Daily Sepsis Research Analysis
Three studies advance sepsis science and practice: a secondary analysis of two RCTs shows that phenotype-based subgrouping does not reliably personalize treatment benefits for sepsis resuscitation; a national cohort from England and Wales reveals very rapid mortality in invasive group A streptococcal infections, emphasizing pre-culture diagnostics; and an Australian multi-hospital validation demonstrates poor PPV of ICD-based algorithms for hospital-onset bloodstream infection, improved by remov
Summary
Three studies advance sepsis science and practice: a secondary analysis of two RCTs shows that phenotype-based subgrouping does not reliably personalize treatment benefits for sepsis resuscitation; a national cohort from England and Wales reveals very rapid mortality in invasive group A streptococcal infections, emphasizing pre-culture diagnostics; and an Australian multi-hospital validation demonstrates poor PPV of ICD-based algorithms for hospital-onset bloodstream infection, improved by removing unspecified sepsis codes.
Research Themes
- Limits of phenotype-based precision medicine in sepsis resuscitation
- Rapid lethality in invasive GAS sepsis and the need for pre-culture diagnostics
- Improving surveillance accuracy: refining ICD-based detection of hospital-onset bloodstream infections
Selected Articles
1. Relationship Between Phenotyping and Individualized Absolute Risk Differences in Sepsis: A Secondary Analysis of Two Approaches in Two Multicenter Trials.
Using data from the ProCESS and ARISE randomized trials, supervised models estimating individualized absolute risk differences showed wide within-subgroup variability in the effect of early goal-directed therapy. Even where average effects suggested benefit (β or nonhyperinflammatory) or harm (γ or hyperinflammatory), many individuals were predicted to experience the opposite effect.
Impact: This study challenges the assumption that phenotype-based subgrouping can reliably personalize sepsis resuscitation and introduces individualized absolute risk modeling as a superior approach to estimate treatment effects.
Clinical Implications: Clinicians should be cautious about deploying phenotype-restricted protocols for sepsis resuscitation and consider individualized risk modeling where feasible. Future trials should randomize or stratify based on predicted individualized benefit rather than phenotype alone.
Key Findings
- Average EGDT effects differed by clinical (α, β, γ, δ) and biologic (hyperinflammatory vs nonhyperinflammatory) subphenotypes.
- Within-subgroup iARDs ranged from substantial harm to benefit; in the β subtype, average mortality reduction was 8.5% (95% CI, -0.4 to 17.5) but 39% of patients were predicted to be harmed.
- Phenotype-based subgrouping did not reliably identify individuals who would benefit from EGDT.
Methodological Strengths
- Secondary analysis of two large multicenter RCTs (ProCESS and ARISE) with standardized outcomes
- Use of supervised effect modeling to estimate individualized absolute risk differences with internal validation
Limitations
- Post hoc modeling susceptible to overfitting and residual confounding despite randomization in source trials
- Biomarker-based subphenotypes available only in a subset; generalizability beyond EGDT context is uncertain
Future Directions: Prospective trials that randomize based on predicted individualized benefit; integration of multi-omics and real-time data to refine iARD models; external validation across diverse settings.
2. Mortality Among Patients With Invasive Group A Streptococcal Infections Caused by the M1UK Lineage: A Retrospective Cohort Study in England and Wales.
In a national cohort of 4952 emm1 iGAS cases, 30-day case fatality rates were high and similar between M1UK and M1global lineages after adjustment. Notably, 63.7% of deaths occurred within one day of diagnostic sampling and most pediatric deaths occurred at or before sampling.
Impact: This study provides large-scale, lineage-resolved mortality data and highlights the ultra-rapid lethality of emm1 iGAS, shaping priorities for pre-culture diagnostics and prevention policies.
Clinical Implications: Clinicians should prioritize early recognition and empiric therapy for suspected iGAS, supported by rapid molecular diagnostics where available. Public health strategies should emphasize prevention and prehospital recognition, and trial designs must account for the narrow therapeutic window.
Key Findings
- 30-day CFR: M1UK 24.4%, M1global 22.3%, M123SNP 10.5%, M113SNP 10.3%.
- After age and sex adjustment, lineage was not a significant predictor of 7- or 30-day mortality.
- 63.7% of deaths occurred within 1 day of diagnostic sampling; among children <15 years, 56.3% died before sampling and 95.6% within 1 day of sampling.
Methodological Strengths
- Large national linked dataset over 12+ years with lineage assignment via WGS or allele-specific PCR
- Adjusted analyses and time-to-event evaluation highlighting clinical timelines
Limitations
- Lineage assignment available for a subset (1356/4952), potentially introducing selection bias
- Unmeasured confounders (e.g., time to antibiotics, source control) and limited clinical covariates
Future Directions: Implement rapid pre-culture diagnostics and evaluate their impact on outcomes; assess vaccine or prophylaxis strategies; incorporate ultra-early endpoints in iGAS trials.
3. Performance of the Australian hospital-acquired complication algorithm for detecting hospital-onset bloodstream infections.
Across five Australian hospitals, the ICD-based HAC algorithm had a PPV of 0.28 and NPV of 1.00 for hospital-onset bloodstream infection. Unspecified sepsis codes were major false-positive drivers; removing them increased PPV to 0.53.
Impact: This study provides actionable refinements to administrative algorithms that influence surveillance, quality metrics, and reimbursement for sepsis-related complications.
Clinical Implications: Hospitals should not rely solely on ICD-based HAC flags for HO-BSI; integrate laboratory-confirmed surveillance and consider excluding unspecified sepsis codes to improve accuracy of quality reporting.
Key Findings
- PPV 0.28 (95% CI 0.23–0.34) and NPV 1.00 (95% CI 0.98–1.00) for HO-BSI detection by the HAC algorithm.
- Unspecified sepsis ICD codes accounted for 35.8% (sepsis, unspecified) and 18.4% (newborn bacterial sepsis, unspecified) of HO-BSI flags with very poor PPV (0.06 and 0.03).
- Removing unspecified sepsis codes increased PPV to 0.53 (95% CI 0.45–0.62).
Methodological Strengths
- Multicenter evaluation with blinded manual adjudication against surveillance definitions
- Large administrative dataset (352,917 episodes) and iterative algorithm testing
Limitations
- Manual review sample size was limited (50 positives and 50 negatives per site)
- Findings may be specific to Australian coding practices and the 2016–2017 period
Future Directions: Validate refined algorithms nationally; integrate microbiology, timing of blood cultures, and NLP from clinical notes; assess impact on quality metrics and incentives.