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

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. ### Competing Interest Statement Under a license agreement between Bayesian Health and the Johns Hopkins University, Dr. Henry, Dr. Saria, and Johns Hopkins University are entitled to revenue distributions. Additionally, the University owns equity in Bayesian Health. Dr. Saria also has grants from Gordon and Betty Moore Foundation, the National Science Foundation, the National Institutes of Health, Defense Advanced Research Projects Agency, the Food and Drug Administration, and the American Heart Association; she is a founder of and holds equity in Bayesian Health; she is the scientific advisory board member for PatientPing; and she has received honoraria for talks from a number of biotechnology, research, and health-tech companies. This arrangement has been reviewed and approved by the Johns Hopkins University in accordance with its conflict-of-interest policies. The other authors declare no disclosures of conflicts of interest. ### Funding Statement The authors gratefully acknowledge the following sources of funding: the Gordon and Betty Moore Foundation (award #3926), the National Science Foundation Future of Work at the Human-technology Frontier (award #1840088), and the Alfred P. Sloan Foundation research fellowship (2018). This information or content and conclusions are those of the authors and should not be construed as the official position or policy of, nor should any endorsements be inferred by the NSF the U.S. Government. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study was approved by the Johns Hopkins University internal review board (IRB No. 00252594) and a waiver of consent was obtained. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes The data are not publicly available because they are from electronic health records approved for limited use by Johns Hopkins University investigators.