Daily Sepsis Research Analysis
Large-scale surveillance from India quantifies CLABSI burden and extreme carbapenem resistance, informing prevention priorities. A target trial emulation suggests early albumin may increase sepsis-associated AKI without short-term survival benefit. A multi-biomarker, machine-learning classifier outperformed NEWS-2 and procalcitonin for ED sepsis identification.
Summary
Large-scale surveillance from India quantifies CLABSI burden and extreme carbapenem resistance, informing prevention priorities. A target trial emulation suggests early albumin may increase sepsis-associated AKI without short-term survival benefit. A multi-biomarker, machine-learning classifier outperformed NEWS-2 and procalcitonin for ED sepsis identification.
Research Themes
- Healthcare-associated infection surveillance and prevention (CLABSI) with AMR profiling
- Fluid resuscitation choices and kidney risk in sepsis (albumin and SA-AKI)
- Composite biomarker panels with machine learning for sepsis diagnosis
Selected Articles
1. Profile of central line-associated bloodstream infections in adult, paediatric, and neonatal intensive care units of hospitals participating in a health-care-associated infection surveillance network in India: a 7-year multicentric study.
A 7-year, multicenter surveillance across ~200 ICUs in India reported a pooled CLABSI rate of 8.83/1,000 central line-days, with the highest rate in neonatal ICUs. Klebsiella pneumoniae and Acinetobacter baumannii predominated, showing extremely high carbapenem resistance (77.7% and 87.1%). These data establish national benchmarks to target prevention and antimicrobial stewardship.
Impact: This is the first standardized, large-scale CLABSI surveillance from India, quantifying burden and resistance patterns that directly inform prevention bundles and stewardship.
Clinical Implications: Implement CLABSI bundles with emphasis on neonatal ICUs; prioritize infection control resources; tailor empiric therapy and stewardship to address high carbapenem resistance in A. baumannii and K. pneumoniae.
Key Findings
- Pooled CLABSI rate: 8.83 per 1,000 central line-days (adult 8.68; pediatric 6.71; neonatal 13.86).
- Predominant pathogens: K. pneumoniae (22.8%) and A. baumannii (20.4%) among 10,042 isolates.
- Very high carbapenem resistance: A. baumannii 87.1% (1607/1846) and K. pneumoniae 77.7% (1589/2046) among tested isolates.
Methodological Strengths
- Standardized, multicenter surveillance with centralized data quality checks
- Large denominator data (patient-days and central line-days) enabling robust rate calculations
Limitations
- Observational surveillance cannot infer causality or patient-level risk adjustment
- Potential heterogeneity in practice and reporting across sites
Future Directions: Evaluate impact of targeted CLABSI prevention bundles and stewardship interventions using interrupted time series; incorporate patient-level severity and outcomes to refine risk-adjusted benchmarking.
2. Pentraxin-3, MyD88, GLP-1, and PD-L1: Performance assessment and composite algorithmic analysis for sepsis identification.
In an ED cohort of 388 patients, several host-response biomarkers (notably MyD88, PD-L1, and pentraxin-3) showed strong discrimination for bacterial infection, sepsis, and 30-day mortality. A three-biomarker XGBoost classifier (pentraxin-3, MyD88, GLP-1) achieved AUROC 0.89 for sepsis, outperforming NEWS-2 (0.83) and procalcitonin (0.81).
Impact: Demonstrates a practical multi-marker algorithm that improves sepsis identification over current tools, with potential to accelerate triage and therapy.
Clinical Implications: Adopting composite biomarker panels with machine learning could enhance early sepsis recognition beyond NEWS-2 or single biomarkers, enabling earlier antibiotics and source control.
Key Findings
- MyD88, PD-L1, and pentraxin-3 showed high AUROCs: bacterial infection ≥0.87, sepsis ≥0.81, 30-day mortality ≥0.71.
- Seven of nine biomarkers significantly discriminated all three endpoints.
- An XGBoost model using pentraxin-3, MyD88, and GLP-1 achieved AUROC 0.89 for sepsis, exceeding NEWS-2 (0.83) and procalcitonin (0.81).
Methodological Strengths
- Prospective recruitment at ED presentation with predefined biomarker panel
- Direct comparison with established clinical tools (NEWS-2) and a standard biomarker (procalcitonin)
Limitations
- Single-cohort study with modest sample size (n=388) and need for external validation
- Algorithm performance may depend on assay platform and local prevalence; implementation logistics not addressed
Future Directions: Externally validate and calibrate the classifier across diverse ED settings; assess impact on time-to-antibiotics and outcomes in a pragmatic trial.
3. Early use of albumin may increase the risk of sepsis-associated acute kidney injury in sepsis patients: a target trial emulation.
Using a clone-censor-weight target trial emulation over 2008–2022, early albumin administration was associated with a 3.47% higher risk of SA-AKI without a meaningful 7-day survival benefit. Findings were robust across sensitivity analyses, emphasizing careful risk-benefit assessment of albumin in sepsis resuscitation.
Impact: Challenges routine early albumin use by linking it to higher SA-AKI risk using state-of-the-art causal inference, with immediate implications for fluid strategies.
Clinical Implications: De-emphasize routine early albumin in sepsis resuscitation absent clear indications; monitor renal function closely if albumin is used; prioritize trials to confirm harm or identify subgroups who may benefit.
Key Findings
- Early albumin administration increased SA-AKI risk by 3.47% (95% CI 1.76–5.23) versus no albumin.
- No meaningful difference in 7-day all-cause mortality (relative difference 0.05%, 95% CI −2.30 to 2.45).
- Robustness demonstrated via clone-censor-weight adjustment, new-user design, competing risk analyses, and sensitivity checks.
Methodological Strengths
- Target trial emulation with clone-censor-weight method to mitigate immortal time bias
- New-user design and competing risk analyses with extensive sensitivity analyses
Limitations
- Observational emulation susceptible to residual confounding and indication bias
- Single-center dataset may limit generalizability; exposure timing and dose details may vary
Future Directions: Conduct randomized trials or instrumental-variable analyses to confirm causality and explore effect heterogeneity by baseline kidney function and hemodynamics.