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Validation Study of Artificial Neural Network Models for Prediction of Methicillin-Resistant Staphylococcus aureus Carriage

Published online by Cambridge University Press:  02 January 2015

Cheng-Chuan Hsu ,
Yusen E. Lin ,
Yao-Shen Chen ,
Yung-Ching Liu  and
Robert R. Muder
Cheng-Chuan Hsu
Affiliation:
Graduate Institute of Environmental Education, National Kaohsiung Normal University, Kaohsiung, Taiwan
Yusen E. Lin*
Affiliation:
Graduate Institute of Environmental Education, National Kaohsiung Normal University, Kaohsiung, Taiwan
Yao-Shen Chen
Affiliation:
Graduate Institute of Environmental Education, National Kaohsiung Normal University, Kaohsiung, Taiwan Section of Infectious Diseases, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
Yung-Ching Liu
Affiliation:
Section of Infectious Diseases, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
Robert R. Muder
Affiliation:
Infectious Diseases Section, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, Pennsylvania
*
Graduate Institute of Environmental Education National Kaohsiung Normal University, 62 Shen-chong Road, Yanchao, Kaohsiung, Taiwan824 (easonlin@nknucc.nknu.edu.tw)
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Abstract

Objective.

Use of active surveillance cultures for methicillin-resistant Staphylococcus aureus (MRSA) for all patients admitted to the intensive care unit has been shown to reduce nosocomial transmission. However, the cost-effectiveness and the utility of implementing use of active surveillance cultures nationwide remain controversial. We sought to develop an artificial neural network (ANN) model that would predict the likelihood of MRSA colonization.

Setting.

Two acute care hospitals, one in Pittsburgh (hospital A) and one in Kaohsiung, Taiwan (hospital B).

Methods.

Nasal cultures were performed for all patients admitted to the hospitals. A total of 46 potential risk factors in hospital A and 86 potential risk factors in hospital B associated with MRSA colonization were assessed. Culture results were obtained; 75% of the data were used for training our ANN model, and the remaining 25% were used for validating our ANN model. The culture results were the “gold standard” for determining the accuracy of the model predictions.

Results.

The ANN model predictions were accurate 95.2% of the time for hospital A (sensitivity, 94.3%; specificity, 96.0%) and 94.2% of the time for hospital B (sensitivity, 96.6%; specificity, 91.8%), integrating all potential risk factors into the model. Only 17 potential risk factors were needed for the hospital A ANN model (accuracy, 90.9%; sensitivity, 98.5%; specificity, 83.4%), and only 20 potential risk factors were needed for the hospital B ANN model (accuracy, 90.5%; sensitivity, 96.6%; specificity, 84.3%), if the minimal risk factor method was used. Cross-validation analysis showed an average accuracy of 85.6% (sensitivity, 91.3%; specificity, 80.0%).

Conclusion.

Our ANN model can be used to predict with an accuracy of more than 90% which patients carry MRSA. The false-negative rates were significantly lower than the false-positive rates in the ANN predictions, which can serve as a safety buffer in case of patient misclassification.

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 29 , Issue 7 , July 2008 , pp. 607 - 614
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2008

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