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Machine learning consensus clustering for inflammatory subtype analysis in stroke and its impact on mortality risk: a
Zhang Chunjuan1, Wang Yulong1, Zhou Xicheng1
1Haiyan People's Hospital, Jiaxing, Zhejiang, China.
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 (p < 0.001), independent of potential confounders; Consensus clustering divided patients into two inflammatory subgroups via SIRI and NPAR, with subgroup 2 having significantly higher markers and mortality risks than subgroup 1 (p < 0.001); LASSO regression analysis showed subgroup 2 had higher risk scores and shorter overall survival than subgroup 1 [HR, 1.99 (1.61-2.45), p < 0.001]; WQS regression, Cox regression, and machine learning methods identified NPAR and SIRI as hub prognostic inflammatory markers; The nomogram prognostic model with NPAR and SIRI demonstrated the best net benefit for predicting 1, 3, 5 and 10-year overall survival in stroke patients.
Conclusion:
This study shows NPAR and SIRI were key prognostic inflammatory markers and positively correlated with mortality risk (p < 0.001) for stroke patients. Patients would been divided into 2 inflammatory subtypes via them, with subtype 2 having higher values and mortality risks (p < 0.001). It suggests that enhanced monitoring and management for patients with high SIRI and NPAR levels to improve survival outcomes.
