Sepsis, November 2 edition

Using daily prothrombin time and D-dimer measurements, the investigators identified three longitudinal phenotypes: Compensate, Coupled, and Consumptive. The Consumptive phenotype, characterized by severe coagulation-factor consumption and blunted fibrinolysis, had the highest 28-day mortality at 52.1% and independently predicted death in both the derivation and external validation cohorts.

Impact: This study converts routinely available serial coagulation tests into clinically interpretable dynamic phenotypes and demonstrates reproducibility across an external multicenter cohort. It provides a plausible framework for identifying patients who may require intensified monitoring or phenotype-specific trials.

Clinical Implications: Serial prothrombin time and D-dimer trends may improve early recognition of a high-risk consumptive coagulopathy phenotype beyond single measurements. Before routine implementation, the score requires prospective clinical-impact studies and evaluation of whether phenotype-guided anticoagulant, transfusion, or adjunctive strategies improve outcomes.

Future Directions: Future studies should prospectively test automated trajectory calculation at the bedside, assess transportability across diverse healthcare systems, and conduct phenotype-enriched trials of coagulation, fibrinolysis, and transfusion interventions.

Patient microbiome profiling, mouse cecal ligation and puncture experiments, metabolomics, macrophage studies, and genetic and pharmacologic validation identified a protective F. prausnitzii–18β-glycyrrhetinic acid axis. The metabolite reduced inflammatory signaling through MAPK and nuclear factor kappa B (NF-κB), with ATP-binding cassette subfamily C member 1 (ABCC1) implicated as a functionally relevant host mediator.

Impact: The work links a sepsis-associated loss of a commensal bacterium to a defined metabolite and host transporter, providing a mechanistic bridge from microbiome observation to therapeutic hypothesis. It also illustrates a potentially host-directed strategy that could complement antimicrobial and supportive care.

Clinical Implications: The findings support future development of microbiome-derived metabolites, defined bacterial products, or ABCC1-modulating approaches for sepsis. They are not yet sufficient to justify probiotic or metabolite treatment in patients because efficacy, dosing, safety, pharmacokinetics, and effects in clinically relevant polymicrobial sepsis populations remain unestablished.

Future Directions: Future research should replicate the microbiome and metabolite associations in larger, geographically diverse patient cohorts, define the causal role of ABCC1 in clinically relevant polymicrobial models, and evaluate purified metabolite or microbiome-based interventions with pharmacokinetic and toxicology studies.

This systematic review and meta-analysis found moderate short-term mortality discrimination for machine-learning models, with pooled AUCs of 0.82 in training datasets and 0.79 in validation datasets. Logistic regression, random forest, and XGBoost performed similarly, while evidence was strongest for sepsis-associated encephalopathy and sepsis-induced coagulopathy; long-term mortality evidence was extremely limited.

Impact: Rather than highlighting isolated high-performing algorithms, this study quantifies the distinction between apparent training performance and validation performance across complications. Its conclusion supports cautious implementation and emphasizes external validation, calibration, and clinically meaningful outcome assessment.

Clinical Implications: Machine-learning models may support early prognostic risk stratification, but current evidence does not justify replacing clinician judgment or using models as autonomous treatment triggers. Implementation should require independent external validation, calibration monitoring, assessment of dataset shift and bias, and prospective evaluation of whether model use improves care.

Future Directions: Future work should use prospective, preregistered, multicenter studies with transparent reporting, calibration and decision-curve analyses, fairness assessment, data and code sharing, and true external validation. Randomized implementation studies are needed to determine whether model-supported care improves mortality or resource use.