Causal Discovery on the Effect of Antipsychotic Drugs on Delirium Patients in the ICU using Large EHR Dataset
Delirium occurs in about 80% cases in the Intensive Care Unit (ICU) and is
associated with a longer hospital stay, increased mortality and other related
issues. Delirium does not have any biomarker-based diagnosis and is commonly
treated with antipsychotic drugs (APD). However, multiple studies have shown
controversy over the efficacy or safety of APD in treating delirium. Since
randomized controlled trials (RCT) are costly and time-expensive, we aim to
approach the research question of the efficacy of APD in the treatment of
delirium using retrospective cohort analysis. We plan to use the Causal
inference framework to look for the underlying causal structure model,
leveraging the availability of large observational data on ICU patients. To
explore safety outcomes associated with APD, we aim to build a causal model for
delirium in the ICU using large observational data sets connecting various
covariates correlated with delirium. We utilized the MIMIC III database, an
extensive electronic health records (EHR) dataset with 53,423 distinct hospital
admissions. Our null hypothesis is: there is no significant difference in
outcomes for delirium patients under different drug-group in the ICU. Through
our exploratory, machine learning based and causal analysis, we had findings
such as: mean length-of-stay and max length-of-stay is higher for patients in
Haloperidol drug group, and haloperidol group has a higher rate of death in a
year compared to other two-groups. Our generated causal model explicitly shows
the functional relationships between different covariates. For future work, we
plan to do time-varying analysis on the dataset.