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

Author

Listed:
  • Ching-Heng Lin

    (Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan 333, Taiwan
    Bachelor Program in Artificial Intelligence, Chang Gung University, Taoyuan 333, Taiwan)

  • Ya-Wen Kuo

    (Department of Nursing, Chang Gung University of Science and Technology, Chiayi Campus, Chiayi 613, Taiwan
    Associate Research Fellow, Chang Gung Memorial Hospital, Chiayi 613, Taiwan)

  • Yen-Chu Huang

    (Department of Neurology, Chiayi Chang Gung Memorial Hospital, Chiayi 613, Taiwan
    College of Medicine, Chang Gung University, Taoyuan 333, Taiwan)

  • Meng Lee

    (Department of Neurology, Chiayi Chang Gung Memorial Hospital, Chiayi 613, Taiwan
    College of Medicine, Chang Gung University, Taoyuan 333, 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
    Division of Rheumatology, Allergy, and Immunology, Chang Gung Memorial Hospital, Taoyuan 333, Taiwan)

  • Jiann-Der Lee

    (Department of Neurology, Chiayi Chang Gung Memorial Hospital, Chiayi 613, Taiwan
    College of Medicine, Chang Gung University, Taoyuan 333, Taiwan)

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.

Suggested Citation

  • Ching-Heng Lin & Ya-Wen Kuo & Yen-Chu Huang & Meng Lee & Yi-Wei Huang & Chang-Fu Kuo & Jiann-Der Lee, 2023. "Development and Validation of a Novel Score for Predicting Long-Term Mortality after an Acute Ischemic Stroke," IJERPH, MDPI, vol. 20(4), pages 1-12, February.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:4:p:3043-:d:1063062
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    References listed on IDEAS

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    1. Wenjuan Wang & Martin Kiik & Niels Peek & Vasa Curcin & Iain J Marshall & Anthony G Rudd & Yanzhong Wang & Abdel Douiri & Charles D Wolfe & Benjamin Bray, 2020. "A systematic review of machine learning models for predicting outcomes of stroke with structured data," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-16, June.
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