Corticosteroids decrease the duration of organ dysfunction in a range of infectious critical illnesses, but their risk and benefit are not fully defined using this construct. This retrospective multicenter study aimed to evaluate the association between usage of corticosteroids and mortality of patients with infectious critical illness by emulating a target trial framework. The study employed a novel stratification method with predictive machine learning (ML) subphenotyping based on organ dysfunction trajectory. Our analysis revealed that corticosteroids' effectiveness varied depending on the stratification method. The ML-based approach identified four distinct subphenotypes, two of which had a large enough sample size in our patient cohorts for further evaluation: "Rapidly Improving" (RI) and "Rapidly Worsening," (RW) which showed divergent responses to corticosteroid treatment. Specifically, the RW group either benefited or were not harmed from corticosteroids, whereas the RI group appeared to derive harm. In the development cohort, which comprised of a combination of patients from the eICU and MIMIC-IV datasets, hazard ratio estimates for the primary outcome, 28-day mortality, in the RW group was 1.05 (95% CI: 0.96 - 1.04) whereas for the RW group, it was 1.40 (95% CI: 1.28 - 1.54). For the validation cohort, which comprised of patients from the Critical carE Database for Advanced Research, estimates for 28-day mortality for the RW and RI groups were 1.24 (95% CI: 1.05 - 1.46) and 1.34 (95% CI: 1.14 - 1.59), respectively. For secondary outcomes, the RW group had a shorter time to ICU discharge and time to cessation of mechanical ventilation with corticosteroid treatment, where the RI group again demonstrated harm. The findings support matching treatment strategies to empirically observed pathobiology and offer a more nuanced understanding of corticosteroid utility. Our results have implications for the design and interpretation of both observational studies and randomized controlled trials (RCTs), suggesting the need for stratification methods that account for the differential response to standard of care.

E.S. received personal fees from Axle Informatics outside of stated work.

The authors would like to acknowledge the support from National Science Foundation awards (nos. 1750326 and 2212175), National Institute of Health awards (nos. RF1AG072449, R01AG080624, R01AG076448, RF1AG084178, R01AG076234, R01AG080991 and R01MH 124740), National Institute of Health award NHLBI K23 HL151876-01A1, and National Institute of General Medical Sciences award number K23 GM151730-01 for this study.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

IRB of Weill Cornell Medicine gave ethical approval for this work.

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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).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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The de-identified data utilized in this study for the development cohort (MIMIC-IV) and the validation cohort (eICU) can be accessed upon the approval of a formal proposal and the execution of a Data Access Agreement via Physio Net (https://physionet.org/). Access for the CEDAR validation cohort is not encompassed by our existing data transfer agreements. The source code pertinent to this research is publicly accessible. The primary repository is hosted on https://github.com/surajraj99/Corticosteroids-in-Patients-with-Critical-Illness. Source code for determining subtypes can be found in https://github.com/xuzhenxing2019/sepsis_subphenotype.

https://github.com/surajraj99/Corticosteroids-in-Patients-with-Critical-Illness

https://github.com/xuzhenxing2019/sepsis_subphenotype