Daily Sepsis Research Analysis
Analyzed 35 papers and selected 3 impactful papers.
Summary
Three impactful sepsis studies emerged: a host-based multi-omics analysis defined and externally validated a 4-protein biomarker panel distinguishing CRKP from CSKP sepsis; a LMIC-focused systematic review/meta-analysis compared bedside sepsis screening tools, supporting tiered strategies; and a large MIMIC-IV cohort revealed a U-shaped association between admission lactate and 28-day mortality in sepsis-associated ARDS, challenging linear risk assumptions.
Research Themes
- Host-response multi-omics biomarkers for drug-resistant sepsis
- Sepsis screening performance in resource-limited settings
- Nonlinear prognostic modeling in sepsis-associated ARDS
Selected Articles
1. Serum proteomic profiling of sepsis patients reveals a protein-based diagnostic model, with metabolomic insights into carbapenem-resistant
Host-focused serum proteomics and metabolomics in CRKP sepsis identified 85 DEPs and 128 metabolites and produced a validated 4-protein panel (IGHV1-8, ITGA2, PKP1, IGFBP6) that differentiated CRKP from CSKP with AUC 0.92. Integrated pathways implicated antigen presentation, phagosome, coagulation/immune signaling, and one-carbon metabolism (MAT2B).
Impact: This is the first host-based multi-omics study in CRKP sepsis with independent targeted validation, shifting the focus from pathogen to host response and yielding a clinically actionable biomarker panel.
Clinical Implications: If prospectively validated, the 4-protein panel could enable early, non-invasive discrimination of CRKP from CSKP to expedite appropriate therapy and antimicrobial stewardship, while pathway insights inform host-directed interventions.
Key Findings
- A 4-protein serum panel (IGHV1-8, ITGA2, PKP1, IGFBP6) differentiated CRKP from CSKP with test AUC 0.920.
- Targeted PRM validation in an independent cohort confirmed differential expression (e.g., IGFBP6 for CRKP vs CSKP; APOA2 for CSKP vs controls).
- Eighty-five DEPs were enriched in antigen presentation and phagosome pathways; a 51-protein cluster increased stepwise from controls to CSKP to CRKP.
- Metabolomics found 128 differential metabolites and highlighted dysregulated cysteine/methionine and folate-mediated one-carbon metabolism, with MAT2B linking omics layers.
Methodological Strengths
- Integrated proteomics and metabolomics with machine-learning classification and independent PRM validation.
- Functional enrichment and trend analyses provided coherent host-pathway context across omics layers.
Limitations
- Cohort sizes and clinical spectrum are not detailed in the abstract, limiting assessment of generalizability.
- Observational design without prospective clinical utility assessment; potential confounding and pre-analytical variability.
Future Directions: Prospective multicenter validation, assay standardization for bedside deployment, and impact studies on time-to-effective therapy and outcomes; evaluate performance across mixed infections and other resistant pathogens.
INTRODUCTION: Sepsis caused by carbapenem-resistant Klebsiella pneumoniae (CRKP) is associated with high mortality. Current research is predominantly pathogen-centric, creating a knowledge gap regarding the host's systemic molecular response, which is critical for understanding outcomes and developing diagnostic strategies. This study aimed to characterize the host serum proteomic and metabolomic landscape of CRKP sepsis and to develop a proteomics-derived biomarker panel for differential diagnosis, while integrating metabolomic data to gain mechanistic insights into host pathways. METHODS: Serum samples from sepsis patients, including culture-negative controls, and individuals infected with carbapenem-susceptible or carbapenem-resistant Klebsiella pneumoniae (CSKP and CRKP), underwent in-depth proteomic and metabolomic profiling. Differential expression, functional enrichment, and trend analyses were performed to characterize host molecular alterations associated with carbapenem resistance. Machine learning approaches were applied to construct diagnostic models based on host-derived molecular features. Candidate biomarker proteins were further validated using targeted parallel reaction monitoring (PRM) in an independent cohort. RESULTS: We identified 85 differentially expressed proteins (DEPs) distinguishing CRKP from CSKP, enriched in viral infection-related pathways, antigen presentation, and phagosome function. Trend analysis revealed a protein cluster (51 proteins) with stepwise increased abundance from controls to CSKP to CRKP, implicating coagulation, immune signaling, and metabolic dysfunction. Based on proteomic data, a four-protein biomarker panel (IGHV1-8, ITGA2, PKP1, IGFBP6) effectively differentiated CRKP from CSKP (test AUC = 0.920). Targeted proteomic validation in an independent cohort confirmed the differential expression of key model proteins, including IGFBP6 (CRKP vs. CSKP) and APOA2 (CSKP vs. controls). Metabolomics identified 128 differential metabolites in CRKP vs. CSKP, with enrichment in thermogenesis and amino acid/fatty acid degradation pathways. Integrated pathway analysis of proteomic and metabolomic data highlighted dysregulation in cysteine/methionine metabolism and the folate-mediated one-carbon pool, with MAT2B as a key connecting protein. CONCLUSION: This first host-based multi-omics study of CRKP sepsis suggests distinct molecular signatures linked to resistance and disease severity. The independently validated biomarkers show high diagnostic potential, offering a preliminary foundation for early, non-invasive diagnosis and precision intervention strategies.
2. Sepsis screening tools in resource-limited settings: a systematic review and meta-analysis of diagnostic accuracy in low- and middle-income countries.
Across 27 LMIC studies (30,310 patients), NEWS showed the highest AUROC (0.77), while qSOFA had AUROC 0.74 with limited rule-out capacity (LR− 0.59). No tool was universally optimal; tiered selection based on resources is recommended, and GRADE assessments noted heterogeneity.
Impact: Provides the most comprehensive LMIC-specific synthesis of sepsis screening performance, directly informing triage in resource-limited settings.
Clinical Implications: In settings without labs, NEWS and qSOFA are reasonable for initial screening, but qSOFA should not be used alone to exclude sepsis; where possible, incorporate SOFA/SIRS and implement tiered protocols aligned with local capacity.
Key Findings
- NEWS achieved the highest pooled AUROC (0.77, 95% CI 0.73–0.81) among evaluated tools.
- qSOFA showed AUROC 0.74 with LR+ 3.00 and LR− 0.59, indicating moderate rule-in but poor rule-out utility.
- SIRS had high sensitivity (0.86) but low specificity (0.32), while SOFA and UVA had AUROCs of 0.75 and 0.74, respectively.
- Substantial heterogeneity existed across studies; GRADE assessments reflected variable certainty.
Methodological Strengths
- Comprehensive, multi-database search with inclusion of Global Index Medicus and GRADE certainty assessment.
- Bivariate random-effects meta-analysis enabling pooled sensitivity, specificity, and AUROC estimates.
Limitations
- High between-study heterogeneity and overlapping confidence intervals limit definitive ranking of tools.
- Variable reference standards and case definitions across LMIC settings may bias pooled estimates.
Future Directions: Prospective, setting-specific validation with standardized case definitions; evaluation of tiered algorithms incorporating resource availability and impact on time-to-antibiotics and mortality.
BACKGROUND: Sepsis causes disproportionate mortality in low- and middle-income countries (LMICs), yet evidence on screening tool performance in these resource-limited settings remains fragmented. OBJECTIVE: This systematic review and meta-analysis aimed to evaluate and compare the diagnostic accuracy of sepsis screening tools-ranging from purely clinical assessments (qSOFA, NEWS, MEWS, UVA) to those incorporating laboratory parameters (SIRS, SOFA)-for sepsis identification in adult populations within LMICs. METHODS: A comprehensive search of PubMed, Embase, Cochrane Library, Web of Science, and Global Index Medicus was conducted from inception through June 2025. Eligible studies evaluated sepsis screening tools ranging from purely clinical bedside assessments (qSOFA, NEWS, MEWS, UVA) to those requiring basic laboratory parameters (SIRS, SOFA), enabling comparison across the resource-availability spectrum in LMICs. Bivariate random-effects models were employed to calculate pooled sensitivity, specificity, and area under the hierarchical summary receiver operating characteristic curve (AUROC). Evidence quality was assessed using the GRADE approach. RESULTS: Twenty-seven studies encompassing 30,310 patients across 14 LMICs were included. qSOFA demonstrated pooled sensitivity of 0.51 (95% CI: 0.42-0.60) and specificity of 0.83 (95% CI: 0.77-0.88) with AUROC of 0.74. SIRS exhibited high sensitivity (0.86) but poor specificity (0.32). NEWS achieved the highest point estimate of discriminative ability (AUROC 0.77, 95% CI: 0.73-0.81), followed by SOFA (AUROC 0.75, 95% CI: 0.71-0.79) and UVA (AUROC 0.74, 95% CI: 0.70-0.78), although confidence intervals overlapped substantially across tools. qSOFA yielded a positive likelihood ratio (LR+) of 3.00 and a negative likelihood ratio (LR-) of 0.59, indicating moderate rule-in but limited rule-out utility. Substantial heterogeneity was observed across studies (I CONCLUSION: No single screening tool demonstrates optimal performance across all metrics in LMIC populations. When analysis is restricted to purely clinical tools requiring no laboratory parameters, NEWS (AUROC 0.77) and qSOFA (AUROC 0.74) demonstrate comparable discriminative ability with broadly overlapping confidence intervals, supporting their consideration in the most resource-constrained settings. However, the limited rule-out capacity of qSOFA (LR - 0.59) suggests it should not be used as a standalone screening tool. Tool selection should be guided by local healthcare priorities and available laboratory capacity, with tiered screening strategies potentially optimizing sepsis recognition while ensuring efficient resource allocation.
3. Nonlinear correlation between lactate levels and 28-day all-cause mortality in patients with sepsis complicated by acute respiratory distress syndrome (ARDS): a retrospective study based on the MIMIC-IV database.
In 3,214 septic ARDS patients from MIMIC-IV, admission lactate exhibited a significant U-shaped relationship with 28-day mortality. Adjusted risks were higher for lactate 1.50–4.05 mmol/L, but not for ≥4.05 mmol/L compared with <1.50 mmol/L; findings were robust to PSM.
Impact: Challenges the prevalent linear interpretation of lactate in sepsis-associated ARDS and proposes data-driven, nonlinear risk strata that could refine resuscitation targets and prognostication.
Clinical Implications: Avoid rigid reliance on fixed lactate thresholds; consider nonlinear risk when triaging and tailoring resuscitation in septic ARDS, and integrate lactate with other features in prognostic models.
Key Findings
- Restricted cubic spline analysis showed a significant U-shaped association between admission lactate and 28-day mortality (P for nonlinearity < 0.001).
- Compared with <1.50 mmol/L, adjusted hazard ratios were 1.25 (95% CI 1.01–1.54) for 1.50–2.25 mmol/L and 1.39 (95% CI 1.13–1.72) for 2.25–4.05 mmol/L; ≥4.05 mmol/L showed no significant increase (aHR 0.88, 95% CI 0.70–1.11).
- Kaplan–Meier curves indicated poorest survival in mid-lactate groups (2 and 3), with convergence between lowest and highest groups by day 14; results persisted after propensity score matching.
Methodological Strengths
- Large ICU cohort with advanced modeling (restricted cubic splines, Boruta selection, multivariable Cox) and PSM sensitivity analyses.
- Quartile stratification and survival analyses corroborated nonlinearity across methods.
Limitations
- Retrospective single-database design with potential residual confounding and selection bias.
- Admission lactate only; lack of serial lactate trajectories limits dynamic risk assessment.
Future Directions: Prospective validation incorporating serial lactate trajectories and external datasets; testing lactate-informed nonlinear risk strata to guide resuscitation targets in randomized trials.
BACKGROUND: Sepsis-associated acute respiratory distress syndrome (ARDS) is a life-threatening condition with high mortality. Although lactate is a widely used prognostic marker in critical illness, its association with mortality in this specific patient population remains inconsistent, and the nature of this relationship has not been systematically characterized. This study aimed to systematically investigate the association between admission serum lactate levels and 28-day all-cause mortality in this cohort and to derive lactate-based risk strata for clinical prognosis. METHODS: A retrospective cohort of 3,214 septic patients with ARDS was identified from the Medical Information Mart for Intensive Care-IV (MIMIC-IV, v3.0) database. Patients were stratified into four groups by lactate quartiles. Restricted cubic spline (RCS) analysis, the Boruta algorithm for covariate selection, multivariate Cox regression, and propensity score matching (PSM) were applied to explore the lactate-mortality association and control confounders. RESULTS: RCS analysis confirmed a significant U-shaped, nonlinear relationship between admission lactate levels and 28-day mortality (P for nonlinearity < 0.001). In quartile-based analyses using multivariable Cox regression, mortality was significantly lower in the lowest lactate group (Group 1: <1.50 mmol/L) compared with higher quartiles (Group 2: 1.50-2.25 mmol/L; Group 3: 2.25-4.05 mmol/L; Group 4: ≥4.05 mmol/L) (19.4%, 26.1%, 28.7%, and 25.8%, respectively; overall P = 0.001). After full covariate adjustment, compared with Group 1, mortality risk was significantly elevated in Group 2 (adjusted hazard ratio [aHR] = 1.25, 95% CI: 1.01-1.54; P = 0.039) and Group 3 (aHR = 1.39, 95% CI: 1.13-1.72; P = 0.002), but not in Group 4 (aHR = 0.88, 95% CI: 0.70-1.11; P = 0.295). Kaplan-Meier survival analysis further demonstrated the lowest survival probability in Group 2 and Group 3, with convergence of survival curves between Group 1 and Group 4 occurring around day 14. After propensity score matching to control for confounders, the non-linear relationship remained robust, and no significant mortality difference was observed between Group 1 and Group 4, further supporting the U-shaped association. CONCLUSIONS: This study reveals a U-shaped relationship between lactate and 28-day mortality in sepsis-associated ARDS, providing a significant refinement to the conventional linear prognostic model. The derived lactate thresholds provide a novel framework for risk stratification and may help guide phenotype-specific clinical management.