J Korean Acad Nurs.  2011 Jun;41(3):423-431. 10.4040/jkan.2011.41.3.423.

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

• Cho IS
• Chung E

• KMID: 1015492
• DOI: http://doi.org/10.4040/jkan.2011.41.3.423

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.

Keyword

Pressure ulcer; Bayesian prediction; Logistic models; Risk assessment; Data mining

MeSH Terms

Adult
Aged
Area Under Curve
Bayes Theorem
Cohort Studies
Female
Humans
Logistic Models
Male
Medical Records
Middle Aged
*Predictive Value of Tests
Pressure Ulcer/epidemiology/*prevention & control
Retrospective Studies
Risk Assessment

Figure

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  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.

• 

  Figure 2 A summary of analytic procedures, Nhospital-day is the number of hospital-day, BN stands for Bayesian Network.

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