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

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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

Original languageEnglish
Pages (from-to)e646-e656
JournalThe Lancet Digital Health
Volume4
Issue number9
Early online date23 Aug 2022
DOIs
E-pub ahead of print23 Aug 2022
Published1 Sep 2022

Bibliographical note

Funding Information: Doctaly Assist (Phil Tyler and Prad Velayuthan, Doctaly) provided the data extraction and Systematized Nomenclature of Medicine (SNOMED) coding of their chat-bot data. Mark Ashworth and Ibidun Fakoya (King's College London, London, UK), evaluated the initial set-up of the COVID monitoring programme in Southeast London. Eamon O’Doherty (Northwest London Clinical Commissioning Group, London, UK) extracted the relevant data tables from northwest London Whole Systems Integrated Care to enable the data analysis in iCare. Sneha Anand (Oxford University, Oxford, UK) managed the governance, data transfer, and linkage in ORCHID. Matt Widdows (South Central Ambulance Service NHS Trust) coordinated the development of the RECAP template with Adastra and extracted the relevant CCAS data for analysis. Merlin Dunlop (Ardens, Oxford, UK) assisted in the SNOMED coding of the RECAP-V0 templates and their distribution. Ashnee Dhondee (northwest London Clinical Research Network, London, UK) helped recruit practices to the study. Patients and practices of the Royal College of General Practitioners Research and Surveillance Centre and other networks shared data for this research. All those acknowledged have seen a copy of the final draft paper. The study was funded by the Community Jameel and the Imperial College President's Excellence Fund, the Economic and Social Research Council, the UK Research and Innovation, and Health Data Research UK. The authors gratefully acknowledge infrastructure support from the National Institute for Health and Care Research (NIHR) Imperial Patient Safety Translational Research Centre, the NIHR Imperial Biomedical Research Centre and the NIHR Oxford Biomedical Research Centre. This research was in part enabled by the Imperial Clinical Analytics Research and Evaluation (iCARE) environment and Whole System Integrated Care and used the iCARE and WSIC team and data resources. The project was supported by the NIHR CRN Urgent Public Health Study. Funding Information: Doctaly Assist (Phil Tyler and Prad Velayuthan, Doctaly) provided the data extraction and Systematized Nomenclature of Medicine (SNOMED) coding of their chat-bot data. Mark Ashworth and Ibidun Fakoya (King's College London, London, UK), evaluated the initial set-up of the COVID monitoring programme in Southeast London. Eamon O'Doherty (Northwest London Clinical Commissioning Group, London, UK) extracted the relevant data tables from northwest London Whole Systems Integrated Care to enable the data analysis in iCare. Sneha Anand (Oxford University, Oxford, UK) managed the governance, data transfer, and linkage in ORCHID. Matt Widdows (South Central Ambulance Service NHS Trust) coordinated the development of the RECAP template with Adastra and extracted the relevant CCAS data for analysis. Merlin Dunlop (Ardens, Oxford, UK) assisted in the SNOMED coding of the RECAP-V0 templates and their distribution. Ashnee Dhondee (northwest London Clinical Research Network, London, UK) helped recruit practices to the study. Patients and practices of the Royal College of General Practitioners Research and Surveillance Centre and other networks shared data for this research. All those acknowledged have seen a copy of the final draft paper. The study was funded by the Community Jameel and the Imperial College President's Excellence Fund, the Economic and Social Research Council, the UK Research and Innovation, and Health Data Research UK. The authors gratefully acknowledge infrastructure support from the National Institute for Health and Care Research (NIHR) Imperial Patient Safety Translational Research Centre, the NIHR Imperial Biomedical Research Centre and the NIHR Oxford Biomedical Research Centre. This research was in part enabled by the Imperial Clinical Analytics Research and Evaluation (iCARE) environment and Whole System Integrated Care and used the iCARE and WSIC team and data resources. The project was supported by the NIHR CRN Urgent Public Health Study. Publisher Copyright: © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license

King's Authors

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.

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