ArticleInternational journal of environmental research and public health2023
Development and Validation of a Novel Score for Predicting Long-Term Mortality after an Acute Ischemic Stroke.
Article in International journal of environmental research and public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Artificial Intelligence Models for Time-to-Event Outcome Applied in Cardiovascular Disease Risk Prediction.Journal of medical systems · 2024Pooled it
- Use of artificial intelligence in the management of stroke: scoping review.Frontiers in radiology · 2025Review
- Development and Validation of a Predictive Score for Three-Year Mortality in Acute Ischemic Stroke Patients.Medicina (Kaunas, Lithuania) · 2024Article
- A comparison of random survival forest and Cox regression for prediction of mortality in patients with hemorrhagic stroke.BMC medical informatics and decision making · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLong-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).
methodsThe 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.
resultsAll 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.
conclusionsReliable prediction models for long-term poststroke mortality were developed using information routinely available to clinicians during hospitalization.
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