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

File Description SizeFormat 
Lancet Digital Health manuscript_second_revision_clean.docxWorking Paper50.99 kBMicrosoft WordView/Open
Title: Remote covid 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
Hussain, L
Macartney, J
Brown, M
Browne, B
Warren, C
Chowla, R
Heaversedge, J
Greenhalgh, T
De Lusignan, S
Mayer, E
Delaney, B
Item Type: Working Paper
Abstract: <jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>Accurate assessment of COVID-19 severity in the community is essential for best patient care and efficient use of services and requires a risk prediction score that is COVID-19 specific and adequately validated in a community setting. Following a qualitative phase to identify signs, symptoms and risk factors, we sought to develop and validate two COVID-19-specific risk prediction scores RECAP-GP (without peripheral oxygen saturation (SpO2)) and RECAP-O2 (with SpO2).</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>Prospective cohort study using multivariable logistic regression for model development. Data on signs and symptoms (model predictors) were collected on community-based patients with suspected COVID-19 via primary care electronic health records systems and linked with secondary data on hospital admission (primary outcome) within 28 days of symptom onset. Data sources: RECAP-GP: Oxford-Royal College of General Practitioners Research and Surveillance Centre (RSC) primary care practices (development), Northwest London (NWL) primary care practices, NHS COVID-19 Clinical Assessment Service (CCAS) (validation). RECAP-O2: Doctaly Assist platform (development, and validation in subsequent sample). Estimated sample size was 2,880 per model.</jats:p></jats:sec><jats:sec><jats:title>Findings</jats:title><jats:p>Data were available from 8,311 individuals. Observations, such SpO2, were mostly missing in NWL, RSC, and CCAS data; however, SpO2 was available for around 70% of Doctaly patients. In the final predictive models, RECAP-GP included sex, age, degree of breathlessness, temperature symptoms, and presence of hypertension (Area Under the Curve (AUC): 0.802, Validation Negative Predictive Value (NPV) of ‘low risk’ 98.8%. RECAP-O2 included age, degree of breathlessness, fatigue, and SpO2 at rest (AUC: 0.843), Validation NPV of ‘low risk’ 99.4%.</jats:p></jats:sec><jats:sec><jats:title>Interpretation</jats:title><jats:p>Both RECAP models are a valid tool in the assessment of 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 at home and SpO2 is available, RECAP-O2 is useful to assess the need for further treatment escalation.</jats:p></jats:sec><jats:sec><jats:title>Research in context panel</jats:title><jats:sec><jats:title>Evidence before the study</jats:title><jats:p>This study was conceived during the first COVID-19 wave in the UK (March - April 2020), as members of the research team contributed to the development of national clinical guidelines for COVID-19 management in the community and to the Oxford COVID-19 rapid review to track signs and symptoms of COVID-19 internationally. The review was carried out according to Cochrane Collaboration standards for rapid reviews and identified systematic reviews and large-scale observational studies describing the signs and symptoms of COVID-19. Evidence gathered showed worsening of COVID-19 symptoms around the 7th day of disease and challenges in identifying patients with higher likelihood of severity to increase their monitoring. To this end, tools such NEWS2 have been used in the UK to assess COVID-19 patients in primary care, but they do not capture the characteristics of COVID-19 infection and/or are not suitable for community remote assessment. Several COVID-19 risk scores have been developed. QCOVID provides a risk of mortality considering patients’ existing risk factors but does not include acute signs and symptoms. ISARIC 4C Deterioration model has been specifically developed for hospital settings. In England, the NHS has implemented the Oximetry @home strategy to monitor patients with acute COVID-19 deemed at risk (older than 64 years old or with comorbidities) by providing pulse oximeters; however, the criteria for monitoring or for escalation of care have not been validated. There is, therefore, the need to develop a risk prediction score to establish COVID-19 patients’ risk of deterioration to be used in the community for both face to face or remote consultation.</jats:p></jats:sec><jats:sec><jats:title>Added value of this study</jats:title><jats:p>We developed and validated two COVID-19 specific risk prediction scores. One to be used in the initial remote assessment of patients with acute COVID-19 to assess need for monitoring (RECAP-GP). The second one to assess the need for further treatment escalation and includes peripheral saturation of oxygen among the model predictors (RECAP-O2). To our knowledge, this is the first COVID-19 specific risk prediction score to assess and monitor COVID-19 patients’ risk of deterioration remotely. This will be a valuable resource to complement the use of oximetry in the community clinical decision-making when assessing a patient with acute COVID-19.</jats:p></jats:sec><jats:sec><jats:title>Implications of all available evidence</jats:title><jats:p>To manage pandemic waves and their demand on healthcare, acute COVID-19 patients require close monitoring in the community and prompt escalation of their treatment. Guidance available so far relies on unvalidated tools and clinician judgement to assess deterioration. COVID-19 specific community-based risk prediction scores such as RECAP may contribute to reducing the uncertainty in the assessment and monitoring of COVID-19 patients, increase safety in clinical practice and improve outcomes by facilitating appropriate treatment escalation.</jats:p></jats:sec></jats:sec>
Issue Date: 15-Jun-2022
DOI: 10.1101/2021.12.23.21268279
ISSN: 2589-7500
Publisher: MedRxiv
Copyright Statement: ©2022 The Author(s)
Sponsor/Funder: Imperial College Healthcare NHS Trust- BRC Funding
National Institute for Health Research
National Institute for Health Research
Imperial College Healthcare NHS Trust- BRC Funding
NHS North West London CCG
Funder's Grant Number: RDB04
Publication Status: Published
Appears in Collections:Department of Surgery and Cancer
Institute of Global Health Innovation