Daily Sepsis Research Analysis
Three studies advanced sepsis science today: a post hoc analysis of the ADRENAL RCT linked hydrocortisone to a lower need for kidney replacement therapy in septic shock; a multi-cohort deep-learning model (ORAKLE) accurately predicted MAKE30 using dynamic ICU time-series; and a mechanistically novel TLR4-mimetic peptide (OP18) scavenged multiple DAMPs and ameliorated lethal sepsis in preclinical models.
Summary
Three studies advanced sepsis science today: a post hoc analysis of the ADRENAL RCT linked hydrocortisone to a lower need for kidney replacement therapy in septic shock; a multi-cohort deep-learning model (ORAKLE) accurately predicted MAKE30 using dynamic ICU time-series; and a mechanistically novel TLR4-mimetic peptide (OP18) scavenged multiple DAMPs and ameliorated lethal sepsis in preclinical models.
Research Themes
- Corticosteroids and renal outcomes in septic shock
- Dynamic AI prognostication for sepsis-associated AKI (MAKE30)
- DAMP-targeted therapeutics via TLR4-mimetic peptides
Selected Articles
1. Hydrocortisone and Risk Factors for Kidney Replacement Therapy in Septic Shock.
In a post hoc analysis of 3,161 ADRENAL trial participants with septic shock, hydrocortisone was associated with a lower incidence of new kidney replacement therapy compared with placebo (adjusted OR 0.79). Among patients who initiated KRT, hydrocortisone did not improve days alive and free of KRT.
Impact: This large, multicenter analysis leveraging randomized allocation provides clinically actionable evidence that hydrocortisone may reduce progression to KRT in septic shock, a meaningful patient-centered outcome.
Clinical Implications: When administering hydrocortisone for refractory septic shock per guidelines, clinicians may anticipate a reduced likelihood of initiating KRT, though prospective trials with kidney-specific endpoints are needed before changing protocols.
Key Findings
- Hydrocortisone reduced new KRT requirement vs placebo (21% vs 24%; OR 0.84, 95% CI 0.70–0.99; P=0.04).
- Adjusted analysis confirmed lower odds of new KRT with hydrocortisone (OR 0.79, 95% CI 0.66–0.95; P=0.01).
- No improvement in days alive and free of KRT among those who initiated KRT (mean difference 1.28 days; P=0.65).
Methodological Strengths
- Leverages randomized allocation from a large multicenter RCT (ADRENAL)
- Adjusted analyses accounting for key confounders associated with KRT
Limitations
- Post hoc analysis not originally powered for kidney endpoints
- Practice variability in initiating KRT across centers may influence findings
Future Directions: Conduct prospective, kidney endpoint-focused RCTs to validate steroid effects on SA-AKI progression and KRT initiation; explore timing, dosing, and interaction with vasopressor requirements and infection source.
2. ORAKLE: Optimal Risk prediction for mAke30 in patients with sepsis associated AKI using deep LEarning.
Using three large ICU datasets (n=30,783), ORAKLE leveraged dynamic time-series features to predict MAKE30 in sepsis-associated AKI with AUROCs of 0.84, 0.83, and 0.85 across development and external validation cohorts and demonstrated good calibration (Brier 0.21). Performance exceeded Cox and XGBoost baselines.
Impact: The study delivers an externally validated, dynamically updated risk model for a patient-centered renal outcome, enabling more precise prognostication and potential workflow integration for early nephrology interventions.
Clinical Implications: If integrated into EHRs, ORAKLE could support real-time risk stratification, guide early nephrology consultation, optimize dialysis preparedness, and inform enrollment in AKI interventional trials.
Key Findings
- ORAKLE achieved AUROC 0.84 (MIMIC-IV), 0.83 (SICdb), and 0.85 (eICU-CRD) for MAKE30 prediction.
- Calibration was good across cohorts (Brier score 0.21).
- Outperformed Cox and XGBoost models in AUROC and AUPRC across internal and external test sets.
Methodological Strengths
- Large multi-cohort development with external validation
- Dynamic time-series survival modeling (Dynamic DeepHit) with calibration assessment
Limitations
- Retrospective design with potential dataset shift and unmeasured confounders
- No prospective impact evaluation or fairness assessment by subgroups
Future Directions: Prospective, EHR-integrated implementation trials to assess clinical impact, equity, and clinician-in-the-loop use; model updating for drift and site-specific recalibration.
3. Identification of a multiple DAMP scavenger mimicking the DAMP-binding site of TLR4 to ameliorate lethal sepsis.
OP18 is a rationally designed TLR4-mimetic peptide that binds multiple DAMPs (eCIRP, HMGB1, histone H3) and promotes their macrophage-mediated clearance, thereby ameliorating lethal sepsis in preclinical models. This multi-DAMP scavenging strategy offers a novel host-directed therapeutic avenue.
Impact: Introduces a first-in-class multi-DAMP scavenger with a defined TLR4-mimetic binding motif, addressing a central pathophysiologic axis of sepsis beyond pathogen-directed therapy.
Clinical Implications: While preclinical, OP18 suggests a host-directed adjunct that could attenuate maladaptive inflammation in sepsis; rigorous toxicology, pharmacokinetics, large-animal validation, and early-phase trials will be essential before clinical use.
Key Findings
- Designed OP18 by mapping a shared 15-aa DAMP-binding site on TLR4 recognized by eCIRP, HMGB1, and histone H3.
- OP18 bound multiple DAMPs and enhanced macrophage-mediated clearance.
- OP18 ameliorated lethal sepsis in preclinical models, indicating therapeutic potential.
Methodological Strengths
- Rational peptide design targeting a conserved TLR4 DAMP-binding motif
- In vivo preclinical efficacy demonstrating amelioration of lethal sepsis
Limitations
- Preclinical study without human safety/PK data
- Mechanistic details and peptide optimization (stability, immunogenicity) require further work
Future Directions: Advance to large-animal sepsis models, optimize peptide stability and delivery, define biomarker-enriched populations, and initiate phase I safety trials.