Remote COVID-19 Assessment in Primary Care (RECAP) risk prediction tool: derivation and real-world validation studies

Ana Espinosa-Gonzalez, Denys Prociuk, Francesca Fiorentino, Christian Ramtale, Ella Mi, Emma Mi, Ben Glampson, Ana Luisa Neves, Cecilia Okusi, Laiba Husain, Jack Macartney, Martina Brown, Ben Browne, Caroline Warren, Rachna Chowla, Jonty Heaversedge, Trisha Greenhalgh, Simon De Lusignan, Erik Mayer, Brendan C Delaney

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)e646-e656
JournalThe Lancet Digital Health
Volume4
Issue number9
Early online date23 Aug 2022
DOIs
Publication statusPublished - 1 Sept 2022

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