LAUSR

Finding safe and effective treatments for acute heart failure syndrome (AHFS) is a high priority. More than 80% of patients with AHFS who present to emergency departments are treated identically with intravenous diuretics, despite recognition of the syndrome's heterogeneity. We hypothesize that matching patient profiles with "personalized" AHFS treatments will improve outcomes. Matching multiple heterogeneous clinical profiles with a number of potentially effective treatments requires an adaptive trial design that can adjust for these complexities. We propose a Bayesian response-adaptive randomization trial design for AHFS patients. Baseline information collected for each patient with AHFS prior to randomization includes blood pressure, renal function, and dyspnea severity. The primary outcome is discharge readiness within 23h of presentation and no unplanned emergency visits or admissions for acute heart failure within 7days of discharge. We use a Bayesian logistic regression model to characterize the association between primary outcome and patient profile. We adaptively randomize patients to one of five treatments, basing the randomization probability on the cumulative data from the ongoing trial and fitting results from the regression model. Simulations show high probability of selecting the best treatment corresponding to the patient's profile while allocating more patients to the efficacious treatments within the trial.