Predicting pressure injury risk in hospitalised patients using machine learning with electronic health records: a US multilevel cohort study

Objective To predict the risk of hospital-acquired pressure injury using machine learning compared with standard care.Design We obtained electronic health records (EHRs) to structure a multilevel cohort of hospitalised patients at risk for pressure injury and then calibrate a machine learning model to predict future pressure injury risk. Optimisation methods combined with multilevel logistic regression were used to develop a predictive algorithm of patient-specific shifts in risk over time. Machine learning methods were tested, including random forests, to identify predictive features for the algorithm. We reported the results of the regression approach as well as the area under the receiver operating characteristics (ROC) curve for predictive models.Setting Hospitalised inpatients.Participants EHRs of 35 001 hospitalisations over 5 years across 2 academic hospitals.Main outcome measure Longitudinal shifts in pressure injury risk.Results The predictive algorithm with features generated by machine learning achieved significantly improved prediction of pressure injury risk (p