Abstract Details

Patients hospitalized with COVID-19 have significantly higher risk of developing stroke compared to the general population. Predicting stroke in these patients, particularly those who are encephalopathic or ventilator-dependent, remains a challenge.

The aim of this study is to leverage advanced analytics to develop an accurate, individualized risk prediction paradigm for stroke in hospitalized patients with COVID-19.

Patients admitted to a network of hospitals in the Austin metropolitan area with laboratory-confirmed COVID-19 were analyzed from March 2020 to June 2021. Using each patient’s demographic, medication, laboratory and clinical assessment data, several machine-learning algorithms were developed to predict probability of stroke.

A total of 8,183 patients hospitalized with COVID-19 met our inclusion criteria. Among this cohort, 174 patients experienced a stroke. Of the machine learning algorithms that were developed and evaluated for stroke prediction, the random forest model achieved the highest prediction accuracy with a strong overall discriminatory performance. The features with the most significant prognostic value include ventilator dependence, history of hyperlipidemia, history of cancer and administration of aspirin. 

Machine learning models are capable of multi-dimensional analysis and can be leveraged to predict risk of stroke in patients hospitalized with COVID-19. Further studies are underway to incorporate a final model into the clinical pipeline for automated, patient-specific risk stratification.