Multicenter target trial emulation to evaluate corticosteroids for sepsis stratified by predicted organ dysfunction trajectory.

TitleMulticenter target trial emulation to evaluate corticosteroids for sepsis stratified by predicted organ dysfunction trajectory.
Publication TypeJournal Article
Year of Publication2025
AuthorsRajendran S, Xu Z, Pan W, Zang C, Siempos I, Torres L, Xu J, Bian J, Schenck EJ, Wang F
JournalNat Commun
Volume16
Issue1
Pagination4450
Date Published2025 May 13
ISSN2041-1723
KeywordsAdrenal Cortex Hormones, Aged, Female, Humans, Machine Learning, Male, Middle Aged, Multiple Organ Failure, Organ Dysfunction Scores, Pneumonia, Respiratory Distress Syndrome, Retrospective Studies, Sepsis, Shock, Septic
Abstract

Corticosteroids decrease the duration of organ dysfunction in sepsis and a range of overlapping and complementary infectious critical illnesses, including septic shock, pneumonia and the acute respiratory distress syndrome (ARDS). The risk and benefit of corticosteroids are not fully defined using the construct of organ dysfunction duration. This retrospective multicenter, proof-of-concept study aimed to evaluate the association between usage of corticosteroids and mortality of patients with sepsis, pneumonia and ARDS by emulating a target trial framework stratified by predicted organ dysfunction trajectory. The study employed a two staged machine learning (ML) methodology to first subphenotype based on organ dysfunction trajectory then predict this defined trajectory. Once patients were classified by predicted trajectory we conducted a target trial emulation. Our analysis revealed that the association between corticosteroid use and 28-day mortality varied by predicted trajectory and between cohorts.Our findings suggest that matching treatment strategies to empirically observed pathobiology may offer a more nuanced understanding of corticosteroid utility.

DOI10.1038/s41467-025-59643-z
Alternate JournalNat Commun
PubMed ID40360520
PubMed Central IDPMC12075795
Grant ListK23 HL151876 / HL / NHLBI NIH HHS / United States
RF1 AG084178 / AG / NIA NIH HHS / United States
R01 AG080624 / AG / NIA NIH HHS / United States
K23 GM151730 / GM / NIGMS NIH HHS / United States
RF1 AG072449 / AG / NIA NIH HHS / United States
R01 AG076448 / AG / NIA NIH HHS / United States
R01 AG080991 / AG / NIA NIH HHS / United States
R01 AG076234 / AG / NIA NIH HHS / United States
R01 MH124740 / MH / NIMH NIH HHS / United States
Division: 
Institute of Artificial Intelligence for Digital Health
Category: 
Faculty Publication