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      Identification of a hyperinflammatory sepsis phenotype using protein biomarker and clinical data in the ProCESS randomized trial

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          Abstract

          Sepsis is a heterogeneous syndrome and phenotypes have been proposed using clinical data. Less is known about the contribution of protein biomarkers to clinical sepsis phenotypes and their importance for treatment effects in randomized trials of resuscitation. The objective is to use both clinical and biomarker data in the Protocol-Based Care for Early Septic Shock (ProCESS) randomized trial to determine sepsis phenotypes and to test for heterogeneity of treatment effect by phenotype comparing usual care to protocolized early, goal-directed therapy(EGDT). In this secondary analysis of a subset of patients with biomarker sampling in the ProCESS trial (n = 543), we identified sepsis phenotypes prior to randomization using latent class analysis of 20 clinical and biomarker variables. Logistic regression was used to test for interaction between phenotype and treatment arm for 60-day inpatient mortality. Among 543 patients with severe sepsis or septic shock in the ProCESS trial, a 2-class model best fit the data (p = 0.01). Phenotype 1 (n = 66, 12%) had increased IL-6, ICAM, and total bilirubin and decreased platelets compared to phenotype 2 (n = 477, 88%, p < 0.01 for all). Phenotype 1 had greater 60-day inpatient mortality compared to Phenotype 2 (41% vs 16%; p < 0.01). Treatment with EGDT was associated with worse 60-day inpatient mortality compared to usual care (58% vs. 23%) in Phenotype 1 only (p-value for interaction = 0.05). The 60-day inpatient mortality was similar comparing EGDT to usual care in Phenotype 2 (16% vs. 17%). We identified 2 sepsis phenotypes using latent class analysis of clinical and protein biomarker data at randomization in the ProCESS trial. Phenotype 1 had increased inflammation, organ dysfunction and worse clinical outcomes compared to phenotype 2. Response to EGDT versus usual care differed by phenotype.

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          Incidence and Trends of Sepsis in US Hospitals Using Clinical vs Claims Data, 2009-2014

          Estimates from claims-based analyses suggest that the incidence of sepsis is increasing and mortality rates from sepsis are decreasing. However, estimates from claims data may lack clinical fidelity and can be affected by changing diagnosis and coding practices over time.
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            Subphenotypes in acute respiratory distress syndrome: latent class analysis of data from two randomised controlled trials.

            Subphenotypes have been identified within heterogeneous diseases such as asthma and breast cancer, with important therapeutic implications. We assessed whether subphenotypes exist within acute respiratory distress syndrome (ARDS), another heterogeneous disorder.
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              Derivation, Validation, and Potential Treatment Implications of Novel Clinical Phenotypes for Sepsis

              Question Are clinical sepsis phenotypes identifiable at hospital presentation correlated with the biomarkers of host response and clinical outcomes and relevant for understanding the heterogeneity of treatment effects? Findings In this retrospective analysis using data from 63 858 patients in 3 observational cohorts, 4 novel sepsis phenotypes (α, β, γ , and δ) with different demographics, laboratory values, and patterns of organ dysfunction were derived, validated, and shown to correlate with biomarkers and mortality. In the simulations using data from 3 randomized clinical trials involving 4737 patients, the outcomes related to the treatments were sensitive to changes in the distribution of these phenotypes. Meaning Four novel clinical phenotypes of sepsis were identified that correlated with host-response patterns and clinical outcomes and may help inform the design and interpretation of clinical trials. Importance Sepsis is a heterogeneous syndrome. Identification of distinct clinical phenotypes may allow more precise therapy and improve care. Objective To derive sepsis phenotypes from clinical data, determine their reproducibility and correlation with host-response biomarkers and clinical outcomes, and assess the potential causal relationship with results from randomized clinical trials (RCTs). Design, Settings, and Participants Retrospective analysis of data sets using statistical, machine learning, and simulation tools. Phenotypes were derived among 20 189 total patients (16 552 unique patients) who met Sepsis-3 criteria within 6 hours of hospital presentation at 12 Pennsylvania hospitals (2010-2012) using consensus k means clustering applied to 29 variables. Reproducibility and correlation with biological parameters and clinical outcomes were assessed in a second database (2013-2014; n = 43 086 total patients and n = 31 160 unique patients), in a prospective cohort study of sepsis due to pneumonia (n = 583), and in 3 sepsis RCTs (n = 4737). Exposures All clinical and laboratory variables in the electronic health record. Main Outcomes and Measures Derived phenotype (α, β, γ , and δ) frequency, host-response biomarkers, 28-day and 365-day mortality, and RCT simulation outputs. Results The derivation cohort included 20 189 patients with sepsis (mean age, 64 [SD, 17] years; 10 022 [50%] male; mean maximum 24-hour Sequential Organ Failure Assessment [SOFA] score, 3.9 [SD, 2.4]). The validation cohort included 43 086 patients (mean age, 67 [SD, 17] years; 21 993 [51%] male; mean maximum 24-hour SOFA score, 3.6 [SD, 2.0]). Of the 4 derived phenotypes, the α phenotype was the most common (n = 6625; 33%) and included patients with the lowest administration of a vasopressor; in the β phenotype (n = 5512; 27%), patients were older and had more chronic illness and renal dysfunction; in the γ phenotype (n = 5385; 27%), patients had more inflammation and pulmonary dysfunction; and in the δ phenotype (n = 2667; 13%), patients had more liver dysfunction and septic shock. Phenotype distributions were similar in the validation cohort. There were consistent differences in biomarker patterns by phenotype. In the derivation cohort, cumulative 28-day mortality was 287 deaths of 5691 unique patients (5%) for the α phenotype; 561 of 4420 (13%) for the β phenotype; 1031 of 4318 (24%) for the γ phenotype; and 897 of 2223 (40%) for the δ phenotype. Across all cohorts and trials, 28-day and 365-day mortality were highest among the δ phenotype vs the other 3 phenotypes ( P  < .001). In simulation models, the proportion of RCTs reporting benefit, harm, or no effect changed considerably (eg, varying the phenotype frequencies within an RCT of early goal-directed therapy changed the results from >33% chance of benefit to >60% chance of harm). Conclusions and Relevance In this retrospective analysis of data sets from patients with sepsis, 4 clinical phenotypes were identified that correlated with host-response patterns and clinical outcomes, and simulations suggested these phenotypes may help in understanding heterogeneity of treatment effects. Further research is needed to determine the utility of these phenotypes in clinical care and for informing trial design and interpretation. In this study, Sepsis-3 investigators use electronic health record and trial data from patients with sepsis within 6 hours of hospital presentation to define clinical phenotypes that correlate with host-response patterns, sepsis biomarkers, mortality, and treatment effects.
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                Author and article information

                Contributors
                seymourcw@upmc.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                14 March 2024
                14 March 2024
                2024
                : 14
                : 6234
                Affiliations
                [1 ]GRID grid.21925.3d, ISNI 0000 0004 1936 9000, Clinical Research, Investigation, and Systems Modeling of Acute Illness (CRISMA) Center, ; Pittsburgh, PA USA
                [2 ]Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, ( https://ror.org/01an3r305) Pittsburgh, PA USA
                [3 ]Department of Critical Care Medicine, University of Pittsburgh, ( https://ror.org/01an3r305) Pittsburgh, PA USA
                [4 ]Department of Medicine, University of Pittsburgh, ( https://ror.org/01an3r305) Pittsburgh, PA USA
                [5 ]Department of Psychiatry, University of California San Francisco, ( https://ror.org/043mz5j54) San Francisco, CA USA
                [6 ]Department of Emergency Medicine, University of Pittsburgh, ( https://ror.org/01an3r305) Pittsburgh, PA USA
                [7 ]Multidisciplinary Acute Care Research Organization (MACRO), Pittsburgh, PA USA
                [8 ]Department of Emergency Medicine, Beth Israel Deaconess Medical Center, ( https://ror.org/04drvxt59) Boston, MA USA
                [9 ]GRID grid.239395.7, ISNI 0000 0000 9011 8547, Center for Vascular Biology Research, , Beth Israel Deaconess Medical Center, ; Boston, MA USA
                [10 ]Division of Pulmonary and Critical Care Medicine, Department of Medicine and Anesthesia, University of California San Francisco, ( https://ror.org/043mz5j54) San Francisco, CA USA
                [11 ]Present Address: Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, ( https://ror.org/01an3r305) 3459 Fifth Avenue, NW628, Pittsburgh, PA 15213 USA
                Article
                55667
                10.1038/s41598-024-55667-5
                10940677
                38485953
                62a773a8-90b9-49d6-b9a5-139877140ff0
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 18 December 2023
                : 26 February 2024
                Funding
                Funded by: NIH
                Award ID: T32HL007820
                Award ID: R35HL140026
                Award ID: R35GM119519
                Award Recipient :
                Categories
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                © Springer Nature Limited 2024

                Uncategorized
                sepsis,phenotypes,biomarkers,prognostic markers,machine learning
                Uncategorized
                sepsis, phenotypes, biomarkers, prognostic markers, machine learning

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