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Citation

BibTex format

@article{Fiorentino:2021:10.2196/30083,
author = {Fiorentino, F and Prociuk, D and Espinosa, Gonzalez AB and Neves, AL and Husain, L and Ramtale, S and Mi, E and Mi, E and Macartney, J and Anand, S and Sherlock, J and Saravanakumar, K and Mayer, E and de, Lusignan S and Greenhalgh, T and Delaney, B},
doi = {10.2196/30083},
journal = {JMIR Research Protocols},
title = {An early warning risk prediction tool (RECAP-V1)  for patients diagnosed with COVID-19: the protocol for a statistical analysis plan},
url = {http://dx.doi.org/10.2196/30083},
volume = {10},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Background:Since the start of the Covid-19 pandemic efforts have been made to develop early warning risk scores to help clinicians decide which patient is likely to deteriorate and require hospitalisation. The RECAP (Remote COVID Assessment in Primary Care) study investigates the predictive risk of hospitalisation, deterioration, and death of patients with confirmed COVID-19, based on a set of parameters chosen through a Delphi process done by clinicians. The study aims to use rich data collected remotely through the use of electronic data templates integrated in the electronic health systems of a number of general practices across the UK to construct accurate predictive models that will use pre-existing conditions and monitoring data of a patient’s clinical parameters such as blood oxygen saturation to make reliable predictions as to the patient’s risk of hospital admission, deterioration, and death.Objective:We outline the statistical methods to build the prediction model to be used in the prioritisation of patients in the primary care setting. The statistical analysis plan for the RECAP study includes as primary outcome the development and validation of the RECAP-V1 prediction model. Such prediction model will be adapted as a three-category risk score split into red (high risk), amber (medium risk), and green (low risk) for any patient with suspected covid-19. The model will predict risk of deterioration, hospitalisation, and death.Methods:After the data has been collected, we will assess the degree of missingness and use a combination of traditional data imputation using multiple imputation by chained equations, as well as more novel machine learning approaches to impute the missing data for the final analysis. For predictive model development we will use multiple logistic regressions to construct the model on a training dataset, as well as validating the model on an independent dataset. The model will also be applied for multiple different datasets
AU  - Fiorentino,F
AU  - Prociuk,D
AU  - Espinosa,Gonzalez AB
AU  - Neves,AL
AU  - Husain,L
AU  - Ramtale,S
AU  - Mi,E
AU  - Mi,E
AU  - Macartney,J
AU  - Anand,S
AU  - Sherlock,J
AU  - Saravanakumar,K
AU  - Mayer,E
AU  - de,Lusignan S
AU  - Greenhalgh,T
AU  - Delaney,B
DO  - 10.2196/30083
PY  - 2021///
SN  - 1929-0748
TI  - An early warning risk prediction tool (RECAP-V1)  for patients diagnosed with COVID-19: the protocol for a statistical analysis plan
T2  - JMIR Research Protocols
UR  - http://dx.doi.org/10.2196/30083
UR  - https://preprints.jmir.org/preprint/30083
UR  - http://hdl.handle.net/10044/1/90255
VL  - 10
ER  -
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