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

Autor:	Espinosa-Gonzalez A; Institute of Global Health Innovation, Department of Surgery and Cancer, Imperial College London, London, UK., Prociuk D; Institute of Global Health Innovation, Department of Surgery and Cancer, Imperial College London, London, UK., Fiorentino F; Institute of Global Health Innovation, Department of Surgery and Cancer, Imperial College London, London, UK; Nightingale-Saunders Clinical Trials & Epidemiology Unit, King's Clinical Trials Unit, King's College London, London, UK., Ramtale C; Institute of Global Health Innovation, Department of Surgery and Cancer, Imperial College London, London, UK., Mi E; Institute of Global Health Innovation, Department of Surgery and Cancer, Imperial College London, London, UK., Mi E; Institute of Global Health Innovation, Department of Surgery and Cancer, Imperial College London, London, UK., Glampson B; Department of Surgery and Cancer, Imperial College Healthcare NHS Trust, London, UK., Neves AL; Institute of Global Health Innovation, Department of Surgery and Cancer, Imperial College London, London, UK., Okusi C; Nuffield Department of Primary Care, University of Oxford, Oxford, UK., Husain L; Nuffield Department of Primary Care, University of Oxford, Oxford, UK., Macartney J; Nuffield Department of Primary Care, University of Oxford, Oxford, UK., Brown M; South Central Ambulance Service NHS Trust, Otterboure, UK., Browne B; South Central Ambulance Service NHS Trust, Otterboure, UK., Warren C; South Central Ambulance Service NHS Trust, Otterboure, UK., Chowla R; King's Health Partners, London, UK., Heaversedge J; South East London NHS Clinical Commissioning Group, London, UK., Greenhalgh T; Nuffield Department of Primary Care, University of Oxford, Oxford, UK., de Lusignan S; Nuffield Department of Primary Care, University of Oxford, Oxford, UK., Mayer E; Institute of Global Health Innovation, Department of Surgery and Cancer, Imperial College London, London, UK., Delaney BC; Institute of Global Health Innovation, Department of Surgery and Cancer, Imperial College London, London, UK. Electronic address: brendan.delaney@imperial.ac.uk.
Jazyk:	angličtina
Zdroj:	The Lancet. Digital health [Lancet Digit Health] 2022 Sep; Vol. 4 (9), pp. e646-e656. Date of Electronic Publication: 2022 Jul 28.
DOI:	10.1016/S2589-7500(22)00123-6
Abstrakt:	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 [SpO 2 ]) and RECAP-oxygen saturation score (RECAP-O2; with SpO 2 ). 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 SpO 2 , were mostly missing in the northwest London, RCGP-RSC, and CCAS data; however, SpO 2 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 SpO 2 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 SpO 2 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. Competing Interests: Declaration of interests SdL is Director of the Royal College of General Practitioners Research and Surveillance Centre, the English primary care sentinel system. This work funds part of his academic position, and the network and its Trusted Research Environment were part of this study, funded using the university standard cost template. SdL reports grants through his University from AstraZeneca, Eli Lilly, GSK, Sanofi, Seqirus, and Takeda, none have a direct link to this study and has been a member of Advisory Boards for AstraZeneca, Sanofi, and Seqirus, none have a direct link to this study. All other authors declare no competing of interests. (Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)
Databáze:	MEDLINE
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