TY  - JOUR
AU  - Reese, Thomas J
AU  - Domenico, Henry J
AU  - Hernandez, Antonio
AU  - Byrne, Daniel W
AU  - Moore, Ryan P
AU  - Williams, Jessica B
AU  - Douthit, Brian J
AU  - Russo, Elise
AU  - McCoy, Allison B
AU  - Ivory, Catherine H
AU  - Steitz, Bryan D
AU  - Wright, Adam
PY  - 2024
DA  - 2024/5/8
TI  - Implementable Prediction of Pressure Injuries in Hospitalized Adults: Model Development and Validation
JO  - JMIR Med Inform
SP  - e51842
VL  - 12
KW  - patient safety
KW  - electronic health record
KW  - EHR
KW  - implementation
KW  - predictive analytics
KW  - prediction
KW  - injury
KW  - pressure injury
KW  - hospitalization
KW  - adult
KW  - development
KW  - routine care
KW  - prediction model
KW  - pressure sore
AB  - Background: Numerous pressure injury prediction models have been developed using electronic health record data, yet hospital-acquired pressure injuries (HAPIs) are increasing, which demonstrates the critical challenge of implementing these models in routine care. Objective: To help bridge the gap between development and implementation, we sought to create a model that was feasible, broadly applicable, dynamic, actionable, and rigorously validated and then compare its performance to usual care (ie, the Braden scale). Methods: We extracted electronic health record data from 197,991 adult hospital admissions with 51 candidate features. For risk prediction and feature selection, we used logistic regression with a least absolute shrinkage and selection operator (LASSO) approach. To compare the model with usual care, we used the area under the receiver operating curve (AUC), Brier score, slope, intercept, and integrated calibration index. The model was validated using a temporally staggered cohort. Results: A total of 5458 HAPIs were identified between January 2018 and July 2022. We determined 22 features were necessary to achieve a parsimonious and highly accurate model. The top 5 features included tracheostomy, edema, central line, first albumin measure, and age. Our model achieved higher discrimination than the Braden scale (AUC 0.897, 95% CI 0.893-0.901 vs AUC 0.798, 95% CI 0.791-0.803). Conclusions: We developed and validated an accurate prediction model for HAPIs that surpassed the standard-of-care risk assessment and fulfilled necessary elements for implementation. Future work includes a pragmatic randomized trial to assess whether our model improves patient outcomes. 
SN  - 2291-9694
UR  - https://medinform.jmir.org/2024/1/e51842
UR  - https://doi.org/10.2196/51842
DO  - 10.2196/51842
ID  - info:doi/10.2196/51842
ER  -