Multicenter target trial emulation to evaluate corticosteroids for sepsis stratified by predicted organ dysfunction trajectory
Suraj Rajendran, Zhenxing Xu, Weishen Pan, Chengxi Zang, Ilias Ι. Siempos, Lisa K. Torres, Jie Xu, Jiang Bian, Edward J. Schenck, Fei Wang
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. Corticosteroids shorten organ dysfunction in sepsis and related illnesses, but their risks and benefits remain unclear. Here, the authors show that machine learning-based trajectory prediction can reveal how corticosteroid effects vary by patient profile.