  Bacterial infections are responsible for high mortality worldwide.
Antimicrobial resistance underlying the infection, and multifaceted patient's
clinical status can hamper the correct choice of antibiotic treatment.
Randomized clinical trials provide average treatment effect estimates but are
not ideal for risk stratification and optimization of therapeutic choice, i.e.,
individualized treatment effects (ITE). Here, we leverage large-scale
electronic health record data, collected from Southern US academic clinics, to
emulate a clinical trial, i.e., 'target trial', and develop a machine learning
model of mortality prediction and ITE estimation for patients diagnosed with
acute bacterial skin and skin structure infection (ABSSSI) due to
methicillin-resistant Staphylococcus aureus (MRSA). ABSSSI-MRSA is a
challenging condition with reduced treatment options - vancomycin is the
preferred choice, but it has non-negligible side effects. First, we use
propensity score matching to emulate the trial and create a treatment
randomized (vancomycin vs. other antibiotics) dataset. Next, we use this data
to train various machine learning methods (including boosted/LASSO logistic
regression, support vector machines, and random forest) and choose the best
model in terms of area under the receiver characteristic (AUC) through
bootstrap validation. Lastly, we use the models to calculate ITE and identify
possible averted deaths by therapy change. The out-of-bag tests indicate that
SVM and RF are the most accurate, with AUC of 81% and 78%, respectively, but
BLR/LASSO is not far behind (76%). By calculating the counterfactuals using the
BLR/LASSO, vancomycin increases the risk of death, but it shows a large
variation (odds ratio 1.2, 95% range 0.4-3.8) and the contribution to outcome
probability is modest. Instead, the RF exhibits stronger changes in ITE,
suggesting more complex treatment heterogeneity.
