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Cho and Chung: Predictive Bayesian Network Model Using Electronic Patient Records for Prevention of Hospital-Acquired Pressure Ulcers
Original Article
Journal of Korean Academy of Nursing

Journal of Korean Academy of Nursing 2011; 41(3): 423-431.

Published online: 13 June 2011

DOI: https://doi.org/10.4040/jkan.2011.41.3.423

Predictive Bayesian Network Model Using Electronic Patient Records for Prevention of Hospital-Acquired Pressure Ulcers

In Sook Cho1, Eunja Chung2

1Associated Professor, Department of Nursing, Inha University, Incheon, Korea.

2Director, Department of Nursing, Seoul National University Bundang Hospital, Seongnam, Korea.

Address reprint requests to: Cho, In Sook. Department of Nursing, Inha University, Yonghyeon 4-dong, Nam-gu, Incheon 402-751, Korea. Tel: +82-32-860-8201, Fax: +82-32-874-5880, insook.cho@inha.ac.kr

Received 16 July 2010       Accepted 7 June 2011

© 2011 Korean Society of Nursing Science

Abstract

Purpose

The study was designed to determine the discriminating ability of a Bayesian network (BN) for predicting risk for pressure ulcers.

Methods

Analysis was done using a retrospective cohort, nursing records representing 21,114 hospital days, 3,348 patients at risk for ulcers, admitted to the intensive care unit of a tertiary teaching hospital between January 2004 and January 2007. A BN model and two logistic regression (LR) versions, model-I and -II, were compared, varying the nature, number and quality of input variables. Classification competence and case coverage of the models were tested and compared using a threefold cross validation method.

Results

Average incidence of ulcers was 6.12%. Of the two LR models, model-I demonstrated better indexes of statistical model fits. The BN model had a sensitivity of 81.95%, specificity of 75.63%, positive and negative predictive values of 35.62% and 96.22% respectively. The area under the receiver operating characteristic (AUROC) was 85.01% implying moderate to good overall performance, which was similar to LR model-I. However, regarding case coverage, the BN model was 100% compared to 15.88% of LR.

Conclusion

Discriminating ability of the BN model was found to be acceptable and case coverage proved to be excellent for clinical use.

Keywords: Pressure ulcer, Bayesian prediction, Logistic models, Risk assessment, Data mining

Figures and Tables

Figure 1
Procedure for extracting data from the clinical data repository (CDR) of the research hospital. Npt and Nhospital-day are the numbers of patients and hospital-day, respectively.
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Figure 2
A summary of analytic procedures, Nhospital-day is the number of hospital-day, BN stands for Bayesian Network.
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Table 1
Group Comparison of Demographic Characteristics between Ulcer Group and Risk Group
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Table 2
Multiple Logistic Regression Models Examining Risks of Pressure Ulcers
jkan-41-423-i002

*Confidence intervals (95% CI) not containing the null value (1.00) are statistically significant at p<.05; †p<.001 for χ2 tests; The Akaike information criterion (AIC), a measure of statistical model fit, was used to compare the amount of information explained across the logistic regression models. A lower AIC value indicates a model is a better fit for the observed data.

ER=emergency room; OR=odds ratio; CI=confidence interval.

Table 3
Results of the Sensitivity, Specificity, Positive Predictive Value, and Negative Predictive Value
jkan-41-423-i003

PPV=positive predictive value; NPV=negative predictive value.

Table 4
Test Characteristics of the Predictive Models
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Table 5
Case Coverage by the Predictive Models
jkan-41-423-i005

Notes

This work was supported by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korea government (MOST) (No. 2009-0053032).

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