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Development and evaluation of a clinical decision support system for the prediction of methicillin resistant Staphylococcus aureaus surgical site infections in patients undergoing major surgical procedures in the United States
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Wilson, Kevin A.. Development and evaluation of a clinical decision support system for the prediction of methicillin resistant Staphylococcus aureaus surgical site infections in patients undergoing major surgical procedures in the United States. Retrieved from https://doi.org/doi:10.7282/t3-gmn8-t474

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TitleDevelopment and evaluation of a clinical decision support system for the prediction of methicillin resistant Staphylococcus aureaus surgical site infections in patients undergoing major surgical procedures in the United States
NameWilson, Kevin A. (author); Srinivasan, Shankar (chair); Coffman, Frederick (internal member); Gohel, Suril (internal member); Rutgers University; School of Health Professions
Date Created2019
Other Date2019-08 (degree)
SubjectBiomedical Informatics, MRSA, Staphylococcal infections -- Diagnosis, Staphylococcus aureus, Methicillin resistance
Extent1 online resource (xiv, 196 pages) : illustrations
DescriptionMethicillin-resistant Staphylococcus aureus (MRSA) is the leading cause of antibiotic resistance related mortality in surgical patients. Effective prediction of MRSA and MRSA-related SSI would facilitate the prophylactic use of appropriate antibiotics or application of other prevention techniques, which have been shown to improve clinical outcomes. While there is a range of factors that have been shown to increase the risk of MRSA-related infections, research is less clear on the best approaches to developing predictive models for incorporation into a clinical decision support system. This study compared two common modeling approaches — logistic regression (LR) and artificial neural networks (ANN) — for the prediction of MRSA infection and MRSA-related SSI in patients undergoing major surgical procedures (MSPs) in the United States.

The data source for analysis is the National Inpatient Sample, which contains approximately 7 million discharges each year. A descriptive analysis was performed to identify potential predictors for each of three research hypotheses and ANN and LR models were developed and evaluated for the prediction of: (1) MRSA infection in patients undergoing MSPs; (2) MRSA-related SSI in patients undergoing MSPs; and (3) MRSA-related SSI in patients with S. aureus infection.

The ANN model performed best for Hypothesis 1, with an AUC of 0.87, sensitivity of 0.86 and specificity of 0.74; the LR model achieved an AUC of 0.85, sensitivity of 0.79 and specificity of 0.75. For Hypothesis 2, the ANN model achieved an AUC of 0.86, sensitivity of 0.73 and specificity of 0.87; the LR model achieved an AUC of 0.85, sensitivity of 0.77 and specificity of 0.76. For Hypothesis 3, the ANN model achieved an AUC of 0.67, sensitivity of 0.57 and specificity of 0.67; the LR model achieved an AUC of 0.68, sensitivity of 0.61 and specificity of 0.64.

This study assessed the feasibility of LR and ANN for the prediction of MRSA-related infections in surgical patients using a range of demographic, clinical, procedural, and hospital-related factors. The results showed that both algorithms are effective modeling approaches with reasonable sensitivity and specificity and suggest that a clinical decision support tool based on either model could be informative in clinical practice. ?
NotePh.D.
NoteIncludes bibliographical references
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/t3-gmn8-t474
LanguageEnglish
CollectionSchool of Health Professions ETD Collection
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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