Causal Discovery on the Effect of Antipsychotic Drugs on Delirium Patients in the ICU using Large Observational EHR Dataset

Delirium occurs in about 80% of cases in the Intensive Care Unit (ICU) and is associated with an extended hospital stay, increased mortality, and other complications. Delirium lacks biomarker-based diagnosis and is frequently treated with antipsychotic drugs (APD), despite numerous studies debating its efficacy. Since randomized controlled trials (RCT) are expensive and time-consuming, we approach the research question of estimating the efficacy and safety outcomes of APD in treating delirium through retrospective cohort analysis. We employed the Causal inference framework to explore the underlying causal model for Delirium patient cohort. We focus on building a structural causal model for delirium in the ICU using large observational data sets linking various delirium-related covariates. We utilized an extensive electronic health records (EHR) dataset (MIMIC-III) to curate delirium data cohort. Our null hypothesis examines any significant differences in outcomes (30-day mortality and ICU length of stay) among delirium patients under different drug-groups (Haloperidol, other drugs, and no drugs). Our causal exploration presents a specialized pipeline through causal model generation, expert knowledge augmentation and average treatment effect estimation. Through our exploratory, machine learning driven, and causal analysis, we estimate and compare effects of antipsychotic drug groups on patients’ survival timeline and ICU length-of-stay.