A Causal Framework for Evaluating ICU Discharge Strategies

Abstract

In this applied paper, we address the difficult open problem of when to discharge patients from the Intensive Care Unit. This can be conceived as an optimal stopping scenario with three added challenges: 1) the evaluation of a stopping strategy from observational data is itself a complex causal inference problem, 2) the composite objective is to minimize the length of intervention and maximize the outcome, but the two cannot be collapsed to a single dimension, and 3) the recording of variables stops when the intervention is discontinued. Our contributions are two-fold. First, we generalize the implementation of the g-formula Python package, providing a framework to evaluate stopping strategies for problems with the aforementioned structure, including positivity and coverage checks. Second, with a fully open-source pipeline, we apply this approach to MIMIC-IV, a public ICU dataset, demonstrating the potential for strategies that improve upon current care.

Figures (8)

• Figure 1: The ICU discharge trade-off represented in the space of potential outcomes for our multi-objective stopping problem. Given a fixed dataset, each strategy returns an average 90-day mortality on the y-axis as well as an average length of ICU stay on the x-axis. The red box defines the set of strategies that constitute an improvement over current clinical practice, i.e. strategies leading to lower mortality and lower utilization of ICU beds. The blue dashed area defines the set of strategies we are actually able to evaluate given the available data.
• Figure 2: Positivity and coverage diagnostics over time for all evaluated dynamic strategies separated between patients kept and discharged. Top: coverage rate (fraction of history-compatible observations at epoch $t$ for which the clinician's action matches the target strategy's prescription). Bottom: absolute match count (fraction of history-compatible observations at epoch $t$ for which the clinician's action matches the target strategy's prescription and the decision at time $t$).
• Figure 3: Positivity diagnostics over time for all evaluated strategies. Top: coverage rate $\rho_t$ (fraction of history-compatible observations at epoch $t$ for which the clinician's action matches the target strategy's prescription). Middle: effective sample size ratio $\mathrm{ESS}_t / N_t$ among matching observations (IS weight evenness; 1 = uniform, $1/N_t$ = fully concentrated). Bottom: absolute match count $N_t$. The red dashed line in the top and middle panels marks the 0.3 warning threshold.
• Figure 4: PCA positivity scatter plots for the three static threshold strategy at 3 days, across decision epochs $t \in \{12, 24, \ldots, 120\}$ h (columns). Each panel projects the first two PCA components of the covariate space. Rows 1 & 3: blue dots indicate patients for whom the strategy prescribes keep and the clinician kept; grey dots indicate patients for whom the strategy prescribes keep but the clinician discharged (positivity violation). Rows 2 & 4: orange dots indicate agreement on discharge; grey dots indicate patients the strategy would discharge but clinicians retained (positivity violation). Panel titles report cohort size $n$ and coverage rate $\rho_t$.
• Figure 5: PCA positivity scatter plots for the dynamic strategies (Natural course, Knight strategy, and DS1 strategy; top to bottom), across decision epochs $t \in \{12, 24, \ldots, 120\}$ h (columns). Layout and colour coding are identical to Figure \ref{['fig:positivity_pca_static']}.