Explainable machine learning for predicting mechanical ventilation in Alzheimer's patients with pneumonia: a SHAP-guided XGBoost nomogram based on MIMIC-IV.

Alzheimer's disease (AD) patients are particularly vulnerable to pneumonia and subsequent respiratory failure due to neurodegeneration-induced dysphagia and immunosenescence. Accurate prediction of mechanical ventilation (MV) need in this population remains a critical but unmet clinical challenge. This retrospective cohort study utilized the MIMIC-IV database to identify 793 critically ill AD patients diagnosed with pneumonia. Clinical variables were extracted and filtered using LASSO regression and multivariate logistic regression to identify key predictors. Five machine learning algorithms, including XGBoost, were constructed and compared to evaluate MV prediction performance. Model interpretability was enhanced using SHAP analysis, and a nomogram was developed based on the best-performing model. Among the included patients, 213 (26.9%) required mechanical ventilation during ICU admission. The XGBoost model outperformed all other algorithms, achieving the highest AUC in both the training (0.849) and validation (0.744) sets, as well as superior F1-score (0.507) and balanced accuracy (0.776). SHAP analysis identified COPD, temperature, and gender as the most influential predictors. The final nomogram incorporating seven key variables (Gender, AKI, COPD, OASIS, Temperature, PLT, and antibiotic use) achieved robust discrimination (validation Set AUC = 0.780), excellent calibration, and consistent clinical benefit in decision curve analysis. The derived NomoScore stratified patients into quartiles with escalating MV risk (p < 0.001), and retained predictive accuracy with Cohen's kappa values of 0.40 (training) and 0.363 (validation). The model demonstrated superior net benefit over conventional scoring systems and has the potential to support bedside decision-making in cognitively impaired ICU populations. This study presents a validated, interpretable XGBoost-based model and clinical nomogram for predicting mechanical ventilation risk in AD patients with pneumonia. The framework offers a precision tool to aid ICU triage and guide ventilatory strategies.