Interpretable Machine Learning Prediction Model for Predicting Mortality Risk of ICU Patients With Pressure Ulcers...

This study was to create an interpretable machine learning model to predict the risk of mortality within 90 days for ICU patients suffering from pressure ulcers.

We retrospectively analysed 1774 ICU pressure ulcer patients from the Medical Information Mart for Intensive Care (MIMIC)-IV database.

We used the LASSO regression and the Boruta algorithm for feature selection. The dataset was split into training and test sets at a 7:3 ratio for constructing machine learning models. We employed logistic regression and nine other machine learning algorithms to build the prediction model. Restricted cubic spline (RCS) was used to analyse the linear relationship between the Braden score and the outcome, whereas the SHAP (Shapley additive explanations) method was applied to visualise the model's characteristics.

This study compared the predictive ability of the Braden Scale with other scoring systems (SOFA, APSIII, Charlson, SAPSII). The results showed that the Braden Scale model had the highest performance, and SHAP analysis indicated that the Braden Scale is an important influencing factor for the risk of 90-day mortality in the ICU. The restricted cubic spline curve demonstrated a significant negative correlation between the Braden Scale and mortality. Subgroup analysis showed no significant interaction effects among subgroups except for age.

The machine learning-enhanced Braden Scale has been developed to forecast the 90-day mortality risk for ICU patients suffering from pressure ulcers, and its efficacy as a clinically reliable tool has been substantiated.

Patients or public members were not directly involved in this study.