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Peer-reviewedCC BYFrontiers in neurology

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

  • Liang Liu1
  • Zhilei Huang2
  • Binbin Gui1
  • Lin Xu1
  • Yansong Liu1
  • Yaolou Wang1
  • Wei Zhang1
  • Haowei Luo3
  • Xiangtong Zhang1
  1. 1Department of Neurosurgery, The First Affiliated Hospital of Harbin Medical University , , ,
  2. 2Department of Orthopedics, The First Affiliated Hospital of Harbin Medical University , , ,
  3. 3Department of General Surgery, The First Affiliated Hospital of Harbin Medical University , , ,
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Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

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Cite

BibTeXRIS
BibTeX
@article{liu2026prognostic,
  title = {{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}},
  author = {Liang Liu and Zhilei Huang and Binbin Gui and Lin Xu and Yansong Liu and Yaolou Wang and Wei Zhang and Haowei Luo and Xiangtong Zhang},
  journal = {Frontiers in neurology},
  year = {2026},
  month = sep,
  doi = {10.3389/fneur.2026.1864288},
  url = {https://europepmc.org/article/PMC/PMC13661331},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - 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
AU  - Liang Liu
AU  - Zhilei Huang
AU  - Binbin Gui
AU  - Lin Xu
AU  - Yansong Liu
AU  - Yaolou Wang
AU  - Wei Zhang
AU  - Haowei Luo
AU  - Xiangtong Zhang
PY  - 2026
DA  - 2026/09/30
JO  - Frontiers in neurology
DO  - 10.3389/fneur.2026.1864288
UR  - https://europepmc.org/article/PMC/PMC13661331
AB  - Background: 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.  Methods: 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.  Results: 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.  Conclusion: 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.
SN  - 1664-2295
ER  -