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.

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.

Objective Sepsis hospitalizations are frequently followed by hospital readmissions, often for recurrent sepsis. However, it is unclear how often sepsis readmissions are for relapsed/recrudescent versus new infections. The aim of this study was to assess the extent to which 90-day readmissions for recurrent sepsis are due to infection of the same site and same pathogen as the initial episode. Design Retrospective cohort study. Setting University of Michigan Health System. Patients All hospitalizations (May 15, 2013 to May 14, 2015) with a principal ICD-9-CM diagnosis of septicemia (038.x), severe sepsis (995.92) or septic shock (785.52), as well as all subsequent hospitalizations and sepsis readmissions within 90 days. We determined organism and site of sepsis through manual chart abstraction. Interventions None Measurements and Main Results We identified 472 readmissions within 90 days of sepsis, of which 137 (29.1%) were for sepsis. In sepsis readmissions, the site and organisms were most commonly urinary (29.2%), gastrointestinal (20.4%), gram negative (29.9%), gram positive (16.8%) and culture negative (30.7%). 94 (68.6%) readmissions were for infection at the same site as initial sepsis hospitalization. 19% of readmissions were confirmed to be same site and same organism. However, accounting for the uncertainty from culture-negative sepsis, as many as 53.2% of readmissions could plausibly due to infections with both the same organism and same site. Conclusions Of the patients readmitted with sepsis within 90 days, two thirds had infection at the same site as their initial admission. Just 19% had infection confirmed to be from the same site and organism as the initial sepsis hospitalization. Half of readmissions were definitively for new infections, while an additional 34% were unclear since cultures were negative in one of the hospitalizations.

A taxonomic revolution isoccurringinmedicine.Spurred by the halcyon vision of targeted "precision" therapy and enabled by access to massive electronic health data sets, high-throughput multichannel, molecular diagnostic assays, and advances in the understanding of disease biology, researchers have generated a plethora of new disease subclassifications (eTable and eFigure in the Supplement). Variably termed "phenotypes," "endotypes," or "subtypes," these patient groups can share symptoms, biology, or prognosis and are proposed as the basis for precision care.The fast-paced research of SARS-CoV-2 has followed suit, with more than 60 subtypes proposed in the last year (eTable and eFigure in the Supplement). These subtypes range from simple classifications such as the H or L phenotypes of COVID-19-related acute respiratory distress syndrome to emerging groups organized from machine learning methods on large data sets. This Viewpoint examines the complexity of COVID-19 subtype classification and the implications 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 PARTICIPANTSIn 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 Ն18 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

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 65 days (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.

Background: There is wide heterogeneity in sepsis in causative pathogens, host response, organ dysfunction, and outcomes. Clinical and biologic phenotypes of sepsis are proposed, but the role of pathogen data on sepsis classification is unknown.Methods: We conducted a secondary analysis of the Recombinant Human Activated Protein C (rhAPC) Worldwide Evaluation in Severe Sepsis (PROWESS) Study. We used latent class analysis (LCA) to identify sepsis phenotypes using, (i) only clinical variables (“host model”) and, (ii) combining clinical with microbiology variables (e.g., site of infection, culture-derived pathogen type, and anti-microbial resistance characteristics, “host-pathogen model”). We describe clinical characteristics, serum biomarkers, and outcomes of host and host-pathogen models. We tested the treatment effects of rhAPC by phenotype using Kaplan-Meier curves.Results: Among 1,690 subjects with severe sepsis, latent class modeling derived a 4-class host model and a 4-class host-pathogen model. In the host model, alpha type (N = 327, 19%) was younger and had less shock; beta type (N=518, 31%) was older with more comorbidities; gamma type (N = 532, 32%) had more pulmonary dysfunction; delta type (N = 313, 19%) had more liver, renal and hematologic dysfunction and shock. After the addition of microbiologic variables, 772 (46%) patients changed phenotype membership, and the median probability of phenotype membership increased from 0.95 to 0.97 (P < 0.01). When microbiology data were added, the contribution of individual variables to phenotypes showed greater change for beta and gamma types. In beta type, the proportion of abdominal infections (from 20 to 40%) increased, while gamma type patients had an increased rate of lung infections (from 50 to 78%) with worsening pulmonary function. Markers of coagulation such as d-dimer and plasminogen activator inhibitor (PAI)-1 were greater in the beta type and lower in the gamma type. The 28 day mortality was significantly different for individual phenotypes in host and host-pathogen models (both P < 0.01). The treatment effect of rhAPC obviously changed in gamma type when microbiology data were added (P-values of log rank test changed from 0.047 to 0.780).Conclusions: Sepsis host phenotype assignment was significantly modified when microbiology data were added to clinical variables, increasing cluster cohesiveness and homogeneity.

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