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Infect Control Hosp Epidemiol. 2019 Apr;40(4):400-407. doi: 10.1017/ice.2019.17. Epub 2019 Mar 4.

A methodological comparison of risk scores versus decision trees for predicting drug-resistant infections: A case study using extended-spectrum beta-lactamase (ESBL) bacteremia.

Author information

1
Department of Epidemiology,Johns Hopkins Bloomberg School of Public Health,Baltimore,Maryland.
2
Department of Epidemiology and Public Health,University of Maryland School of Medicine,Baltimore,Maryland.
3
Division 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 practical 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.

PMID:
30827286
DOI:
10.1017/ice.2019.17

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