Prognostic value of platelet-to-white blood cell ratio in critically ill patients with non-traumatic subarachnoid hemorrhage: a machine learning-driven study of the public database and the hospital information system.

Authors

Liang Liu, Zhilei Huang, Binbin Gui, Lin Xu, Yansong Liu, Yaolou Wang, Wei Zhang, Haowei Luo, Xiangtong Zhang

Published in

Frontiers in neurology. Volume 17. Pages 1864288. Epub Sep 30, 2026.

Abstract

Assessment of prognosis is crucial for managing non-traumatic subarachnoid hemorrhage (NSAH) patients. The study aimed to explore the association between platelet-to-white blood cell ratio (PWR) and all-cause mortality in this high-risk population and to develop machine learning models to improve prognostic accuracy.
Using data from the Medical Information Mart for Intensive Care (MIMIC-IV). The observational endpoints were 30-day and 365-day mortality. Patients were categorized into four groups based on the quartiles of PWR. Kaplan-Meier curves, multivariate Cox regression analyses, restricted cubic splines (RCS), and subgroup analyses were applied. We also developed machine learning models that include PWR and clinical features selected with the Boruta and LASSO algorithms to predict 30-day mortality. Model performance was evaluated by the receiver operating characteristic (ROC) curve and the area under the curve (AUC). Shapley Additive exPlanations (SHAP) method was utilized to provide model interpretability. External validations in the eICU-Collaborative Research Database (eICU-CRD) and the Hospital Information System (HIS) of the First Affiliated Hospital of Harbin Medical University were used to assess generalizability.
A total of 1,176 patients with critically ill NSAH were enrolled from MIMIC-IV, predominantly male (58.8%), with a median age of 62 years. In multivariable Cox models, higher PWR quartiles were independently associated with lower risk of 30-day (HR = 0.55, 95% CI 0.35, 0.86, p = 0.009) and 365-day (HR = 0.53, 95% CI 0.37, 0.77, p = 0.001) mortality after fully adjusting. RCS suggested a linear association. No significant interaction was observed by subgroup analysis across all subgroups. The top-performing machine learning model attained an AUC of 0.868 during internal validation (MIMIC-IV). External validations yielded AUC values of 0.874 (eICU-CRD) and 0.763 (HIS). SHAP analysis revealed a correlation between decreased PWR and an elevated predicted risk of mortality.
PWR is an independent predictor of short- and long-term mortality in ICU patients with NSAH. Machine learning models incorporating PWR significantly improve risk stratification, thereby providing precious support for the early identification and personalized management of patients in the ICU.

PMID:
42876490
Bibliographic data and abstract were imported from PubMed on 15 Oct 2026.

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