Predicting pressure injury risk in hospitalised patients using machine learning with electronic health records: a US
William V Padula1,2,3, David G Armstrong3,4, Peter J Pronovost5,6
1Department of Pharmaceutical & Health Economics, University of Southern California Mann School of Pharmacy & Pharmaceutical Sciences, Los Angeles, CA, USA padula@usc.edu.
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<0.001) with an area under the ROC curve of 0.72; whereas standard care only achieved an area under the ROC curve of 0.52. At a specificity of 0.50, the predictive algorithm achieved a sensitivity of 0.75.
Conclusions:
These data could help hospitals conserve resources within a critical period of patient vulnerability of hospital-acquired pressure injury which is not reimbursed by US Medicare; thus, conserving between 30 000 and 90 000 labour-hours per year in an average 500-bed hospital. Hospitals can use this predictive algorithm to initiate a quality improvement programme for pressure injury prevention and further customise the algorithm to patient-specific variation by facility.
