The pan-immune-inflammation value predicts stroke-associated pneumonia and poor outcome in spontaneous intracerebral hemorrhage: a machine learning approach

 You're that incompetent you don't know predictions DO NOTHING TOWARDS RECOVERY! Solve the correct problem!  You've known of the need to prevent pneumonia for years! I'd have you all fired for incompetence!

• 11% Stroke-associated pneumonia (21 posts to October 2020)

The pan-immune-inflammation value predicts stroke-associated pneumonia and poor outcome in spontaneous intracerebral hemorrhage: a machine learning approach

• 

  Anni Chen †

• J

  Jiahui Wang †

• Q

  Qiaoying Huang †

• 

  Chaoxiong Shen

• 

  Zhizhou Hu

• 

  Xiaohong Hu *

• Department of Neurology, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, Fujian, China

Abstract

Objective: 

Stroke-associated pneumonia (SAP) constitutes a major complication following spontaneous intracerebral hemorrhage (sICH), posing a significant clinical challenge for accurate prediction. This study aimed to evaluate whether integrating the pan-immune-inflammation value (PIV) enhances the predictive performance of machine learning (ML) models for SAP and poor functional outcome.

Methods: 

A retrospective cohort of 371 sICH patients was analyzed. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO). Predictive models for SAP and poor outcome [modified Rankin Scale score (mRS) > 2 at 90 days] were developed and compared across nine ML algorithms. Model performance was assessed by discrimination (area under the receiver operating characteristic curve, AUC), calibration, and decision curve analysis (DCA). Interpretability was achieved via SHapley Additive exPlanations (SHAP).

Results: 

Elevated PIV was independently associated with SAP [odds ratio (OR) 13.55, 95% confidence interval (CI) 4.14–44.39; p < 0.0001] and poor 90-day functional outcome (OR 25.27, 95% CI 7.75–82.34; p < 0.0001). Among the algorithms, extreme Gradient Boosting (XGBoost) and logistic regression (LR) demonstrated the highest predictive performance for poor outcome (AUC 0.912) and SAP (AUC 0.856), respectively. Incorporating PIV significantly improved discriminatory ability (XGBoost: 0.926–0.942; LR: 0.905–0.931; both p < 0.05). The models demonstrated good calibration, provided net clinical benefit on DCA, and were interpretable through SHAP analysis, which consistently identified PIV as a critical predictor.

Interpretation: 

The integration of PIV into interpretable ML models significantly improves the accuracy of predicting SAP and functional outcome after sICH. This strategy, combining a systemic inflammatory biomarker with explainable ML, holds promise for advancing personalized risk stratification in neurocritical care.