External validation of a clinical risk score to predict hospital admission and in-hospital mortality in COVID-19 patients

Abstract

Background: Identification of patients with novel coronavirus disease 2019 (COVID-19) requiring hospital admission or at high-risk of in-hospital mortality is essential to guide patient triage and to provide timely treatment for higher risk hospitalized patients. Methods: A retrospective multi-centre (8 hospital) cohort at Beaumont Health, Michigan, USA, reporting on COVID-19 patients diagnosed between 1 March and 1 April 2020 was used for score validation. The COVID-19 Risk of Complications Score was automatically computed by the EHR. Multivariate logistic regression models were built to predict hospital admission and in-hospital mortality using individual variables constituting the score. Validation was performed using both discrimination and calibration. Results: Compared to Green scores, Yellow Scores (OR: 5.72) and Red Scores (OR: 19.1) had significantly higher odds of admission (both p < .0001). Similarly, Yellow Scores (OR: 4.73) and Red Scores (OR: 13.3) had significantly higher odds of in-hospital mortality than Green Scores (both p < .0001). The cross-validated C-Statistics for the external validation cohort showed good discrimination for both hospital admission (C ¼ 0.79 (95% CI: 0.77-0.81)) and in-hospital mortality (C ¼ 0.75 (95% CI: 0.71-0.78)). Conclusions: The COVID-19 Risk of Complications Score predicts the need for hospital admission and in-hospital mortality patients with COVID-19.

ABC2-SPH risk score for in-hospital mortality in COVID-19 patients: development, external validation and comparison with other available scores

“…Different from what has been mistakenly suggested 36,[67][68][69] , the results from this study do not suggest that patients from low-risk group may be discharged for home treatment. No score so far has specifically tested this hypothesis.…”

Development of An Individualized Risk Prediction Model for COVID-19 Using Electronic Health Record Data

“…Accordingly, several predictive models that seek to optimize hospital resource management and clinical decisions have been developed (Jehi et al, 2020a;Jehi et al, 2020b;Gong et al, 2020;Liang et al, 2020;Wynants et al, 2020;Zhao et al, 2020). To a large degree, these informatic tools leverage the vast and rich health information available from Electronic Health Record (EHR) data (Jehi et al, 2020b;Oetjens et al, 2020;Osborne et al, 2020;Vaid et al, 2020;Wang et al, 2021a;Wang et al, 2021b;Estiri et al, 2021;Halalau et al, 2021;Schwab et al, 2021). EHR systems contain longitudinal data about patients' demographics, health history, current and past medications, hospital admissions, procedures, current and past symptoms and conditions.…”

Determinants of COVID-19 outcomes: A systematic review

“…Age and sex were the common demographic risk factors. Of the 20 studies on age and COVID-19 mortality, 16 identified increasing age as a significant determinant of COVID-19 mortality, with effect sizes ranging from 1.04 to 20.6 and 95% CI from 1.01 to 22.68 (9, 10, 11, 16, 17, 18, 24, 25, 26, 27, 28, 29, 30, 32, 33, 35). Also, 8 of the 20 studies on gender/sex and COVID-19 found men to have a higher risk of COVID-19 mortality than women (10, 11, 16, 17, 24, 26, 28, 32).…”

“…Of biological/medical risk factors, the review identified diabetes (n=6), Chronic kidney/renal disease (CKD) (n=5), hypertension (n=4), C-reactive protein (CRP) (n=4), BMI (n=4), dyspnoea (n=3), COPD (n=3), cancer (n=3), coronary heart disease (n=2), asthma (n=2) and D-dimer (n=2) as determinants of COVID-19 mortality. Of the 10 studies that included CKD in their analysis, 5 found it a significant determinant of COVID-19 mortality (18, 25, 26, 30, 35). Additionally, out of the 5 studies that investigated the influence of CRP on COVID-19 mortality, 4 showed that elevated CRP in the blood (at least >5mg/L) increases the risk of COVID-19 death (11, 24, 26, 29).…”