Risk stratification models for stroke in patients hospitalized with COVID-19 infection.
Academic Article
Overview
abstract
OBJECTIVES: To derive models that identify patients with COVID-19 at high risk for stroke. MATERIALS AND METHODS: We used data from the AHA's Get With The Guidelines® COVID-19 Cardiovascular Disease Registry to generate models for predicting stroke risk among adults hospitalized with COVID-19 at 122 centers from March 2020-March 2021. To build our models, we used data on demographics, comorbidities, medications, and vital sign and laboratory values at admission. The outcome was a cerebrovascular event (stroke, TIA, or cerebral vein thrombosis). First, we used Cox regression with cross validation techniques to identify factors associated with the outcome in both univariable and multivariable analyses. Then, we assigned points for each variable based on corresponding coefficients to create a prediction score. Second, we used machine learning techniques to create risk estimators using all available covariates. RESULTS: Among 21,420 patients hospitalized with COVID-19, 312 (1.5%) had a cerebrovascular event. Using traditional Cox regression, we created/validated a COVID-19 stroke risk score with a C-statistic of 0.66 (95% CI, 0.60-0.72). The CANDLE score assigns 1 point each for prior cerebrovascular disease, afebrile temperature, no prior pulmonary disease, history of hypertension, leukocytosis, and elevated systolic blood pressure. CANDLE stratified risk of an acute cerebrovascular event according to low- (0-1: 0.2% risk), medium- (2-3: 1.1% risk), and high-risk (4-6: 2.1-3.0% risk) groups. Machine learning estimators had similar discriminatory performance as CANDLE: C-statistics, 0.63-0.69. CONCLUSIONS: We developed a practical clinical score, with similar performance to machine learning estimators, to help stratify stroke risk among patients hospitalized with COVID-19.
