Enterobacteriaceae bacteremia, particularly when associated with antimicrobial resistance, can result in increased mortality, emphasizing the need for timely effective therapy. Clinical risk prediction models are promising tools, stratifying patients based on their risk of resistance due to ESBL and carbapenemase-producing Enterobacteriaceae in bloodstream infections (BSIs) and, thereby, improving therapeutic decisions. This systematic review and meta-analysis synthesized the literature on the performance of these models. Searches of PubMed and EMBASE led to the identification of 10 relevant studies with 6106 unique patient encounters. Nine studies concerned ESBL prediction, and one focused on the prediction of carbapenemases. For the two ESBL model derivation studies, the discrimination performance showed sensitivities of 53-85% and specificities of 93-95%. Among the four ESBL model derivation and validation studies, the sensitivities were 43-88%, and the specificities were 77-99%. The sensitivity and specificity for the subsequent external validation studies were 7-37% and 88-96%, respectively. For the three external validation studies, only two models were evaluated across multiple studies, with a pooled AUROC of 65-71%, with one study omitting the sensitivity/specificity. Only two studies measured clinical utility through hypothetical therapy assessments. Given the limited evidence on their interventional application, it would be beneficial to further assess these or future models, to better understand their clinical utility and ensure their safe and impactful implementation.
Keywords: ESBL production; Enterobacteriaceae bacteremia; antimicrobial resistance; antimicrobial stewardship; carbapenemase production; clinical utility of predictive models; risk prediction scoring systems.