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, SC and Mi, E and Mi, E and Macartney, J and Anand, SN and Sherlock, J and Saravanakumar, K and Mayer, E and de, Lusignan S and Greenhalgh, T and Delaney, BC},
doi = {10.2196/30083},
journal = {JMIR Research Protocols},
pages = {e30083--e30083},
title = {An Early Warning Risk Prediction Tool (RECAP-V1) for Patients Diagnosed With COVID-19: Protocol for a Statistical Analysis Plan},
url = {http://dx.doi.org/10.2196/30083},
volume = {10},
year = {2021}
}
RIS format (EndNote, RefMan)
TY - JOUR
AB - <jats:sec> <jats:title>Background</jats:title> <jats:p>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 hospitalization. The RECAP (Remote COVID-19 Assessment in Primary Care) study investigates the predictive risk of hospitalization, deterioration, and death of patients with confirmed COVID-19, based on a set of parameters chosen through a Delphi process performed by clinicians. We aim to use rich data collected remotely through the use of electronic data templates integrated in the electronic health systems of several general practices across the United Kingdom to construct accurate predictive models. The models will be based on preexisting conditions and monitoring data of a patient’s clinical parameters (eg, blood oxygen saturation) to make reliable predictions as to the patient’s risk of hospital admission, deterioration, and death.</jats:p> </jats:sec> <jats:sec> <jats:title>Objective</jats:title> <jats:p>This statistical analysis plan outlines the statistical methods to build the prediction model to be used in the prioritization of patients in the primary care setting. The statistical analysis plan for the RECAP study includes the development and validation of the RECAP-V1 prediction model as a primary outcome. This 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 the risk of deterioration and hospitalization.</jats:p> </jats:sec> <jats:sec> <jats:title>Methods</jats:title> <jats:p>After the data have been collected, we will assess the degree of missingness and use a combination
AU - Fiorentino,F
AU - Prociuk,D
AU - Espinosa,Gonzalez AB
AU - Neves,AL
AU - Husain,L
AU - Ramtale,SC
AU - Mi,E
AU - Mi,E
AU - Macartney,J
AU - Anand,SN
AU - Sherlock,J
AU - Saravanakumar,K
AU - Mayer,E
AU - de,Lusignan S
AU - Greenhalgh,T
AU - Delaney,BC
DO - 10.2196/30083
EP - 30083
PY - 2021///
SP - 30083
TI - An Early Warning Risk Prediction Tool (RECAP-V1) for Patients Diagnosed With COVID-19: Protocol for a Statistical Analysis Plan
T2 - JMIR Research Protocols
UR - http://dx.doi.org/10.2196/30083
UR - https://doi.org/10.2196/30083
VL - 10
ER -
