Causal machine learning to investigate the effectiveness of vancomycin as the empiric treatment choice in patients hospitalized with community acquired pneumonia

Community-acquired pneumonia (CAP) is a significant cause of mortality among older adults. The early initiation of appropriate empiric antimicrobial treatment is crucial, yet the role of vancomycin, especially regarding its impact on patient outcomes, is controversial. This study investigates the effects of vancomycin on 30-day mortality and acute kidney failure in older adults hospitalized with CAP using observational data. Statistical analyses included causal random forests to identify treatment effect heterogeneity and targeted maximum likelihood estimation (TMLE) for estimating average treatment effects. Vancomycin use was linked to higher 30-day mortality as well as acute kidney failure, in particular in patients on mechanical ventilation. This study underscores the necessity of re-evaluating empirical antibiotic strategies in CAP, advocating for a more tailored approach to minimize unnecessary risks.