Daily Sepsis Research Analysis
Analyzed 35 papers and selected 3 impactful papers.
Summary
Three impactful studies advance sepsis care along complementary fronts: a translational study identifies miR-144 as a regulator and predictor of glucocorticoid responsiveness in ARDS; a national cohort shows no adjusted mortality difference between TZP-resistant vs TZP-susceptible E. coli/K. pneumoniae bloodstream infections; and a meta-analysis finds interpretable AI models perform well for sepsis detection but only moderately for mortality prediction, with widespread risk of bias.
Research Themes
- Precision steroid therapy and biomarkers in sepsis-related ARDS
- Antimicrobial resistance and outcome in bloodstream infections
- Interpretable AI for early sepsis detection and risk stratification
Selected Articles
1. MiR-144 regulates glucocorticoid sensitivity in acute respiratory distress syndrome via the GRβ/NF-κB axis.
In a sepsis-induced ARDS rat model, miR-144 inhibition augmented, while miR-144 mimic attenuated, the efficacy of glucocorticoids. Mechanistically, sepsis increased miR-144, GRβ, and NF-κB and reduced GRα; GC therapy reversed these changes, and miR-144 inhibition further suppressed GRβ/NF-κB. In 63 ARDS patients, higher baseline miR-144 correlated with glucocorticoid resistance and effectively predicted poor response.
Impact: This study links a specific microRNA to steroid responsiveness in ARDS, offering a mechanistic biomarker to personalize glucocorticoid therapy and reduce harm from ineffective treatment.
Clinical Implications: Baseline miR-144 measurement could help stratify ARDS (including sepsis-associated ARDS) patients for glucocorticoid therapy, prioritizing likely responders and avoiding exposure in probable non-responders.
Key Findings
- MiR-144 inhibition enhanced glucocorticoid efficacy in a sepsis-induced ARDS rat model; miR-144 mimic attenuated it.
- Sepsis increased miR-144, GRβ, and NF-κB and reduced GRα in lung tissue; GC therapy reversed these changes, and miR-144 inhibition further suppressed GRβ/NF-κB.
- In 63 ARDS patients, GC-resistant individuals had higher baseline miR-144, GRβ, and NF-κB; miR-144 predicted GC resistance.
Methodological Strengths
- Translational design combining sepsis-induced ARDS animal model with a prospective clinical cohort.
- Mechanistic interrogation of the GRβ/NF-κB axis with concordant molecular readouts in vivo and in patients.
Limitations
- Single-center clinical cohort with modest sample size may limit generalizability.
- Heterogeneity of steroid regimens and lack of interventional validation for miR-144-guided therapy.
Future Directions: Prospective multicenter validation of miR-144 as a predictive biomarker and interventional trials testing miR-144-guided steroid therapy in ARDS, including sepsis-associated ARDS.
BACKGROUND: MicroRNA-144 (miR-144) has been implicated in inflammation and glucocorticoid (GC) receptor regulation, but its role in GC sensitivity during acute respiratory distress syndrome (ARDS) is unclear. This study aimed to investigate the impact of miR-144 on GC therapeutic efficacy, elucidate its underlying molecular mechanisms in ARDS, and validate its clinical application value. METHODS: First, we established a sepsis-induced ARDS rat model with five groups (n = 6 each): control, model, GC, GC + miR-144 mimic, or GC + miR-144 inhibitor. Body weight, inflammatory markers, lung wet/dry ratio, histopathology, and miR-144/GRα/GRβ/NF-κB mRNA were assessed after the 5-day treatment.
2. Mortality After Piperacillin-Tazobactam-Resistant vs -Susceptible Bloodstream Infection.
Among 34,379 adults with monomicrobial E. coli or K. pneumoniae bloodstream infection, 5.9% (E. coli) and 9.5% (K. pneumoniae) were TZP-resistant. After multivariable adjustment, 30-day mortality did not differ significantly between TZP-resistant and TZP-susceptible infections for either species (E. coli AHR 1.09; K. pneumoniae AHR 1.15).
Impact: A large, nationwide cohort refines understanding of the prognostic impact of TZP resistance in common Gram-negative BSIs, informing empirical therapy and stewardship policies.
Clinical Implications: Do not assume higher short-term mortality solely due to TZP resistance in E. coli or K. pneumoniae BSI; empirical choices should consider local resistance patterns, source control, and patient factors rather than resistance phenotype alone.
Key Findings
- 34,379 patients with first monomicrobial E. coli (81.9%) or K. pneumoniae (18.1%) BSI were analyzed using national registries with no loss to follow-up.
- TZP resistance prevalence: 5.9% in E. coli (1,689/28,649) and 9.5% in K. pneumoniae (597/6,314).
- Adjusted 30-day mortality hazards were not significantly different for TZP-resistant versus susceptible infections (E. coli AHR 1.09, 95% CI 0.94-1.27; K. pneumoniae AHR 1.15, 95% CI 0.94-1.41).
Methodological Strengths
- Nationwide, population-based cohort with large sample size and near real-time registry linkage.
- Robust multivariable adjustment with minimal censoring and no loss to follow-up.
Limitations
- Residual confounding is possible in observational data; antibiotic regimens and source control details were not fully captured.
- Findings from Denmark may not generalize to regions with different resistance mechanisms or treatment practices.
Future Directions: Integrate treatment variables (timing/appropriateness), source control, and resistance mechanisms in multinational cohorts to refine outcome associations and guide stewardship.
IMPORTANCE: Use of piperacillin-tazobactam (TZP) as treatment for bloodstream infections (BSIs) has increased over time in Europe, as has antimicrobial resistance to TZP in some European countries. OBJECTIVE: To evaluate mortality associated with TZP-resistant BSIs in the Danish health care system. DESIGN, SETTING, AND PARTICIPANTS: In this cohort study, data collected in near real time were obtained from Danish national health registers from November 15, 2018, through November 21, 2024. Participants were patients aged 18 years or older with monomicrobial Escherichia coli or Klebsiella pneumoniae BSI, restricted to the first positive (index) blood culture with a TZP-susceptibility test result during the follow-up period.
3. Early clinical decision support using interpretable artificial intelligence in acute illness (sepsis and infection): A systematic review and meta-analysis.
Across 15 studies, interpretable AI models showed good discrimination for sepsis detection (pooled AUC ≈ 0.88 with sensitivity 0.69 and specificity 0.90) but only moderate performance for mortality prediction (AUC ≈ 0.75). Fourteen studies had high overall risk of bias, and none directly assessed clinician adoption or patient-level implementation outcomes.
Impact: Provides a synthesis focused on interpretable AI for sepsis, quantifying performance and highlighting pervasive bias and key evidence gaps that must be addressed before clinical deployment.
Clinical Implications: Interpretable AI tools for early sepsis detection show promise but should undergo rigorous prospective, multicenter validation with calibration, decision-curve analyses, and assessment of clinician trust and workflow impact before deployment.
Key Findings
- For sepsis detection/diagnosis (n=8 studies), pooled sensitivity was 0.685 and specificity 0.898 with AUC 0.879; sensitivity analysis yielded similar AUC 0.890.
- For mortality prediction (n=5 studies), pooled sensitivity was 0.618 and specificity 0.778 with AUC 0.753, indicating moderate performance.
- Fourteen of 15 studies had high overall risk of bias, mainly due to analytical concerns; none evaluated clinician behavior or implementation outcomes.
Methodological Strengths
- Focused synthesis of interpretable/explainable AI models with bivariate random-effects meta-analysis for diagnostic accuracy.
- Explicit risk-of-bias assessment and sensitivity analyses to test robustness.
Limitations
- High heterogeneity and predominantly high risk of bias across included studies limit confidence.
- Lack of prospective multicenter validations and absence of implementation outcomes (e.g., clinician trust, workflow changes).
Future Directions: Conduct prospective multicenter trials of interpretable AI for sepsis with calibration, temporal validation, decision-curve analyses, and measurement of clinician adoption and patient outcomes.
BACKGROUND: Sepsis and severe infections remain major causes of morbidity and mortality worldwide, particularly in acute care settings where early recognition is critical. Artificial intelligence (AI)-based clinical decision support systems (CDSS) have emerged as promising tools for improving early diagnosis and prognostic assessment. However, limited interpretability and transparency remain key barriers to clinical adoption. This systematic review and meta-analysis aimed to evaluate the diagnostic and prognostic performance of interpretable AI models in acute illness related to sepsis and infection. METHODS: A systematic literature search was conducted in April 2026 across PubMed, Scopus, and the Cochrane Library.