Addressing the ‘coin flip model’ and the role of ‘process of care’ variables in the analysis of TREWS

Abstract

Across two recent papers, Henry et al. (Nature Medicine, 2022) and Adams et al. (Nature Medicine, 2022) evaluated a deployed machine learning-based early warning system for sepsis, the Targeted Real-time Early Warning System (TREWS) for sepsis, finding that provider interactions with the tool were associated with reduced time to antibiotics and improved patient outcomes. In a subsequent commentary, Nemati et al. (medRxiv, 2022) assert that "the findings of Adams et al. are likely to be severely biased due to the failure to adjust for 'processes of care'-related confounding factors." In this response to Nemati et al., we argue that this conclusion is based on unrealistic assumptions about provider behavior that do not match the data reported in Adams et al. We further show that adjusting for 'process of care'-related variables does not change the conclusions of Adams et al.