Stratified delirium risk using prescription medication data in a state-wide cohort

Abstract

Objective: Delirium is a common condition associated with increased morbidity and mortality. Medication side effects are a possible source of modifiable delirium risk and provide an opportunity to improve delirium predictive models. This study characterized the risk for delirium diagnosis by applying a previously validated algorithm for calculating central nervous system adverse effect burden arising from a full medication list.

Method: Using a cohort of hospitalized adult (age 18-65) patients from the Massachusetts All-Payers Claims Database, we calculated medication burden following hospital discharge and characterized risk of new coded delirium diagnosis over the following 90 days. We applied the resulting model to a held-out test cohort.

Results: The cohort included 62,180 individuals of whom 1.6% (1019) went on to have a coded delirium diagnosis. In the training cohort (43,527 individuals), the medication burden feature was positively associated with delirium diagnosis (OR = 5.75, 95% CI 4.34-7.63) and this association persisted (aOR = 1.95; 1.31-2.92) after adjusting for demographics, clinical features, prescribed medications, and anticholinergic risk score. In the test cohort, the trained model produced an area under the curve of 0.80 (0.78-0.82). This performance was similar across subgroups of age and gender.

Conclusion: Aggregating brain-related medication adverse effects facilitates identification of individuals at high risk of subsequent delirium diagnosis.

Keywords: Cohort study; Data mining; Delirium; Feature engineering; Pharmacovigilance; Predictive modeling.

Figure 1:

Predictive lift by decile of predicted risk in the independent testing set

Figure 2:

Kaplan-Meier curves of time to delirium outcome in the testing cohort stratified by (A) quartile of predicted delirium risk and (B) quartile of raw univariate medication burden score.