Explainable SHAP-XGBoost models for pressure injuries among patients requiring with mechanical ventilation in intensive care unit

Abstract

pressure injuries are significant concern for ICU patients on mechanical ventilation. Early prediction is crucial for enhancing patient outcomes and reducing healthcare costs. This study aims to develop a predictive model using machine learning techniques, specifically XGBoost combined with SHAP, to identify key risk factors of pressure ulcers in this population. Utilizing the MIMIC-IV 2.2 database, we included a cohort of 29,448 mechanically ventilated patients in ICU intensive unit. These patients were divided into a training set (20,614 patients, 70%) and an internal validation set (8,834 patients, 30%). Of these, 2,052 patients developed pressure injuries. We applied the XGBoost algorithm to build the predictive model and used SHAP analysis to identify the top ten factors influencing pressure ulcer development: 'sepsis', 'age', 'the count of platelet', 'length of ICU stay', 'PaO2/FiO2 ratio', 'hemoglobin concentration', 'admission type', 'renal disease', 'albumin concentration', and 'ethnicity'. The predictive model achieved an area under the ROC curve (AUC) of 0.797 (95% CI: 0.786-0.808) in the training set and 0.739 (95% CI: 0.721-0.758) in the validation set. Calibration curves demonstrated good fit, and the decision curve analysis indicated clinical utility. This study successfully developed a machine learning model that accurately predicts the risk of pressure ulcers in ICU patients with mechanical ventilation. This model could serve as a useful tool for guiding early interventions, ultimately reducing the incidence of pressure injuries in this vulnerable population. The integration of SHAP analysis offers insights into the most critical factors.