{"doi":"10.3410/f.726629397.793577235","title":"Faculty Opinions recommendation of Acute respiratory distress syndrome subphenotypes respond differently to randomized fluid management strategy.","abstract":"RATIONALE: We previously identified two acute respiratory distress syndrome (ARDS) subphenotypes in two separate randomized controlled trials with differential response to positive end-expiratory pressure.OBJECTIVES: To identify these subphenotypes in a third ARDS cohort, to test whether subphenotypes respond differently to fluid management strategy, and to develop a practical model for subphenotype identification.METHODS: We used latent class analysis of baseline clinical and plasma biomarker data to identify subphenotypes in FACTT (Fluid and Catheter Treatment Trial; n = 1,000). Logistic regression was used to test for an interaction between subphenotype and treatment for mortality. We used stepwise modeling to generate a model for subphenotype identification in FACTT and validated its accuracy in the two cohorts in which we previously identified ARDS subphenotypes.MEASUREMENTS AND MAIN RESULTS: We confirmed that a two-class (two-subphenotype) model best described the study population. Subphenotype 2 was again characterized by higher inflammatory biomarkers and hypotension. Fluid management strategy had significantly different effects on 90-day mortality in the two subphenotypes (P = 0.0039 for interaction); mortality in subphenotype 1 was 26% with fluid-liberal strategy versus 18% with fluid-conservative, whereas mortality in subphenotype 2 was 40% with fluid-liberal strategy versus 50% in fluid-conservative. A three-variable model of IL-8, bicarbonate, and tumor necrosis factor receptor-1 accurately classified the subphenotypes.CONCLUSIONS: This analysis confirms the presence of two ARDS subphenotypes that can be accurately identified with a limited number of variables and that responded differently to randomly assigned fluid management. These findings support the presence of ARDS subtypes that may require different treatment approaches. PMID: 27513822 Funding information This work was supported by: NHLBI NIH HHS, United States Grant ID: R01 HL110969 NHLBI NIH HHS, United States Grant ID: K24 HL133390 NHLBI NIH HHS, United States Grant ID: R21 HL112656 NHLBI NIH HHS, United States Grant ID: K23 HL116800 NHLBI NIH HHS, United States Grant ID: F32 HL129680 NHLBI NIH HHS, United States Grant ID: K24 HL103836 NHLBI NIH HHS, United States Grant ID: R01 HL131621 NHLBI NIH HHS, United States Grant ID: T32 HL007185 NHLBI NIH HHS, United States Grant ID: N01 HR016146 More Less keyboard_arrow_down","journal":"Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature","year":2020,"id":132868,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9543,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":15405,"name":"Mark E. Nunnally","orcid":"0000-0003-2129-4733","position":0,"is_corresponding":true}],"reference_count":19,"raw_metadata":null,"created_at":"2026-07-18T23:16:14.071209Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}