Sepsis Subclasses: A Framework for Development and Interpretation.

Sepsis is defined as a dysregulated host response to infection that leads to life-threatening acute organ dysfunction. It afflicts approximately 50 million people worldwide annually and is often deadly, even when evidence-based guidelines are applied promptly. Many randomized trials tested therapies for sepsis over the past 2 decades, but most have not proven beneficial. This may be because sepsis is a heterogeneous syndrome, characterized by a vast set of clinical and biologic features. Combinations of these features, however, may identify previously unrecognized groups, or "subclasses" with different risks of outcome and response to a given treatment. As efforts to identify sepsis subclasses become more common, many unanswered questions and challenges arise. These include: 1) the semantic underpinning of sepsis subclasses, 2) the conceptual goal of subclasses, 3) considerations about study design, data sources, and statistical methods, 4) the role of emerging data types, and 5) how to determine whether subclasses represent "truth." We discuss these challenges and present a framework for the broader study of sepsis subclasses. This framework is intended to aid in the understanding and interpretation of sepsis subclasses, provide a mechanism for explaining subclasses generated by different methodologic approaches, and guide clinicians in how to consider subclasses in bedside care.

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

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.

Feasibility of Embedding a Scalable, Virtually Enabled Biorepository in the Electronic Health Record for Precision Medicine.

Importance: A cornerstone of precision medicine is the identification and use of biomarkers that help subtype patients for targeted treatment. Such an approach requires the development and subsequent interrogation of large-scale biobanks linked to well-annotated clinical data. Traditional means of creating these data-linked biobanks are costly and lengthy, especially in acute conditions that require time-sensitive clinical data and biospecimens. Objectives: To develop a virtually enabled biorepository and electronic health record (EHR)-embedded, scalable cohort for precision medicine (VESPRE) and compare the feasibility, enrollment, and costs of VESPRE with those of a traditional study design in acute care. Design, Setting, and Participants: In a prospective cohort study, the EHR-embedded screening alert was generated for 3428 patients, and 2199 patients (64%) were eligible and screened. Of these, 1027 patients (30%) were enrolled. VESPRE was developed for regulatory compliance, feasibility, internal validity, and cost in a prospective cohort of 1027 patients (aged ≥18 years) with sepsis-3 within 6 hours of presentation to the emergency department. The VESPRE infrastructure included (1) automated EHR screening, (2) remnant blood collection for creation of a virtually enabled biorepository, and (3) automated clinical data abstraction. The study was conducted at an academic institution in southwestern Pennsylvania from October 17, 2017, to June 6, 2019. Main Outcomes and Measures: Regulatory compliance, enrollment, internal validity of automated screening, biorepository acquisition, and costs. Results: Of the 1027 patients enrolled in the study, 549 were included in the proof-of-concept analysis (305 [56%] men); median (SD) age was 59 (17) years. VESPRE collected 12 963 remnant blood and urine samples and demonstrated adequate feasibility for clinical, biomarker, and microbiome analyses. Over the 20-month test, the total cost beyond the existing operations infrastructure was $39 417.50 ($14 880.00 project management, $22 717.50 laboratory supplies/staff, and $1820.00 data management)-approximately $39 per enrolled patient vs $239 per patient for a traditional cohort study. Conclusions and Relevance: Results of this study suggest that, in a large US health system that collects data using a common EHR platform and centralized laboratory system, VESPRE, a large-scale, inexpensive EHR-embedded infrastructure for precision medicine can be used. Tested in the sepsis setting, VESPRE appeared to capture a high proportion of eligible patients at low incremental cost.

Shorter Versus Longer Courses of Antibiotics for Infection in Hospitalized Patients: A Systematic Review and Meta-Analysis.

BACKGROUND: Infection is a leading cause of hospitalization with high morbidity and mortality, but there are limited data to guide the duration of antibiotic therapy. PURPOSE: Systematic review to compare outcomes of shorter versus longer antibiotic courses among hospitalized adults and adolescents. DATA SOURCES: MEDLINE and Embase databases, 1990-2017. STUDY SELECTION: Inclusion criteria were human randomized controlled trials (RCTs) in English comparing a prespecified short course of antibiotics to a longer course for treatment of infection in hospitalized adults and adolescents aged 12 years and older. DATA EXTRACTION: Two authors independently extracted study characteristics, methods of statistical analysis, outcomes, and risk of bias. DATA SYNTHESIS: Of 5187 unique citations identified, 19 RCTs comprising 2867 patients met our inclusion criteria, including the following: 9 noninferiority trials, 1 superiority design trial, and 9 pilot studies. Across 13 studies evaluating 1727 patients, no significant difference in clinical efficacy was observed (d = 1.6% [95% confidence interval (CI), -1.0%-4.2%]). No significant difference was detected in microbiologic cure (8 studies, d = 1.2% [95% CI, -4.1%-6.4%]), short-term mortality (8 studies, d = 0.3% [95% CI, -1.2%-1.8%]), longer-term mortality (3 studies, d = -0.4% [95% CI, -6.3%-5.5%]), or recurrence (10 studies, d = 2.1% [95% CI, -1.2%-5.3%]). Heterogeneity across studies was not significant for any of the primary outcomes. CONCLUSIONS: Based on the available literature, shorter courses of antibiotics can be safely utilized in hospitalized patients with common infections, including pneumonia, urinary tract infection, and intra-abdominal infection, to achieve clinical and microbiologic resolution without adverse effects on mortality or recurrence.

Increased healthcare facility use in veterans surviving sepsis hospitalization.

PURPOSE: We sought to measure inpatient healthcare utilization among U.S. Veteran Affairs beneficiaries surviving sepsis hospitalization, and to examine how post-sepsis utilization varies by select patient characteristics. MATERIALS AND METHODS: Retrospective cohort study of 26,561 Veterans who survived sepsis hospitalization in 2009. Using difference-in-differences analysis, we compared changes in healthcare utilization in one year before and one year after sepsis hospitalization by Veteran age, illness severity, and recent nursing facility use. RESULTS: Median days in a healthcare facility increased from 5 to 10. Veterans with recent nursing facility use spent a median 65days (or 86% of days alive) in a healthcare facility in the year after sepsis. Older age, greater illness severity, and recent nursing home use were each associated with spending more days, and a greater proportion of days alive, in a healthcare facility during the year after sepsis. However, none of these characteristics was associated with a greater rise in utilization after sepsis. CONCLUSIONS: Veterans surviving sepsis experience high rates of post-sepsis mortality and significant increases in healthcare facility use. Recent nursing facility use is strongly predictive of greater post-sepsis healthcare utilization.