Evidence map›Paper›PMID 33939619›Full record

ArticleJMIR research protocols2021

Predicting Risk of Hospital Admission in Patients With Suspected COVID-19 in a Community Setting: Protocol for Development and Validation of a Multivariate Risk Prediction Tool.

Ana Belen Espinosa-Gonzalez, Ana Luisa Neves, Francesca Fiorentino, Denys Prociuk, Laiba Husain, Sonny Christian Ramtale, Emma Mi, Ella Mi, Jack Macartney, Sneha N Anand and 6 more

Open access · goldAbstract read
In one paragraph

Article in JMIR research protocols, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.4field-weighted citation impact, top 20% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 10 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors at 4 institutions in 2 countries.

Ana Belen Espinosa-GonzalezDepartment of Surgery and Cancer, Imperial College London, London, United Kingdom.ORCID https://orcid.org/0000-0002-8226-416X
Ana Luisa NevesPatient Safety Translational Research Centre, Institute of Global Health Innovation, Imperial College London, London, United Kingdom.ORCID https://orcid.org/0000-0002-7107-7211
Francesca FiorentinoDepartment of Surgery and Cancer, Imperial College London, London, United Kingdom.ORCID https://orcid.org/0000-0001-9817-6634
Denys ProciukDepartment of Surgery and Cancer, Imperial College London, London, United Kingdom.ORCID https://orcid.org/0000-0002-6117-1933
Laiba HusainNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.ORCID https://orcid.org/0000-0001-9889-0889
Sonny Christian RamtaleDepartment of Surgery and Cancer, Imperial College London, London, United Kingdom.ORCID https://orcid.org/0000-0002-5578-1639
Emma MiDepartment of Surgery and Cancer, Imperial College London, London, United Kingdom.ORCID https://orcid.org/0000-0001-8587-5495
Ella MiDepartment of Surgery and Cancer, Imperial College London, London, United Kingdom.ORCID https://orcid.org/0000-0002-1906-7956
Jack MacartneyNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.ORCID https://orcid.org/0000-0003-0678-2659
Sneha N AnandNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.ORCID https://orcid.org/0000-0002-0538-3974
Julian SherlockNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.ORCID https://orcid.org/0000-0001-7427-1936
Kavitha SaravanakumarWhole Systems Integrated Care, North West London Clinical Commissioning Group, London, United Kingdom.ORCID https://orcid.org/0000-0002-7045-4062
Erik MayerDepartment of Surgery and Cancer, Imperial College London, London, United Kingdom.ORCID https://orcid.org/0000-0002-5509-4580
Simon de LusignanNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.ORCID https://orcid.org/0000-0002-8553-2641
Trisha GreenhalghNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.ORCID https://orcid.org/0000-0003-2369-8088
Brendan C DelaneyDepartment of Surgery and Cancer, Imperial College London, London, United Kingdom.ORCID https://orcid.org/0000-0002-3518-0131
Imperial College London · GBUniversity of Oxford · GBLondon Clinic · GBUniversidade do Porto · PT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDuring the pandemic, remote consultations have become the norm for assessing patients with signs and symptoms of COVID-19 to decrease the risk of transmission. This has intensified the clinical uncertainty already experienced by primary care clinicians when assessing patients with suspected COVID-19 and has prompted the use of risk prediction scores, such as the National Early Warning Score (NEWS2), to assess severity and guide treatment. However, the risk prediction tools available have not been validated in a community setting and are not designed to capture the idiosyncrasies of COVID-19 infection.

objectiveThe objective of this study is to produce a multivariate risk prediction tool, RECAP-V1 (Remote COVID-19 Assessment in Primary Care), to support primary care clinicians in the identification of those patients with COVID-19 that are at higher risk of deterioration and facilitate the early escalation of their treatment with the aim of improving patient outcomes.

methodsThe study follows a prospective cohort observational design, whereby patients presenting in primary care with signs and symptoms suggestive of COVID-19 will be followed and their data linked to hospital outcomes (hospital admission and death). Data collection will be carried out by primary care clinicians in four arms: North West London Clinical Commissioning Groups (NWL CCGs), Oxford-Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC), Covid Clinical Assessment Service (CCAS), and South East London CCGs (Doctaly platform). The study involves the use of an electronic template that incorporates a list of items (known as RECAP-V0) thought to be associated with disease outcome according to previous qualitative work. Data collected will be linked to patient outcomes in highly secure environments. We will then use multivariate logistic regression analyses for model development and validation.

resultsRecruitment of participants started in October 2020. Initially, only the NWL CCGs and RCGP RSC arms were active. As of March 24, 2021, we have recruited a combined sample of 3827 participants in these two arms. CCAS and Doctaly joined the study in February 2021, with CCAS starting the recruitment process on March 15, 2021. The first part of the analysis (RECAP-V1 model development) is planned to start in April 2021 using the first half of the NWL CCGs and RCGP RSC combined data set. Posteriorly, the model will be validated with the rest of the NWL CCGs and RCGP RSC data as well as the CCAS and Doctaly data sets. The study was approved by the Research Ethics Committee on May 27, 2020 (Integrated Research Application System number: 283024, Research Ethics Committee reference number: 20/NW/0266) and badged as National Institute of Health Research Urgent Public Health Study on October 14, 2020.

conclusionsWe believe the validated RECAP-V1 early warning score will be a valuable tool for the assessment of severity in patients with suspected COVID-19 in the community, either in face-to-face or remote consultations, and will facilitate the timely escalation of treatment with the potential to improve patient outcomes.

trial registrationISRCTN registry ISRCTN13953727; https://www.isrctn.com/ISRCTN13953727. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/29072.

Indexed as

COVID-19 severityearly warning scoreelectronic health recordshospital admissionprimary carerisk prediction tool

Identifiers

PMID33939619
PMCPMC8153031
OpenAlexW3141459533

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.