Imperial College London
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DrAndreaWeisse

Faculty of Medicine, Department of Infectious Disease

Honorary Lecturer
 
 
 
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andrea.weisse

 
 
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Location

 

Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Myall:2022:10.1016/S2589-7500(22)00093-0,
author = {Myall, A and Price, J and Peach, R and Abbas, M and Mookerjee, S and Zhu, N and Ahmad, I and Ming, D and Ramzan, F and Teixeira, D and Graf, C and Weisse, A and Harbarth, S and Holmes, A and Barahona, M},
doi = {10.1016/S2589-7500(22)00093-0},
journal = {The Lancet Digital Health},
pages = {e573--e583},
title = {Predicting hospital-onset COVID-19 infections using dynamic networks of patient contact: an international retrospective cohort study},
url = {http://dx.doi.org/10.1016/S2589-7500(22)00093-0},
volume = {4},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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 51157 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; 40057 inpatients) and from the same UK group after COVID-19 surges (from April 2 to Aug 13, 2021; 43375 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&middo
AU  - Myall,A
AU  - Price,J
AU  - Peach,R
AU  - Abbas,M
AU  - Mookerjee,S
AU  - Zhu,N
AU  - Ahmad,I
AU  - Ming,D
AU  - Ramzan,F
AU  - Teixeira,D
AU  - Graf,C
AU  - Weisse,A
AU  - Harbarth,S
AU  - Holmes,A
AU  - Barahona,M
DO  - 10.1016/S2589-7500(22)00093-0
EP  - 583
PY  - 2022///
SN  - 2589-7500
SP  - 573
TI  - Predicting hospital-onset COVID-19 infections using dynamic networks of patient contact: an international retrospective cohort study
T2  - The Lancet Digital Health
UR  - http://dx.doi.org/10.1016/S2589-7500(22)00093-0
UR  - http://hdl.handle.net/10044/1/96620
VL  - 4
ER  -
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