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Original Article
Predictive Bayesian Network Model Using Electronic Patient Records for Prevention of Hospital-Acquired Pressure Ulcers
In Sook Cho, Eunja Chung
Journal of Korean Academy of Nursing 2011;41(3):423-431.
DOI: https://doi.org/10.4040/jkan.2011.41.3.423
Published online: June 13, 2011

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: July 16, 2010   • Accepted: June 7, 2011

© 2011 Korean Society of Nursing Science

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  • 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
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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.
jkan-41-423-g001.jpg
Figure 2
A summary of analytic procedures, Nhospital-day is the number of hospital-day, BN stands for Bayesian Network.
jkan-41-423-g002.jpg
Table 1
Group Comparison of Demographic Characteristics between Ulcer Group and Risk Group
jkan-41-423-i001.jpg
Table 2
Multiple Logistic Regression Models Examining Risks of Pressure Ulcers
jkan-41-423-i002.jpg

*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.jpg

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

Table 4
Test Characteristics of the Predictive Models
jkan-41-423-i004.jpg
Table 5
Case Coverage by the Predictive Models
jkan-41-423-i005.jpg

Figure & Data

REFERENCES

    Citations

    Citations to this article as recorded by  
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      Predictive Bayesian Network Model Using Electronic Patient Records for Prevention of Hospital-Acquired Pressure Ulcers
      J Korean Acad Nurs. 2011;41(3):423-431.   Published online June 13, 2011
      DOI: https://doi.org/10.4040/jkan.2011.41.3.423
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    Predictive Bayesian Network Model Using Electronic Patient Records for Prevention of Hospital-Acquired Pressure Ulcers
    Image Image
    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.

    Figure 1

    Figure 2

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

    Group Comparison of Demographic Characteristics between Ulcer Group and Risk Group

    Multiple Logistic Regression Models Examining Risks of Pressure Ulcers

    *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.

    Results of the Sensitivity, Specificity, Positive Predictive Value, and Negative Predictive Value

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

    Test Characteristics of the Predictive Models

    Case Coverage by the Predictive Models
    Table 1 Group Comparison of Demographic Characteristics between Ulcer Group and Risk Group

    Table 2 Multiple Logistic Regression Models Examining Risks of Pressure Ulcers

    *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

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

    Table 4 Test Characteristics of the Predictive Models

    Table 5 Case Coverage by the Predictive Models

    Table 1

    Table 2

    Table 3

    Table 4

    Table 5

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