Daily Sepsis Research Analysis
Three studies advance sepsis care across mechanisms, prognostics, and antimicrobial stewardship. A European multicenter analysis links persistent candidemia and poor guideline adherence to higher mortality, an externally validated nomogram improves ICU mortality prediction in sepsis-associated encephalopathy, and multi-omics work reveals a dual microbiome–steroid biosynthesis mechanism by which Shenmai Injection mitigates sepsis-associated acute lung injury.
Summary
Three studies advance sepsis care across mechanisms, prognostics, and antimicrobial stewardship. A European multicenter analysis links persistent candidemia and poor guideline adherence to higher mortality, an externally validated nomogram improves ICU mortality prediction in sepsis-associated encephalopathy, and multi-omics work reveals a dual microbiome–steroid biosynthesis mechanism by which Shenmai Injection mitigates sepsis-associated acute lung injury.
Research Themes
- Quality of antifungal care and persistent candidemia
- Mechanistic microbiome–metabolism crosstalk in sepsis lung injury
- Externally validated prognostic modeling for sepsis-associated encephalopathy
Selected Articles
1. Risk factors for persistent candidemia and prognostic implications: Results from the ECMM Candida III study.
In a 60-center European cohort sub-analysis (n=258 with FUBCs), 20.2% had persistent candidemia. Lower EQUAL Candida Scores—reflecting poorer adherence to guideline-recommended management, including initial echinocandin use—predicted persistence, and persistent candidemia ≥5 days independently doubled mortality risk alongside ICU admission.
Impact: Directly links persistent candidemia and guideline adherence with mortality, offering actionable targets—early echinocandin therapy and systematic FUBCs—to improve sepsis outcomes.
Clinical Implications: Ensure prompt echinocandin initiation, adherence to EQUAL Candida recommendations (FUBCs, source control, ophthalmologic exam), and heightened attention to cases with candidemia persisting ≥5 days to mitigate mortality risk.
Key Findings
- 20.2% (52/258) had persistent candidemia; median candidemia duration was 6 days.
- Initial echinocandin use was less frequent in persistent cases (61.5% vs. 78.2%; p=0.014).
- Lower EQUAL Candida Scores independently predicted persistence (OR 0.003; 95% CI 0.0002-0.07; p<0.001).
- Persistent candidemia ≥5 days (HR 2.06; 95% CI 1.26-3.37; p=0.004) and ICU admission (HR 1.59; 95% CI 1.02-2.50; p=0.039) independently predicted mortality.
Methodological Strengths
- Multicenter European cohort with standardized data capture
- Multivariable logistic and Cox models with attention to immortal time bias
- Use of EQUAL Candida Score to quantify guideline adherence
Limitations
- Sub-analysis limited to 40.8% with available FUBCs; potential selection bias
- Observational design precludes causal inference and residual confounding may persist
- Timing and frequency of FUBCs were not standardized across centers
Future Directions: Prospective interventions to improve EQUAL compliance (early echinocandin, source control, FUBCs), and trials testing standardized FUBC protocols to reduce persistence and mortality.
OBJECTIVES: Candidemia is a severe complication in critically ill and immunocompromised patients, and is associated with high morbidity and mortality. While early fungal clearance may improve outcomes, the association between follow-up blood culture (FUBCs) results and clinical outcomes remains insufficiently explored. This sub-analysis of the ECMM Candida III study investigates predictors of persistent candidemia and the impact of positive FUBC results on clinical outcomes. METHODS: The multicenter ECMM Candida III study enrolled adults with culture-proven candidemia from 60 European centers (2018-2019). This sub-analysis included patients with at least one FUBC result reported (n = 258; 40.8%). Statistical analysis used SPSS 29 and R. Binary logistic regression was used to identify predictors of persistent candidemia. To assess mortality risk factors, Cox proportional hazards regression models were constructed. RESULTS: Of 258 patients, 52 (20.2%) had persistent candidemia based on positive FUBCs (median duration of candidemia 6 days). Utilization of echinocandins as first line treatment was less frequent (61.5% vs. 78.2%; p=0.014) in those with positive FUBCs. Mortality was significantly higher in the FUBC-positive group (50% vs. 32%; p=0.016). In the multivariable logistic regression model, lower EQUAL Candida Scores, reflecting reduced adherence to guideline-recommended management, were independently associated with persistent candidemia (OR 0.003, 95% CI 0.0002-0.07; p<0.001). Univariable Cox regression identified persistent candidemia ≥5 days (HR 2.16; 95% CI 1.33-3.53; p= 0.002) as a significant predictor of mortality. In the multivariable Cox regression model, intensive care unit (ICU) admission (HR 1.59, 95% CI 1.02-2.50; p= 0.039) and persistent candidemia ≥5 days (HR 2.06, 95% CI 1.26-3.37; p= 0.004) remained independent predictors of mortality. CONCLUSION: Persistent candidemia was predicted by poor adherence to treatment guidelines, as shown by low EQUAL Candida Scores, particularly due to the lack of initial echinocandin use. After controlling immortal time bias, persistent candidemia ≥5 days and ICU admission remained independent predictors of mortality in the multivariable model.
2. Shenmai injection attenuates sepsis-associated acute lung injury by remodeling gut microbiota and restoring steroid hormone biosynthesis.
Using a murine SA-ALI model with integrated network pharmacology, metabolomics, metagenomics, and validation, Shenmai Injection reduced inflammation, restored barrier integrity, and suppressed NF-κB signaling. Active constituents (ophiopogonin A/B, luteolin) targeted steroid biosynthesis enzymes, while gut microbiota remodeling (↓Chlamydiaceae, ↑Lactobacillaceae) correlated with androgen metabolites.
Impact: Elucidates a dual mechanism—microbiota remodeling and steroid hormone biosynthesis modulation—linking host–microbe metabolism to sepsis lung injury recovery, offering a multi-target therapeutic concept.
Clinical Implications: Although preclinical, findings support evaluating SMI or its active components as adjunctive therapy for SA-ALI, with potential biomarkers (steroid metabolites, microbiome signatures) to guide response.
Key Findings
- SMI reduced pulmonary inflammation, restored blood–gas barrier, and suppressed NF-κB activation in SA-ALI mice.
- Network pharmacology and metabolomics implicated steroid biosynthesis modulation via AKR1C3, HSD17B1/2, and SULT1E1 targeted by ophiopogonin A/B and luteolin.
- Metagenomics showed decreased Chlamydiaceae (notably Chlamydia abortus) and increased Lactobacillaceae after SMI.
- Intestinal androstenedione/androsterone levels negatively correlated with Chlamydia abortus abundance during recovery.
Methodological Strengths
- Integrated multi-omics (network pharmacology, metabolomics, metagenomics) with in vivo validation
- Use of dual-hit SA-ALI model (LPS + Poly(I:C)) to capture bacterial/viral mimicry
- Identification of discrete active constituents and enzymatic targets
Limitations
- Mouse model limits generalizability to human sepsis
- Complex herbal formulation poses standardization and dosing challenges
- LPS/Poly(I:C) model may not recapitulate full heterogeneity of human SA-ALI
Future Directions: Isolate and standardize active components, perform dose–response and safety studies, test microbiome-targeted strategies, and conduct early-phase clinical trials with metabolic/microbiome biomarkers.
Sepsis-associated acute lung injury (SA-ALI), a critical complication of sepsis, is characterized by immune dysregulation-induced pulmonary dysfunction. Shenmai Injection (SMI) is a standardized herbal preparation consisting of Panax ginseng C.A.Mey (Hongshen) and Ophiopogon japonicus (Thunb.) Ker Gawl (Maidong), traditionally used for qi-replenishing, collapse-stabilizing, and lung-moistening therapy. Although clinically utilized in the management of SA-ALI, the specific mechanisms by which it acts against SA-ALI necessitate further investigation. The present study endeavors to comprehensively determine the therapeutic efficacy of SMI against SA-ALI through an integrated approach combining network pharmacology, metabolomics, metagenomic sequencing, and experimental validation. In this study, murine SA-ALI was established using lipopolysaccharide (LPS) and Poly(I:C). Results indicated that SMI administration significantly attenuated pulmonary inflammation, restored blood-gas barrier integrity, reduced serum pro-inflammatory cytokines and suppressed NF-κB pathway activation in SA-ALI mice. Network pharmacology elucidated the multi-targeted mechanism of SMI in modulating steroid hormone biosynthesis. Integrated metabolomics and target analysis revealed that ophiopogonin A/B and luteolin in SMI alleviates metabolic dysregulation by targeting key enzymes, including AKR1C3, HSD17B1/2, and SULT1E1. Metagenomic profiling demonstrated SMI-mediated gut microbiota remodeling, marked by suppression of pathogenic Chlamydiaceae (particularly Chlamydia abortus) and enrichment of commensal Lactobacillaceae. Correlation analysis showed that intestinal androstenedione and androsterone levels during SMI treatment recovery were negatively correlated with Chlamydia abortus abundance. In conclusion, SMI enhances the recovery from sepsis-associated SA-ALI by dual modulation of gut microbial ecology and host metabolic homeostasis, thereby establishing its potential as a multi-mechanistic therapeutic candidate for sepsis-related organ injury.
3. A nomogram for predicting ICU mortality of sepsis associated encephalopathy: a retrospective cohort study based on MIMIC-IV and eICU-CRD.
From 5,242 training and 3,103 external validation cases, an eight-variable nomogram for SAE ICU mortality achieved AUROC 0.832 (train) and 0.825 (validation), outperforming SAPS II, SOFA, and GCS. Calibration and decision-curve analyses supported reliability and clinical net benefit.
Impact: Provides an externally validated, higher-performing prognostic tool for SAE, enabling earlier risk stratification than standard ICU scores.
Clinical Implications: Integrate the nomogram into ICU workflows to triage SAE patients for early neuroprotective and organ support strategies, while planning prospective impact evaluations before routine adoption.
Key Findings
- Training and validation cohorts included 5,242 and 3,103 patients, respectively.
- Eight predictors were selected via LASSO; the nomogram achieved AUROC 0.832 (train) and 0.825 (validation).
- Outperformed SAPS II, SOFA, and GCS; showed good calibration (Hosmer–Lemeshow p=0.129 train, 0.583 validation) and superior decision-curve net benefit.
Methodological Strengths
- Large, multi-database design with external validation
- Transparent variable selection (LASSO) and comprehensive performance assessment (ROC, calibration, DCA)
Limitations
- Retrospective design with potential misclassification of SAE and unmeasured confounding
- Generalizability beyond US ICU databases and the need for prospective impact assessment
- Model inputs limited to variables available in MIMIC/eICU datasets
Future Directions: Prospective validation and impact trials, EHR integration with real-time updating, and calibration across diverse health systems and sepsis phenotypes.
BACKGROUND: Sepsis-associated encephalopathy (SAE) is a fatal complication of sepsis, with a high mortality rate worldwide. This study aimed to reduce mortality and improve the quality of life of patients with SAEs by developing a practical nomogram to predict the risk factors associated with ICU mortality. METHOD: The MIMIC database was used as the training set to build the model, and the eICU-CRD served as the validation set for external verification. LASSO regression analysis was conducted to identify predictive variables and develop the nomogram model. Receiver Operating Characteristic (ROC) curves were generated to assess the model's discriminative ability. Model calibration was assessed using calibration curves and the Hosmer-Lemeshow goodness-of-fit tests. Clinical decision curves were plotted to assess the model's net benefit and evaluate its clinical applicability. RESULTS: A total of 5,242 patients from the MIMIC database and 3,103 from the eICU-CRD were included in the study. LASSO regression, identified eight predictive variables for inclusion in the final model. The nomogram was evaluated against standard ICU scoring systems, including SAPS II, SOFA and GOS scores, with AUROC values of 0.832, 0.769, 0.607, and 0.575, respectively, in the training set. Conversely, the validation set demonstrated AUROC values of 0.825, 0.715, 0.714, and 0.587. P-values from the Hosmer-Lemeshow goodness-of-fit test for both the training and validation sets were 0.129 and 0.583, respectively, indicating a good fit quality. DCA revealed that the nomogram consistently provides greater net benefits compared to SAPS II, SOFA, and GCS scores. CONCLUSION: Developing mortality prediction models for SAE patients in the ICU can facilitate early intervention strategies and potentially reduce mortality rates in this high-risk population. CLINICAL TRIAL NUMBER: Not applicable.