Validation study of artificial neural network models for prediction of methicillin-resistant Staphylococcus aureus carriage.
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.


METHODS
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%).


CONCLUSIONS
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.
Graduate Institute of Environmental Education, National Kaohsiung Normal University, Kaohsiung, Taiwan.
Cheng-Chuan
CC
Hsu
Yusen E
YE
Lin
Yao-Shen
YS
Chen
Yung-Ching
YC
Liu
Robert R
RR
Muder
D000465:Algorithms; D002353:Carrier State / Q000382:microbiology*; D006761:Hospitals; D006801:Humans; D016106:Methicillin Resistance; D016571:Neural Networks, Computer; D009666:Nose / Q000382:microbiology; D010414:Pennsylvania; D011237:Predictive Value of Tests; D012307:Risk Factors; D012680:Sensitivity and Specificity; D013203:Staphylococcal Infections / Q000382:microbiology*; D013211:Staphylococcus aureus / Q000187:drug effects / Q000302:isolation & purification*; D013624:Taiwan