Infection Control & Hospital Epidemiology (2019), 40,400–407 doi:10.1017/ice.2019.17
Original Article
A methodological comparison of risk scores versus decision trees for predicting drug-resistant infections: A case study using extended-spectrum beta-lactamase (ESBL) bacteremia
Katherine E. Goodman1 , Justin Lessler1, Anthony D. Harris2, Aaron M. Milstone1,3 and Pranita D. Tamma3
1Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, 2Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland and 3Division of Infectious Diseases, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland
Abstract
Background: Timely identification of multidrug-resistant gram-negative infections remains an epidemiological challenge. Statistical models for predicting drug resistance can offer utility where rapid diagnostics are unavailable or resource-impractical. Logistic regression–derived risk scores are common in the healthcare epidemiology literature. Machine learning–derived decision trees are an alternative approach for developing decision support tools. Our group previously reported on a decision tree for predicting ESBL bloodstream infections. Our objective in the current study was to develop a risk score from the same ESBL dataset to compare these 2 methods and to offer general guiding principles for using each approach.
Methods: Using a dataset of 1,288 patients with Escherichia coli or Klebsiella spp bacteremia, we generated a risk score to predict the likelihood that a bacteremic patient was infected with an ESBL-producer. We evaluated discrimination (original and cross-validated models) using receiver operating characteristic curves and C statistics. We compared risk score and decision tree performance, and we reviewed their prac- tical and methodological attributes.
Results: In total, 194 patients (15%) were infected with ESBL-producing bacteremia. The clinical risk score included 14 variables, compared to the 5 decision-tree variables. The positive and negative predictive values of the risk score and decision tree were similar (>90%), but the C statistic of the risk score (0.87) was 10% higher.
Conclusions: A decision tree and risk score performed similarly for predicting ESBL infection. The decision tree was more user-friendly, with fewer variables for the end user,whereas the risk score offered higher discrimination and greater flexibility for adjusting sensitivity and specificity.
(Received 14 October 2018; accepted 13 January 2019)
Multidrug-resistant gram-negative (MDRGN) organisms re- present a growing clinical threat. These bacteria can spread rapidly among vulnerable hospitalized populations, and MDRGN infec- tions are associated with significant morbidity and mortality.1,2 Timely identification can limit nosocomial transmission and improve patient outcomes by facilitating prompt initiation of appropriate treatment.3,4 However, rapid diagnostics that can be readily incorporated into routine laboratory workflows are limited or lacking for many MDRGNs, posing clinical and epidemiological challenges Extended-spectrum β-lactamase (ESBL)–producing bacteria, which can hydrolyze most β-lactam antibiotics other than carbapenems, are a representative example of these MDRGNs. Currently, no phenotypic method has been endorsed by the
Clinical and Laboratory Standards Institute (CLSI) for ESBL detec- tion.5 Although molecular methods for identifying ESBL genes are
Author for correspondence: Katherine E. Goodman, Email:
kgoodma7@jhu.edu and
Pranita D. Tamma, Email:
ptamma1@jhmi.edu Cite this article: Goodman KE, et al. (2019). A methodological comparison of risk
scores versus decision trees for predicting drug-resistant infections: A case study using extended-spectrum beta-lactamase (ESBL) bacteremia. Infection Control & Hospital Epidemiology, 40: 400–407,
https://doi.org/10.1017/ice.2019.17
© 2019 by The Society for Healthcare Epidemiology of America. All rights reserved.
commercially available, these assays do not include a comprehen- sive list of known ESBL genes and would require frequent panel updates to detect emerging ESBLs.6,7 Molecular diagnostics can also be resource-intensive and are often not cost-effective for laboratories in regions where ESBL prevalence is low, and they are cost-prohibitive for developing areas of the world where ESBL prevalence is high. Statistical models for identifyingMDRGN infections can provide
important information in settingswhere rapid diagnostics are unavail- able or are resource-impractical. One particular approach, generating a logistic regression–derived risk score, is common in the healthcare epidemiology literature. However, classification and regression tree (CART) analysis or “recursive partitioning,” aformofmachine learning, is analternative approach fordeveloping this type of decision support tool. Our group previously developed a CART decision tree for predicting ESBL bloodstream infections.8 Since publication, there has been interest in whether a risk score derived from the same population could achieve greater predictive accuracy while remaining sufficiently simple to incorporate into practice. We performed a case study of the development of a risk score from the same ESBL dataset as our original decision tree to
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