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Chelliah A, Wood DA, Canas LS, Shuaib H, Currie S, Fatania K, Frood R, Rowland-Hill C, Thust S, Wastling SJ, Tenant S, Foweraker K, Williams M, Wang Q, Roman A, Dragos C, MacDonald M, Lau YH, Linares CA, Bassiouny A, Luis A, Young T, Brock J, Chandy E, Beaumont E, Lam TC, Welsh L, Lewis J, Mathew R, Kerfoot E, Brown R, Beasley D, Glendenning J, Brazil L, Swampillai A, Ashkan K, Ourselin S, Modat M, Booth TC. Glioblastoma and Radiotherapy: a multi-center AI study for Survival Predictions from MRI (GRASP study). Neuro Oncol 2024:noae017. [PMID: 38285679 DOI: 10.1093/neuonc/noae017] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Indexed: 01/31/2024] Open
Abstract
BACKGROUND The aim was to predict survival of glioblastoma at eight months after radiotherapy (a period allowing for completing a typical course of adjuvant temozolomide), by applying deep learning to the first brain MRI after radiotherapy completion. METHODS Retrospective and prospective data were collected from 206 consecutive glioblastoma, IDH-wildtype patients diagnosed between March 2014-February 2022 across 11 UK centers. Models were trained on 158 retrospective patients from three centers. Holdout test sets were retrospective (n=19; internal validation), and prospective (n=29; external validation from eight distinct centers).Neural network branches for T2-weighted and contrast-enhanced T1-weighted inputs were concatenated to predict survival. A non-imaging branch (demographics/MGMT/treatment data) was also combined with the imaging model. We investigated the influence of individual MR sequences; non-imaging features; and weighted dense blocks pretrained for abnormality detection. RESULTS The imaging model outperformed the non-imaging model in all test sets (area under the receiver-operating characteristic curve, AUC p=0.038) and performed similarly to a combined imaging/non-imaging model (p>0.05). Imaging, non-imaging, and combined models applied to amalgamated test sets gave AUCs of 0.93, 0.79, and 0.91. Initializing the imaging model with pretrained weights from 10,000s of brain MRIs improved performance considerably (amalgamated test sets without pretraining 0.64; p=0.003). CONCLUSIONS A deep learning model using MRI images after radiotherapy, reliably and accurately determined survival of glioblastoma. The model serves as a prognostic biomarker identifying patients who will not survive beyond a typical course of adjuvant temozolomide, thereby stratifying patients into those who might require early second-line or clinical trial treatment.
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Affiliation(s)
| | | | | | - Haris Shuaib
- King's College London, London, United Kingdom
- Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Stuart Currie
- Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
| | - Kavi Fatania
- Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
- Nottingham University Hospitals NHS Trust, Nottingham, United Kingdom
| | - Russell Frood
- Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
| | | | - Stefanie Thust
- University College London Hospitals NHS Foundation Trust, London, United Kingdom
- University College London, London, United Kingdom
- Nottingham University Hospitals NHS Trust, Nottingham, United Kingdom
- University of Nottingham, Nottingham, United Kingdom
| | - Stephen J Wastling
- University College London Hospitals NHS Foundation Trust, London, United Kingdom
- University College London, London, United Kingdom
| | - Sean Tenant
- The Christie NHS Foundation Trust, Withington, Manchester, United Kingdom
| | | | - Matthew Williams
- Imperial College Healthcare NHS Trust, London, United Kingdom
- Imperial College London, London, United Kingdom
| | - Qiquan Wang
- Imperial College Healthcare NHS Trust, London, United Kingdom
- Imperial College London, London, United Kingdom
| | - Andrei Roman
- Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
- Oncology Institute Prof. Dr. Ion Chiricuta, Cluj-Napoca, Romania
| | - Carmen Dragos
- Buckinghamshire Healthcare NHS Trust, Amersham, United Kingdom
| | | | - Yue Hui Lau
- King's College Hospital NHS Foundation Trust, London, United Kingdom
| | | | - Ahmed Bassiouny
- King's College London, London, United Kingdom
- Mansoura University, Mansoura, Egypt
| | - Aysha Luis
- King's College London, London, United Kingdom
- King's College Hospital NHS Foundation Trust, London, United Kingdom
| | - Thomas Young
- Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Juliet Brock
- Brighton and Sussex University Hospitals NHS Trust, England, United Kingdom
| | - Edward Chandy
- Brighton and Sussex University Hospitals NHS Trust, England, United Kingdom
| | - Erica Beaumont
- Lancashire Teaching Hospitals NHS Foundation Trust, England, United Kingdom
| | - Tai-Chung Lam
- Lancashire Teaching Hospitals NHS Foundation Trust, England, United Kingdom
| | - Liam Welsh
- The Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Joanne Lewis
- Newcastle upon Tyne Hospitals NHS Foundation Trust, England, United Kingdom
| | - Ryan Mathew
- Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
- University of Leeds, Leeds, UK
| | | | | | - Daniel Beasley
- King's College London, London, United Kingdom
- Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | | | - Lucy Brazil
- Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | | | - Keyoumars Ashkan
- King's College London, London, United Kingdom
- King's College Hospital NHS Foundation Trust, London, United Kingdom
| | | | - Marc Modat
- King's College London, London, United Kingdom
- Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Thomas C Booth
- King's College London, London, United Kingdom
- King's College Hospital NHS Foundation Trust, London, United Kingdom
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Roney CH, Solis Lemus JA, Lopez Barrera C, Zolotarev A, Ulgen O, Kerfoot E, Bevis L, Misghina S, Vidal Horrach C, Jaffery OA, Ehnesh M, Rodero C, Dharmaprani D, Ríos-Muñoz GR, Ganesan A, Good WW, Neic A, Plank G, Hopman LHGA, Götte MJW, Honarbakhsh S, Narayan SM, Vigmond E, Niederer S. Constructing bilayer and volumetric atrial models at scale. Interface Focus 2023; 13:20230038. [PMID: 38106921 PMCID: PMC10722212 DOI: 10.1098/rsfs.2023.0038] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 11/15/2023] [Indexed: 12/19/2023] Open
Abstract
To enable large in silico trials and personalized model predictions on clinical timescales, it is imperative that models can be constructed quickly and reproducibly. First, we aimed to overcome the challenges of constructing cardiac models at scale through developing a robust, open-source pipeline for bilayer and volumetric atrial models. Second, we aimed to investigate the effects of fibres, fibrosis and model representation on fibrillatory dynamics. To construct bilayer and volumetric models, we extended our previously developed coordinate system to incorporate transmurality, atrial regions and fibres (rule-based or data driven diffusion tensor magnetic resonance imaging (MRI)). We created a cohort of 1000 biatrial bilayer and volumetric models derived from computed tomography (CT) data, as well as models from MRI, and electroanatomical mapping. Fibrillatory dynamics diverged between bilayer and volumetric simulations across the CT cohort (correlation coefficient for phase singularity maps: left atrial (LA) 0.27 ± 0.19, right atrial (RA) 0.41 ± 0.14). Adding fibrotic remodelling stabilized re-entries and reduced the impact of model type (LA: 0.52 ± 0.20, RA: 0.36 ± 0.18). The choice of fibre field has a small effect on paced activation data (less than 12 ms), but a larger effect on fibrillatory dynamics. Overall, we developed an open-source user-friendly pipeline for generating atrial models from imaging or electroanatomical mapping data enabling in silico clinical trials at scale (https://github.com/pcmlab/atrialmtk).
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Affiliation(s)
- Caroline H. Roney
- School of Engineering and Materials Science, Queen Mary University of London, London, UK
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
| | - Jose Alonso Solis Lemus
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Carlos Lopez Barrera
- School of Engineering and Materials Science, Queen Mary University of London, London, UK
- Center for Research in Advanced Materials S.C (CIMAV), Chihuahua, Mexico
| | - Alexander Zolotarev
- School of Engineering and Materials Science, Queen Mary University of London, London, UK
| | - Onur Ulgen
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
| | - Eric Kerfoot
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
| | - Laura Bevis
- School of Engineering and Materials Science, Queen Mary University of London, London, UK
| | - Semhar Misghina
- School of Engineering and Materials Science, Queen Mary University of London, London, UK
| | - Caterina Vidal Horrach
- School of Engineering and Materials Science, Queen Mary University of London, London, UK
| | - Ovais A. Jaffery
- School of Engineering and Materials Science, Queen Mary University of London, London, UK
| | - Mahmoud Ehnesh
- School of Engineering and Materials Science, Queen Mary University of London, London, UK
| | - Cristobal Rodero
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Dhani Dharmaprani
- College of Medicine and Public Health, Flinders University, Adelaide, Australia
| | - Gonzalo R. Ríos-Muñoz
- Bioengineering Department, Universidad Carlos III de Madrid, Madrid 28911, Spain
- Department of Cardiology, Gregorio Marañón Health Research Institute (IiSGM), Hospital General Universitario Gregorio Marañón, Madrid 28007, Spain
- Center for Biomedical Research in Cardiovascular Disease Network (CIBERCV), Madrid 28029, Spain
| | - Anand Ganesan
- College of Medicine and Public Health, Flinders University, Adelaide, Australia
| | | | | | - Gernot Plank
- Gottfried Schatz Research Center-Biophysics, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | | | | | - Shohreh Honarbakhsh
- Electrophysiology Department, Barts Heart Centre, Barts Health NHS Trust, London, UK
| | - Sanjiv M. Narayan
- Department of Medicine and Cardiovascular Institute, Stanford University, Palo Alto, CA, USA
| | - Edward Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Bordeaux, France
- IMB, UMR 5251, University Bordeaux, Talence 33400, France
| | - Steven Niederer
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
- Turing Research and Innovation Cluster in Digital Twins (TRIC: DT), The Alan Turing Institute, London, UK
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Antonelli M, Penfold RS, Canas LDS, Sudre C, Rjoob K, Murray B, Molteni E, Kerfoot E, Cheetham N, Pujol JC, Polidori L, May A, Wolf J, Modat M, Spector T, Hammers A, Ourselin S, Steves C. SARS-CoV-2 infection following booster vaccination: Illness and symptom profile in a prospective, observational community-based case-control study. J Infect 2023; 87:506-515. [PMID: 37777159 DOI: 10.1016/j.jinf.2023.08.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [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: 07/10/2023] [Revised: 08/02/2023] [Accepted: 08/22/2023] [Indexed: 10/02/2023]
Abstract
BACKGROUND Booster COVID-19 vaccines have shown efficacy in clinical trials and effectiveness in real-world data against symptomatic and severe illness. However, some people still become infected with SARS-CoV-2 following a third (booster) vaccination. This study describes the characteristics of SARS-CoV-2 illness following a third vaccination and assesses the risk of progression to symptomatic disease in SARS-CoV-2 infected individuals with time since vaccination. METHODS This prospective, community-based, case-control study used data from UK-based, adult (≥18 years) users of the COVID Symptom Study mobile application, self-reporting a first positive COVID-19 test between June 1, 2021 and April 1, 2022. To describe the characteristics of SARS-CoV-2 illness following a third vaccination, we selected cases and controls who had received a third and second dose of monovalent vaccination against COVID-19, respectively, and reported a first positive SARS-CoV-2 test at least 7 days after most recent vaccination. Cases and controls were matched (1:1) based on age, sex, BMI, time between first vaccination and infection, and week of testing. We used logistic regression models (adjusted for age, sex, BMI, level of social deprivation and frailty) to analyse associations of disease severity, overall disease duration, and individual symptoms with booster vaccination status. To assess for potential waning of vaccine effectiveness, we compared disease severity, duration, and symptom profiles of individuals testing positive within 3 months of most recent vaccination (reference group) to profiles of individuals infected between 3 and 4, 4-5, and 5-6 months, for both third and second dose. All analyses were stratified by time period, based on the predominant SARS-CoV-2 variant at time of infection (Delta: June 1, 2021-27 Nov, 2021; Omicron: 20 Dec, 2021-Apr 1, 2022). FINDINGS During the study period, 50,162 (Delta period) and 162,041 (Omicron) participants reported a positive SARS-CoV-2 test. During the Delta period, infection following three vaccination doses was associated with lower odds of long COVID (symptoms≥ 4 weeks) (OR=0.83, CI[0.50-1.36], p < 0.0001), hospitalisation (OR=0.55, CI[0.39-0.75], p < 0.0001) and severe symptoms (OR=0.36, CI[0.27-0.49], p < 0.0001), and higher odds of asymptomatic infection (OR=3.45, CI[2.86-4.16], p < 0.0001), compared to infection following only two vaccination doses. During the Omicron period, infection following three vaccination doses was associated with lower odds of severe symptoms (OR=0.48, CI[0.42-0.55], p < 0.0001). During the Delta period, infected individuals were less likely to report almost all individual symptoms after a third vaccination. During the Omicron period, individuals were less likely to report most symptoms after a third vaccination, except for upper respiratory symptoms e.g. sneezing (OR=1.40, CI[1.18-1.35], p < 0.0001), runny nose (OR=1.26, CI[1.18-1.35], p < 0.0001), sore throat (OR=1.17, CI[1.10-1.25], p < 0.0001), and hoarse voice (OR=1.13, CI[1.06-1.21], p < 0.0001), which were more likely to be reported. There was evidence of reduced vaccine effectiveness during both Delta and Omicron periods in those infected more than 3 months after their most recent vaccination, with increased reporting of severe symptoms, long duration illness, and most individual symptoms. INTERPRETATION This study suggests that a third dose of monovalent vaccine may reduce symptoms, severity and duration of SARS-CoV-2 infection following vaccination. For Omicron variants, the third vaccination appears to reduce overall symptom burden but may increase upper respiratory symptoms, potentially due to immunological priming. There is evidence of waning vaccine effectiveness against progression to symptomatic and severe disease and long COVID after three months. Our findings support ongoing booster vaccination promotion amongst individuals at high risk from COVID-19, to reduce severe symptoms and duration of illness, and health system burden. Disseminating knowledge on expected symptoms following booster vaccination may encourage vaccine uptake.
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Affiliation(s)
- Michela Antonelli
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Rose S Penfold
- Ageing and Health Research Group, Usher Institute, University of Edinburgh, Edinburgh, UK; Department of Twin Research and Genetic Epidemiology, King's College London, UK
| | | | - Carole Sudre
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK; Centre for Medical Image Computing, University College London, London, UK
| | - Khaled Rjoob
- Centre for Medical Image Computing, University College London, London, UK
| | - Ben Murray
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Erika Molteni
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Eric Kerfoot
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Nathan Cheetham
- Department of Twin Research and Genetic Epidemiology, King's College London, UK
| | | | | | | | | | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Tim Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, UK
| | - Alexander Hammers
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; King's College London & Guy's and St Thomas' PET Centre, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Claire Steves
- Department of Twin Research and Genetic Epidemiology, King's College London, UK; Department of Ageing and Health, Guys and St Thomas' NHS Foundation Trust, London, UK.
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Cheetham NJ, Penfold R, Giunchiglia V, Bowyer V, Sudre CH, Canas LS, Deng J, Murray B, Kerfoot E, Antonelli M, Rjoob K, Molteni E, Österdahl MF, Harvey NR, Trender WR, Malim MH, Doores KJ, Hellyer PJ, Modat M, Hammers A, Ourselin S, Duncan EL, Hampshire A, Steves CJ. The effects of COVID-19 on cognitive performance in a community-based cohort: a COVID symptom study biobank prospective cohort study. EClinicalMedicine 2023; 62:102086. [PMID: 37654669 PMCID: PMC10466229 DOI: 10.1016/j.eclinm.2023.102086] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 06/16/2023] [Accepted: 06/21/2023] [Indexed: 09/02/2023] Open
Abstract
Background Cognitive impairment has been reported after many types of infection, including SARS-CoV-2. Whether deficits following SARS-CoV-2 improve over time is unclear. Studies to date have focused on hospitalised individuals with up to a year follow-up. The presence, magnitude, persistence and correlations of effects in community-based cases remain relatively unexplored. Methods Cognitive performance (working memory, attention, reasoning, motor control) was assessed in a prospective cohort study of participants from the United Kingdom COVID Symptom Study Biobank between July 12, 2021 and August 27, 2021 (Round 1), and between April 28, 2022 and June 21, 2022 (Round 2). Participants, recruited from the COVID Symptom Study smartphone app, comprised individuals with and without SARS-CoV-2 infection and varying symptom duration. Effects of COVID-19 exposures on cognitive accuracy and reaction time scores were estimated using multivariable ordinary least squares linear regression models weighted for inverse probability of participation, adjusting for potential confounders and mediators. The role of ongoing symptoms after COVID-19 infection was examined stratifying for self-perceived recovery. Longitudinal analysis assessed change in cognitive performance between rounds. Findings 3335 individuals completed Round 1, of whom 1768 also completed Round 2. At Round 1, individuals with previous positive SARS-CoV-2 tests had lower cognitive accuracy (N = 1737, β = -0.14 standard deviations, SDs, 95% confidence intervals, CI: -0.21, -0.07) than negative controls. Deficits were largest for positive individuals with ≥12 weeks of symptoms (N = 495, β = -0.22 SDs, 95% CI: -0.35, -0.09). Effects were comparable to hospital presentation during illness (N = 281, β = -0.31 SDs, 95% CI: -0.44, -0.18), and 10 years age difference (60-70 years vs. 50-60 years, β = -0.21 SDs, 95% CI: -0.30, -0.13) in the whole study population. Stratification by self-reported recovery revealed that deficits were only detectable in SARS-CoV-2 positive individuals who did not feel recovered from COVID-19, whereas individuals who reported full recovery showed no deficits. Longitudinal analysis showed no evidence of cognitive change over time, suggesting that cognitive deficits for affected individuals persisted at almost 2 years since initial infection. Interpretation Cognitive deficits following SARS-CoV-2 infection were detectable nearly two years post infection, and largest for individuals with longer symptom durations, ongoing symptoms, and/or more severe infection. However, no such deficits were detected in individuals who reported full recovery from COVID-19. Further work is needed to monitor and develop understanding of recovery mechanisms for those with ongoing symptoms. Funding Chronic Disease Research Foundation, Wellcome Trust, National Institute for Health and Care Research, Medical Research Council, British Heart Foundation, Alzheimer's Society, European Union, COVID-19 Driver Relief Fund, French National Research Agency.
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Affiliation(s)
- Nathan J. Cheetham
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
| | - Rose Penfold
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
- Edinburgh Delirium Research Group, Ageing and Health, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | | | - Vicky Bowyer
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
| | - Carole H. Sudre
- MRC Unit for Lifelong Health and Ageing, Department of Population Health Sciences, University College London, London, United Kingdom
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Liane S. Canas
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Jie Deng
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Benjamin Murray
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Eric Kerfoot
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Michela Antonelli
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Khaled Rjoob
- MRC Unit for Lifelong Health and Ageing, Department of Population Health Sciences, University College London, London, United Kingdom
| | - Erika Molteni
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Marc F. Österdahl
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
| | - Nicholas R. Harvey
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
| | | | - Michael H. Malim
- Department of Infectious Diseases, King's College London, London, United Kingdom
| | - Katie J. Doores
- Department of Infectious Diseases, King's College London, London, United Kingdom
| | - Peter J. Hellyer
- Centre for Neuroimaging Sciences, King's College London, London, United Kingdom
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Alexander Hammers
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
- King's College London & Guy's and St Thomas' PET Centre, King's College London, London, United Kingdom
| | - Sebastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Emma L. Duncan
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
- Guy's & St Thomas's NHS Foundation Trust, London, United Kingdom
| | - Adam Hampshire
- Department of Brain Sciences, Imperial College London, United Kingdom
| | - Claire J. Steves
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
- Guy's & St Thomas's NHS Foundation Trust, London, United Kingdom
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5
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Canas LS, Molteni E, Deng J, Sudre CH, Murray B, Kerfoot E, Antonelli M, Rjoob K, Capdevila Pujol J, Polidori L, May A, Österdahl MF, Whiston R, Cheetham NJ, Bowyer V, Spector TD, Hammers A, Duncan EL, Ourselin S, Steves CJ, Modat M. Profiling post-COVID-19 condition across different variants of SARS-CoV-2: a prospective longitudinal study in unvaccinated wild-type, unvaccinated alpha-variant, and vaccinated delta-variant populations. Lancet Digit Health 2023; 5:e421-e434. [PMID: 37202336 PMCID: PMC10187990 DOI: 10.1016/s2589-7500(23)00056-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 03/01/2023] [Accepted: 03/03/2023] [Indexed: 05/20/2023]
Abstract
BACKGROUND Self-reported symptom studies rapidly increased understanding of SARS-CoV-2 during the COVID-19 pandemic and enabled monitoring of long-term effects of COVID-19 outside hospital settings. Post-COVID-19 condition presents as heterogeneous profiles, which need characterisation to enable personalised patient care. We aimed to describe post-COVID-19 condition profiles by viral variant and vaccination status. METHODS In this prospective longitudinal cohort study, we analysed data from UK-based adults (aged 18-100 years) who regularly provided health reports via the Covid Symptom Study smartphone app between March 24, 2020, and Dec 8, 2021. We included participants who reported feeling physically normal for at least 30 days before testing positive for SARS-CoV-2 who subsequently developed long COVID (ie, symptoms lasting longer than 28 days from the date of the initial positive test). We separately defined post-COVID-19 condition as symptoms that persisted for at least 84 days after the initial positive test. We did unsupervised clustering analysis of time-series data to identify distinct symptom profiles for vaccinated and unvaccinated people with post-COVID-19 condition after infection with the wild-type, alpha (B.1.1.7), or delta (B.1.617.2 and AY.x) variants of SARS-CoV-2. Clusters were then characterised on the basis of symptom prevalence, duration, demography, and previous comorbidities. We also used an additional testing sample with additional data from the Covid Symptom Study Biobank (collected between October, 2020, and April, 2021) to investigate the effects of the identified symptom clusters of post-COVID-19 condition on the lives of affected people. FINDINGS We included 9804 people from the COVID Symptom Study with long COVID, 1513 (15%) of whom developed post-COVID-19 condition. Sample sizes were sufficient only for analyses of the unvaccinated wild-type, unvaccinated alpha variant, and vaccinated delta variant groups. We identified distinct profiles of symptoms for post-COVID-19 condition within and across variants: four endotypes were identified for infections due to the wild-type variant (in unvaccinated people), seven for the alpha variant (in unvaccinated people), and five for the delta variant (in vaccinated people). Across all variants, we identified a cardiorespiratory cluster of symptoms, a central neurological cluster, and a multi-organ systemic inflammatory cluster. These three main clusers were confirmed in a testing sample. Gastrointestinal symptoms clustered in no more than two specific phenotypes per viral variant. INTERPRETATION Our unsupervised analysis identified different profiles of post-COVID-19 condition, characterised by differing symptom combinations, durations, and functional outcomes. Our classification could be useful for understanding the distinct mechanisms of post-COVID-19 condition, as well as for identification of subgroups of individuals who might be at risk of prolonged debilitation. FUNDING UK Government Department of Health and Social Care, Chronic Disease Research Foundation, The Wellcome Trust, UK Engineering and Physical Sciences Research Council, UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value-Based Healthcare, UK National Institute for Health Research, UK Medical Research Council, British Heart Foundation, UK Alzheimer's Society, and ZOE.
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Affiliation(s)
- Liane S Canas
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
| | - Erika Molteni
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Jie Deng
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Carole H Sudre
- MRC Unit for Lifelong Health and Ageing, Department of Population Health Sciences, University College London, London, UK; Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Benjamin Murray
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Eric Kerfoot
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Michela Antonelli
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Khaled Rjoob
- MRC Unit for Lifelong Health and Ageing, Department of Population Health Sciences, University College London, London, UK
| | | | | | | | - Marc F Österdahl
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Ronan Whiston
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Nathan J Cheetham
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Vicky Bowyer
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Tim D Spector
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Alexander Hammers
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Emma L Duncan
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK; Department of Endocrinology, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Claire J Steves
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
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Yaakub SN, White TA, Kerfoot E, Verhagen L, Hammers A, Fouragnan EF. Pseudo-CTs from T1-weighted MRI for planning of low-intensity transcranial focused ultrasound neuromodulation: An open-source tool. Brain Stimul 2023; 16:75-78. [PMID: 36669697 DOI: 10.1016/j.brs.2023.01.838] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 01/14/2023] [Indexed: 01/19/2023] Open
Affiliation(s)
- Siti N Yaakub
- Brain Research & Imaging Centre, Faculty of Health, University of Plymouth, Plymouth, UK; School of Psychology, Faculty of Health, University of Plymouth, Plymouth, UK; King's College London & Guy's and St Thomas' PET Centre, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
| | - Tristan A White
- Brain Research & Imaging Centre, Faculty of Health, University of Plymouth, Plymouth, UK; School of Psychology, Faculty of Health, University of Plymouth, Plymouth, UK
| | - Eric Kerfoot
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Lennart Verhagen
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Alexander Hammers
- King's College London & Guy's and St Thomas' PET Centre, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Elsa F Fouragnan
- Brain Research & Imaging Centre, Faculty of Health, University of Plymouth, Plymouth, UK; School of Psychology, Faculty of Health, University of Plymouth, Plymouth, UK
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7
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Molteni E, Canas LS, Kläser K, Deng J, Bhopal SS, Hughes RC, Chen L, Murray B, Kerfoot E, Antonelli M, Sudre CH, Pujol JC, Polidori L, May A, Hammers PA, Wolf J, Spector PTD, Steves CJ, Ourselin PS, Absoud M, Modat M, Duncan PEL. Post-vaccination infection rates and modification of COVID-19 symptoms in vaccinated UK school-aged children and adolescents: A prospective longitudinal cohort study. Lancet Reg Health Eur 2022; 19:100429. [PMID: 35821715 PMCID: PMC9263281 DOI: 10.1016/j.lanepe.2022.100429] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
BACKGROUND We aimed to explore the effectiveness of one-dose BNT162b2 vaccination upon SARS-CoV-2 infection, its effect on COVID-19 presentation, and post-vaccination symptoms in children and adolescents (CA) in the UK during periods of Delta and Omicron variant predominance. METHODS In this prospective longitudinal cohort study, we analysed data from 115,775 CA aged 12-17 years, proxy-reported through the Covid Symptom Study (CSS) smartphone application. We calculated post-vaccination infection risk after one dose of BNT162b2, and described the illness profile of CA with post-vaccination SARS-CoV-2 infection, compared to unvaccinated CA, and post-vaccination side-effects. FINDINGS Between August 5, 2021 and February 14, 2022, 25,971 UK CA aged 12-17 years received one dose of BNT162b2 vaccine. The probability of testing positive for infection diverged soon after vaccination, and was lower in CA with prior SARS-CoV-2 infection. Vaccination reduced proxy-reported infection risk (-80·4% (95% CI -0·82 -0·78) and -53·7% (95% CI -0·62 -0·43) at 14-30 days with Delta and Omicron variants respectively, and -61·5% (95% CI -0·74 -0·44) and -63·7% (95% CI -0·68 -0.59) after 61-90 days). Vaccinated CA who contracted SARS-CoV-2 during the Delta period had milder disease than unvaccinated CA; during the Omicron period this was only evident in children aged 12-15 years. Overall disease profile was similar in both vaccinated and unvaccinated CA. Post-vaccination local side-effects were common, systemic side-effects were uncommon, and both resolved within few days (3 days in most cases). INTERPRETATION One dose of BNT162b2 vaccine reduced risk of SARS-CoV-2 infection for at least 90 days in CA aged 12-17 years. Vaccine protection varied for SARS-CoV-2 variant type (lower for Omicron than Delta variant), and was enhanced by pre-vaccination SARS-CoV-2 infection. Severity of COVID-19 presentation after vaccination was generally milder, although unvaccinated CA also had generally mild disease. Overall, vaccination was well-tolerated. FUNDING UK Government Department of Health and Social Care, Chronic Disease Research Foundation, The Wellcome Trust, UK Engineering and Physical Sciences Research Council, UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare, UK National Institute for Health Research, UK Medical Research Council, British Heart Foundation and Alzheimer's Society, and ZOE Limited.
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Affiliation(s)
- Erika Molteni
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Liane S. Canas
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Kerstin Kläser
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Jie Deng
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Sunil S. Bhopal
- Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, Tyne and Wear, UK
| | - Robert C. Hughes
- Department of Population Health, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, Keppel Street, London, UK
| | - Liyuan Chen
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Benjamin Murray
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Eric Kerfoot
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Michela Antonelli
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Carole H. Sudre
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- MRC Unit for Lifelong Health and Ageing, Department of Population Health Sciences and Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | | | | | | | | | | | - Prof Tim D. Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Claire J. Steves
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- Department of Aging and Health, Guy's and St Thomas’ NHS Foundation Trust, London, UK
| | | | - Michael Absoud
- Children's Neurosciences, Evelina London Children's Hospital, St Thomas’ Hospital, King's Health Partners, Academic Health Science Centre, London, UK
- Department of Women and Children's Health, Faculty of Life Sciences and Medicine, School of Life Course Sciences, King's College London, London, UK
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Prof Emma L. Duncan
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- Department of Endocrinology, Guy's and St Thomas’ NHS Foundation trust, London, UK
- Corresponding author at: Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, WC2R 2LS, Strand, London, UK.
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8
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Kläser K, Molteni E, Graham M, Canas LS, Österdahl MF, Antonelli M, Chen L, Deng J, Murray B, Kerfoot E, Wolf J, May A, Fox B, Capdevila J, Modat M, Hammers A, Spector TD, Steves CJ, Sudre CH, Ourselin S, Duncan EL. COVID-19 due to the B.1.617.2 (Delta) variant compared to B.1.1.7 (Alpha) variant of SARS-CoV-2: a prospective observational cohort study. Sci Rep 2022; 12:10904. [PMID: 35764879 PMCID: PMC9240087 DOI: 10.1038/s41598-022-14016-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [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] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 05/31/2022] [Indexed: 01/07/2023] Open
Abstract
The Delta (B.1.617.2) variant was the predominant UK circulating SARS-CoV-2 strain between May and December 2021. How Delta infection compares with previous variants is unknown. This prospective observational cohort study assessed symptomatic adults participating in the app-based COVID Symptom Study who tested positive for SARS-CoV-2 from May 26 to July 1, 2021 (Delta overwhelmingly the predominant circulating UK variant), compared (1:1, age- and sex-matched) with individuals presenting from December 28, 2020 to May 6, 2021 (Alpha (B.1.1.7) the predominant variant). We assessed illness (symptoms, duration, presentation to hospital) during Alpha- and Delta-predominant timeframes; and transmission, reinfection, and vaccine effectiveness during the Delta-predominant period. 3581 individuals (aged 18 to 100 years) from each timeframe were assessed. The seven most frequent symptoms were common to both variants. Within the first 28 days of illness, some symptoms were more common with Delta versus Alpha infection (including fever, sore throat, and headache) and some vice versa (dyspnoea). Symptom burden in the first week was higher with Delta versus Alpha infection; however, the odds of any given symptom lasting ≥ 7 days was either lower or unchanged. Illness duration ≥ 28 days was lower with Delta versus Alpha infection, though unchanged in unvaccinated individuals. Hospitalisation for COVID-19 was unchanged. The Delta variant appeared more (1.49) transmissible than Alpha. Re-infections were low in all UK regions. Vaccination markedly reduced the risk of Delta infection (by 69-84%). We conclude that COVID-19 from Delta or Alpha infections is similar. The Delta variant is more transmissible than Alpha; however, current vaccines showed good efficacy against disease. This research framework can be useful for future comparisons with new emerging variants.
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Affiliation(s)
- Kerstin Kläser
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Erika Molteni
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Mark Graham
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Liane S Canas
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Marc F Österdahl
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, Faculty of Life Sciences and Medicine, King's College London, St Thomas' Hospital Campus, 3rd Floor South Wing Block D, Westminster Bridge Road, London, SE1 7EH, UK
- Department of Aging and Health, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Michela Antonelli
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Liyuan Chen
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Jie Deng
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Benjamin Murray
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Eric Kerfoot
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | | | | | | | | | - Marc Modat
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Alexander Hammers
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- King's College London & Guy's and St Thomas' PET Centre, London, UK
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, Faculty of Life Sciences and Medicine, King's College London, St Thomas' Hospital Campus, 3rd Floor South Wing Block D, Westminster Bridge Road, London, SE1 7EH, UK
| | - Claire J Steves
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, Faculty of Life Sciences and Medicine, King's College London, St Thomas' Hospital Campus, 3rd Floor South Wing Block D, Westminster Bridge Road, London, SE1 7EH, UK
- Department of Aging and Health, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Carole H Sudre
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- MRC Unit for Lifelong Health and Ageing, Department of Population Health Sciences, University College London, London, UK
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Emma L Duncan
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, Faculty of Life Sciences and Medicine, King's College London, St Thomas' Hospital Campus, 3rd Floor South Wing Block D, Westminster Bridge Road, London, SE1 7EH, UK.
- Department of Endocrinology, Guy's and St Thomas' NHS Foundation Trust, London, UK.
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9
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Molteni E, Sudre CH, Canas LDS, Bhopal SS, Hughes RC, Chen L, Deng J, Murray B, Kerfoot E, Antonelli M, Graham M, Kläser K, May A, Hu C, Pujol JC, Wolf J, Hammers A, Spector TD, Ourselin S, Modat M, Steves CJ, Absoud M, Duncan EL. Illness Characteristics of COVID-19 in Children Infected with the SARS-CoV-2 Delta Variant. Children (Basel) 2022; 9:652. [PMID: 35626830 PMCID: PMC9140086 DOI: 10.3390/children9050652] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 04/29/2022] [Accepted: 04/30/2022] [Indexed: 12/29/2022]
Abstract
BACKGROUND The Delta (B.1.617.2) SARS-CoV-2 variant was the predominant UK circulating strain between May and November 2021. We investigated whether COVID-19 from Delta infection differed from infection with previous variants in children. METHODS Through the prospective COVID Symptom Study, 109,626 UK school-aged children were proxy-reported between 28 December 2020 and 8 July 2021. We selected all symptomatic children who tested positive for SARS-CoV-2 and were proxy-reported at least weekly, within two timeframes: 28 December 2020 to 6 May 2021 (Alpha (B.1.1.7), the main UK circulating variant) and 26 May to 8 July 2021 (Delta, the main UK circulating variant), with all children unvaccinated (as per national policy at the time). We assessed illness profiles (symptom prevalence, duration, and burden), hospital presentation, and presence of long (≥28 day) illness, and calculated odds ratios for symptoms presenting within the first 28 days of illness. RESULTS 694 (276 younger (5-11 years), 418 older (12-17 years)) symptomatic children tested positive for SARS-CoV-2 with Alpha infection and 706 (227 younger and 479 older) children with Delta infection. Median illness duration was short with either variant (overall cohort: 5 days (IQR 2-9.75) with Alpha, 5 days (IQR 2-9) with Delta). The seven most prevalent symptoms were common to both variants. Symptom burden over the first 28 days was slightly greater with Delta compared with Alpha infection (in younger children, 3 (IQR 2-5) symptoms with Alpha, 4 (IQR 2-7) with Delta; in older children, 5 (IQR 3-8) symptoms with Alpha, 6 (IQR 3-9) with Delta infection ). The odds of presenting several symptoms were higher with Delta than Alpha infection, including headache and fever. Few children presented to hospital, and long illness duration was uncommon, with either variant. CONCLUSIONS COVID-19 in UK school-aged children due to SARS-CoV-2 Delta strain B.1.617.2 resembles illness due to the Alpha variant B.1.1.7., with short duration and similar symptom burden.
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Affiliation(s)
- Erika Molteni
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London WC2R 2LS, UK; (E.M.); (C.H.S.); (L.D.S.C.); (L.C.); (J.D.); (B.M.); (E.K.); (M.A.); (M.G.); (K.K.); (A.H.); (S.O.); (M.M.)
| | - Carole H. Sudre
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London WC2R 2LS, UK; (E.M.); (C.H.S.); (L.D.S.C.); (L.C.); (J.D.); (B.M.); (E.K.); (M.A.); (M.G.); (K.K.); (A.H.); (S.O.); (M.M.)
- MRC Unit for Lifelong Health and Ageing, Department of Population Health Sciences, University College London, London WC1E 6BT, UK
- Centre for Medical Image Computing, Department of Computer Science, University College London, London WC1E 6BT, UK
| | - Liane Dos Santos Canas
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London WC2R 2LS, UK; (E.M.); (C.H.S.); (L.D.S.C.); (L.C.); (J.D.); (B.M.); (E.K.); (M.A.); (M.G.); (K.K.); (A.H.); (S.O.); (M.M.)
| | - Sunil S. Bhopal
- Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK;
| | - Robert C. Hughes
- Department of Population Health, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK;
| | - Liyuan Chen
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London WC2R 2LS, UK; (E.M.); (C.H.S.); (L.D.S.C.); (L.C.); (J.D.); (B.M.); (E.K.); (M.A.); (M.G.); (K.K.); (A.H.); (S.O.); (M.M.)
| | - Jie Deng
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London WC2R 2LS, UK; (E.M.); (C.H.S.); (L.D.S.C.); (L.C.); (J.D.); (B.M.); (E.K.); (M.A.); (M.G.); (K.K.); (A.H.); (S.O.); (M.M.)
| | - Benjamin Murray
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London WC2R 2LS, UK; (E.M.); (C.H.S.); (L.D.S.C.); (L.C.); (J.D.); (B.M.); (E.K.); (M.A.); (M.G.); (K.K.); (A.H.); (S.O.); (M.M.)
| | - Eric Kerfoot
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London WC2R 2LS, UK; (E.M.); (C.H.S.); (L.D.S.C.); (L.C.); (J.D.); (B.M.); (E.K.); (M.A.); (M.G.); (K.K.); (A.H.); (S.O.); (M.M.)
| | - Michela Antonelli
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London WC2R 2LS, UK; (E.M.); (C.H.S.); (L.D.S.C.); (L.C.); (J.D.); (B.M.); (E.K.); (M.A.); (M.G.); (K.K.); (A.H.); (S.O.); (M.M.)
| | - Mark Graham
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London WC2R 2LS, UK; (E.M.); (C.H.S.); (L.D.S.C.); (L.C.); (J.D.); (B.M.); (E.K.); (M.A.); (M.G.); (K.K.); (A.H.); (S.O.); (M.M.)
| | - Kerstin Kläser
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London WC2R 2LS, UK; (E.M.); (C.H.S.); (L.D.S.C.); (L.C.); (J.D.); (B.M.); (E.K.); (M.A.); (M.G.); (K.K.); (A.H.); (S.O.); (M.M.)
| | - Anna May
- ZOE Limited London, London SE1 7RW, UK; (A.M.); (C.H.); (J.C.P.); (J.W.)
| | - Christina Hu
- ZOE Limited London, London SE1 7RW, UK; (A.M.); (C.H.); (J.C.P.); (J.W.)
| | | | - Jonathan Wolf
- ZOE Limited London, London SE1 7RW, UK; (A.M.); (C.H.); (J.C.P.); (J.W.)
| | - Alexander Hammers
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London WC2R 2LS, UK; (E.M.); (C.H.S.); (L.D.S.C.); (L.C.); (J.D.); (B.M.); (E.K.); (M.A.); (M.G.); (K.K.); (A.H.); (S.O.); (M.M.)
- King’s College London & Guy’s and St Thomas’ PET Centre, London WC2R 2LS, UK
| | - Timothy D. Spector
- Department of Twin Research and Genetic Epidemiology, King’s College London, London WC2R 2LS, UK; (T.D.S.); (C.J.S.)
| | - Sebastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London WC2R 2LS, UK; (E.M.); (C.H.S.); (L.D.S.C.); (L.C.); (J.D.); (B.M.); (E.K.); (M.A.); (M.G.); (K.K.); (A.H.); (S.O.); (M.M.)
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London WC2R 2LS, UK; (E.M.); (C.H.S.); (L.D.S.C.); (L.C.); (J.D.); (B.M.); (E.K.); (M.A.); (M.G.); (K.K.); (A.H.); (S.O.); (M.M.)
| | - Claire J. Steves
- Department of Twin Research and Genetic Epidemiology, King’s College London, London WC2R 2LS, UK; (T.D.S.); (C.J.S.)
- Department of Aging and Health, Guy’s and St Thomas’ NHS Foundation Trust, London SE1 7EH, UK
| | - Michael Absoud
- Children’s Neurosciences, Evelina London Children’s Hospital, St Thomas’ Hospital, King’s Health Partners, Academic Health Science Centre, London SE1 7EH, UK
- Department of Women and Children’s Health, Faculty of Life Sciences and Medicine, School of Life Course Sciences, King’s College London, London WC2R 2LS, UK
| | - Emma L. Duncan
- Department of Twin Research and Genetic Epidemiology, King’s College London, London WC2R 2LS, UK; (T.D.S.); (C.J.S.)
- Department of Endocrinology, Guy’s and St Thomas’ NHS Foundation Trust, London SE1 7EH, UK
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10
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Molteni E, Sudre CH, Canas LDS, Bhopal SS, Hughes RC, Chen L, Deng J, Murray B, Kerfoot E, Antonelli M, Graham M, Kläser K, May A, Hu C, Pujol JC, Wolf J, Hammers A, Spector TD, Ourselin S, Modat M, Steves CJ, Absoud M, Duncan EL. Illness Characteristics of COVID-19 in Children Infected with the SARS-CoV-2 Delta Variant. Children (Basel) 2022; 9:children9050652. [PMID: 35626830 DOI: 10.1101/2021.10.06.21264467] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 04/29/2022] [Accepted: 04/30/2022] [Indexed: 05/22/2023]
Abstract
BACKGROUND The Delta (B.1.617.2) SARS-CoV-2 variant was the predominant UK circulating strain between May and November 2021. We investigated whether COVID-19 from Delta infection differed from infection with previous variants in children. METHODS Through the prospective COVID Symptom Study, 109,626 UK school-aged children were proxy-reported between 28 December 2020 and 8 July 2021. We selected all symptomatic children who tested positive for SARS-CoV-2 and were proxy-reported at least weekly, within two timeframes: 28 December 2020 to 6 May 2021 (Alpha (B.1.1.7), the main UK circulating variant) and 26 May to 8 July 2021 (Delta, the main UK circulating variant), with all children unvaccinated (as per national policy at the time). We assessed illness profiles (symptom prevalence, duration, and burden), hospital presentation, and presence of long (≥28 day) illness, and calculated odds ratios for symptoms presenting within the first 28 days of illness. RESULTS 694 (276 younger (5-11 years), 418 older (12-17 years)) symptomatic children tested positive for SARS-CoV-2 with Alpha infection and 706 (227 younger and 479 older) children with Delta infection. Median illness duration was short with either variant (overall cohort: 5 days (IQR 2-9.75) with Alpha, 5 days (IQR 2-9) with Delta). The seven most prevalent symptoms were common to both variants. Symptom burden over the first 28 days was slightly greater with Delta compared with Alpha infection (in younger children, 3 (IQR 2-5) symptoms with Alpha, 4 (IQR 2-7) with Delta; in older children, 5 (IQR 3-8) symptoms with Alpha, 6 (IQR 3-9) with Delta infection ). The odds of presenting several symptoms were higher with Delta than Alpha infection, including headache and fever. Few children presented to hospital, and long illness duration was uncommon, with either variant. CONCLUSIONS COVID-19 in UK school-aged children due to SARS-CoV-2 Delta strain B.1.617.2 resembles illness due to the Alpha variant B.1.1.7., with short duration and similar symptom burden.
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Affiliation(s)
- Erika Molteni
- School of Biomedical Engineering & Imaging Sciences, King's College London, London WC2R 2LS, UK
| | - Carole H Sudre
- School of Biomedical Engineering & Imaging Sciences, King's College London, London WC2R 2LS, UK
- MRC Unit for Lifelong Health and Ageing, Department of Population Health Sciences, University College London, London WC1E 6BT, UK
- Centre for Medical Image Computing, Department of Computer Science, University College London, London WC1E 6BT, UK
| | - Liane Dos Santos Canas
- School of Biomedical Engineering & Imaging Sciences, King's College London, London WC2R 2LS, UK
| | - Sunil S Bhopal
- Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Robert C Hughes
- Department of Population Health, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Liyuan Chen
- School of Biomedical Engineering & Imaging Sciences, King's College London, London WC2R 2LS, UK
| | - Jie Deng
- School of Biomedical Engineering & Imaging Sciences, King's College London, London WC2R 2LS, UK
| | - Benjamin Murray
- School of Biomedical Engineering & Imaging Sciences, King's College London, London WC2R 2LS, UK
| | - Eric Kerfoot
- School of Biomedical Engineering & Imaging Sciences, King's College London, London WC2R 2LS, UK
| | - Michela Antonelli
- School of Biomedical Engineering & Imaging Sciences, King's College London, London WC2R 2LS, UK
| | - Mark Graham
- School of Biomedical Engineering & Imaging Sciences, King's College London, London WC2R 2LS, UK
| | - Kerstin Kläser
- School of Biomedical Engineering & Imaging Sciences, King's College London, London WC2R 2LS, UK
| | - Anna May
- ZOE Limited London, London SE1 7RW, UK
| | | | | | | | - Alexander Hammers
- School of Biomedical Engineering & Imaging Sciences, King's College London, London WC2R 2LS, UK
- King's College London & Guy's and St Thomas' PET Centre, London WC2R 2LS, UK
| | - Timothy D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London WC2R 2LS, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, King's College London, London WC2R 2LS, UK
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London WC2R 2LS, UK
| | - Claire J Steves
- Department of Twin Research and Genetic Epidemiology, King's College London, London WC2R 2LS, UK
- Department of Aging and Health, Guy's and St Thomas' NHS Foundation Trust, London SE1 7EH, UK
| | - Michael Absoud
- Children's Neurosciences, Evelina London Children's Hospital, St Thomas' Hospital, King's Health Partners, Academic Health Science Centre, London SE1 7EH, UK
- Department of Women and Children's Health, Faculty of Life Sciences and Medicine, School of Life Course Sciences, King's College London, London WC2R 2LS, UK
| | - Emma L Duncan
- Department of Twin Research and Genetic Epidemiology, King's College London, London WC2R 2LS, UK
- Department of Endocrinology, Guy's and St Thomas' NHS Foundation Trust, London SE1 7EH, UK
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Antonelli M, Penfold RS, Merino J, Sudre CH, Molteni E, Berry S, Canas LS, Graham MS, Klaser K, Modat M, Murray B, Kerfoot E, Chen L, Deng J, Österdahl MF, Cheetham NJ, Drew DA, Nguyen LH, Pujol JC, Hu C, Selvachandran S, Polidori L, May A, Wolf J, Chan AT, Hammers A, Duncan EL, Spector TD, Ourselin S, Steves CJ. Risk factors and disease profile of post-vaccination SARS-CoV-2 infection in UK users of the COVID Symptom Study app: a prospective, community-based, nested, case-control study. Lancet Infect Dis 2022; 22:43-55. [PMID: 34480857 PMCID: PMC8409907 DOI: 10.1016/s1473-3099(21)00460-6] [Citation(s) in RCA: 435] [Impact Index Per Article: 217.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Revised: 07/20/2021] [Accepted: 07/26/2021] [Indexed: 12/15/2022]
Abstract
BACKGROUND COVID-19 vaccines show excellent efficacy in clinical trials and effectiveness in real-world data, but some people still become infected with SARS-CoV-2 after vaccination. This study aimed to identify risk factors for post-vaccination SARS-CoV-2 infection and describe the characteristics of post-vaccination illness. METHODS This prospective, community-based, nested, case-control study used self-reported data (eg, on demographics, geographical location, health risk factors, and COVID-19 test results, symptoms, and vaccinations) from UK-based, adult (≥18 years) users of the COVID Symptom Study mobile phone app. For the risk factor analysis, cases had received a first or second dose of a COVID-19 vaccine between Dec 8, 2020, and July 4, 2021; had either a positive COVID-19 test at least 14 days after their first vaccination (but before their second; cases 1) or a positive test at least 7 days after their second vaccination (cases 2); and had no positive test before vaccination. Two control groups were selected (who also had not tested positive for SARS-CoV-2 before vaccination): users reporting a negative test at least 14 days after their first vaccination but before their second (controls 1) and users reporting a negative test at least 7 days after their second vaccination (controls 2). Controls 1 and controls 2 were matched (1:1) with cases 1 and cases 2, respectively, by the date of the post-vaccination test, health-care worker status, and sex. In the disease profile analysis, we sub-selected participants from cases 1 and cases 2 who had used the app for at least 14 consecutive days after testing positive for SARS-CoV-2 (cases 3 and cases 4, respectively). Controls 3 and controls 4 were unvaccinated participants reporting a positive SARS-CoV-2 test who had used the app for at least 14 consecutive days after the test, and were matched (1:1) with cases 3 and 4, respectively, by the date of the positive test, health-care worker status, sex, body-mass index (BMI), and age. We used univariate logistic regression models (adjusted for age, BMI, and sex) to analyse the associations between risk factors and post-vaccination infection, and the associations of individual symptoms, overall disease duration, and disease severity with vaccination status. FINDINGS Between Dec 8, 2020, and July 4, 2021, 1 240 009 COVID Symptom Study app users reported a first vaccine dose, of whom 6030 (0·5%) subsequently tested positive for SARS-CoV-2 (cases 1), and 971 504 reported a second dose, of whom 2370 (0·2%) subsequently tested positive for SARS-CoV-2 (cases 2). In the risk factor analysis, frailty was associated with post-vaccination infection in older adults (≥60 years) after their first vaccine dose (odds ratio [OR] 1·93, 95% CI 1·50-2·48; p<0·0001), and individuals living in highly deprived areas had increased odds of post-vaccination infection following their first vaccine dose (OR 1·11, 95% CI 1·01-1·23; p=0·039). Individuals without obesity (BMI <30 kg/m2) had lower odds of infection following their first vaccine dose (OR 0·84, 95% CI 0·75-0·94; p=0·0030). For the disease profile analysis, 3825 users from cases 1 were included in cases 3 and 906 users from cases 2 were included in cases 4. Vaccination (compared with no vaccination) was associated with reduced odds of hospitalisation or having more than five symptoms in the first week of illness following the first or second dose, and long-duration (≥28 days) symptoms following the second dose. Almost all symptoms were reported less frequently in infected vaccinated individuals than in infected unvaccinated individuals, and vaccinated participants were more likely to be completely asymptomatic, especially if they were 60 years or older. INTERPRETATION To minimise SARS-CoV-2 infection, at-risk populations must be targeted in efforts to boost vaccine effectiveness and infection control measures. Our findings might support caution around relaxing physical distancing and other personal protective measures in the post-vaccination era, particularly around frail older adults and individuals living in more deprived areas, even if these individuals are vaccinated, and might have implications for strategies such as booster vaccinations. FUNDING ZOE, the UK Government Department of Health and Social Care, the Wellcome Trust, the UK Engineering and Physical Sciences Research Council, UK Research and Innovation London Medical Imaging and Artificial Intelligence Centre for Value Based Healthcare, the UK National Institute for Health Research, the UK Medical Research Council, the British Heart Foundation, and the Alzheimer's Society.
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Affiliation(s)
- Michela Antonelli
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Rose S Penfold
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK; Department of Ageing and Health, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Jordi Merino
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Programs in Metabolism, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Programs in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Carole H Sudre
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK; Centre for Medical Image Computing, University College London, London, UK
| | - Erika Molteni
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Sarah Berry
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Liane S Canas
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Mark S Graham
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Kerstin Klaser
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Marc Modat
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Benjamin Murray
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Eric Kerfoot
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Liyuan Chen
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Jie Deng
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Marc F Österdahl
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK; Department of Ageing and Health, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Nathan J Cheetham
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - David A Drew
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Long H Nguyen
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | | | | | | | | | | | - Andrew T Chan
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Alexander Hammers
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; King's College London and Guy's and St Thomas' PET Centre, London, UK
| | - Emma L Duncan
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK; Department of Endocrinology, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Claire J Steves
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK; Department of Ageing and Health, Guy's and St Thomas' NHS Foundation Trust, London, UK.
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12
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Klaser K, Thompson EJ, Nguyen LH, Sudre CH, Antonelli M, Murray B, Canas LS, Molteni E, Graham MS, Kerfoot E, Chen L, Deng J, May A, Hu C, Guest A, Selvachandran S, Drew DA, Modat M, Chan AT, Wolf J, Spector TD, Hammers A, Duncan EL, Ourselin S, Steves CJ. Anxiety and depression symptoms after COVID-19 infection: results from the COVID Symptom Study app. J Neurol Neurosurg Psychiatry 2021; 92:1254-1258. [PMID: 34583944 PMCID: PMC8599635 DOI: 10.1136/jnnp-2021-327565] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [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: 07/08/2021] [Accepted: 09/07/2021] [Indexed: 01/05/2023]
Abstract
BACKGROUND Mental health issues have been reported after SARS-CoV-2 infection. However, comparison to prevalence in uninfected individuals and contribution from common risk factors (eg, obesity and comorbidities) have not been examined. We identified how COVID-19 relates to mental health in the large community-based COVID Symptom Study. METHODS We assessed anxiety and depression symptoms using two validated questionnaires in 413148 individuals between February and April 2021; 26998 had tested positive for SARS-CoV-2. We adjusted for physical and mental prepandemic comorbidities, body mass index (BMI), age and sex. FINDINGS Overall, 26.4% of participants met screening criteria for general anxiety and depression. Anxiety and depression were slightly more prevalent in previously SARS-CoV-2-positive (30.4%) vs SARS-CoV-2-negative (26.1%) individuals. This association was small compared with the effect of an unhealthy BMI and the presence of other comorbidities, and not evident in younger participants (≤40 years). Findings were robust to multiple sensitivity analyses. Association between SARS-CoV-2 infection and anxiety and depression was stronger in individuals with recent (<30 days) versus more distant (>120 days) infection, suggesting a short-term effect. INTERPRETATION A small association was identified between SARS-CoV-2 infection and anxiety and depression symptoms. The proportion meeting criteria for self-reported anxiety and depression disorders is only slightly higher than prepandemic.
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Affiliation(s)
- Kerstin Klaser
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Ellen J Thompson
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Long H Nguyen
- Clinical & Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Harvard Medical School, Boston, Massachusetts, USA
- Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Harvard Medical School, Boston, Massachusetts, USA
| | - Carole H Sudre
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Department of Medical Physics and Bioengineering, UCL Centre for Medical Image Computing (CMIC), London, UK
- MRC Unit for Lifelong Health and Ageing, Department of Population Science and Experimental Medicine, University College London, London, UK
| | - Michela Antonelli
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Benjamin Murray
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Liane S Canas
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Erika Molteni
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Mark S Graham
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Eric Kerfoot
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Liyuan Chen
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Jie Deng
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | | | | | | | | | - David A Drew
- Clinical & Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Harvard Medical School, Boston, Massachusetts, USA
- Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Harvard Medical School, Boston, Massachusetts, USA
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Andrew T Chan
- Clinical & Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Harvard Medical School, Boston, Massachusetts, USA
- Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Alexander Hammers
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- PET Centre, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Emma L Duncan
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Claire J Steves
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
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13
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Canas LS, Österdahl MF, Deng J, Hu C, Selvachandran S, Polidori L, May A, Molteni E, Murray B, Chen L, Kerfoot E, Klaser K, Antonelli M, Hammers A, Spector T, Ourselin S, Steves C, Sudre CH, Modat M, Duncan EL. Disentangling post-vaccination symptoms from early COVID-19. EClinicalMedicine 2021; 42:101212. [PMID: 34873584 PMCID: PMC8635464 DOI: 10.1016/j.eclinm.2021.101212] [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] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 10/28/2021] [Accepted: 11/08/2021] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Identifying and testing individuals likely to have SARS-CoV-2 is critical for infection control, including post-vaccination. Vaccination is a major public health strategy to reduce SARS-CoV-2 infection globally. Some individuals experience systemic symptoms post-vaccination, which overlap with COVID-19 symptoms. This study compared early post-vaccination symptoms in individuals who subsequently tested positive or negative for SARS-CoV-2, using data from the COVID Symptom Study (CSS) app. METHODS We conducted a prospective observational study in 1,072,313 UK CSS participants who were asymptomatic when vaccinated with Pfizer-BioNTech mRNA vaccine (BNT162b2) or Oxford-AstraZeneca adenovirus-vectored vaccine (ChAdOx1 nCoV-19) between 8 December 2020 and 17 May 2021, who subsequently reported symptoms within seven days (N=362,770) (other than local symptoms at injection site) and were tested for SARS-CoV-2 (N=14,842), aiming to differentiate vaccination side-effects per se from superimposed SARS-CoV-2 infection. The post-vaccination symptoms and SARS-CoV-2 test results were contemporaneously logged by participants. Demographic and clinical information (including comorbidities) were recorded. Symptom profiles in individuals testing positive were compared with a 1:1 matched population testing negative, including using machine learning and multiple models considering UK testing criteria. FINDINGS Differentiating post-vaccination side-effects alone from early COVID-19 was challenging, with a sensitivity in identification of individuals testing positive of 0.6 at best. Most of these individuals did not have fever, persistent cough, or anosmia/dysosmia, requisite symptoms for accessing UK testing; and many only had systemic symptoms commonly seen post-vaccination in individuals negative for SARS-CoV-2 (headache, myalgia, and fatigue). INTERPRETATION Post-vaccination symptoms per se cannot be differentiated from COVID-19 with clinical robustness, either using symptom profiles or machine-derived models. Individuals presenting with systemic symptoms post-vaccination should be tested for SARS-CoV-2 or quarantining, to prevent community spread. FUNDING UK Government Department of Health and Social Care, Wellcome Trust, UK Engineering and Physical Sciences Research Council, UK National Institute for Health Research, UK Medical Research Council and British Heart Foundation, Chronic Disease Research Foundation, Zoe Limited.
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Key Words
- AUC, Area under the curve
- BMI, Body mass index
- CI, Confidence interval
- COVID-19 detection
- COVID-19, Coronavirus disease 2019
- CSS, COVID Symptoms Study
- DI, Data invalid
- Early detection
- IQR, inter quartile range
- KCL, King's College London
- LFAT, Lateral flow antigen test
- LR, Logistic Regression
- Mobile technology
- NHS UK, National Health Service of the United Kingdom
- O-AZ, Oxford-AstraZeneca adenovirus-vectored vaccine
- PB, Pfizer-BoiNTech mRNA vaccine
- RF, Random forest
- ROC, Receiver operating curve
- SARS-CoV-2, Severe acute respiratory syndrome-related coronavirus-2
- Self-reported symptoms
- Side-effects
- UK, United Kingdom of Great Britain and Nothern Ireland
- Vaccination
- bMEM, Bayesian mixed-effect model
- rtPCR, Reverse transcription polymerase chain reaction
- severe acute respiratory syndrome‐related coronavirus 2 (SARS-CoV-2)
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Affiliation(s)
- Liane S. Canas
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Marc F. Österdahl
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, UK
| | - Jie Deng
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | | | | | | | | | - Erika Molteni
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Benjamin Murray
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Liyuan Chen
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Eric Kerfoot
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Kerstin Klaser
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Michela Antonelli
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Alexander Hammers
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- King's College London & Guy's and St Thomas’ PET Centre, London, UK
| | - Tim Spector
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Claire Steves
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, UK
| | - Carole H. Sudre
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Medical Research Council Unit for Lifelong Health and Ageing, Department of Population Science and Experimental Medicine. UK
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Marc Modat
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Emma L. Duncan
- Department of Twin Research and Genetic Epidemiology, Kings College London, London, UK
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14
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Murray B, Kerfoot E, Chen L, Deng J, Graham MS, Sudre CH, Molteni E, Canas LS, Antonelli M, Klaser K, Visconti A, Hammers A, Chan AT, Franks PW, Davies R, Wolf J, Spector TD, Steves CJ, Modat M, Ourselin S. Accessible data curation and analytics for international-scale citizen science datasets. Sci Data 2021; 8:297. [PMID: 34811392 PMCID: PMC8608807 DOI: 10.1038/s41597-021-01071-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [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] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 09/29/2021] [Indexed: 11/21/2022] Open
Abstract
The Covid Symptom Study, a smartphone-based surveillance study on COVID-19 symptoms in the population, is an exemplar of big data citizen science. As of May 23rd, 2021, over 5 million participants have collectively logged over 360 million self-assessment reports since its introduction in March 2020. The success of the Covid Symptom Study creates significant technical challenges around effective data curation. The primary issue is scale. The size of the dataset means that it can no longer be readily processed using standard Python-based data analytics software such as Pandas on commodity hardware. Alternative technologies exist but carry a higher technical complexity and are less accessible to many researchers. We present ExeTera, a Python-based open source software package designed to provide Pandas-like data analytics on datasets that approach terabyte scales. We present its design and capabilities, and show how it is a critical component of a data curation pipeline that enables reproducible research across an international research group for the Covid Symptom Study.
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Affiliation(s)
- Benjamin Murray
- King's College London, School of Biomedical Engineering & Imaging Sciences, London, SE1 7EU, United Kingdom.
| | - Eric Kerfoot
- King's College London, School of Biomedical Engineering & Imaging Sciences, London, SE1 7EU, United Kingdom
| | - Liyuan Chen
- King's College London, School of Biomedical Engineering & Imaging Sciences, London, SE1 7EU, United Kingdom
| | - Jie Deng
- King's College London, School of Biomedical Engineering & Imaging Sciences, London, SE1 7EU, United Kingdom
| | - Mark S Graham
- King's College London, School of Biomedical Engineering & Imaging Sciences, London, SE1 7EU, United Kingdom
| | - Carole H Sudre
- King's College London, School of Biomedical Engineering & Imaging Sciences, London, SE1 7EU, United Kingdom
- University College London, MRC Unit for Lifelong Health and Ageing, Department of Population Health Sciences, London, WC1E 7HB, United Kingdom
- University College London, Centre for Medical Image Computing, London, WC1E 6BT, United Kingdom
| | - Erika Molteni
- King's College London, School of Biomedical Engineering & Imaging Sciences, London, SE1 7EU, United Kingdom
| | - Liane S Canas
- King's College London, School of Biomedical Engineering & Imaging Sciences, London, SE1 7EU, United Kingdom
| | - Michela Antonelli
- King's College London, School of Biomedical Engineering & Imaging Sciences, London, SE1 7EU, United Kingdom
| | - Kerstin Klaser
- King's College London, School of Biomedical Engineering & Imaging Sciences, London, SE1 7EU, United Kingdom
| | - Alessia Visconti
- King's College London, Department of Twin Research and Genetic Epidemiology, Westminster Bridge Road, London, SE1 7EH, United Kingdom
| | - Alexander Hammers
- King's College London, School of Biomedical Engineering & Imaging Sciences, London, SE1 7EU, United Kingdom
| | - Andrew T Chan
- Massachusetts General Hospital, 55 Fruit Street, GRJ 825C, Boston, MA, 02116, United States
| | - Paul W Franks
- Lund University, Diabetes Centre, CRC, SUS Malmö, Jan Waldenströms gata 35, House 91:12, SE-214 28, Malmö, Sweden
| | - Richard Davies
- Zoe Limited, 164 Westminster Bridge Road, London, SE1 7RW, United Kingdom
| | - Jonathan Wolf
- Zoe Limited, 164 Westminster Bridge Road, London, SE1 7RW, United Kingdom
| | - Tim D Spector
- King's College London, Department of Twin Research and Genetic Epidemiology, Westminster Bridge Road, London, SE1 7EH, United Kingdom
| | - Claire J Steves
- King's College London, Department of Twin Research and Genetic Epidemiology, Westminster Bridge Road, London, SE1 7EH, United Kingdom
| | - Marc Modat
- King's College London, School of Biomedical Engineering & Imaging Sciences, London, SE1 7EU, United Kingdom
| | - Sebastien Ourselin
- King's College London, School of Biomedical Engineering & Imaging Sciences, London, SE1 7EU, United Kingdom
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15
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Molteni E, Sudre CH, Canas LS, Bhopal SS, Hughes RC, Antonelli M, Murray B, Kläser K, Kerfoot E, Chen L, Deng J, Hu C, Selvachandran S, Read K, Capdevila Pujol J, Hammers A, Spector TD, Ourselin S, Steves CJ, Modat M, Absoud M, Duncan EL. Illness duration and symptom profile in symptomatic UK school-aged children tested for SARS-CoV-2. Lancet Child Adolesc Health 2021; 5:708-718. [PMID: 34358472 PMCID: PMC8443448 DOI: 10.1016/s2352-4642(21)00198-x] [Citation(s) in RCA: 235] [Impact Index Per Article: 78.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 06/28/2021] [Accepted: 06/29/2021] [Indexed: 12/16/2022]
Abstract
BACKGROUND In children, SARS-CoV-2 infection is usually asymptomatic or causes a mild illness of short duration. Persistent illness has been reported; however, its prevalence and characteristics are unclear. We aimed to determine illness duration and characteristics in symptomatic UK school-aged children tested for SARS-CoV-2 using data from the COVID Symptom Study, one of the largest UK citizen participatory epidemiological studies to date. METHODS In this prospective cohort study, data from UK school-aged children (age 5-17 years) were reported by an adult proxy. Participants were voluntary, and used a mobile application (app) launched jointly by Zoe Limited and King's College London. Illness duration and symptom prevalence, duration, and burden were analysed for children testing positive for SARS-CoV-2 for whom illness duration could be determined, and were assessed overall and for younger (age 5-11 years) and older (age 12-17 years) groups. Children with longer than 1 week between symptomatic reports on the app were excluded from analysis. Data from symptomatic children testing negative for SARS-CoV-2, matched 1:1 for age, gender, and week of testing, were also assessed. FINDINGS 258 790 children aged 5-17 years were reported by an adult proxy between March 24, 2020, and Feb 22, 2021, of whom 75 529 had valid test results for SARS-CoV-2. 1734 children (588 younger and 1146 older children) had a positive SARS-CoV-2 test result and calculable illness duration within the study timeframe (illness onset between Sept 1, 2020, and Jan 24, 2021). The most common symptoms were headache (1079 [62·2%] of 1734 children), and fatigue (954 [55·0%] of 1734 children). Median illness duration was 6 days (IQR 3-11) versus 3 days (2-7) in children testing negative, and was positively associated with age (Spearman's rank-order rs 0·19, p<0·0001). Median illness duration was longer for older children (7 days, IQR 3-12) than younger children (5 days, 2-9). 77 (4·4%) of 1734 children had illness duration of at least 28 days, more commonly in older than younger children (59 [5·1%] of 1146 older children vs 18 [3·1%] of 588 younger children; p=0·046). The commonest symptoms experienced by these children during the first 4 weeks of illness were fatigue (65 [84·4%] of 77), headache (60 [77·9%] of 77), and anosmia (60 [77·9%] of 77); however, after day 28 the symptom burden was low (median 2 symptoms, IQR 1-4) compared with the first week of illness (median 6 symptoms, 4-8). Only 25 (1·8%) of 1379 children experienced symptoms for at least 56 days. Few children (15 children, 0·9%) in the negatively tested cohort had symptoms for at least 28 days; however, these children experienced greater symptom burden throughout their illness (9 symptoms, IQR 7·7-11·0 vs 8, 6-9) and after day 28 (5 symptoms, IQR 1·5-6·5 vs 2, 1-4) than did children who tested positive for SARS-CoV-2. INTERPRETATION Although COVID-19 in children is usually of short duration with low symptom burden, some children with COVID-19 experience prolonged illness duration. Reassuringly, symptom burden in these children did not increase with time, and most recovered by day 56. Some children who tested negative for SARS-CoV-2 also had persistent and burdensome illness. A holistic approach for all children with persistent illness during the pandemic is appropriate. FUNDING Zoe Limited, UK Government Department of Health and Social Care, Wellcome Trust, UK Engineering and Physical Sciences Research Council, UK Research and Innovation London Medical Imaging and Artificial Intelligence Centre for Value Based Healthcare, UK National Institute for Health Research, UK Medical Research Council, British Heart Foundation, and Alzheimer's Society.
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Affiliation(s)
- Erika Molteni
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, School of Life Course Sciences, King's College London, London, UK
| | - Carole H Sudre
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, School of Life Course Sciences, King's College London, London, UK; MRC Unit for Lifelong Health and Ageing, Department of Population Health Sciences and Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Liane S Canas
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, School of Life Course Sciences, King's College London, London, UK
| | - Sunil S Bhopal
- Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Robert C Hughes
- Department of Population Health, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Michela Antonelli
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, School of Life Course Sciences, King's College London, London, UK
| | - Benjamin Murray
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, School of Life Course Sciences, King's College London, London, UK
| | - Kerstin Kläser
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, School of Life Course Sciences, King's College London, London, UK
| | - Eric Kerfoot
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, School of Life Course Sciences, King's College London, London, UK
| | - Liyuan Chen
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, School of Life Course Sciences, King's College London, London, UK
| | - Jie Deng
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, School of Life Course Sciences, King's College London, London, UK
| | | | | | | | | | - Alexander Hammers
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, School of Life Course Sciences, King's College London, London, UK; King's College London and Guy's and St Thomas' PET Centre, London, UK
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, Faculty of Life Sciences and Medicine, School of Life Course Sciences, King's College London, London, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, School of Life Course Sciences, King's College London, London, UK
| | - Claire J Steves
- Department of Twin Research and Genetic Epidemiology, Faculty of Life Sciences and Medicine, School of Life Course Sciences, King's College London, London, UK; Department of Ageing and Health, Guys and St Thomas' NHS Foundation Trust, London, UK
| | - Marc Modat
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, School of Life Course Sciences, King's College London, London, UK
| | - Michael Absoud
- Department of Women and Children's Health, Faculty of Life Sciences and Medicine, School of Life Course Sciences, King's College London, London, UK; Children's Neurosciences, Evelina London Children' Hospital, St Thomas' Hospital, King's Health Partners, Academic Health Science Centre, London, UK
| | - Emma L Duncan
- Department of Twin Research and Genetic Epidemiology, Faculty of Life Sciences and Medicine, School of Life Course Sciences, King's College London, London, UK; Department of Endocrinology, Guys and St Thomas' NHS Foundation Trust, London, UK.
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16
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Miller R, Kerfoot E, Mauger C, Ismail TF, Young AA, Nordsletten DA. An Implementation of Patient-Specific Biventricular Mechanics Simulations With a Deep Learning and Computational Pipeline. Front Physiol 2021; 12:716597. [PMID: 34603077 PMCID: PMC8481785 DOI: 10.3389/fphys.2021.716597] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [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: 05/28/2021] [Accepted: 08/06/2021] [Indexed: 02/04/2023] Open
Abstract
Parameterised patient-specific models of the heart enable quantitative analysis of cardiac function as well as estimation of regional stress and intrinsic tissue stiffness. However, the development of personalised models and subsequent simulations have often required lengthy manual setup, from image labelling through to generating the finite element model and assigning boundary conditions. Recently, rapid patient-specific finite element modelling has been made possible through the use of machine learning techniques. In this paper, utilising multiple neural networks for image labelling and detection of valve landmarks, together with streamlined data integration, a pipeline for generating patient-specific biventricular models is applied to clinically-acquired data from a diverse cohort of individuals, including hypertrophic and dilated cardiomyopathy patients and healthy volunteers. Valve motion from tracked landmarks as well as cavity volumes measured from labelled images are used to drive realistic motion and estimate passive tissue stiffness values. The neural networks are shown to accurately label cardiac regions and features for these diverse morphologies. Furthermore, differences in global intrinsic parameters, such as tissue anisotropy and normalised active tension, between groups illustrate respective underlying changes in tissue composition and/or structure as a result of pathology. This study shows the successful application of a generic pipeline for biventricular modelling, incorporating artificial intelligence solutions, within a diverse cohort.
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Affiliation(s)
- Renee Miller
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Eric Kerfoot
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Charlène Mauger
- Auckland MR Research Group, University of Auckland, Auckland, New Zealand
| | - Tevfik F. Ismail
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Alistair A. Young
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Auckland MR Research Group, University of Auckland, Auckland, New Zealand
| | - David A. Nordsletten
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Department of Biomedical Engineering and Cardiac Surgery, University of Michigan, Ann Arbor, MI, United States
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17
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Xue W, Li J, Hu Z, Kerfoot E, Clough J, Oksuz I, Xu H, Grau V, Guo F, Ng M, Li X, Li Q, Liu L, Ma J, Grinias E, Tziritas G, Yan W, Atehortúa A, Garreau M, Jang Y, Debus A, Ferrante E, Yang G, Hua T, Li S. Left Ventricle Quantification Challenge: A Comprehensive Comparison and Evaluation of Segmentation and Regression for Mid-Ventricular Short-Axis Cardiac MR Data. IEEE J Biomed Health Inform 2021; 25:3541-3553. [PMID: 33684050 PMCID: PMC7611810 DOI: 10.1109/jbhi.2021.3064353] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Automatic quantification of the left ventricle (LV) from cardiac magnetic resonance (CMR) images plays an important role in making the diagnosis procedure efficient, reliable, and alleviating the laborious reading work for physicians. Considerable efforts have been devoted to LV quantification using different strategies that include segmentation-based (SG) methods and the recent direct regression (DR) methods. Although both SG and DR methods have obtained great success for the task, a systematic platform to benchmark them remains absent because of differences in label information during model learning. In this paper, we conducted an unbiased evaluation and comparison of cardiac LV quantification methods that were submitted to the Left Ventricle Quantification (LVQuan) challenge, which was held in conjunction with the Statistical Atlases and Computational Modeling of the Heart (STACOM) workshop at the MICCAI 2018. The challenge was targeted at the quantification of 1) areas of LV cavity and myocardium, 2) dimensions of the LV cavity, 3) regional wall thicknesses (RWT), and 4) the cardiac phase, from mid-ventricle short-axis CMR images. First, we constructed a public quantification dataset Cardiac-DIG with ground truth labels for both the myocardium mask and these quantification targets across the entire cardiac cycle. Then, the key techniques employed by each submission were described. Next, quantitative validation of these submissions were conducted with the constructed dataset. The evaluation results revealed that both SG and DR methods can offer good LV quantification performance, even though DR methods do not require densely labeled masks for supervision. Among the 12 submissions, the DR method LDAMT offered the best performance, with a mean estimation error of 301 mm 2 for the two areas, 2.15 mm for the cavity dimensions, 2.03 mm for RWTs, and a 9.5% error rate for the cardiac phase classification. Three of the SG methods also delivered comparable performances. Finally, we discussed the advantages and disadvantages of SG and DR methods, as well as the unsolved problems in automatic cardiac quantification for clinical practice applications.
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Affiliation(s)
- Wufeng Xue
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China; Department of Medical Imaging, Western University, London, ON N6A 3K7, Canada
| | - Jiahui Li
- Beijing University of Post and Telecommunication, Beijing, China
| | | | - Eric Kerfoot
- School of Biomedical Engineering & Imaging Sciences, King’s College London, UK
| | - James Clough
- School of Biomedical Engineering & Imaging Sciences, King’s College London, UK
| | - Ilkay Oksuz
- School of Biomedical Engineering & Imaging Sciences, King’s College London, UK
| | - Hao Xu
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Vicente Grau
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Fumin Guo
- Sunnybrook Research Institute, Department of Medical Biophysics, University of Toronto, Canada
| | - Matthew Ng
- Sunnybrook Research Institute, Department of Medical Biophysics, University of Toronto, Canada
| | - Xiang Li
- Department of Radiology, Massachusetts General Hospital, Boston, USA
| | - Quanzheng Li
- Department of Radiology, Massachusetts General Hospital, Boston, USA
| | - Lihong Liu
- Pingan Technology (Shenzhen) Co.Ltd. Elias Grinias and Georgios Tziritas are with Department of Computer Science, University of Crete, Heraklion, Greece
| | - Jin Ma
- Pingan Technology (Shenzhen) Co.Ltd. Elias Grinias and Georgios Tziritas are with Department of Computer Science, University of Crete, Heraklion, Greece
| | - Elias Grinias
- Department of Computer Science, University of Crete, Heraklion, Greece
| | - Georgios Tziritas
- Department of Computer Science, University of Crete, Heraklion, Greece
| | - Wenjun Yan
- Department of Electrical Engineering, Fudan University, Shanghai, China
| | - Angélica Atehortúa
- LTSI UMR 1099, F-35000 Rennes, France; Universidad Nacional de Colombia, Bogotá, Colombia
| | | | - Yeonggul Jang
- Brain Korea 21 PLUS Project for Medical Science, Yonsei University
| | - Alejandro Debus
- Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH-UNL/CONICET, Santa Fe, Argentina
| | - Enzo Ferrante
- Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH-UNL/CONICET, Santa Fe, Argentina
| | - Guanyu Yang
- Centre de Recherche en Information Biomédicale Sino-Français (CRIBs), Southeast University, Nanjing, China; LIST, Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing, China
| | - Tiancong Hua
- Centre de Recherche en Information Biomedicale Sino-Francais (CRIBs), Southeast University, Nanjing, China
| | - Shuo Li
- Department of Medical Imaging, Western University, London, ON N6A 3K7, Canada
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18
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Klaser K, Thompson EJ, Nguyen LH, Sudre CH, Antonelli M, Murray B, Canas LS, Molteni E, Graham MS, Kerfoot E, Chen L, Deng J, May A, Hu C, Guest A, Selvachandran S, Drew DA, Modat M, Chan AT, Wolf J, Spector TD, Hammers A, Duncan EL, Ourselin S, Steves CJ. Anxiety and depression symptoms after COVID-19 infection: results from the COVID Symptom Study app. medRxiv 2021:2021.07.07.21260137. [PMID: 34268526 PMCID: PMC8282115 DOI: 10.1101/2021.07.07.21260137] [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] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
BACKGROUND Mental health issues have been reported after SARS-CoV-2 infection. However, comparison to prevalence in uninfected individuals and contribution from common risk factors (e.g., obesity, comorbidities) have not been examined. We identified how COVID-19 relates to mental health in the large community-based COVID Symptom Study. METHODS We assessed anxiety and depression symptoms using two validated questionnaires in 413,148 individuals between February and April 2021; 26,998 had tested positive for SARS-CoV-2. We adjusted for physical and mental pre-pandemic comorbidities, BMI, age, and sex. FINDINGS Overall, 26.4% of participants met screening criteria for general anxiety and depression. Anxiety and depression were slightly more prevalent in previously SARS-CoV-2 positive (30.4%) vs. negative (26.1%) individuals. This association was small compared to the effect of an unhealthy BMI and the presence of other comorbidities, and not evident in younger participants (≤40 years). Findings were robust to multiple sensitivity analyses. Association between SARS-CoV-2 infection and anxiety and depression was stronger in individuals with recent (<30 days) vs. more distant (>120 days) infection, suggesting a short-term effect. INTERPRETATION A small association was identified between SARS-CoV-2 infection and anxiety and depression symptoms. The proportion meeting criteria for self-reported anxiety and depression disorders is only slightly higher than pre-pandemic. FUNDING Zoe Limited, National Institute for Health Research, Chronic Disease Research Foundation, National Institutes of Health, Medical Research Council UK.
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Affiliation(s)
- Kerstin Klaser
- School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EU, UK
| | - Ellen J Thompson
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK
| | - Long H Nguyen
- Clinical & Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, 100 Cambridge Street, Boston, MA, USA
- Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, 100 Cambridge Street, Boston, MA, USA
| | - Carole H Sudre
- School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EU, UK
- MRC Unit for Lifelong Health and Ageing, Department of Population Science and Experimental Medicine, University College London, UK
- Centre for Medical Image Computing, Department of Computer Science, University College London, UK
| | - Michela Antonelli
- School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EU, UK
| | - Benjamin Murray
- School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EU, UK
| | - Liane S Canas
- School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EU, UK
| | - Erika Molteni
- School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EU, UK
| | - Mark S Graham
- School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EU, UK
| | - Eric Kerfoot
- School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EU, UK
| | - Liyuan Chen
- School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EU, UK
| | - Jie Deng
- School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EU, UK
| | | | | | | | | | - David A Drew
- Clinical & Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, 100 Cambridge Street, Boston, MA, USA
- Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, 100 Cambridge Street, Boston, MA, USA
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EU, UK
| | - Andrew T Chan
- Clinical & Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, 100 Cambridge Street, Boston, MA, USA
- Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, 100 Cambridge Street, Boston, MA, USA
| | | | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK
| | - Alexander Hammers
- School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EU, UK
- King's College London & Guy's and St Thomas' PET Centre, London, UK
| | - Emma L Duncan
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EU, UK
| | - Claire J Steves
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK
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19
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Ruijsink B, Puyol-Antón E, Li Y, Bai W, Kerfoot E, Razavi R, King AP. Quality-aware semi-supervised learning for CMR segmentation. Stat Atlases Comput Models Heart 2021; 2020:97-107. [PMID: 34286332 PMCID: PMC7611307 DOI: 10.1007/978-3-030-68107-4_10] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
One of the challenges in developing deep learning algorithms for medical image segmentation is the scarcity of annotated training data. To overcome this limitation, data augmentation and semi-supervised learning (SSL) methods have been developed. However, these methods have limited effectiveness as they either exploit the existing data set only (data augmentation) or risk negative impact by adding poor training examples (SSL). Segmentations are rarely the final product of medical image analysis -they are typically used in downstream tasks to infer higher-order patterns to evaluate diseases. Clinicians take into account a wealth of prior knowledge on biophysics and physiology when evaluating image analysis results. We have used these clinical assessments in previous works to create robust quality-control (QC) classifiers for automated cardiac magnetic resonance (CMR) analysis. In this paper, we propose a novel scheme that uses QC of the downstream task to identify high quality outputs of CMR segmentation networks, that are subsequently utilised for further network training. In essence, this provides quality-aware augmentation of training data in a variant of SSL for segmentation networks (semiQCSeg). We evaluate our approach in two CMR segmentation tasks (aortic and short axis cardiac volume segmentation) using UK Biobank data and two commonly used network architectures (U-net and a Fully Convolutional Network) and compare against supervised and SSL strategies. We show that semiQCSeg improves training of the segmentation networks. It decreases the need for labelled data, while outperforming the other methods in terms of Dice and clinical metrics. SemiQCSeg can be an efficient approach for training segmentation networks for medical image data when labelled datasets are scarce.
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Affiliation(s)
- Bram Ruijsink
- School of Biomedical Engineering & Imaging Sciences, King's College London, UK
- St Thomas' Hospital NHS Foundation Trust, London, UK
- Department of Cardiology, University Medical Centre Utrecht, The Netherlands
| | - Esther Puyol-Antón
- School of Biomedical Engineering & Imaging Sciences, King's College London, UK
| | - Ye Li
- School of Biomedical Engineering & Imaging Sciences, King's College London, UK
| | - Wenja Bai
- Biomedical Image Analysis Group, Imperial College London, UK
| | - Eric Kerfoot
- School of Biomedical Engineering & Imaging Sciences, King's College London, UK
| | - Reza Razavi
- School of Biomedical Engineering & Imaging Sciences, King's College London, UK
- St Thomas' Hospital NHS Foundation Trust, London, UK
| | - Andrew P King
- School of Biomedical Engineering & Imaging Sciences, King's College London, UK
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20
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Lourenco A, Kerfoot E, Dibblin C, Chubb H, Bharath A, Correia T, Varela M. Automatic estimation of left atrial function from short axis CINE-MRI using machine learning. Eur Heart J 2020. [DOI: 10.1093/ehjci/ehaa946.0229] [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] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
Introduction
The importance of atrial mechanical dysfunction in atrial and ventricular pathologies is becoming increasingly recognised. Although machine learning (ML) tools have the ability to automatically estimate atrial function, to date ML techniques have not been used to automatically estimate atrial volumes and functional parameters directly from short axis CINE MRI.
Purpose
We introduce a convolutional neural network (CNN) to automatically segment the left atria (LA) in CINE-MRI. As a demonstration of the clinical utility of this technique, we calculated LA and left ventricular (LV) ejection fractions automatically from CINE images.
Methods
Short axis CINE MRI stacks, covering both ventricles and atria, were obtained in a 1.5T Philips Ingenia scanner. A 2D bSSFP ECG-gated protocol was used (FA=60°, TE/TR=1.5/2.9 ms), typical FOV =385 x 310 x 150 mm3, acquisition matrix = 172 x 140, slice thickness = 10 mm, reconstructed with resolution 1.25 x 1.25 x 10 mm3, 30–50 cardiac phases. Images were collected from 37 AF patients in sinus rythm at the time of scan (31–72 years old, 75% male, 18 with paroxysmal AF (PAF), 19 with persistent AF (persAF)).
To automatically segment the LA, we used a dedicated CNN that follows a U-Net architecture and was trained in 715 images of the LA, manually segmented by an expert. Data augmentation techniques that included noise addition and linear and non-linear image transforms were also used to increase the training dataset. Ventricular structures, including the LV blood pool, were automatically segmented in these images using a CNN previously trained for this task.
Volumetric time plots of LA and LV volume were produced and used to automatically compute maximal and minimal volumes, from which LA and LV ejection fractions (EFs) were assessed. A Bland-Altman analysis compared these automatically computed LA volumes and LA EFs with clinical manual estimates from the same scanning session.
Results
The CNN achieved very good quality LA segmentations when compared to manual ones (Fig a,b): Dice coefficients (0.90±0.07), median contour distances (0.50±1.12mm) and Hausdorff distances (6.70±6.16mm). Bland-Altman analyses show very good agreement between automatic and manual LA volumes and EFs (Fig e). A moderate linear correlation between LA and LV EFs in AF patients was found (Fig d). The measured LA EF was higher for PAF (29±8%) than PersAF patients (21±11%), although non-significantly (t-test p-value: 0.10).
Conclusions
We present a reliable automatic method to perform LA segmentations from CINE MRI across the entire cardiac cycle. This approachs opens up the possibility of automatically calculating more sophisticated biomarkers of LA function which take into account information about LA volumes across the entire cardiac cycle, including biomarkers of LA booster pump function.
Figure 1
Funding Acknowledgement
Type of funding source: Foundation. Main funding source(s): British Heart Foundation; EPSRC/Wellcome Centre for Medical Engineering
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Affiliation(s)
- A Lourenco
- King's College London, Division of Imaging Sciences and Biomedical Engineering, London, United Kingdom
| | - E Kerfoot
- King's College London, Division of Imaging Sciences and Biomedical Engineering, London, United Kingdom
| | - C Dibblin
- King's College London, Division of Imaging Sciences and Biomedical Engineering, London, United Kingdom
| | - H Chubb
- King's College London, Division of Imaging Sciences and Biomedical Engineering, London, United Kingdom
| | - A Bharath
- Imperial College London, London, United Kingdom
| | - T Correia
- King's College London, Division of Imaging Sciences and Biomedical Engineering, London, United Kingdom
| | - M Varela
- Imperial College London, London, United Kingdom
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21
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Mendonca Costa C, Neic A, Kerfoot E, Porter B, Sieniewicz B, Gould J, Sidhu B, Chen Z, Plank G, Rinaldi CA, Bishop MJ, Niederer SA. Pacing in proximity to scar during cardiac resynchronization therapy increases local dispersion of repolarization and susceptibility to ventricular arrhythmogenesis. Heart Rhythm 2019; 16:1475-1483. [PMID: 30930329 PMCID: PMC6774764 DOI: 10.1016/j.hrthm.2019.03.027] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Indexed: 12/24/2022]
Abstract
BACKGROUND Cardiac resynchronization therapy (CRT) increases the risk of ventricular tachycardia (VT) in patients with ischemic cardiomyopathy (ICM) when the left ventricular (LV) epicardial lead is implanted in proximity to scar. OBJECTIVE The purpose of this study was to determine the mechanisms underpinning this risk by investigating the effects of pacing on local electrophysiology (EP) in relation to scar that provides a substrate for VT in ICM patients undergoing CRT. METHODS Imaging data from ICM patients (n = 24) undergoing CRT were used to create patient-specific LV anatomic computational models including scar morphology. Simulations of LV epicardial pacing at 0.2-4.5 cm from the scar were performed using EP models of chronic infarct and heart failure (HF). Dispersion of repolarization and the vulnerable window were computed as surrogates for VT risk. RESULTS Simulations predict that pacing in proximity to scar (0.2 cm) compared to more distant pacing to a scar (4.5 cm) significantly (P <.01) increased dispersion of repolarization in the vicinity of the scar and widened (P <.01) the vulnerable window, increasing the likelihood of unidirectional block. Moreover, slow conduction during HF further increased dispersion (∼194%). Analysis of variance and post hoc tests show significantly (P <.01) reduced repolarization dispersion when pacing ≥3.5 cm from the scar compared to pacing at 0.2 cm. CONCLUSION Increased dispersion of repolarization in the vicinity of the scar and widening of the vulnerable window when pacing in proximity to scar provides a mechanistic explanation for VT induction in ICM-CRT with lead placement proximal to scar. Pacing 3.5 cm or more from scar may avoid increasing VT risk in ICM-CRT patients.
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Affiliation(s)
| | - Aurel Neic
- Medical University of Graz, Graz, Austria
| | | | | | | | | | | | - Zhong Chen
- King's College London, London, United Kingdom
| | | | - Christopher A Rinaldi
- King's College London, London, United Kingdom; Guy's and St Thomas' Hospital, London, United Kingdom
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22
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Dillon-Murphy D, Marlevi D, Ruijsink B, Qureshi A, Chubb H, Kerfoot E, O'Neill M, Nordsletten D, Aslanidi O, de Vecchi A. Modeling Left Atrial Flow, Energy, Blood Heating Distribution in Response to Catheter Ablation Therapy. Front Physiol 2019; 9:1757. [PMID: 30618785 PMCID: PMC6302108 DOI: 10.3389/fphys.2018.01757] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [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: 05/11/2018] [Accepted: 11/20/2018] [Indexed: 11/16/2022] Open
Abstract
Introduction: Atrial fibrillation (AF) is a widespread cardiac arrhythmia that commonly affects the left atrium (LA), causing it to quiver instead of contracting effectively. This behavior is triggered by abnormal electrical impulses at a specific site in the atrial wall. Catheter ablation (CA) treatment consists of isolating this driver site by burning the surrounding tissue to restore sinus rhythm (SR). However, evidence suggests that CA can concur to the formation of blood clots by promoting coagulation near the heat source and in regions with low flow velocity and blood stagnation. Methods: A patient-specific modeling workflow was created and applied to simulate thermal-fluid dynamics in two patients pre- and post-CA. Each model was personalized based on pre- and post-CA imaging datasets. The wall motion and anatomy were derived from SSFP Cine MRI data, while the trans-valvular flow was based on Doppler ultrasound data. The temperature distribution in the blood was modeled using a modified Pennes bioheat equation implemented in a finite-element based Navier-Stokes solver. Blood particles were also classified based on their residence time in the LA using a particle-tracking algorithm. Results: SR simulations showed multiple short-lived vortices with an average blood velocity of 0.2-0.22 m/s. In contrast, AF patients presented a slower vortex and stagnant flow in the LA appendage, with the average blood velocity reduced to 0.08–0.14 m/s. Restoration of SR also increased the blood kinetic energy and the viscous dissipation due to the presence of multiple vortices. Particle tracking showed a dramatic decrease in the percentage of blood remaining in the LA for longer than one cycle after CA (65.9 vs. 43.3% in patient A and 62.2 vs. 54.8% in patient B). Maximum temperatures of 76° and 58°C were observed when CA was performed near the appendage and in a pulmonary vein, respectively. Conclusion: This computational study presents novel models to elucidate relations between catheter temperature, patient-specific atrial anatomy and blood velocity, and predict how they change from SR to AF. The models can quantify blood flow in critical regions, including residence times and temperature distribution for different catheter positions, providing a basis for quantifying stroke risks.
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Affiliation(s)
- Desmond Dillon-Murphy
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - David Marlevi
- School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Bram Ruijsink
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Ahmed Qureshi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Henry Chubb
- Department of Cardiothoracic Surgery, Stanford University, Palo Alto, CA, United States
| | - Eric Kerfoot
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Mark O'Neill
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - David Nordsletten
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Oleg Aslanidi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Adelaide de Vecchi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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23
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Hadjicharalambous M, Asner L, Chabiniok R, Sammut E, Wong J, Peressutti D, Kerfoot E, King A, Lee J, Razavi R, Smith N, Carr-White G, Nordsletten D. Non-invasive Model-Based Assessment of Passive Left-Ventricular Myocardial Stiffness in Healthy Subjects and in Patients with Non-ischemic Dilated Cardiomyopathy. Ann Biomed Eng 2016. [PMID: 27605213 DOI: 10.1007/s10439‐016‐1721‐4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
Patient-specific modelling has emerged as a tool for studying heart function, demonstrating the potential to provide non-invasive estimates of tissue passive stiffness. However, reliable use of model-derived stiffness requires sufficient model accuracy and unique estimation of model parameters. In this paper we present personalised models of cardiac mechanics, focusing on improving model accuracy, while ensuring unique parametrisation. The influence of principal model uncertainties on accuracy and parameter identifiability was systematically assessed in a group of patients with dilated cardiomyopathy ([Formula: see text]) and healthy volunteers ([Formula: see text]). For all cases, we examined three circumferentially symmetric fibre distributions and two epicardial boundary conditions. Our results demonstrated the ability of data-derived boundary conditions to improve model accuracy and highlighted the influence of the assumed fibre distribution on both model fidelity and stiffness estimates. The model personalisation pipeline-based strictly on non-invasive data-produced unique parameter estimates and satisfactory model errors for all cases, supporting the selected model assumptions. The thorough analysis performed enabled the comparison of passive parameters between volunteers and dilated cardiomyopathy patients, illustrating elevated stiffness in diseased hearts.
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Affiliation(s)
- Myrianthi Hadjicharalambous
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St. Thomas' Hospital, London, SE1 7EH, UK.
| | - Liya Asner
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St. Thomas' Hospital, London, SE1 7EH, UK
| | - Radomir Chabiniok
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St. Thomas' Hospital, London, SE1 7EH, UK.,Inria and Paris-Saclay University, Bâtiment Alan Turing, 1 rue Honoré d'Estienne d'Orves, Campus de l'Ecole Polytechnique, 91120, Palaiseau, France
| | - Eva Sammut
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St. Thomas' Hospital, London, SE1 7EH, UK
| | - James Wong
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St. Thomas' Hospital, London, SE1 7EH, UK
| | - Devis Peressutti
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St. Thomas' Hospital, London, SE1 7EH, UK
| | - Eric Kerfoot
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St. Thomas' Hospital, London, SE1 7EH, UK
| | - Andrew King
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St. Thomas' Hospital, London, SE1 7EH, UK
| | - Jack Lee
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St. Thomas' Hospital, London, SE1 7EH, UK
| | - Reza Razavi
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St. Thomas' Hospital, London, SE1 7EH, UK
| | - Nicolas Smith
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St. Thomas' Hospital, London, SE1 7EH, UK.,Department of Engineering Science, University of Auckland, 20 Symonds St, Auckland, 1010, New Zealand
| | - Gerald Carr-White
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St. Thomas' Hospital, London, SE1 7EH, UK
| | - David Nordsletten
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St. Thomas' Hospital, London, SE1 7EH, UK
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Hadjicharalambous M, Asner L, Chabiniok R, Sammut E, Wong J, Peressutti D, Kerfoot E, King A, Lee J, Razavi R, Smith N, Carr-White G, Nordsletten D. Non-invasive Model-Based Assessment of Passive Left-Ventricular Myocardial Stiffness in Healthy Subjects and in Patients with Non-ischemic Dilated Cardiomyopathy. Ann Biomed Eng 2016; 45:605-618. [PMID: 27605213 PMCID: PMC5479360 DOI: 10.1007/s10439-016-1721-4] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [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: 03/09/2016] [Accepted: 08/24/2016] [Indexed: 11/29/2022]
Abstract
Patient-specific modelling has emerged as a tool for studying heart function, demonstrating the potential to provide non-invasive estimates of tissue passive stiffness. However, reliable use of model-derived stiffness requires sufficient model accuracy and unique estimation of model parameters. In this paper we present personalised models of cardiac mechanics, focusing on improving model accuracy, while ensuring unique parametrisation. The influence of principal model uncertainties on accuracy and parameter identifiability was systematically assessed in a group of patients with dilated cardiomyopathy (\documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$n=3$$\end{document}n=3) and healthy volunteers (\documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$n=5$$\end{document}n=5). For all cases, we examined three circumferentially symmetric fibre distributions and two epicardial boundary conditions. Our results demonstrated the ability of data-derived boundary conditions to improve model accuracy and highlighted the influence of the assumed fibre distribution on both model fidelity and stiffness estimates. The model personalisation pipeline—based strictly on non-invasive data—produced unique parameter estimates and satisfactory model errors for all cases, supporting the selected model assumptions. The thorough analysis performed enabled the comparison of passive parameters between volunteers and dilated cardiomyopathy patients, illustrating elevated stiffness in diseased hearts.
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Affiliation(s)
- Myrianthi Hadjicharalambous
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St. Thomas' Hospital, London, SE1 7EH, UK.
| | - Liya Asner
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St. Thomas' Hospital, London, SE1 7EH, UK
| | - Radomir Chabiniok
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St. Thomas' Hospital, London, SE1 7EH, UK.,Inria and Paris-Saclay University, Bâtiment Alan Turing, 1 rue Honoré d'Estienne d'Orves, Campus de l'Ecole Polytechnique, 91120, Palaiseau, France
| | - Eva Sammut
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St. Thomas' Hospital, London, SE1 7EH, UK
| | - James Wong
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St. Thomas' Hospital, London, SE1 7EH, UK
| | - Devis Peressutti
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St. Thomas' Hospital, London, SE1 7EH, UK
| | - Eric Kerfoot
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St. Thomas' Hospital, London, SE1 7EH, UK
| | - Andrew King
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St. Thomas' Hospital, London, SE1 7EH, UK
| | - Jack Lee
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St. Thomas' Hospital, London, SE1 7EH, UK
| | - Reza Razavi
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St. Thomas' Hospital, London, SE1 7EH, UK
| | - Nicolas Smith
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St. Thomas' Hospital, London, SE1 7EH, UK.,Department of Engineering Science, University of Auckland, 20 Symonds St, Auckland, 1010, New Zealand
| | - Gerald Carr-White
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St. Thomas' Hospital, London, SE1 7EH, UK
| | - David Nordsletten
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St. Thomas' Hospital, London, SE1 7EH, UK
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Lamata P, Sinclair M, Kerfoot E, Lee A, Crozier A, Blazevic B, Land S, Lewandowski AJ, Barber D, Niederer S, Smith N. An automatic service for the personalization of ventricular cardiac meshes. J R Soc Interface 2013; 11:20131023. [PMID: 24335562 PMCID: PMC3869175 DOI: 10.1098/rsif.2013.1023] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [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] [Indexed: 01/12/2023] Open
Abstract
Computational cardiac physiology has great potential to improve the management of cardiovascular diseases. One of the main bottlenecks in this field is the customization of the computational model to the anatomical and physiological status of the patient. We present a fully automatic service for the geometrical personalization of cardiac ventricular meshes with high-order interpolation from segmented images. The method is versatile (able to work with different species and disease conditions) and robust (fully automatic results fulfilling accuracy and quality requirements in 87% of 255 cases). Results also illustrate the capability to minimize the impact of segmentation errors, to overcome the sparse resolution of dynamic studies and to remove the sometimes unnecessary anatomical detail of papillary and trabecular structures. The smooth meshes produced can be used to simulate cardiac function, and in particular mechanics, or can be used as diagnostic descriptors of anatomical shape by cardiologists. This fully automatic service is deployed in a cloud infrastructure, and has been made available and accessible to the scientific community.
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Affiliation(s)
- Pablo Lamata
- Department of Biomedical Engineering, King's College of London, St Thomas' Hospital, , London SE1 7EH, UK
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Kerfoot E, Lamata P, Niederer S, Hose R, Spaan J, Smith N. Share and enjoy: anatomical models database--generating and sharing cardiovascular model data using web services. Med Biol Eng Comput 2013; 51:1181-90. [PMID: 23436208 DOI: 10.1007/s11517-012-1023-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2012] [Accepted: 12/19/2012] [Indexed: 11/26/2022]
Abstract
Sharing data between scientists and with clinicians in cardiac research has been facilitated significantly by the use of web technologies. The potential of this technology has meant that information sharing has been routinely promoted through databases that have encouraged stakeholder participation in communities around these services. In this paper we discuss the Anatomical Model Database (AMDB) (Gianni et al. Functional imaging and modeling of the heart. Springer, Heidelberg, 2009; Gianni et al. Phil Trans Ser A Math Phys Eng Sci 368:3039-3056, 2010) which both facilitate a database-centric approach to collaboration, and also extends this framework with new capabilities for creating new mesh data. AMDB currently stores cardiac geometric models described in Gianni et al. (Functional imaging and modelling of the heart. Springer, Heidelberg, 2009), a number of additional cardiac models describing geometry and functional properties, and most recently models generated using a web service. The functional models represent data from simulations in geometric form, such as electrophysiology or mechanics, many of which are present in AMDB as part of a benchmark study. Finally, the heartgen service has been added for producing left or bi-ventricle models derived from binary image data using the methods described in Lamata et al. (Med Image Anal 15:801-813, 2011). The results can optionally be hosted on AMDB alongside other community-provided anatomical models. AMDB is, therefore, a unique database storing geometric data (rather than abstract models or image data) combined with a powerful web service for generating new geometric models.
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Niederer SA, Kerfoot E, Benson AP, Bernabeu MO, Bernus O, Bradley C, Cherry EM, Clayton R, Fenton FH, Garny A, Heidenreich E, Land S, Maleckar M, Pathmanathan P, Plank G, Rodríguez JF, Roy I, Sachse FB, Seemann G, Skavhaug O, Smith NP. Verification of cardiac tissue electrophysiology simulators using an N-version benchmark. Philos Trans A Math Phys Eng Sci 2011; 369:4331-51. [PMID: 21969679 PMCID: PMC3263775 DOI: 10.1098/rsta.2011.0139] [Citation(s) in RCA: 142] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Ongoing developments in cardiac modelling have resulted, in particular, in the development of advanced and increasingly complex computational frameworks for simulating cardiac tissue electrophysiology. The goal of these simulations is often to represent the detailed physiology and pathologies of the heart using codes that exploit the computational potential of high-performance computing architectures. These developments have rapidly progressed the simulation capacity of cardiac virtual physiological human style models; however, they have also made it increasingly challenging to verify that a given code provides a faithful representation of the purported governing equations and corresponding solution techniques. This study provides the first cardiac tissue electrophysiology simulation benchmark to allow these codes to be verified. The benchmark was successfully evaluated on 11 simulation platforms to generate a consensus gold-standard converged solution. The benchmark definition in combination with the gold-standard solution can now be used to verify new simulation codes and numerical methods in the future.
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Affiliation(s)
- Steven A Niederer
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London, UK.
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Kerfoot E, Domeier E. Pulmonary function measurements of large animals using the capacitance respirometer. Lab Anim Sci 1972; 22:854-9. [PMID: 4345306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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