Objectives There are no established mortality risk equations specifically for unplanned emergency medical admissions which include patients with SARS-19 (COVID-19). We aim to develop and validate a computer-aided risk score (CARMc19) for predicting mortality risk by combining COVID-19 status, the first electronically recorded blood test results and the National Early Warning Score (NEWS2). Design Logistic regression model development and validation study. Setting Two acute hospitals (York Hospital - model development data; Scarborough Hospital - external validation data). Participants Adult (aged ≥16 years) medical admissions discharged over a 24-month period with electronic NEWS and blood test results recorded on admission. We used logistic regression modelling to predict the risk of in-hospital mortality using two models: (1) CARMc19-N: Age+sex+NEWS2 including subcomponents+COVID19; (2) CARMc19-NB: CARMc19-N in conjunction with seven blood test results and acute kidney injury score. Model performance was evaluated according to discrimination (c-statistic), calibration (graphically) and clinical usefulness at NEWS2 thresholds of 4+, 5+, 6+. Results The risk of in-hospital mortality following emergency medical admission was similar in development and validation datasets (8.4% vs 8.2%). The c-statistics for predicting mortality for CARMc19-NB is better than CARMc19-N in the validation dataset (CARMc19-NB=0.88 (95% CI 0.86 to 0.90) vs CARMc19-N=0.86 (95% CI 0.83 to 0.88)). Both models had good calibration (CARMc19-NB=1.01 (95% CI 0.88 to 1.14) and CARMc19-N:0.95 (95% CI 0.83 to 1.06)). At all NEWS2 thresholds (4+, 5+, 6+) model, CARMc19-NB had better sensitivity and similar specificity. Conclusions We have developed a validated CARMc19 scores with good performance characteristics for predicting the risk of in-hospital mortality. Since the CARMc19 scores place no additional data collection burden on clinicians, it may now be carefully introduced and evaluated in hospitals with sufficient informatics infrastructure.
CITATION STYLE
Faisal, M., Mohammed, M., Richardson, D., Fiori, M., & Beatson, K. (2022). Development and validation of automated computer-aided risk scores to predict in-hospital mortality for emergency medical admissions with COVID-19: A retrospective cohort development and validation study. BMJ Open, 12(8). https://doi.org/10.1136/bmjopen-2021-050274
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