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Risk Prediction for Poor Outcome and Death in Hospital In-Patients with COVID-19: Derivation in Wuhan, China and External Validation in London, UK

27 Pages Posted: 4 Jun 2020

See all articles by Huayu Zhang

Huayu Zhang

University of Edinburgh - Centre for Medical Informatics

Ting Shi

University of Edinburgh - Centre for Global Health

Xiaodong Wu

Tongji University - Department of Respiratory and Critical Care Medicine

Xin Zhang

People's Liberation Army Joint Logistic Support Force 920th Hospital - Department of Pulmonary and Critical Care Medicine

Kun Wang

Tongji University - Department of Pulmonary and Critical Care Medicine

Daniel Bean

University of London - Faculty of Psychiatry, Psychology & Neuroscience - Department of Biostatistics and Health Informatics

Richard Dobson

University of London - Faculty of Psychiatry, Psychology & Neuroscience - Department of Biostatistics and Health Informatics

James T. Teo

King’s College Hospital, Department of Neurosciences

Jiaxing Sun

Tongji University - Department of Pulmonary and Critical Care Medicine

Pei Zhao

Tongji University - Department of Pulmonary and Critical Care Medicine

Chenghong Li

Jianghan University - Department of Pulmonary and Critical Care Medicine

Kevin Dhaliwal

University of Edinburgh - Queen’s Medical Research Institute - Centre for Inflammation Research

Honghan Wu

Nanjing University of Information Science & Technology - School of Computer and Software

Qiang Li

Tongji University - Department of Respiratory and Critical Care Medicine

Bruce Guthrie

University of Edinburgh

More...

Abstract

Background: Accurate risk prediction of clinical outcome would usefully inform clinical decisions and intervention targeting in COVID-19. The aim of this study was to derive and validate risk prediction models for poor outcome and death in adult inpatients with COVID-19.  

Methods: Model derivation using data from Wuhan, China used logistic regression with death and poor outcome (death or severe disease) as outcomes. Predictors were demographic, comorbidity, symptom and laboratory test variables. The best performing models were externally validated in data from London, UK.  

Findings: 4.3% of the derivation cohort (n=775) died and 9.7% had a poor outcome, compared to 34.1% and 42.9% of the validation cohort (n=226). In derivation, prediction models based on age, sex, neutrophil count, lymphocyte count, platelet count, C-reactive protein and creatinine had excellent discrimination (death c-index=0.91, poor outcome c-index=0.88), with good-to-excellent calibration. Using two cut-offs to define low, high and very-high risk groups, derivation patients were stratified in groups with observed death rates of 0.34%, 15.0% and 28.3% and poor outcome rates 0.63%, 8.9% and 58.5%. External validation discrimination was good (c-index death=0.74, poor outcome=0.72) as was calibration. However, observed rates of death were 16.5%, 42.9% and 58.4% and poor outcome 26.3%, 28.4% and 64.8% in predicted low, high and very-high risk groups.  

Interpretation: Our prediction model using demography and routinely-available laboratory tests performed very well in internal validation in the lower-risk derivation population, but less well in the much higher-risk external validation population. Further external validation is needed. Collaboration to create larger derivation datasets, and to rapidly externally validate all proposed prediction models in a range of populations is needed, before routine implementation of any risk prediction tool in clinical care.  

Funding Statement: HW and HZ are supported by Medical Research Council and Health Data Research UK Grant (MR/S004149/1), Industrial Strategy Challenge Grant (MC_PC_18029) and Wellcome Institutional Translation Partnership Award (PIII054). RD is supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. DMB is funded by a UKRI Innovation Fellowship as part of Health Data Research UK MR/S00310X/1 (https://www.hdruk.ac.uk). KD is supported by LifeArc STOPCOVID award. This work uses data provided by patients and collected by the NHS as part of their care and support. XW is supported by National Natural Science Foundation of China (grant number:81700006). QL is supported by National Key R&D Program (2018YFC1313700), National Natural Science Foundation of China (grant number: 81870064) and the “Gaoyuan” project of Pudong Health and Family Planning Commission (PWYgy2018-06).

Declaration of Interests: The authors declare no competing interests.

Ethics Approval Statement: The derivation study was approved by the Research Ethics Committee of Shanghai Dongfang Hospital and Taikang Tongji Hospital. The external validation study operated under London South East Research Ethics Committee (reference 18/LO/2048) approval granted to the King’s Electronic Records Research Interface (KERRI).

Keywords: COVID-19; SARS-CoV-2; prognosis; Risk Prediction Model; External Validation; China; United Kingdom

Suggested Citation

Zhang, Huayu and Shi, Ting and Wu, Xiaodong and Zhang, Xin and Wang, Kun and Bean, Daniel and Dobson, Richard and Teo, James T. and Sun, Jiaxing and Zhao, Pei and Li, Chenghong and Dhaliwal, Kevin and Wu, Honghan and Li, Qiang and Guthrie, Bruce, Risk Prediction for Poor Outcome and Death in Hospital In-Patients with COVID-19: Derivation in Wuhan, China and External Validation in London, UK (4/27/2020). Available at SSRN: https://ssrn.com/abstract=3590468 or http://dx.doi.org/10.2139/ssrn.3590468

Huayu Zhang

University of Edinburgh - Centre for Medical Informatics

Old College
South Bridge
Edinburgh, Scotland EH8 9JY
United Kingdom

Ting Shi

University of Edinburgh - Centre for Global Health

Old College
South Bridge
Edinburgh, Scotland EH8 9JY
United Kingdom

Xiaodong Wu

Tongji University - Department of Respiratory and Critical Care Medicine

Shanghai
China

Xin Zhang

People's Liberation Army Joint Logistic Support Force 920th Hospital - Department of Pulmonary and Critical Care Medicine

Kunming
China

Kun Wang

Tongji University - Department of Pulmonary and Critical Care Medicine

Shanghai
China

Daniel Bean

University of London - Faculty of Psychiatry, Psychology & Neuroscience - Department of Biostatistics and Health Informatics

London, SE5 8AF
United Kingdom

Richard Dobson

University of London - Faculty of Psychiatry, Psychology & Neuroscience - Department of Biostatistics and Health Informatics

London, SE5 8AF
United Kingdom

James T. Teo

King’s College Hospital, Department of Neurosciences

London
United Kingdom

Jiaxing Sun

Tongji University - Department of Pulmonary and Critical Care Medicine

Shanghai
China

Pei Zhao

Tongji University - Department of Pulmonary and Critical Care Medicine

Shanghai
China

Chenghong Li

Jianghan University - Department of Pulmonary and Critical Care Medicine

Wuhan
China

Kevin Dhaliwal

University of Edinburgh - Queen’s Medical Research Institute - Centre for Inflammation Research

Edinburgh
United Kingdom

Honghan Wu

Nanjing University of Information Science & Technology - School of Computer and Software

No.219, Ningliu Road
Nanjing, Jiangsu 21004
China

Qiang Li

Tongji University - Department of Respiratory and Critical Care Medicine ( email )

Shanghai
China

Bruce Guthrie (Contact Author)

University of Edinburgh ( email )

Old College
South Bridge
Edinburgh, Scotland EH8 9JY
United Kingdom
+44 7948 267 273 (Phone)

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