Prediction of Inpatient Pressure Ulcers Based on Routine Healthcare Data Using Machine Learning Methodology

Despite the relevance of pressure ulcers (PU) in inpatient care, the predictive power and role of care-related risk factors (e.g. surgical anesthesia) remain unclear. We investigated the predictability of PU incidence and its association with multiple care variables. We included all somatic cases between 2014 and 2018 with length of stay ≥2 days in a German university hospital. For regression analyses and prediction we used Bayesian Additive Regression Trees (BART) as nonparametric modeling approach. To assess predictive accuracy, we compared BART and logistic regression (LR) using area under the curve (AUC) and confusion matrices. The analysis of 149,006 cases revealed high predictive variable importance and associations between incident PU and intensive care with ventilation, age, surgical anesthesia (≥1 hour) and number of care-involved wards. Despite high AUCs (LR: 0.89; BART: 0.9), the confusion matrices showed a higher number of false negative (LR: 816; BART: 826) than true positive (LR: 138; BART: 68) predictions. In summary, particularly intensive care with ventilation, age, anesthesia and number of care-involved wards were associated with incident PU. Using surgical anesthesia as a proxy for immobility, our results suggest hourly repositioning. High rates of false negative predictions indicate a general challenge in the predictability of PU.