J Korean Acad Nurs.  2019 Oct;49(5):575-585. 10.4040/jkan.2019.49.5.575.

Development and Evaluation of Electronic Health Record Data-Driven Predictive Models for Pressure Ulcers

• Park SK
• Park HA
• Hwang H

• KMID: 2461857
• DOI: http://doi.org/10.4040/jkan.2019.49.5.575

Abstract

PURPOSE
The purpose of this study was to develop predictive models for pressure ulcer incidence using electronic health record (EHR) data and to compare their predictive validity performance indicators with that of the Braden Scale used in the study hospital.
METHODS
A retrospective case-control study was conducted in a tertiary teaching hospital in Korea. Data of 202 pressure ulcer patients and 14,705 non-pressure ulcer patients admitted between January 2015 and May 2016 were extracted from the EHRs. Three predictive models for pressure ulcer incidence were developed using logistic regression, Cox proportional hazards regression, and decision tree modeling. The predictive validity performance indicators of the three models were compared with those of the Braden Scale.
RESULTS
The logistic regression model was most efficient with a high area under the receiver operating characteristics curve (AUC) estimate of 0.97, followed by the decision tree model (AUC 0.95), Cox proportional hazards regression model (AUC 0.95), and the Braden Scale (AUC 0.82). Decreased mobility was the most significant factor in the logistic regression and Cox proportional hazards models, and the endotracheal tube was the most important factor in the decision tree model.
CONCLUSION
Predictive validity performance indicators of the Braden Scale were lower than those of the logistic regression, Cox proportional hazards regression, and decision tree models. The models developed in this study can be used to develop a clinical decision support system that automatically assesses risk for pressure ulcers to aid nurses.

Keyword

Pressure Ulcer; Electronic Health Records; Patient Safety; Data Mining

MeSH Terms

Case-Control Studies
Data Mining
Decision Support Systems, Clinical
Decision Trees
Electronic Health Records*
Hospitals, Teaching
Humans
Incidence
Korea
Logistic Models
Patient Safety
Pressure Ulcer*
Proportional Hazards Models
Retrospective Studies
ROC Curve
Ulcer

Figure

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  Figure 1. Decision tree model.

Reference

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