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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 ([alpha], [beta], [gamma], and [delta]), (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 [gamma] (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., Author(s): Rombout B. E. van Amstel [sup.1] [sup.2], Jason N. Kennedy [sup.3], Brendon P. Scicluna [sup.4] [sup.5], Lieuwe D. J. Bos [sup.1] [sup.2], Hessel Peters-Sengers [sup.6] [sup.7], Joe M. Butler [...]

This cohort study characterizes heterogeneity in cardiac function prior to sepsis and describes associations with hospitalization outcomes and mortality. [ABSTRACT FROM AUTHOR]

Background: Sepsis is common, deadly, and heterogenous. Prior analyses of patients with sepsis and septic shock in New York State showed a risk-adjusted association between more rapid antibiotic administration and bundled care completion, but not an intravenous fluid bolus, with reduced in-hospital mortality. However, it is unknown if clinically identifiable sepsis subtypes modify these associations. Methods: Secondary analysis of patients with sepsis and septic shock enrolled in the New York State Department of Health cohort from January 1, 2015 to December 31, 2016. Patients were classified as clinical sepsis subtypes (α, β, γ, δ-types) using the Sepsis ENdotyping in Emergency CAre (SENECA) approach. Exposure variables included time to 3-h sepsis bundle completion, antibiotic administration, and intravenous fluid bolus completion. Then logistic regression models evaluated the interaction between exposures, clinical sepsis subtypes, and in-hospital mortality. Results: 55,169 hospitalizations from 155 hospitals were included (34% α, 30% β, 19% γ, 17% δ). The α-subtype had the lowest (N = 1,905, 10%) and δ-subtype had the highest (N = 3,776, 41%) in-hospital mortality. Each hour to completion of the 3-h bundle (aOR, 1.04 [95%CI, 1.02–1.05]) and antibiotic initiation (aOR, 1.03 [95%CI, 1.02–1.04]) was associated with increased risk-adjusted in-hospital mortality. The association differed across subtypes (p-interactions < 0.05). For example, the outcome association for the time to completion of the 3-h bundle was greater in the δ-subtype (aOR, 1.07 [95%CI, 1.05–1.10]) compared to α-subtype (aOR, 1.02 [95%CI, 0.99–1.04]). Time to intravenous fluid bolus completion was not associated with risk-adjusted in-hospital mortality (aOR, 0.99 [95%CI, 0.97–1.01]) and did not differ among subtypes (p-interaction = 0.41). Conclusion: Timely completion of a 3-h sepsis bundle and antibiotic initiation was associated with reduced risk-adjusted in-hospital mortality, an association modified by clinically identifiable sepsis subtype. [ABSTRACT FROM AUTHOR]

Importance: The quick Sequential (Sepsis-Related) Organ Failure Assessment (qSOFA) score has not been well-evaluated in low- and middle-income countries (LMICs).Objective: To assess the association of qSOFA with excess hospital death among patients with suspected infection in LMICs and to compare qSOFA with the systemic inflammatory response syndrome (SIRS) criteria.Design, Settings, and Participants: Retrospective secondary analysis of 8 cohort studies and 1 randomized clinical trial from 2003 to 2017. This study included 6569 hospitalized adults with suspected infection in emergency departments, inpatient wards, and intensive care units of 17 hospitals in 10 LMICs across sub-Saharan Africa, Asia, and the Americas.Exposures: Low (0), moderate (1), or high (≥2) qSOFA score (range, 0 [best] to 3 [worst]) or SIRS criteria (range, 0 [best] to 4 [worst]) within 24 hours of presentation to study hospital.Main Outcomes and Measures: Predictive validity (measured as incremental hospital mortality beyond that predicted by baseline risk factors, as a marker of sepsis or analogous severe infectious course) of the qSOFA score (primary) and SIRS criteria (secondary).Results: The cohorts were diverse in enrollment criteria, demographics (median ages, 29-54 years; males range, 36%-76%), HIV prevalence (range, 2%-43%), cause of infection, and hospital mortality (range, 1%-39%). Among 6218 patients with nonmissing outcome status in the combined cohort, 643 (10%) died. Compared with a low or moderate score, a high qSOFA score was associated with increased risk of death overall (19% vs 6%; difference, 13% [95% CI, 11%-14%]; odds ratio, 3.6 [95% CI, 3.0-4.2]) and across cohorts (P < .05 for 8 of 9 cohorts). Compared with a low qSOFA score, a moderate qSOFA score was also associated with increased risk of death overall (8% vs 3%; difference, 5% [95% CI, 4%-6%]; odds ratio, 2.8 [95% CI, 2.0-3.9]), but not in every cohort (P < .05 in 2 of 7 cohorts). High, vs low or moderate, SIRS criteria were associated with a smaller increase in risk of death overall (13% vs 8%; difference, 5% [95% CI, 3%-6%]; odds ratio, 1.7 [95% CI, 1.4-2.0]) and across cohorts (P < .05 for 4 of 9 cohorts). qSOFA discrimination (area under the receiver operating characteristic curve [AUROC], 0.70 [95% CI, 0.68-0.72]) was superior to that of both the baseline model (AUROC, 0.56 [95% CI, 0.53-0.58; P < .001) and SIRS (AUROC, 0.59 [95% CI, 0.57-0.62]; P < .001).Conclusions and Relevance: When assessed among hospitalized adults with suspected infection in 9 LMIC cohorts, the qSOFA score identified infected patients at risk of death beyond that explained by baseline factors. However, the predictive validity varied among cohorts and settings, and further research is needed to better understand potential generalizability. [ABSTRACT FROM AUTHOR]

Recent studies by Wallin and others have described the characteristics of single sympathetic axons in conscious humans using high impedence percutaneous electrodes. While these procedures do not allow for repeated recording from identified axons over several days, they have described the properties and responses of individual sympathetic axons for the first time. In our study, we sought to characterize the responses of single fibers in renal sympathetic nerves to various stimuli including stress in conscious rats. Without trying to test the longevity of the recordings specifically, we found we were able to record from individual axons for up to two weeks. Using relatively standard procedures, we identified single units using several criteria. These units have a consistent amplitude and morphology and respond to acute stress stimuli in a similar manner for several days. The activity of many axons was entrained to the cardiac cycle. Moreover, the large amplitude of many of these single units complicated the traditional analysis of multiunit activity using a moving average. The analysis of multiunit activity in these high fidelity recordings requires a different methodology. We conclude that it is possible to record from single axons for long periods and that this will allow the examination of progressive changes in activity during the development of cardiovascular disease. [ABSTRACT FROM AUTHOR]

Objectives: The optimal approach for resuscitation in septic shock remains unclear despite multiple randomized controlled trials (RCTs). Our objective was to investigate whether previously uncharacterized variation across individuals in their response to resuscitation strategies may contribute to conflicting average treatment effects in prior RCTs., Design: We randomly split study sites from the Australian Resuscitation of Sepsis Evaluation (ARISE) and Protocolized Care for Early Septic Shock (ProCESS) trials into derivation and validation cohorts. We trained machine learning models to predict individual absolute risk differences (iARDs) in 90-day mortality in derivation cohorts and tested for heterogeneity of treatment effect (HTE) in validation cohorts and swapped these cohorts in sensitivity analyses. We fit the best-performing model in a combined dataset to explore roles of patient characteristics and individual components of early goal-directed therapy (EGDT) to determine treatment responses., Setting: Eighty-one sites in Australia, New Zealand, Hong Kong, Finland, Republic of Ireland, and the United States., Patients: Adult patients presenting to the emergency department with severe sepsis or septic shock., Interventions: EGDT vs. usual care., Measurements and Main Results: A local-linear random forest model performed best in predicting iARDs. In the validation cohort, HTE was confirmed, evidenced by an interaction between iARD prediction and treatment (p < 0.001). When patients were grouped based on predicted iARDs, treatment response increased from the lowest to the highest quintiles (absolute risk difference [95% CI], -8% [-19% to 4%] and relative risk reduction, 1.34 [0.89-2.01] in quintile 1 suggesting harm from EGDT, and 12% [1-23%] and 0.64 [0.42-0.96] in quintile 5 suggesting benefit). Sensitivity analyses showed similar findings. Pre-intervention albumin contributed the most to HTE. Analyses of individual EGDT components were inconclusive., Conclusions: Treatment response to EGDT varied across patients in two multicenter RCTs with large benefits for some patients while others were harmed. Patient characteristics, including albumin, were most important in identifying HTE., Competing Interests: Drs. Shah’s, Talisa’s, Chang’s, Tang’s, Cooper’s, Angus’s, and Yende’s institutions received funding from the National Institutes of Health (NIH; GM141081). Dr. Shah’s institution received funding from the NIH (K23GM122069, R21HL168070). Dr. Shah, Dr. Talisa, Dr. Chang, Dr. Triantafyllou, Dr. Tang, Mr. Kennedy, Dr. Cooper, Dr. Yealy, Dr. Seymour, Dr. Angus, and Dr. Yende received support for article research from the NIH. Dr. Talisa disclosed the study was supported by the NIH (UL1TR001857; S10OD028483). Dr. Triantafyllou, Mr. Kennedy, Dr. Cooper, and Dr. Seymour’s institutions received funding from the NIH (R01HL164835). Dr. Mayr’s institution received funding from the NIH (K23GM132688); he disclosed government work; and he received personal fees from Baxter for serving on a racial disparities advisory board outside the submitted work. Dr. Higgins’ institution received funding from the National Health and Medical Research Council (NHMRC) (GNT2008447); she received support for article research from the NHMRC. Mr. Mouncey’s institution received funding from the National Institute for Health and Care Research Health Technology Assessment program; he received support for article research from the National Institute for Health and Care Research. Mr. Kennedy’s and Dr. Seymour’s institutions received funding from the NIH (R21GM144851). Mr. Kennedy’s, Dr. Cooper’s, and Dr. Seymour’s institution received funding from the National Institute for General Medical Sciences (R35GM119519). Dr. Yealy’s institution received funding from the NIH. Dr. Seymour received funding from Beckman Coulter and Octapharma. Dr. Angus received funding from AM-Pharma and Abionyx. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2024 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.)