Daily Sepsis Research Analysis

BACKGROUND: Sepsis is a dysregulated host response to infection resulting in life-threatening organ failure. Although immune dysregulation is central to the sepsis definition, immunomodulation trials enrol participants based on clinical severity, not the extent of dysregulation, which could contribute to treatment heterogeneity. A pragmatic way to quantify immune dysregulation could improve prognostication, help to evaluate treatment responses, and identify individuals most likely to benefit from immunomodulation. We aimed to construct a parsimonious machine-learning tool that defines and quantifies immune dysregulation, thereby supporting biologically informed immunomodulation. METHODS: In this multicohort analysis and reanalysis of a randomised controlled trial, the primary objective was to derive and validate a categorical and continuous immune dysregulation score that is independent of clinical presentation or outcome. We measured 35 plasma biomarkers reflecting key host response domains in individuals with community-acquired pneumonia (CAP) across different care settings (emergency department, general ward, and intensive care unit) and disease severities using data from three independent cohorts. We applied unsupervised trajectory inference analysis to identify an immune dysregulation gradient captured as discrete immune dysregulation stages (Dysregulated Immune Profile [DIP]) and a continuous score (cDIP; 0-1). We developed two parsimonious machine-learning models to predict the DIP stages and cDIP scores based on 35 biomarkers, and validated their ability to capture immune dysregulation and predict clinical outcomes in five independent cohorts. On the basis of our hypothesis that only individuals with severe immune dysregulation benefit from immunomodulation, we carried out a post-hoc analysis of a randomised trial evaluating hydrocortisone in severe CAP (CAPE COD trial, NCT02517489), assessing treatment effects across DIP stages and the cDIP continuum, and how hydrocortisone influenced dysregulation trajectories over time. FINDINGS: We organised 398 participants with CAP along a continuum of immune dysregulation from mild to severe on the basis of 35 plasma biomarkers, yielding three dysregulation stages (DIP1-3) and a continuous score (cDIP). Clinical severity proved to be an inadequate proxy for immune dysregulation. A three-biomarker machine-learning framework (procalcitonin, soluble TREM-1, and IL-6) accurately predicted the degree of dysregulation derived from 35 biomarkers (DIP stage accuracy 91·2%; cDIP root mean square error 0·056). Although the framework was not designed for outcome prediction, increased immune dysregulation-reflected in DIP and cDIP-was associated with a gradual rise in mortality (cDIP odds ratio [OR] 1·26 [95% CI 1·13-1·40] per 10% increase, p<0·0001) and secondary infections (OR 1·50 [1·22-1·93] per 10% increase, p=0·0005), independent of clinical severity. The three-biomarker tool was validated in five external cohorts of varying infections, severities, and care settings (n=1191). Reanalysis of the CAPE COD trial showed that hydrocortisone conferred a survival benefit only in participants classified as severely dysregulated by our model (30-day mortality: DIP3 OR 0·25 [0·05-0·85], p=0·042; cDIP ≥0·63 OR 0·21 [0·10-0·72], p=0·011), accompanied by faster immune recovery (time × treatment interaction, p<0·0001). No such effect modification was observed when stratifying participants by clinical severity. INTERPRETATION: We have provided a publicly available three-biomarker framework to determine the extent of host response dysregulation with potential value for precision-guided immunomodulatory therapy. FUNDING: EU Horizon 2020.

BACKGROUND: Optimal strategies to reduce antimicrobial resistance (AMR) and their effect on newborns in low-income and middle-income countries (LMICs) remain unclear. We assessed the effectiveness of AMR mitigation strategies for newborn care in LMICs. METHODS: A systematic review and meta-analysis was conducted. We searched MEDLINE, Embase, CINAHL, Global Index Medicus, Cochrane Central Register of Controlled Trials, and grey literature from Jan 1, 2000, to Nov 20, 2025, for randomised or quasi-randomised trials, quasi-experimental studies, observational or implementation studies, and programme evaluations. We included studies comparing any intervention, policy, or strategy designed to mitigate AMR development and spread (intervention) among newborns receiving facility-based or community-based care in LMICs (population), with standard practices or no intervention (comparator), on a range of outcomes including clinical and antibiotic use outcomes (outcome). Strategies to reduce AMR were categorised as regulation (structural or organisational actions), education (health-care worker trainings), or optimisation (responsible antimicrobial use). We pooled data from included studies to estimate the effectiveness of each of the three strategy types or a combination thereof. Given the low-resource context, we also narratively synthesised the available evidence on barriers and facilitators to implementing strategies to reduce AMR in newborn care settings (PROSPERO CRD42023388338). FINDINGS: Of 3688 studies screened, 31 facility-based studies were included. Regulation reduced the risk of newborns receiving at least one antimicrobial by 21% (risk ratio 0·79 [95% CI 0·77-0·80]), but had no effect on neonatal sepsis risk. Optimisation reduced culture-positive sepsis risk by 32% (0·68 [0·55-0·83]) and risk of newborns on antibiotics by 13% (0·87 [0·78-0·98]), but had no effect on neonatal mortality risk. Regulation and optimisation did not significantly reduce neonatal mortality due to nosocomial bloodstream infection (BSI) risk (0·62 [0·31-1·25]). Regulation, education, and optimisation reduced neonatal mortality risk by 27% (0·73 [0·57-0·93]) and multidrug-resistant organism infections or colonisation risk by 29% (0·71 [0·52-0·97]). Regulation, education, and optimisation also decreased the risk of newborns receiving antibiotics by 29% (0·71 [0·61-0·81]) and the risk of duration of antibiotic therapy exceeding 5 days by 64% (0·36 [0·14-0·93]). Effect direction plots revealed overall positive directions of effect for outcomes including neonatal mortality (72·72%), neonatal mortality due to nosocomial BSI (100%), sepsis (75%), and drug-resistant (100%) and multidrug-resistant (80%) infection and colonisation. Effect direction plots also showed decreased overall antibiotic use (94·7%), access (71·4%) and watch (88·9%) antibiotic use, and duration of antibiotic therapy (83·3%) after strategies to reduce AMR were implemented. Common implementation barriers included delays in reporting culture test results, health-care worker non-adherence to infection prevention and control measures, and challenges in antibiotic prescribing for culture-negative newborns with sepsis-like presentation. INTERPRETATION: To improve clinical outcomes, interventions targeting the control of antimicrobials alone will not suffice. Our results showed that three types of interventions (regulation, education, and optimisation) must be taken together to reduce AMR. These results can inform and accelerate guidance development for multi-dimensional, holistic, and integrated maternal and newborn care programmes in LMICs. FUNDING: The Bill & Melinda Gates Foundation.

OBJECTIVES: To evaluate the association between socioeconomic position (SEP) and mortality in patients with sepsis or septic shock. DATA SOURCES: We searched MEDLINE, Embase, and Cochrane CENTRAL from inception to August 11, 2025. STUDY SELECTION: We included English-language observational studies that evaluated the association between SEP indicators and mortality in adults with sepsis and/or septic shock. DATA EXTRACTION: Two reviewers independently and in duplicate performed data extraction and risk-of-bias assessment using the Quality in Prognosis Studies tool. We pooled adjusted odds ratios (aORs) or adjusted hazard ratios (aHRs) using random-effects models and assessed certainty of evidence using the Grading of Recommendations Assessment, Development, and Evaluation approach. DATA SYNTHESIS: We included 13 observational studies involving 3,951,677 patients. Lack of private insurance (aOR, 1.34; 95% CI, 1.19-1.51; high certainty) was associated with increased mortality while lower neighborhood socioeconomic status (aOR, 1.35; 95% CI, 1.29-1.41; moderate certainty) and lower income (aOR, 1.06; 95% CI, 1.01-1.11; aHR, 1.51; 95% CI, 1.01-2.25; moderate certainty) were probably associated with increased mortality. Less education (aOR, 1.33; 95% CI, 1.14-1.55; low certainty) and unemployment (aOR, 1.91; 95% CI, 1.00-3.63; low certainty) may be associated with increased mortality. CONCLUSIONS: We found that several indicators of SEP were associated with increased short-term mortality in patients with sepsis and septic shock. These findings underscore the need for routine collection of equity-relevant variables in sepsis research to inform health policy and support equitable care delivery. Given that some of these variables are potentially modifiable, targeted interventions may help improve outcomes and reduce disparities in disadvantaged populations.