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Implementable Prediction of Pressure Injuries in Hospitalized Adults: Model Development and Validation.

Thomas J Reese¹, Henry J Domenico², Antonio Hernandez³

¹1, Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States.

JMIR Medical Informatics

|May 9, 2024

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This summary is machine-generated.

A new hospital-acquired pressure injury (HAPI) prediction model was developed using electronic health record data. This accurate HAPI model surpasses current standard care, offering improved risk assessment for better patient outcomes.

Area of Science:

Healthcare Informatics
Clinical Prediction Modeling
Patient Safety

Background:

Hospital-acquired pressure injuries (HAPIs) remain a significant challenge, with increasing incidence despite numerous prediction models.
Existing models often face implementation barriers in routine clinical practice.

Purpose of the Study:

To develop a feasible, broadly applicable, dynamic, and actionable HAPI prediction model.
To rigorously validate the model and compare its performance against the standard care (Braden scale).

Main Methods:

Utilized electronic health record data from 197,991 adult hospital admissions.
Employed logistic regression with LASSO for risk prediction and feature selection.
Validated the model using a temporally staggered cohort and compared performance using AUC, Brier score, and calibration metrics.

Keywords:

EHR adult development electronic health record hospitalization implementation injury patient safety prediction prediction model predictive analytics pressure injury pressure sore routine care

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Main Results:

Identified 22 essential features for a parsimonious and accurate HAPI prediction model.
The top predictors included tracheostomy, edema, central line, albumin, and age.
The developed model demonstrated superior discrimination compared to the Braden scale (AUC 0.897 vs. 0.798).

Conclusions:

An accurate and validated HAPI prediction model was developed, outperforming standard risk assessment.
The model meets key criteria for successful clinical implementation.
Future research will involve a pragmatic trial to evaluate the model's impact on patient outcomes.