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dc.contributor.authorEspinosa-Gonzalez, Ana
dc.contributor.authorProciuk, Denys
dc.contributor.authorFiorentino, Francesca
dc.contributor.authorRamtale, Christian
dc.contributor.authorMi, Ella
dc.contributor.authorGlampson, Ben
dc.contributor.authorNeves, Ana Luisa
dc.contributor.authorOkusi, Cecilia
dc.contributor.authorHusain, Laiba
dc.contributor.authorMacartney, Jack
dc.contributor.authorBrown, Martina
dc.contributor.authorBrowne, Ben
dc.contributor.authorWarren, Caroline
dc.contributor.authorChowla, Rachna
dc.contributor.authorHeaversedge, Jonty
dc.contributor.authorGreenhalgh, Trisha
dc.contributor.authorde Lusignan, Simon
dc.contributor.authorMayer, Erik
dc.contributor.authorDelaney, Brendan C
dc.date.accessioned2022-08-25T14:45:08Z
dc.date.available2022-08-25T14:45:08Z
dc.identifier.citationEspinosa-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. Remote COVID-19 Assessment in Primary Care (RECAP) risk prediction tool: derivation and real-world validation studies. Lancet Digit Health. 2022 Jul 28;4(9):e646–56. doi: 10.1016/S2589-7500(22)00123-6. Epub ahead of print. PMID: 35909058; PMCID: PMC9333950en_US
dc.identifier.other10.1016/S2589-7500(22)00123-6
dc.identifier.urihttp://hdl.handle.net/20.500.12904/15738
dc.descriptionThis article relates to a research study that included patients or members of the workforce as study participants from GP practices in Nottingham and Nottinghamshire.en_US
dc.description.abstractBackground: 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.
dc.titleRemote COVID-19 Assessment in Primary Care (RECAP) risk prediction tool: derivation and real-world validation studiesen_US
dc.typeArticleen_US
rioxxterms.funderDefault funderen_US
rioxxterms.identifier.projectDefault projecten_US
rioxxterms.versionNAen_US
rioxxterms.typeJournal Article/Reviewen_US
refterms.dateFOA2022-08-25T14:45:08Z
refterms.panelUnspecifieden_US
refterms.dateFirstOnline2022-07
html.description.abstractBackground: 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.en_US
rioxxterms.funder.project94a427429a5bcfef7dd04c33360d80cden_US


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