Randomized Clinical Trials or Convenient Controls: TREWS or FALSE?

Abstract

We read with interest the Adams et al. (doi: https://doi.org/10.1038/s41591-022-01894-0) report of the TREWS machine learning (ML)-based sepsis early warning system. The authors conclude that large-scale randomized trials are needed to confirm their observations, but assert that their findings indicate the potential for the TREWS system to identify sepsis patients early and improve patient outcomes, including a significant decrease in mortality. However, this conclusion is based upon a comparison of those whose alert was confirmed vs. not confirmed within 3 hours, rather than random allocation to TREWS vs. no TREWS. Using data from over 650,000 patient encounters across two distinct healthcare systems, we show 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.

Competing Interest Statement

Drs. Nemati and Shashikumar are co-founders and scientific advisors to Healcisio Inc., a UCSD startup that has been setup in accordance with the UCSD's conflicts of interest management policies.

Funding Statement

Dr. Nemati has received fundings from the National Institutes of Health (R01LM013998, R01HL157985, R35GM143121), and the Gordon and Betty Moore Foundation (#GBMF9052). Dr. Holder is supported by the National Institute of General Medical Sciences of the National Institutes of Health (K23GM146092). Dr. Wardi is supported by the National Institute of General Medical Sciences of the National Institutes of Health (K23GM37182).

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:

Analysis of the de-identified data utilized in our thought experiment was conducted in accordance to the approved Institutional Review Board (IRB) protocols of UC San Diego (IRB#191940; Cohort A) and Grady Hospital (IRB#110675; Cohort B), and the requirement for informed consent was waived, as the use of de-identified retrospective data does not require patient consent under the Health Insurance Portability and Accountability Act (HIPAA) privacy regulations.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the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

Data Availability

Access to de-identified UCSD and Grady cohort may be made available via approval from UCSD and Grady Institutional Review Board (IRB).

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