Remote COVID-19 assessment in primary care (RECAP) risk prediction tool: derivation and real-world validation studies

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Title: Remote COVID-19 assessment in primary care (RECAP) risk prediction tool: derivation and real-world validation studies
Authors: Espinosa-Gonzalez, A
Prociuk, D
Fiorentino, F
Ramtale, C
Mi, E
Mi, E
Glampson, B
Neves, AL
Okusi, C
Husain, L
Macartney, J
Brown, M
Browne, B
Warren, C
Chowla, R
Heaversedge, J
Greenhalgh, T
De Lusignan, S
Mayer, E
Delaney, BC
Item Type: Journal Article
Abstract: BACKGROUND: Accurate assessment of COVID-19 severity in the community is essential for patient care and requires COVID-19-specific risk prediction scores adequately validated in a community setting. Following a qualitative phase to identify signs, symptoms, and risk factors, we aimed to develop and validate two COVID-19-specific risk prediction scores. Remote COVID-19 Assessment in Primary Care-General Practice score (RECAP-GP; without peripheral oxygen saturation [SpO2]) and RECAP-oxygen saturation score (RECAP-O2; with SpO2). METHODS: RECAP was a prospective cohort study that used multivariable logistic regression. Data on signs and symptoms (predictors) of disease were collected from community-based patients with suspected COVID-19 via primary care electronic health records and linked with secondary data on hospital admission (outcome) within 28 days of symptom onset. Data sources for RECAP-GP were Oxford-Royal College of General Practitioners Research and Surveillance Centre (RCGP-RSC) primary care practices (development set), northwest London primary care practices (validation set), and the NHS COVID-19 Clinical Assessment Service (CCAS; validation set). The data source for RECAP-O2 was the Doctaly Assist platform (development set and validation set in subsequent sample). The two probabilistic risk prediction models were built by backwards elimination using the development sets and validated by application to the validation datasets. Estimated sample size per model, including the development and validation sets was 2880 people. FINDINGS: Data were available from 8311 individuals. Observations, such as SpO2, were mostly missing in the northwest London, RCGP-RSC, and CCAS data; however, SpO2 was available for 1364 (70·0%) of 1948 patients who used Doctaly. In the final predictive models, RECAP-GP (n=1863) included sex (male and female), age (years), degree of breathlessness (three point scale), temperature symptoms (two point scale), and presence of hypertension (yes or no); the area under the curve was 0·80 (95% CI 0·76-0·85) and on validation the negative predictive value of a low risk designation was 99% (95% CI 98·1-99·2; 1435 of 1453). RECAP-O2 included age (years), degree of breathlessness (two point scale), fatigue (two point scale), and SpO2 at rest (as a percentage); the area under the curve was 0·84 (0·78-0·90) and on validation the negative predictive value of low risk designation was 99% (95% CI 98·9-99·7; 1176 of 1183). INTERPRETATION: Both RECAP models are valid tools to assess COVID-19 patients in the community. RECAP-GP can be used initially, without need for observations, to identify patients who require monitoring. If the patient is monitored and SpO2 is available, RECAP-O2 is useful to assess the need for treatment escalation. FUNDING: Community Jameel and the Imperial College President's Excellence Fund, the Economic and Social Research Council, UK Research and Innovation, and Health Data Research UK.
Issue Date: Sep-2022
Date of Acceptance: 15-Jun-2022
URI: http://hdl.handle.net/10044/1/98943
DOI: 10.1016/S2589-7500(22)00123-6
ISSN: 2589-7500
Publisher: Elsevier
Start Page: e646
End Page: e656
Journal / Book Title: The Lancet Digital Health
Volume: 4
Issue: 9
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: Imperial College Healthcare NHS Trust- BRC Funding
National Institute for Health Research
Cancer Research UK
National Institute for Health Research
Imperial College Healthcare NHS Trust
Imperial College Healthcare NHS Trust
Imperial College Healthcare NHS Trust- BRC Funding
Imperial College Healthcare NHS Trust- BRC Funding
Health Data Research UK
NHS North West London CCG
Funder's Grant Number: RDB04
RDE07 79560
25310
RDF03
FR775
FR775
RDF01
RDF01
HDRUK2021.0175
XXKSARAVANAKUMAR
Keywords: COVID-19
Dyspnea
Female
Humans
Male
Primary Health Care
Prospective Studies
Risk Factors
Humans
Dyspnea
Risk Factors
Prospective Studies
Primary Health Care
Female
Male
COVID-19
Publication Status: Published
Conference Place: England
Online Publication Date: 2022-07-28
Appears in Collections:Department of Surgery and Cancer
Faculty of Medicine
Institute of Global Health Innovation



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