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An early warning risk prediction tool (RECAP-V1) for patients diagnosed with COVID-19: the protocol for a statistical analysis plan

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Title: An early warning risk prediction tool (RECAP-V1) for patients diagnosed with COVID-19: the protocol for a statistical analysis plan
Authors: Fiorentino, F
Prociuk, D
Espinosa Gonzalez, AB
Neves, AL
Husain, L
Ramtale, S
Mi, E
Mi, E
Macartney, J
Anand, S
Sherlock, J
Saravanakumar, K
Mayer, E
De Lusignan, S
Greenhalgh, T
Delaney, B
Item Type: Journal Article
Abstract: Background: Since the start of the Covid-19 pandemic efforts have been made to develop early warning risk scores to help clinicians decide which patient is likely to deteriorate and require hospitalisation. The RECAP (Remote COVID Assessment in Primary Care) study investigates the predictive risk of hospitalisation, deterioration, and death of patients with confirmed COVID-19, based on a set of parameters chosen through a Delphi process done by clinicians. The study aims to use rich data collected remotely through the use of electronic data templates integrated in the electronic health systems of a number of general practices across the UK to construct accurate predictive models that will use pre-existing conditions and monitoring data of a patient’s clinical parameters such as blood oxygen saturation to make reliable predictions as to the patient’s risk of hospital admission, deterioration, and death. Objective: We outline the statistical methods to build the prediction model to be used in the prioritisation of patients in the primary care setting. The statistical analysis plan for the RECAP study includes as primary outcome the development and validation of the RECAP-V1 prediction model. Such prediction model will be adapted as a three-category risk score split into red (high risk), amber (medium risk), and green (low risk) for any patient with suspected covid-19. The model will predict risk of deterioration, hospitalisation, and death. Methods: After the data has been collected, we will assess the degree of missingness and use a combination of traditional data imputation using multiple imputation by chained equations, as well as more novel machine learning approaches to impute the missing data for the final analysis. For predictive model development we will use multiple logistic regressions to construct the model on a training dataset, as well as validating the model on an independent dataset. The model will also be applied for multiple different datasets to assess both its performance in different patient groups, and applicability for different methods of data collection. Results: As of 5th of May 2021 we have recruited 2280 patients for the main dataset for model development, as well as a further 1741 patients for the validation dataset. Final analysis will commence as soon as data for 2880 are collected. Conclusions: We believe that the methodology for the development of the RECAP V1 prediction model as well as the risk score will provide clinicians with a statistically robust tool to help prioritise Covid-19 patients. Clinical Trial: Trial registration number: NCT04435041
Issue Date: 5-Oct-2021
Date of Acceptance: 5-Jul-2021
URI: http://hdl.handle.net/10044/1/90255
DOI: 10.2196/30083
ISSN: 1929-0748
Publisher: JMIR Publications
Journal / Book Title: JMIR Research Protocols
Volume: 10
Issue: 10
Copyright Statement: ©Francesca Fiorentino, Denys Prociuk, Ana Belen Espinosa Gonzalez, Ana Luisa Neves, Laiba Husain, Sonny Christian Ramtale,Emma Mi, Ella Mi, Jack Macartney, Sneha N Anand, Julian Sherlock, Kavitha Saravanakumar, Erik Mayer, Simon de Lusignan,Trisha Greenhalgh, Brendan C Delaney. Originally published in JMIR Research Protocols (https://www.researchprotocols.org),05.10.2021. This is an open-access article distributed under the terms of the Creative Commons Attribution License(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on https://www.researchprotocols.org, as well as this copyright and license information must beincluded.
Sponsor/Funder: Imperial College Healthcare NHS Trust- BRC Funding
National Institute for Health Research
Cancer Research UK
Imperial College Healthcare NHS Trust
Imperial College Healthcare NHS Trust- BRC Funding
The Health Foundation
Imperial College Healthcare NHS Trust- BRC Funding
Funder's Grant Number: RDB04
n/a
25310
FR775
RDF01
452981
RDF01
Keywords: COVID-19
early warning
modeling
remote assessment
risk score
1103 Clinical Sciences
1117 Public Health and Health Services
Publication Status: Published
Article Number: ARTN e30083
Online Publication Date: 2021-07-05
Appears in Collections:Department of Surgery and Cancer
Faculty of Medicine
Institute of Global Health Innovation
Imperial College London COVID-19



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