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Risk Stratification Models for Stroke in Patients Hospitalized with COVID-19 Infection
Alexander E Merkler1, Cenai Zhang1, Ivan Diaz2
1Clinical and Translational Neuroscience Unit, Department of Neurology and Feil Brain and Mind Research Institute, Weill Cornell Medicine, New York, United States.
Insights
A new COVID-19 stroke risk score, CANDLE, helps identify high-risk patients. This practical tool aids clinicians in stratifying stroke risk for hospitalized COVID-19 patients.
Area of Science:
- Cardiovascular Medicine
- Infectious Diseases
- Neurology
Background:
- Hospitalized COVID-19 patients face increased risk of cerebrovascular events.
- Accurate risk stratification is crucial for timely intervention.
Purpose of the Study:
- To develop and validate predictive models for stroke risk in COVID-19 patients.
- To create a practical clinical score for risk stratification.
Main Methods:
- Utilized data from the AHA's Get With The Guidelines® COVID-19 Cardiovascular Disease Registry (122 centers, March 2020-March 2021).
- Employed Cox regression and machine learning techniques to identify stroke risk factors.
- Developed the COVID-19 Acute Neurological Disease and Long-term Events (CANDLE) score.
Main Results:
- Among 21,420 patients, 1.5% experienced a cerebrovascular event.
- The CANDLE score demonstrated a C-statistic of 0.66 for predicting stroke risk.
- CANDLE effectively stratified patients into low (0.2%), medium (1.1%), and high (2.1-3.0%) risk groups.
Conclusions:
- A practical clinical score (CANDLE) was developed to stratify stroke risk in COVID-19 patients.
- The CANDLE score performs comparably to machine learning estimators.
- This score can aid clinicians in managing stroke risk in hospitalized COVID-19 patients.
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.
