A Causal Framework for Evaluating ICU Discharge Strategies

TL;DR

This paper develops a causal framework to evaluate ICU discharge strategies from observational data, addressing complex challenges in optimal stopping, and demonstrates its application on real-world data to improve patient care.

Contribution

It generalizes the g-formula for evaluating stopping strategies and provides an open-source pipeline applied to MIMIC-IV data for ICU discharge decision-making.

Findings

Framework successfully evaluates ICU discharge strategies

Potential to improve current ICU discharge practices

Open-source tools facilitate causal analysis in healthcare

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.