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Development and external validation of a machine-learning model for classifying documented pressure injury status
Yang Zhao1, Siji Chen2, Chang Pang1
1Department of General Practice, The Second Affiliated Hospital of Shenyang Medical College, Shenyang, Liaoning, 110002, China.
Objective:
To develop and externally validate a machine-learning model for classifying documented pressure injury status during hospitalization among intensive care unit (ICU) patients receiving invasive mechanical ventilation.
Methods:
The development cohort included 1601 patients from MIMIC-IV version 3.1, including 390 with and 1211 without documented pressure injury. Mean values of continuous clinical variables recorded during the 24 h before ICU admission were used. Variables and patients with >30% missing data were excluded, and remaining missing values were imputed using random forest imputation. Features were selected using Boruta and least absolute shrinkage and selection operator (LASSO) regression. Six machine-learning models were compared using 10-fold cross-validation, and XGBoost was subsequently optimized using GridSearchCV with ROC-AUC as the optimization metric and a random seed of 42. SHapley Additive exPlanations (SHAP) were used for model interpretation. External validation was performed in 1576 eICU patients, including 221 with and 1355 without documented pressure injury; the first value recorded after ICU admission was used because continuous pre-ICU data were unavailable.
Results:
XGBoost showed the best overall performance among the six candidate models. After optimization, the model achieved a 10-fold cross-validated area under the receiver operating characteristic curve (AUC) of 0.853 (95% CI, 0.765-0.941) in MIMIC-IV and an AUC of 0.703 (95% CI, 0.669-0.737) in the independent eICU cohort. The final model included sex, white blood cell count, blood glucose, hematocrit, blood urea nitrogen, and anion gap. SHAP analysis identified blood urea nitrogen and anion gap as the strongest contributors to model output.
Conclusion:
The optimized XGBoost model demonstrated moderate external discrimination for classifying documented pressure injury status among ICU patients receiving invasive mechanical ventilation and may provide supplementary information for clinical assessment.