• Faraaz Ali Shah, Victor B Talisa, ChangChung-Chou HCHDivision of General Internal Medicine, University of Pittsburgh, Pittsburgh, PA.Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA., Sofia Triantafyllou, Lu Tang, Florian B Mayr, Alisa M Higgins, Sandra L Peake, Paul Mouncey, David A Harrison, Kimberley M DeMerle, Jason N Kennedy, Gregory F Cooper, Rinaldo Bellomo, Kathy Rowan, Donald M Yealy, Christopher W Seymour, Derek C Angus, and Sachin P Yende.
• Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA.
• Crit. Care Med. 2024 Oct 23.

ObjectivesThe optimal approach for resuscitation in septic shock remains unclear despite multiple randomized controlled trials (RCTs). Our objective was to investigate whether previously uncharacterized variation across individuals in their response to resuscitation strategies may contribute to conflicting average treatment effects in prior RCTs.DesignWe randomly split study sites from the Australian Resuscitation of Sepsis Evaluation (ARISE) and Protocolized Care for Early Septic Shock (ProCESS) trials into derivation and validation cohorts. We trained machine learning models to predict individual absolute risk differences (iARDs) in 90-day mortality in derivation cohorts and tested for heterogeneity of treatment effect (HTE) in validation cohorts and swapped these cohorts in sensitivity analyses. We fit the best-performing model in a combined dataset to explore roles of patient characteristics and individual components of early goal-directed therapy (EGDT) to determine treatment responses.SettingEighty-one sites in Australia, New Zealand, Hong Kong, Finland, Republic of Ireland, and the United States.PatientsAdult patients presenting to the emergency department with severe sepsis or septic shock.InterventionsEGDT vs. usual care.Measurements And Main ResultsA local-linear random forest model performed best in predicting iARDs. In the validation cohort, HTE was confirmed, evidenced by an interaction between iARD prediction and treatment (p < 0.001). When patients were grouped based on predicted iARDs, treatment response increased from the lowest to the highest quintiles (absolute risk difference [95% CI], -8% [-19% to 4%] and relative risk reduction, 1.34 [0.89-2.01] in quintile 1 suggesting harm from EGDT, and 12% [1-23%] and 0.64 [0.42-0.96] in quintile 5 suggesting benefit). Sensitivity analyses showed similar findings. Pre-intervention albumin contributed the most to HTE. Analyses of individual EGDT components were inconclusive.ConclusionsTreatment response to EGDT varied across patients in two multicenter RCTs with large benefits for some patients while others were harmed. Patient characteristics, including albumin, were most important in identifying HTE.Copyright © 2024 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.

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