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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. [ABSTRACT FROM AUTHOR]

Purpose: The heterogeneity in sepsis is held responsible, in part, for the lack of precision treatment. Many attempts to identify subtypes of sepsis patients identify those with shared underlying biology or outcomes. To date, though, there has been limited effort to determine overlap across these previously identified subtypes. We aimed to determine the concordance of critically ill patients with sepsis classified by four previously described subtype strategies. Methods: This secondary analysis of a multicenter prospective observational study included 522 critically ill patients with sepsis assigned to four previously established subtype strategies, primarily based on: (i) clinical data in the electronic health record (α, β, γ, and δ), (ii) biomarker data (hyper- and hypoinflammatory), and (iii–iv) transcriptomic data (Mars1–Mars4 and SRS1–SRS2). Concordance was studied between different subtype labels, clinical characteristics, biological host response aberrations, as well as combinations of subtypes by sepsis ensembles. Results: All four subtype labels could be adjudicated in this cohort, with the distribution of the clinical subtype varying most from the original cohort. The most common subtypes in each of the four strategies were γ (61%), which is higher compared to the original classification, hypoinflammatory (60%), Mars2 (35%), and SRS2 (54%). There was no clear relationship between any of the subtyping approaches (Cramer's V = 0.086–0.456). Mars2 and SRS1 were most alike in terms of host response biomarkers (p = 0.079–0.424), while other subtype strategies showed no clear relationship. Patients enriched for multiple subtypes revealed that characteristics and outcomes differ dependent on the combination of subtypes made. Conclusion: Among critically ill patients with sepsis, subtype strategies using clinical, biomarker, and transcriptomic data do not identify comparable patient populations and are likely to reflect disparate clinical characteristics and underlying biology. [ABSTRACT FROM AUTHOR]

The development of a shared data infrastructure across health systems could improve research, clinical care, and health policy across a spectrum of diseases, including sepsis. Awareness of the potential value of such infrastructure has been heightened by COVID-19, as the lack of a real-time, interoperable data network impaired disease identification, mitigation, and eradication. The Sepsis on FHIR collaboration establishes a dynamic, federated, and interoperable system of sepsis data from 55 hospitals using 2 distinct inpatient electronic health record systems. Here we report on phase 1, a systematic review to identify clinical variables required to define sepsis and its subtypes to produce a concept mapping of elements onto Fast Healthcare Interoperability Resources (FHIR). Relevant papers described consensus sepsis definitions, provided criteria for sepsis, severe sepsis, septic shock, or detailed sepsis subtypes. Studies not written in English, published prior to 1970, or "grey" literature were prospectively excluded. We analyzed 55 manuscripts yielding 151 unique clinical variables. We then mapped variables to their corresponding US Core FHIR resources and specific code values. This work establishes the framework to develop a flexible infrastructure for sharing sepsis data, highlighting how FHIR could enable the extension of this approach to other important conditions relevant to public health. [ABSTRACT FROM AUTHOR]