Development and Validation of a Novel Score for Predicting Long-Term Mortality after an Acute Ischemic Stroke

Ching-Heng Lin, Ya-Wen Kuo, Yen-Chu Huang, Meng Lee, Yi-Wei Huang, Chang-Fu Kuo and Jiann- Der Lee ()
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Ching-Heng Lin: Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan 333, Taiwan
Ya-Wen Kuo: Department of Nursing, Chang Gung University of Science and Technology, Chiayi Campus, Chiayi 613, Taiwan
Yen-Chu Huang: Department of Neurology, Chiayi Chang Gung Memorial Hospital, Chiayi 613, Taiwan
Meng Lee: Department of Neurology, Chiayi Chang Gung Memorial Hospital, Chiayi 613, Taiwan
Yi-Wei Huang: Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan 333, Taiwan
Chang-Fu Kuo: Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan 333, Taiwan
Jiann- Der Lee: Department of Neurology, Chiayi Chang Gung Memorial Hospital, Chiayi 613, Taiwan

IJERPH, 2023, vol. 20, issue 4, 1-12

Abstract: Background: Long-term mortality prediction can guide feasible discharge care plans and coordinate appropriate rehabilitation services. We aimed to develop and validate a prediction model to identify patients at risk of mortality after acute ischemic stroke (AIS). Methods: The primary outcome was all-cause mortality, and the secondary outcome was cardiovascular death. This study included 21,463 patients with AIS. Three risk prediction models were developed and evaluated: a penalized Cox model, a random survival forest model, and a DeepSurv model. A simplified risk scoring system, called the C-HAND (history of Cancer before admission, Heart rate, Age, eNIHSS, and Dyslipidemia) score, was created based on regression coefficients in the multivariate Cox model for both study outcomes. Results: All experimental models achieved a concordance index of 0.8, with no significant difference in predicting poststroke long-term mortality. The C-HAND score exhibited reasonable discriminative ability for both study outcomes, with concordance indices of 0.775 and 0.798. Conclusions: Reliable prediction models for long-term poststroke mortality were developed using information routinely available to clinicians during hospitalization.

Keywords: acute ischemic stroke; mortality; machine learning; clinical prediction rule (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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