Machine Learning Consensus Clustering for Inflammatory Subtype Analysis in Stroke and Its Impact on Mortality Risk: A Study Based on NHANES (1999-2018)

Chunjuan Zhang ^* Yulong Wang Xicheng Zhou Xiaodong Ma ^*

• Haiyan People's Hospital, 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.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.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.