Augmenting Braden Scores With Predictive Modeling to Reduce Hospital-Acquired Pressure Injuries: A Clinical Pilot...

To evaluate the efficacy of a machine learning predictive model integrating the Braden Scale with patient demographic and care-related features, in aiding risk identification and guiding targeted interventions aimed at preventing hospital-acquired pressure injuries (HAPIs).

This clinical pilot study was conducted at a regional medical center in the midwestern United States and included admitted patients ages 18 and older. A gradient boosting trees algorithm was trained using historical patient data and deployed in the electronic health record. Following a validation period, a 6-month pilot study on 2 medical floors involving 3808 admissions evaluated the model's impact. Patients identified by the model as high-risk received supplemental rounding and preventive care. Patients on control floors received standard care.

The primary outcome measures were HAPI events per 1000 census days and the distribution of injury severities. A statistically significant shift in the proportion of deep tissue injuries to full-thickness PIs was observed on pilot floors (odds ratio, 0.09; 95% CI, 0.01-0.88; P =.03). A reduction in the odds of census days with HAPI events to those without was also observed but was not significant in the span of the pilot (odds ratio: 0.69; 95% CI, 0.34-1.39; P =.38).

Predictive modeling shows promise for enhancing HAPI risk identification and guiding targeted prevention in a clinical setting. Further research is needed to confirm that the results are generalizable across diverse health care systems.