An Empirical Method of Detecting Time-Dependent Confounding: An Observational Study of Next Day Delirium in a Medical ICU

Longitudinal research on older persons in the medical intensive care unit (MICU) is often complicated by the time-dependent confounding of concurrently administered interventions such as medications and intubation. Such temporal confounding can bias the respective longitudinal associations between concurrently administered treatments and a longitudinal outcome such as delirium. Although marginal structural models address time-dependent confounding, their application is non-trivial and preferably justified by empirical evidence. Using data from a longitudinal study of older persons in the MICU, we constructed a plausibility score from 0 - 10 where higher values indicate higher plausibility of time-dependent confounding of the association between a time-varying explanatory variable and an outcome. Based on longitudinal plots, measures of correlation, and longitudinal regression, the plausibility scores were compared to the differences in estimates obtained with non-weighted and marginal structural models of next day delirium. The plausibility scores of the three possible pairings of daily doses of fentanyl, haloperidol, and intubation indicated the following: low plausibility for haloperidol and intubation, moderate plausibility for fentanyl and haloperidol, and high plausibility for fentanyl and intubation. Comparing multivariable models of next day delirium with and without adjustment for time-dependent confounding, only intubation's association changed substantively. In our observational study of older persons in the MICU, the plausibility scores were generally reflective of the observed differences between coefficients estimated from non-weighted and marginal structural models

Use of Self-Matching to Control for Stable Patient Characteristics While Addressing Time-Varying Confounding on Treatment Effect: A Case Study of Older Intensive Care Patients

Exposure-crossover design offers a non-experimental option to control for stable baseline confounding through self-matching while examining causal effect of an exposure on an acute outcome. This study extends this approach to longitudinal data with repeated measures of exposure and outcome using data from a cohort of 340 older medical patients in an intensive care unit (ICU). The analytic sample included 92 patients who received ≥1 dose of haloperidol, an antipsychotic medication often used for patients with delirium. Exposure-crossover design was implemented by sampling the 3-day time segments prior (Induction) and posterior (Subsequent) to each treatment episode of receiving haloperidol. In the full cohort, there was a trend of increasing delirium severity scores (Mean±SD: 4.4±1.7) over the course of the ICU stay. After exposure-crossover sampling, the delirium severity score decreased from the Induction (4.9) to the Subsequent (4.1) intervals, with the treatment episode falling in-between (4.5). Based on a GEE Poisson model accounting for self-matching and within-subject correlation, the unadjusted mean delirium severity scores was −0.55 (95% CI: −1.10, −0.01) points lower for the Subsequent than the Induction intervals. The association diminished by 32% (−0.38, 95%CI: −0.99, 0.24) after adjusting only for ICU confounding, while being slightly increased by 7% (−0.60, 95%CI: −1.15, −0.04) when adjusting only for baseline characteristics. These results suggest that longitudinal exposure-crossover design is feasible and capable of partially removing stable baseline confounding through self-matching. Loss of power due to eliminating treatment-irrelevant person-time and uncertainty around allocating person-time to comparison intervals remain methodological challenges