Counterfactual Sepsis Outcome Prediction Under Dynamic and Time-Varying Treatment Regimes.

Abstract

Sepsis is a life-threatening condition that occurs when the body's normal response to an infection is out of balance. A key part of managing sepsis involves the administration of intravenous fluids and vasopressors. In this work, we explore the application of G-Net, a deep sequential modeling framework for g-computation, to predict outcomes under counterfactual fluid treatment strategies in a real-world cohort of sepsis patients. Utilizing observational data collected from the intensive care unit (ICU), we evaluate the performance of multiple deep learning implementations of G-Net and compare their predictive performance with linear models in forecasting patient outcomes and trajectories over time under the observational treatment regime. We then demonstrate that G-Net can generate counterfactual prediction of covariate trajectories that align with clinical expectations across various fluid limiting regimes. Our study demonstrates the potential clinical utility of G-Net in predicting counterfactual treatment outcomes, aiding clinicians in informed decision-making for sepsis patients in the ICU.