Daily Sepsis Research Analysis
Three advances stand out today: a conserved 42-gene immune dysregulation signature (SoM) that stratifies infection severity and predicts treatment response (including potential harm from hydrocortisone in sepsis); a high-performing machine learning model that predicts pediatric sepsis onset daily using EMR data; and preclinical evidence that pro-dermcidin and PEGylated derivatives protect against lethal experimental sepsis via LC3-associated phagocytosis.
Summary
Three advances stand out today: a conserved 42-gene immune dysregulation signature (SoM) that stratifies infection severity and predicts treatment response (including potential harm from hydrocortisone in sepsis); a high-performing machine learning model that predicts pediatric sepsis onset daily using EMR data; and preclinical evidence that pro-dermcidin and PEGylated derivatives protect against lethal experimental sepsis via LC3-associated phagocytosis.
Research Themes
- Precision immunology to stratify sepsis risk and therapy
- AI-driven early detection of pediatric sepsis
- Host-directed immunotherapies enhancing antibacterial phagocytosis
Selected Articles
1. A conserved immune dysregulation signature is associated with infection severity, risk factors prior to infection, and treatment response.
Across 68 cohorts (12,026 blood samples), a conserved 42-gene Severe-or-Mild (SoM) immune signature linked baseline risk factors to infection severity and was modifiable by drugs and lifestyle. The SoM score predicted sepsis patients likely to be harmed by hydrocortisone and was associated with all-cause mortality, suggesting utility for precision immunotherapy and trial stratification.
Impact: This work unifies diverse risk factors under a single immune dysregulation signature that predicts treatment harm/benefit and mortality, enabling precision sepsis therapeutics and better trial design.
Clinical Implications: The SoM score could guide corticosteroid use in sepsis (identifying patients at risk of harm), inform selection of immunomodulatory therapies, and enable baseline immune state profiling for risk stratification.
Key Findings
- Integrated 12,026 blood samples across 68 cohorts to analyze immune dysregulation.
- A 42-gene SoM signature associated with age, sex, obesity, smoking, and comorbidities before infection.
- The SoM signature is modifiable by immunomodulatory drugs and lifestyle changes.
- The SoM score predicted sepsis patients harmed by hydrocortisone and was associated with all-cause mortality.
Methodological Strengths
- Large, multi-cohort integration of single-cell, bulk transcriptomic, and proteomic data (n=12,026 across 68 cohorts).
- Cross-disease validation linking baseline immune state to severity and treatment response.
Limitations
- Observational and integrative design limits causal inference.
- Heterogeneity across cohorts; prospective interventional validation is needed for clinical deployment.
Future Directions: Prospective trials to test SoM-guided corticosteroid and immunomodulator use; embed SoM scoring into EHR pipelines; explore temporal dynamics and responsiveness monitoring.
2. Pro-dermcidin and derivatives as potential therapeutics for lethal experimental sepsis.
Antibody-mediated suppression of pro-dermcidin exacerbated sepsis, whereas supplementation with pro-DCD or PEGylated derivatives conferred protection even when administered 2–24 hours after onset. Benefits correlated with reduced inflammatory biomarkers, tissue injury, and bacteremia, mediated via activation of LC3-associated phagocytosis rather than direct bactericidal activity.
Impact: Introduces a host-directed therapeutic class for sepsis with a clinically relevant treatment window and a defined mechanism (LC3-associated phagocytosis).
Clinical Implications: Pro-DCD-derived agents could complement antibiotics by enhancing LC3-associated bacterial clearance, offering a host-directed strategy for patients with overwhelming inflammation and bacteremia.
Key Findings
- Antibody suppression of pro-DCD worsened sepsis-induced inflammation and liver injury.
- Pro-DCD or PEGylated derivatives protected against sepsis even when given 2–24 hours post-onset.
- Protection correlated with reduced inflammatory biomarkers, tissue damage, and bacteremia.
- Mechanism involved activation of LC3-associated phagocytosis, not direct bactericidal effects.
Methodological Strengths
- Both loss-of-function (antibody suppression) and gain-of-function (supplementation) approaches support causality.
- Demonstrated efficacy with delayed dosing and multi-parametric readouts (cytokines, tissue injury, bacterial counts).
Limitations
- Preclinical animal models; human safety, dosing, and pharmacokinetics are unknown.
- Short-term outcomes; durability of protection and effects in polymicrobial or comorbid settings remain to be tested.
Future Directions: Conduct GLP toxicology, PK/PD, and dose-ranging studies; evaluate efficacy in polymicrobial and comorbidity models; explore combination with antibiotics and immunomodulators; identify biomarkers of response.
3. Machine learning model for daily prediction of pediatric sepsis using Phoenix criteria.
Using 63,875 PICU encounters from two units, a CatBoost model using routinely available EMR variables achieved AUROC 0.98 and AUPRC 0.83 for daily prediction of Phoenix-defined pediatric sepsis. This tool could automate early recognition and prompt management to mitigate organ dysfunction and mortality.
Impact: Demonstrates near-perfect discrimination for pediatric sepsis onset using readily available EMR data across two PICUs, providing a scalable pathway to earlier interventions.
Clinical Implications: Integration into PICU workflows could trigger earlier sepsis bundles, targeted diagnostics, and antimicrobial therapy, potentially reducing organ dysfunction and mortality.
Key Findings
- Analyzed 63,875 PICU encounters; 5,248 met Phoenix Sepsis criteria.
- CatBoost achieved AUROC 0.98 and AUPRC 0.83 for daily sepsis prediction using EMR variables.
- Features included vital signs, labs, demographics, medications, and organ dysfunction scores across two PICUs.
Methodological Strengths
- Large, multi-institution dataset with validation across two PICUs.
- Use of routinely available EMR variables enhances implementability.
Limitations
- Single health system; generalizability beyond two PICUs is uncertain.
- Phoenix criteria dependency and lack of prospective impact evaluation may limit clinical translation.
Future Directions: Prospective implementation studies to measure time-to-antibiotics and outcomes, external validation across diverse PICUs, fairness auditing, and clinician-in-the-loop deployment.