clipping of intracranial aneurysms remains a technically challenging and high-risk area of neurosurgery. We aimed to describe the technical challenges of aneurysm surgery, and the scope for technological innovations to overcome these barriers from the perspective of practising neurovascular surgeons. Materials and Methods Consultant neurovascular surgeons and members of the British Neurovascular Group (BNVG) were electronically invited to participate in an online survey regarding surgery for both ruptured and unruptured aneurysms. The free text survey asked three questions: what do they consider to be the principal technical barriers to aneurysm clipping? What technological advances have previously contributed to improving the safety and efficacy of aneurysm clipping? What technological advances do they anticipate improving the safety and efficacy of aneurysm clipping in the future? A qualitative synthesis of responses was performed using multi-rater emergent thematic analysis. Results The most significant reported historical advances in aneurysm surgery fell into five themes: (1) optimising clip placement, (2) minimising brain retraction, (3) tissue handling, (4) visualisation and orientation, and (5) management of intraoperative rupture. The most frequently reported innovation by far was indocyanine green angiography (84% of respondents). The three most commonly cited future advances were hybrid surgical and endovascular techniques, advances in intraoperative imaging, and patient-specific simulation and planning. Conclusions While some surgeons perceive that the rate of innovation in aneurysm clipping has been dwarfed in recent years by endovascular techniques, surgeons surveyed highlighted a broad range of future technologies that have the potential to continue to improve the safety of aneurysm surgery in the future.
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Affiliation(s)
- W. R. Muirhead
- Department of Neurosurgery, The National Hospital for Neurology and Neurosurgery, London, United Kingdom
- The Wellcome Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
| | - H. Layard Horsfall
- Department of Neurosurgery, The National Hospital for Neurology and Neurosurgery, London, United Kingdom
- The Wellcome Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
| | - D. Z. Khan
- Department of Neurosurgery, The National Hospital for Neurology and Neurosurgery, London, United Kingdom
- The Wellcome Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
| | - C. Koh
- Department of Neurosurgery, The National Hospital for Neurology and Neurosurgery, London, United Kingdom
- The Wellcome Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
| | - P. J. Grover
- Department of Neurosurgery, The National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - A. K. Toma
- Department of Neurosurgery, The National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - P. Castanho
- Department of Neurosurgery, The National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - D. Stoyanov
- The Wellcome Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
| | - H. J. Marcus
- Department of Neurosurgery, The National Hospital for Neurology and Neurosurgery, London, United Kingdom
- The Wellcome Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
| | - M. Murphy
- Department of Neurosurgery, The National Hospital for Neurology and Neurosurgery, London, United Kingdom
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Khan DZ, Kafai Golahmadi A, Mylonas G, Marcus H. 662 Tool-tissue Forces in Surgery: A Systematic Review. Br J Surg 2021. [DOI: 10.1093/bjs/znab134.018] [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
Excessive tool-tissue interaction forces in surgery may result in tissue damage and intraoperative complications, while insufficient forces prevent the completion of the task.
Method
A systematic review of studies exploring tool-tissue forces applied during surgery was performed in accordance with PRISMA guidelines.
Results
45 studies discussing tool-tissue forces during surgical procedures or tasks were included. Mean forces per speciality were as follows: ophthalmology (0.04N), vascular (0.7N), neurosurgery (0.68N), cardiothoracic surgery (1.5N), general surgery (4.7N), otorhinolaryngology (8.5N), obstetrics & gynaecology (mean 8.7N), urology (9.8N) and orthopaedic surgery (210N). Senior surgeons applied 25% less force than novice surgeons, and feedback (haptic, visual or audio) reduced force application by 40% - across specialities and tasks. Nervous tissue required the least amount of force to manipulate (0.4N, n = 17), followed by epithelial (3.8N, n = 18), muscle (4.1N, n = 4) and connective tissue (45.8, n = 10). For manoeuvres, retraction-with-grasping recorded the highest forces (3.65N, n = 13), whilst vessel clamping recorded the lowest (0.5N, n = 2).
Conclusions
The measurement of tool-tissue forces is a novel but rapidly expanding field. Knowledge of the safe range of surgical forces will improve surgical safety whilst maintaining effectiveness. Measuring surgical forces may provide an objective metric for training and assessment. Development of smart instruments, robotics and integrated feedback systems will facilitate this.
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Affiliation(s)
- D Z Khan
- Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
- National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - A Kafai Golahmadi
- Imperial College London School of Medicine, London, United Kingdom
- HARMS Laboratory, The Hamlyn Centre, Department of Surgery & Cancer, Imperial College London, London, United Kingdom
| | - G Mylonas
- HARMS Laboratory, The Hamlyn Centre, Department of Surgery & Cancer, Imperial College London, London, United Kingdom
| | - H Marcus
- Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
- National Hospital for Neurology and Neurosurgery, London, United Kingdom
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5
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Khan DZ, Posa M, Buttimore J, Bance M, Helmy A. 844 The Frequency of Formal Audiological Assessment in Patients with Diagnosed Vestibular Schwannomas – A Single Centre audit. Br J Surg 2021. [DOI: 10.1093/bjs/znab134.493] [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
Patients with vestibular schwannomas (VS) must have formal audiology during their workup, according to multiple national guidelines (including NICE).
Method
This retrospective study sought to audit the percentage of VS cases undergoing formal audiometry at a tertiary neurosurgical centre. An illustrative sample was selected randomly from local databases (2006-2019). Data collected included audiometry types (speech discrimination [SD], pure tone audiometry [PTA]), management pathway (surgery, radiotherapy or conservative) and symptom profile.
Results
200 cases were assessed, 7 were excluded (private patients, patients referred but not formally seen). Of the 193 cases included, 186 (96.4%) had ≥1 Pure Tone assessment [PTA], with 135 (70%) also tested on Speech Discrimination [SD]. The surgical cohort had the highest audiometry rates at 38/38, followed by 116/121 (96%) for surveillance and 32/34 (94%) for radiotherapy subgroups. Reasons for no audiology were: profound hearing loss at presentation (2/7), audiometry at local centres (4/7) and patient compliance (1/7).
Conclusions
Our service is highly compliant (96.4%), consistent across management categories. The rate of SD (70%), a functional measure of hearing impairment, can be improved. Monitoring surgical outcomes via formal audiometry should be standard, particularly surgical approaches that preserve hearing are chosen. Follow-up audiometry regimes can be standardized for those on surveillance pathways.
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Affiliation(s)
- D Z Khan
- Division of Neurosurgery, University of Cambridge and Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - M Posa
- Division of Neurosurgery, University of Cambridge and Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - J Buttimore
- Division of Neurosurgery, University of Cambridge and Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- Department of ENT, University of Cambridge and Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - M Bance
- Department of ENT, University of Cambridge and Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - A Helmy
- Division of Neurosurgery, University of Cambridge and Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
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6
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Khan DZ, Placek MM, Smielewski P, Budohoski KP, Anwar F, Hutchinson PJA, Bance M, Czosnyka M, Helmy A. 817 Robotic Semi-Automated Transcranial Doppler Assessment of Cerebrovascular Autoregulation in Post Concussional Syndrome: Methodological Considerations. Br J Surg 2021. [DOI: 10.1093/bjs/znab135.003] [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
Post-concussive syndrome (PCS) refers to a constellation of physical, cognitive, and emotional symptoms after traumatic brain injury (TBI). Despite its incidence, the underlying mechanisms are unclear. We hypothesised that impaired cerebral autoregulation (CA) is a contributor.
Method
A prospective, observational study was integrated into outpatient clinics at a tertiary neurosurgical centre. Data points included: demographics, symptoms (Post-Concussion Symptom Scale [PCSS]), neuropsychological assessment (Cambridge Neuropsychological Test Automated-Battery [CANTAB]) and cerebrovascular metrics (Mxa co-efficient and the transient hyperaemic-response ratio [THRR]) - via transcranial Doppler (TCD), plethysmography and bespoke software (ICM+).
Results
12 participants were recruited with 2 excluded after unsuccessful cerebrovascular TCD insonation. 10 participants (5 TBI patients, 5 healthy controls) were included in the analysis (median age 26.5, male:female 7:3). Median PCSS scores were 6/126 (TBI subgroup). Median CANTAB percentiles were 78 (healthy controls) and 25 (TBI). Mxa was calculated for 90% and THRR for 50% of participants. Median study time was 127.5 minutes and feedback (n = 6) highlighted the perceived acceptability of the study.
Conclusions
This pilot study has demonstrated a feasible and reproducible assessment of PCS and CA metrics (non-invasively) in a real-world setting. By scaling this methodology, we hope to test whether CA changes are correlated with symptomatic PCS in patients post-TBI.
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Affiliation(s)
- D Z Khan
- Division of Neurosurgery, University of Cambridge and Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - M M Placek
- Brain Physics Laboratory, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - P Smielewski
- Brain Physics Laboratory, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - K P Budohoski
- Division of Neurosurgery, University of Cambridge and Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - F Anwar
- Department of Neurorehabilitation, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - P J A Hutchinson
- Division of Neurosurgery, University of Cambridge and Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - M Bance
- Department of ENT, University of Cambridge and Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - M Czosnyka
- Brain Physics Laboratory, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - A Helmy
- Division of Neurosurgery, University of Cambridge and Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
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Zamanipoor Najafabadi AH, Khan DZ, Muskens IS, Broekman MLD, Dorward NL, van Furth WR, Marcus HJ. 795 Trends in Cerebrospinal Fluid Leak Rates Following the Extended Endoscopic Endonasal Approach for Anterior Skull Base Meningioma: A Meta-Analysis Over the Last 20 Years. Br J Surg 2021. [DOI: 10.1093/bjs/znab134.580] [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
The extended endoscopic approach (EEA) provides direct access for resection of tuberculum sellae (TSM) and olfactory groove meningiomas (OGM) but is associated with cerebrospinal fluid (CSF) leak in up to 25% of patients. To evaluate the impact of improved skull base reconstructive techniques, we assessed published CSF leak percentages in EEA over the last two decades.
Method
Random-effects meta-analyses were performed for studies published between 2004-2020. Outcomes assessed were CSF leak, gross total resection, visual improvement, intraoperative arterial injury and 30-day mortality. For the main analyses, publications were pragmatically grouped based on publication year in three categories: 2004-2010, 2011-2015, and 2016-2020.
Results
We included 29 studies describing 540 TSM and 115 OGM patients. CSF leak incidence dropped over time from 22% (95% CI: 6-43%) in studies published between 2004 and 2010, to 16% (95% CI: 11-23%) between 2011 and 2015, and 4% (95% CI: 1-9%) between 2016 and 2020. Outcomes of gross total resection, visual improvement, intraoperative arterial injury, and 30-day mortality remained stable over time
Conclusions
We report a noticeable decrease in CSF leak over time, which might be attributed to the development of reconstructive techniques (e.g., hadad bassagasteguy flap, and gasket seal), refined multilayer repair protocols, and selected lumbar drain usage.
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Affiliation(s)
- A H Zamanipoor Najafabadi
- Department of Neurosurgery, University Neurosurgical Centre Holland, Leiden University Medical Centre, Haaglanden Medical Centre and Haga Teaching Hospital, Leiden and The Hague, Netherlands
| | - D Z Khan
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
- Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
| | - I S Muskens
- Department of Neurosurgery, University Neurosurgical Centre Holland, Leiden University Medical Centre, Haaglanden Medical Centre and Haga Teaching Hospital, Leiden and The Hague, Netherlands
| | - M L D Broekman
- Department of Neurosurgery, University Neurosurgical Centre Holland, Leiden University Medical Centre, Haaglanden Medical Centre and Haga Teaching Hospital, Leiden and The Hague, Netherlands
| | - N L Dorward
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - W R van Furth
- Department of Neurosurgery, University Neurosurgical Centre Holland, Leiden University Medical Centre, Haaglanden Medical Centre and Haga Teaching Hospital, Leiden and The Hague, Netherlands
| | - H J Marcus
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
- Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
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Abstract
Abstract
Introduction
CRANIAL (CSF Rhinorrhoea After Endonasal Intervention to the Skull Base) is a prospective, multicentre observational study seeking to determine: the scope of skull base repair methods used, and the corresponding rates of postoperative CSF rhinorrhoea in endonasal transsphenoidal (TSA) expanded endonasal approaches (EEA) for skull base tumours.
Method
A prospective, observational cohort pilot study was carried out at eleven neurosurgical units, via NANSIG and BNTRC collaboratives.
Results
192 cases were included – 167 TSA (87%), 25 EEA (13%). The most common (MC) pathologies included: pituitary adenomas (n = 150/192), craniopharyngiomas (n = 7/192) and meningiomas (n = 4/192). The MC skull base repair techniques used were tissue glues (n = 135/192, MC Tisseel®), grafts (n = 94/192, MC fat or Spongostan™) and vascularised flap (52/192, MC nasoseptal). These repairs were most frequently supported by nasal packs (n = 127/192) and lumbar drains (n = 23/197). Biochemically confirmed CSF rhinorrhoea occurred in 10/167 (6%) TSA and 4/25 (16%) EEA. 5 cases required operative management for CSF rhinorrhoea (CSF diversion or direct repair). Qualitative feedback was largely positive (e.g., user-friendly data collection), demonstrating acceptability.
Conclusions
Our pilot experience highlights the acceptability and feasibility of CRANIAL. There is clear precedent for multicentre dissemination of this project, in order to establish a benchmark of contemporary skull base neurosurgery practice.
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Affiliation(s)
- D Z Khan
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - H J Marcus
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
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9
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Kang HS, Yang H, Kim G, Heo H, Nam I, Min CK, Kim C, Baek SY, Choi HJ, Mun G, Park BR, Suh YJ, Shin DC, Hu J, Hong J, Jung S, Kim SH, Kim K, Na D, Park SS, Park YJ, Han JH, Jung YG, Jeong SH, Kim MJ, Lee HG, Lee S, Lee WW, Oh B, Suh HS, Park KH, Lee HS, Khan DZ, Raubenheimer TO, Wu J. FEL performance achieved at PAL-XFEL using a three-chicane bunch compression scheme. J Synchrotron Radiat 2019; 26:1127-1138. [PMID: 31274436 DOI: 10.1107/s1600577519005861] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Accepted: 04/28/2019] [Indexed: 06/09/2023]
Abstract
PAL-XFEL utilizes a three-chicane bunch compression (3-BC) scheme (the very first of its kind in operation) for free-electron laser (FEL) operation. The addition of a third bunch compressor allows for more effective mitigation of coherent synchrotron radiation during bunch compression and an increased flexibility of system configuration. Start-to-end simulations of the effects of radiofrequency jitter on the electron beam performance show that using the 3-BC scheme leads to better performance compared with the two-chicane bunch compression scheme. Together with the high performance of the linac radiofrequency system, it enables reliable operation of PAL-XFEL with unprecedented stability in terms of arrival timing, pointing and intensity; an arrival timing jitter of better than 15 fs, a transverse position jitter of smaller than 10% of the photon beam size, and an FEL intensity jitter of smaller than 5% are consistently achieved.
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Affiliation(s)
- Heung Sik Kang
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Haeryong Yang
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Gyujin Kim
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Hoon Heo
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Inhyuk Nam
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Chang Ki Min
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Changbum Kim
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Soung Youl Baek
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Hyo Jin Choi
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Geonyeong Mun
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Byoung Ryul Park
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Young Jin Suh
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Dong Cheol Shin
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Jinyul Hu
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Juho Hong
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Seonghoon Jung
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Sang Hee Kim
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - KwangHoon Kim
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Donghyun Na
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Soung Soo Park
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Yong Jung Park
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Jang Hui Han
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Young Gyu Jung
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Seong Hun Jeong
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Min Jae Kim
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Hong Gi Lee
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Sangbong Lee
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Woul Woo Lee
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Bonggi Oh
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Hyung Suck Suh
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Ki Hyeon Park
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Heung Soo Lee
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - D Z Khan
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - T O Raubenheimer
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Juhao Wu
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
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10
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Kang HS, Yang H, Kim G, Heo H, Nam I, Min CK, Kim C, Baek SY, Choi HJ, Mun G, Park BR, Suh YJ, Shin DC, Hu J, Hong J, Jung S, Kim SH, Kim K, Na D, Park SS, Park YJ, Han JH, Jung YG, Jeong SH, Kim MJ, Lee HG, Lee S, Lee WW, Oh B, Suh HS, Park KH, Lee HS, Khan DZ, Raubenheimer TO, Wu J. FEL performance achieved at PAL-XFEL using a three-chicane bunch compression scheme. J Synchrotron Radiat 2019. [PMID: 31274436 DOI: 10.1038/s41566-017-0029-8] [Citation(s) in RCA: 166] [Impact Index Per Article: 33.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
PAL-XFEL utilizes a three-chicane bunch compression (3-BC) scheme (the very first of its kind in operation) for free-electron laser (FEL) operation. The addition of a third bunch compressor allows for more effective mitigation of coherent synchrotron radiation during bunch compression and an increased flexibility of system configuration. Start-to-end simulations of the effects of radiofrequency jitter on the electron beam performance show that using the 3-BC scheme leads to better performance compared with the two-chicane bunch compression scheme. Together with the high performance of the linac radiofrequency system, it enables reliable operation of PAL-XFEL with unprecedented stability in terms of arrival timing, pointing and intensity; an arrival timing jitter of better than 15 fs, a transverse position jitter of smaller than 10% of the photon beam size, and an FEL intensity jitter of smaller than 5% are consistently achieved.
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Affiliation(s)
- Heung Sik Kang
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Haeryong Yang
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Gyujin Kim
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Hoon Heo
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Inhyuk Nam
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Chang Ki Min
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Changbum Kim
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Soung Youl Baek
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Hyo Jin Choi
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Geonyeong Mun
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Byoung Ryul Park
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Young Jin Suh
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Dong Cheol Shin
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Jinyul Hu
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Juho Hong
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Seonghoon Jung
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Sang Hee Kim
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - KwangHoon Kim
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Donghyun Na
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Soung Soo Park
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Yong Jung Park
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Jang Hui Han
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Young Gyu Jung
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Seong Hun Jeong
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Min Jae Kim
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Hong Gi Lee
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Sangbong Lee
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Woul Woo Lee
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Bonggi Oh
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Hyung Suck Suh
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Ki Hyeon Park
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Heung Soo Lee
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - D Z Khan
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - T O Raubenheimer
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
| | - Juhao Wu
- Pohang Accelerator Laboratory, 80 Jigokro-127-beongil, Nam-gu, Pohang, Kyungbuk 37673, South Korea
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