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- Title
Predicting 90-Day Prognosis in Ischemic Stroke Patients Post Thrombolysis Using Machine Learning.
- Authors
Abujaber, Ahmad A.; Albalkhi, Ibrahem; Imam, Yahia; Nashwan, Abdulqadir J.; Yaseen, Said; Akhtar, Naveed; Alkhawaldeh, Ibraheem M.
- Abstract
(1) Objective: This study aimed to construct a machine learning model for predicting the prognosis of ischemic stroke patients who underwent thrombolysis, assessed through the modified Rankin Scale (mRS) score 90 days after discharge. (2) Methods: Data were sourced from Qatar's stroke registry covering January 2014 to June 2022. A total of 723 patients with ischemic stroke who had received thrombolysis were included. Clinical variables were examined, encompassing demographics, stroke severity indices, comorbidities, laboratory results, admission vital signs, and hospital-acquired complications. The predictive capabilities of five distinct machine learning models were rigorously evaluated using a comprehensive set of metrics. The SHAP analysis was deployed to uncover the most influential predictors. (3) Results: The Support Vector Machine (SVM) model emerged as the standout performer, achieving an area under the curve (AUC) of 0.72. Key determinants of patient outcomes included stroke severity at admission; admission systolic and diastolic blood pressure; baseline comorbidities, notably hypertension (HTN) and coronary artery disease (CAD); stroke subtype, particularly strokes of undetermined origin (SUO); and hospital-acquired urinary tract infections (UTIs). (4) Conclusions: Machine learning can improve early prognosis prediction in ischemic stroke, especially after thrombolysis. The SVM model is a promising tool for empowering clinicians to create individualized treatment plans. Despite limitations, this study contributes to our knowledge and encourages future research to integrate more comprehensive data. Ultimately, it offers a pathway to improve personalized stroke care and enhance the quality of life for stroke survivors.
- Subjects
QATAR; THROMBOLYTIC therapy; ISCHEMIC stroke; MACHINE learning; STROKE patients; URINARY tract infections; DIASTOLIC blood pressure; PROGNOSIS
- Publication
Journal of Personalized Medicine, 2023, Vol 13, Issue 11, p1555
- ISSN
2075-4426
- Publication type
Article
- DOI
10.3390/jpm13111555