Infection Control & Hospital Epidemiology
forests, Super Learner) offer potential healthcare epidemiology util- ity. We refer interested readers to additional resources that address these approaches and the underlying algorithms in greater technical detail.22,24,26 Overall, timely identification of MDRGN infections remains a
clinical and epidemiological challenge. Rapid detection enables isolation of infected patients and prompt initiation of appropriate antibiotic treatment. Statistical models for predicting drug resis- tance can provide important information in settings when labora- tory diagnostics are challenging to implement. This examination explored 2 alternative decision support tools, logistic regression– derived risk scores and machine learning–derived decision trees, in an inpatient cohort of bacteremic patients to predict ESBL infec- tion. These methodologies offer different strengths and limitations, and we hope that their continued utilization in infectious disease research will assist with improving patient outcomes.
Author ORCIDs. Katherine E. Goodman, Acknowledgments.
0000-0003-2851-775X
Financial support. This work was supported by funding from the Agency for Healthcare Research and Quality (grant no. R36HS025089 to K.E.G.), from the Sherrilyn and Ken Fisher Center for Environmental Infectious Diseases (grants to K.E.G. and A.M.M.), and from the National Institutes of Health (grant no. K23-AI127935 to P.D.T.).
Conflicts of interest. None of the authors report any conflicts of interest. References
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