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Delirium occurs in about 80% cases in the Intensive Care Unit (ICU) and isassociated with a longer hospital stay, increased mortality and other relatedissues. Delirium does not have any biomarker-based diagnosis and is commonlytreated with antipsychotic drugs (APD). However, multiple studies have showncontroversy over the efficacy or safety of APD in treating delirium. Sincerandomized controlled trials (RCT) are costly and time-expensive, we aim toapproach the research question of the efficacy of APD in the treatment ofdelirium using retrospective cohort analysis. We plan to use the Causalinference framework to look for the underlying causal structure model,leveraging the availability of large observational data on ICU patients. Toexplore safety outcomes associated with APD, we aim to build a causal model fordelirium in the ICU using large observational data sets connecting variouscovariates correlated with delirium. We utilized the MIMIC III database, anextensive electronic health records (EHR) dataset with 53,423 distinct hospitaladmissions. Our null hypothesis is: there is no significant difference inoutcomes for delirium patients under different drug-group in the ICU. Throughour exploratory, machine learning based and causal analysis, we had findingssuch as: mean length-of-stay and max length-of-stay is higher for patients inHaloperidol drug group, and haloperidol group has a higher rate of death in ayear compared to other two-groups. Our generated causal model explicitly showsthe functional relationships between different covariates. For future work, weplan to do time-varying analysis on the dataset.

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