ArticleFrontiers in neurology2025
Machine learning consensus clustering for inflammatory subtype analysis in stroke and its impact on mortality risk: a study based on NHANES (1999-2018).
Article in Frontiers in neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predictive Value of Machine Learning for Poststroke Mortality Risk: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Association of peripheral insulin resistance with cognitive impairment in patients with non-disabling ischemic cerebrovascular events and the mediating role of chronic inflammatory biomarkers.European journal of medical research · 2026Article
- Association Between Three Systemic Inflammatory Biomarkers and Diabetic Foot Ulcer: A Cross-Sectional Study and a Clinical Retrospective Study.Mediators of inflammation · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
Background: Our study aims to utilize unsupervised machine learning methods to perform inflammation clustering on stroke patients via novel CBC-derived inflammatory indicators (NLR, PLR, NPAR, SII, SIRI, and AISI), evaluate the mortality risk among these different clusters and construct prognostic models to provide reference for clinical management. Methods: A cross-sectional analysis was conducted using data from stroke participants in the U.S. NHANES 1999-2018. Weighted multivariate logistic regression was used to construct different models; consensus clustering methods were employed to subtype stroke patients based on inflammatory marker levels; LASSO regression analysis was used to construct an inflammatory risk score model to analyze the survival risks of different inflammatory subtypes; WQS regression, Cox regression, as well as XGBoost, random forest, and SVMRFE machine learning methods were used to screen hub markers which affected stroke prognosis; finally, a prognostic nomogram model based on hub inflammatory markers was constructed and evaluated using calibration and DCA curves. Results: A total of 918 stroke patients with a median follow-up of 79 months and 369 deaths. Weighted multivariate logistic regression analysis revealed that high SIRI and NPAR levels were significantly positively correlated with increased all-cause mortality risk in stroke patients ( Conclusion: This study shows NPAR and SIRI were key prognostic inflammatory markers and positively correlated with mortality risk (
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