Daily Sepsis Research Analysis
Three studies advance sepsis precision medicine and frontline care: a prospective multicenter machine-learning study defines clinically actionable sepsis phenotypes aligned with the plasma proteome; a translational multi-omics investigation identifies urinary 3‑methylhistidine as a promising biomarker for sepsis‑associated acute kidney injury; and Ugandan cohort data highlight underrecognized rickettsial infections in febrile sepsis with diagnostic rRNA RT‑PCR performance and clear empirical tre
Summary
Three studies advance sepsis precision medicine and frontline care: a prospective multicenter machine-learning study defines clinically actionable sepsis phenotypes aligned with the plasma proteome; a translational multi-omics investigation identifies urinary 3‑methylhistidine as a promising biomarker for sepsis‑associated acute kidney injury; and Ugandan cohort data highlight underrecognized rickettsial infections in febrile sepsis with diagnostic rRNA RT‑PCR performance and clear empirical treatment implications.
Research Themes
- Data-driven sepsis phenotyping and proteomics
- Biomarkers for sepsis-associated acute kidney injury
- Underrecognized infectious etiologies and empiric therapy in sepsis
Selected Articles
1. Machine learning identifies clinical sepsis phenotypes that translate to the plasma proteome.
A prospective multicenter cohort (n=384) used machine learning to define three sepsis phenotypes with distinct organ failure patterns and mortality risk, mirrored by graded consumption of complement and coagulation proteins. A supervised classifier using seven routine variables enabled early phenotype assignment, creating a path toward phenotype-guided trials.
Impact: Provides a scalable clinical-omics framework to stratify sepsis, enabling hypothesis-driven, phenotype-specific therapies and more efficient trial design.
Clinical Implications: Early phenotype assignment using seven routine variables could aid triage, prognostication, and selection for phenotype-enriched trials. Proteomic signatures point to targetable pathways (complement/coagulation) for precision therapeutics.
Key Findings
- Three sepsis phenotypes were identified; cluster C had highest severity with liver failure and strongest mortality association.
- Plasma proteomics showed graded consumption of complement and coagulation factors with increasing severity across phenotypes.
- A supervised ML model classified phenotypes using seven widely available features (ALT, AST, BE, INR, BPsys, BPdia, aPTT).
Methodological Strengths
- Prospective multicenter cohort with harmonized sampling and mass spectrometry-based proteomics.
- Transparent ML approach linking clinical phenotypes to mechanistic proteomic pathways.
Limitations
- Moderate sample size and single healthcare geography may limit generalizability.
- No interventional validation; phenotype-specific treatment effects remain untested.
Future Directions: External validation across health systems, prospective phenotype-guided trials, and integration of proteomic signatures to select targeted therapies.
2. Urinary 3-methylhistidine as a potential biomarker for sepsis-associated acute kidney injury: multidimensional metabolomics analysis in mice and human.
Integrating mouse RT‑GFR models, untargeted and spatiotemporal renal metabolomics, and a human urine cohort (n=95), the study identifies urinary 3‑methylhistidine as an SA‑AKI biomarker with AUC 0.86 and a combined clinical model AUC 0.89. Spatial analyses localize 3‑MH increases to collecting ducts, supporting biological plausibility.
Impact: Delivers a mechanistically anchored, noninvasive biomarker with immediate diagnostic potential for SA‑AKI—an unmet clinical need in sepsis care.
Clinical Implications: Urinary 3‑MH could support early SA‑AKI screening and risk stratification, enabling timely nephroprotective strategies and enrollment into AKI‑focused trials.
Key Findings
- Urinary 3‑methylhistidine identified as a key metabolite with diagnostic AUC 0.86 (95% CI 0.77–0.95) for SA‑AKI; combined model AUC 0.89.
- Renal spatiotemporal metabolomics localized increased 3‑MH distribution to collecting ducts in SA‑AKI.
- Multi-omics pipeline (mouse RT‑GFR model, tissue/urine metabolomics, human cohort) yielded a biologically plausible, translatable biomarker.
Methodological Strengths
- Integrated multi-omics with both animal models and human validation, including spatial metabolomics.
- Use of RT‑GFR for precise AKI phenotyping and robust machine-learning feature selection.
Limitations
- Human cohort size was modest and from a single setting; external validation is needed.
- Cross-sectional sampling limits assessment of temporal kinetics and clinical cutoffs.
Future Directions: Prospective multicenter validation with serial sampling, assay standardization, and clinical impact studies on early intervention guided by 3‑MH.
3. Rickettsioses as Underrecognized Cause of Hospitalization for Febrile Illness, Uganda.
Analyzing archived samples from Ugandan sepsis and acute febrile illness cohorts (n=329), 10% had rickettsioses. Serum rRNA RT‑PCR showed 75% sensitivity and 91% specificity; thrombocytopenia was strongly associated. Findings support including doxycycline empirically for nonmalarial febrile illness and deploying rRNA RT‑PCR diagnostics.
Impact: Directly informs empiric antibiotic choices and diagnostic strategies for febrile sepsis in sub‑Saharan Africa, where rickettsioses are underrecognized.
Clinical Implications: In regions with high nonmalarial febrile illness, empiric doxycycline should be considered. Implementing rRNA RT‑PCR can improve early pathogen-directed therapy and reduce mortality.
Key Findings
- Rickettsial infections accounted for 10% (33/329) of sepsis/AFI presentations in Uganda.
- Serum rRNA RT‑PCR had 75.0% sensitivity and 91.2% specificity against reference methods.
- Thrombocytopenia was significantly associated with rickettsioses (adjusted OR 3.7; p=0.003), and no patients received tetracycline at admission, supporting empiric doxycycline.
Methodological Strengths
- Dual diagnostic approach using immunofluorescence assay and clinically validated rRNA RT‑PCR on cohort samples.
- Real-world cohorts spanning sepsis and acute febrile illness increase applicability to frontline care.
Limitations
- Use of archived samples limits control over timing relative to symptom onset.
- Regional generalizability may vary; lack of interventional antibiotic outcome data.
Future Directions: Prospective implementation studies of rRNA RT‑PCR in emergency workflows and randomized evaluations of empiric doxycycline strategies in nonmalarial febrile illness.