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Predicting hospital-onset COVID-19 infections using dynamic networks of patient contact: an international retrospective cohort study

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Title: Predicting hospital-onset COVID-19 infections using dynamic networks of patient contact: an international retrospective cohort study
Authors: Myall, A
Price, J
Peach, R
Abbas, M
Mookerjee, S
Zhu, N
Ahmad, I
Ming, D
Ramzan, F
Teixeira, D
Graf, C
Weisse, A
Harbarth, S
Holmes, A
Barahona, M
Item Type: Journal Article
Abstract: Background: Real-time prediction is key to prevention and control of infections associated with health-care settings. Contacts enable spread of many infections, yet most risk prediction frameworks fail to account for their dynamics. We developed, tested, and internationally validated a real-time machine-learning framework, incorporating dynamic patient-contact networks to predict hospital-onset COVID-19 infections (HOCIs) at the individual level. Methods: We report an international retrospective cohort study of our framework, which extracted patient-contact networks from routine hospital data and combined network-derived variables with clinical and contextual information to predict individual infection risk. We trained and tested the framework on HOCIs using the data from 51 157 hospital inpatients admitted to a UK National Health Service hospital group (Imperial College Healthcare NHS Trust) between April 1, 2020, and April 1, 2021, intersecting the first two COVID-19 surges. We validated the framework using data from a Swiss hospital group (Department of Rehabilitation, Geneva University Hospitals) during a COVID-19 surge (from March 1 to May 31, 2020; 40 057 inpatients) and from the same UK group after COVID-19 surges (from April 2 to Aug 13, 2021; 43 375 inpatients). All inpatients with a bed allocation during the study periods were included in the computation of network-derived and contextual variables. In predicting patient-level HOCI risk, only inpatients spending 3 or more days in hospital during the study period were examined for HOCI acquisition risk. Findings: The framework was highly predictive across test data with all variable types (area under the curve [AUC]-receiver operating characteristic curve [ROC] 0·89 [95% CI 0·88–0·90]) and similarly predictive using only contact-network variables (0·88 [0·86–0·90]). Prediction was reduced when using only hospital contextual (AUC-ROC 0·82 [95% CI 0·80–0·84]) or patient clinical (0·64 [0·62–0·66]) variables. A model with only three variables (ie, network closeness, direct contacts with infectious patients [network derived], and hospital COVID-19 prevalence [hospital contextual]) achieved AUC-ROC 0·85 (95% CI 0·82–0·88). Incorporating contact-network variables improved performance across both validation datasets (AUC-ROC in the Geneva dataset increased from 0·84 [95% CI 0·82–0·86] to 0·88 [0·86–0·90]; AUC-ROC in the UK post-surge dataset increased from 0·49 [0·46–0·52] to 0·68 [0·64–0·70]). Interpretation: Dynamic contact networks are robust predictors of individual patient risk of HOCIs. Their integration in clinical care could enhance individualised infection prevention and early diagnosis of COVID-19 and other nosocomial infections. Funding: Medical Research Foundation, WHO, Engineering and Physical Sciences Research Council, National Institute for Health Research (NIHR), Swiss National Science Foundation, and German Research Foundation.
Issue Date: 1-Aug-2022
Date of Acceptance: 25-Apr-2022
URI: http://hdl.handle.net/10044/1/96620
DOI: 10.1016/S2589-7500(22)00093-0
ISSN: 2589-7500
Publisher: Elsevier
Start Page: e573
End Page: e583
Journal / Book Title: The Lancet Digital Health
Volume: 4
Issue: 8
Copyright Statement: © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/)
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
Centers for Disease Control and Prevention
World Health Organization
Funder's Grant Number: EP/N014529/1
Keywords: Science & Technology
Life Sciences & Biomedicine
Medical Informatics
Medicine, General & Internal
General & Internal Medicine
Publication Status: Published
Online Publication Date: 2022-07-19
Appears in Collections:Department of Infectious Diseases
Applied Mathematics and Mathematical Physics
Faculty of Medicine
Imperial College London COVID-19
School of Public Health
Department of Brain Sciences
Faculty of Natural Sciences

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