Daily Sepsis Research Analysis
Three studies point toward precision sepsis care: dynamic liver function testing (ICG clearance) can flag augmented hepatic clearance and predict subtherapeutic levels of hepatically cleared antibiotics; glycemic variability emerges as a high-performing digital prognostic biomarker in sepsis-associated AKI with machine learning support; and FcMBL-based capture of circulating molecular patterns shows prognostic signal through serial kinetics in septic shock.
Summary
Three studies point toward precision sepsis care: dynamic liver function testing (ICG clearance) can flag augmented hepatic clearance and predict subtherapeutic levels of hepatically cleared antibiotics; glycemic variability emerges as a high-performing digital prognostic biomarker in sepsis-associated AKI with machine learning support; and FcMBL-based capture of circulating molecular patterns shows prognostic signal through serial kinetics in septic shock.
Research Themes
- Precision dosing via dynamic organ function phenotyping in sepsis
- Digital biomarkers and machine learning for sepsis-associated organ dysfunction
- Broad-spectrum molecular pattern capture to monitor sepsis trajectory
Selected Articles
1. ICG clearance as an indicator of augmented hepatic clearance and subtherapeutic drug concentrations in septic patients.
In a prospective ICU cohort of 93 septic patients, nearly half exhibited augmented hepatic clearance by ICG testing. ICG plasma disappearance rate strongly predicted subtherapeutic troughs of hepatically or partially cleared agents (e.g., voriconazole, linezolid), with excellent discrimination (AUC 0.853) and actionable cut-offs (~20.65%/min). Findings support dynamic liver function monitoring to personalize antibiotic dosing in sepsis.
Impact: This study operationalizes a practical, dynamic biomarker (ICG-PDR) to detect augmented hepatic clearance and anticipate underexposure to key anti-infectives, directly informing precision dosing in sepsis.
Clinical Implications: In septic patients, consider ICG-PDR testing to identify augmented hepatic clearance and proactively adjust dosing of hepatically cleared drugs (e.g., linezolid, voriconazole), alongside therapeutic drug monitoring. Integrate dynamic liver function into PK models and dosing protocols.
Key Findings
- Augmented hepatic clearance phenotype was present in 49.5% of septic ICU patients (ICG-R15 < 6%).
- ICG-PDR and ICG-R15 strongly correlated with trough levels of hepatically/partially cleared antibiotics (voriconazole, linezolid), but not renally cleared agents.
- ICG-PDR independently predicted subtherapeutic concentrations with excellent performance (AUC 0.853; optimal cut-off ~20.65%/min).
Methodological Strengths
- Prospective ICU cohort with standardized ICG testing and serial pharmacokinetic trough measurements across multiple antibiotics.
- Robust multivariable modeling and ROC analyses with actionable cut-offs enabling clinical translation.
Limitations
- Single-center study with modest sample size; external validity requires confirmation.
- No interventional arm to test ICG-guided dosing on clinical outcomes; correlations limited to selected antibiotics.
Future Directions: Conduct multicenter, interventional trials testing ICG-guided dosing strategies and integrate dynamic hepatic function into population PK models for precision antibiotic therapy.
PURPOSE: To assess indocyanine green (ICG) clearance as a quantitative indicator of augmented hepatic clearance (AHC) in septic patients and to evaluate its impact on subtherapeutic antibiotic concentrations and 28-day mortality. METHODS: This prospective observational study enrolled 93 septic patients admitted to the intensive care unit (ICU), from whom 113 ICG clearance tests were obtained. Patients were stratified into three groups based on ICG retention rate at 15 min (ICG-R15): augmented (AHC, ICG-R15 < 6%), normal (NHC, 6% ≤ ICG-R15 ≤ 12%), and impaired hepatic clearance (IHC, ICG-R15 > 12%). Trough concentrations were measured for the following antibiotics: imipenem/cilastatin, piperacillin/tazobactam, voriconazole, linezolid, vancomycin, cefoperazone/sulbactam, and teicoplanin. Correlations between ICG clearance parameters (ICG-R15 and ICG plasma disappearance rate [PDR] ) and antibiotic concentrations were analyzed. Multivariate logistic regression and ROC analysis were performed to identify independent predictors of subtherapeutic concentrations. RESULTS: Nearly half of the cohort (49.5%, 46/93) presented with AHC. The 28-day mortality rate was highest in the IHC group (48.4%), with no significant difference between the AHC (10.9%) and NHC (12.5%) groups. Trough concentrations of hepatically or partially cleared antibiotics (voriconazole and linezolid) showed strong correlations with ICG-R15 (r = 0.679 and r = 0.626, respectively) and ICG-PDR (r = -0.673 and r = -0.629, respectively; all p < 0.01). No significant correlations were found for renally cleared antibiotics (imipenem, piperacillin). Multivariate analysis identified ICG-PDR as the only independent predictor of subtherapeutic concentrations for hepatically or partially eliminated antibiotics (OR = 0.885, 95% CI: 0.807-0.970, p = 0.009). In ROC analysis, ICG-PDR demonstrated excellent predictive performance for the entire antibiotic cohort (AUC = 0.853, p < 0.001), with a sensitivity of 89.5% and specificity of 72.6% at the optimal cut-off of 20.65%/min. When specifically applied to hepatically cleared antibiotics, it remained a strong predictor (AUC = 0.791, p = 0.003), with a cut-off of 21.55%/min yielding a sensitivity of 81.8% and specificity of 71.1%. CONCLUSION: AHC is a prevalent phenotype in sepsis and identifies a patient subgroup at high risk for subtherapeutic concentrations of hepatically or partially cleared antibiotics. ICG-derived parameters, particularly ICG-PDR, are robust predictors of inadequate drug exposure, supporting the use of dynamic liver function monitoring to optimize antibiotic dosing in septic patients.
2. From glycemic variability to digital signal biomarker: a prognostic and precision medicine framework for sepsis-associated acute kidney injury.
Among 12,268 SA-AKI patients, higher glycemic coefficient of variation robustly predicted 28- and 90-day mortality, with machine-learning models achieving strong discrimination (AUC up to 0.845). Notably, the association was absent in diabetics. The authors propose glycemic variability as a modifiable digital biomarker and outline a mechanistic GO-RI axis for precision ICU nephrology.
Impact: This large-scale, ML-enabled cohort elevates glycemic variability from a correlate to a pragmatic, modifiable signal for dynamic risk stratification in SA-AKI, defining testable precision-medicine hypotheses.
Clinical Implications: Routine monitoring and reduction of glycemic variability—particularly in non-diabetic SA-AKI—may improve outcomes; risk models incorporating GV can prioritize surveillance and tailor glucose modulation strategies.
Key Findings
- In 12,268 SA-AKI patients, higher glycemic CV quartiles were associated with significantly increased 28- and 90-day mortality.
- Subgroup analyses showed the CV–mortality association was absent in diabetic patients.
- ML prognostic models (CoxPH, LASSO, RSF) achieved strong performance (28-day AUCs up to 0.845), positioning GV as a digital biomarker and supporting a GO-RI mechanistic axis.
Methodological Strengths
- Very large cohort with rigorous statistics (restricted cubic splines, KM, multivariable Cox) and ML validation across multiple algorithms.
- Pre-specified subgroup analyses by age, sex, and diabetes status enhance interpretability and generalizability.
Limitations
- Retrospective, single-database design with potential residual confounding and practice-era effects.
- No interventional testing of GV reduction; external validation across health systems is needed.
Future Directions: Prospective interventional trials targeting GV reduction in SA-AKI (stratified by diabetes status) and external, multicenter validation of GV-based risk models with EHR integration.
Sepsis, a condition with substantial global morbidity and mortality, frequently leads to sepsis-associated acute kidney injury (SA-AKI). While glycemic variability (GV) correlates with adverse outcomes in critically ill populations, its prognostic value in SA-AKI remains underexplored. Using the MIMIC-IV database, this large-scale machine learning cohort study examined SA-AKI patients. Restricted cubic spline, Kaplan-Meier analysis, and Cox regression analyses were conducted to evaluate associations between GV, measured by glycemic coefficient of variation (CV) and 28- and 90-day mortality. Subgroup analyses stratified by age, sex, and diabetes status were performed. Prognostic models were developed using Cox proportional hazards (CoxPH), Least absolute shrinkage and selection operator (LASSO), and random survival forests (RSF). Among 12,268 eligible SA-AKI patients, the Boruta algorithm identified glycemic CV as a key prognostic determinant. When stratified by CV quartiles, higher CV quartiles exhibited significantly increased 28-day and 90-day mortality. Subgroup analyses revealed consistent associations except in diabetic patients, where increases in CV showed no correlation with mortality. Machine learning models exhibited strong predictive performance, with 28-day area under the curves (AUCs) of 0.822 (CoxPH), 0.822 (LASSO), and 0.845 (RSF), and 90-day AUCs of 0.819 (CoxPH), 0.820 (LASSO), and 0.837 (RSF). Elevated GV is associated with increased short- and long-term mortality in SA-AKI. Beyond prognostication, these findings position GV as a real-time, modifiable digital biomarker that may underpin a mechanistic Glycemic Oscillation-Induced Renal Injury (GO-RI) Axis. This framework supports future development of machine learning-enabled precision ICU nephrology strategies for dynamic risk stratification and phenotype-specific glycemic modulation in SA-AKI.
3. Kinetics of molecular patterns captured by mannose-binding lectin in septic shock correlate with clinical outcome: a monocentric prospective observational study.
In a prospective septic shock cohort, serial quantification of FcMBL-captured molecular patterns over 5 days revealed large inter-patient variability but prognostically informative kinetics. This broad-spectrum capture assay may complement conventional biomarkers and scores for real-time sepsis trajectory monitoring.
Impact: Introduces a mechanistically grounded, pan-pathogen capture biomarker with temporal resolution, potentially bridging innate immune sensing to bedside sepsis monitoring.
Clinical Implications: If validated, FcMBL kinetics could be integrated with clinical scores to identify deteriorating patients earlier, refine source control/timing, and tailor anti-infective strategies.
Key Findings
- Serial FcMBL-based capture of circulating molecular patterns was feasible in septic shock with sampling every 6–12 hours over 5 days.
- Considerable inter-patient variability was observed, but temporal kinetics carried prognostic value.
- The approach provides a broad-spectrum readout (PAMPs) that may complement existing sepsis biomarkers.
Methodological Strengths
- Prospective, high-frequency serial sampling with a standardized measurement schedule in ICU septic shock.
- Use of engineered FcMBL and magnetic bead platform enables broad PAMP capture with temporal profiling.
Limitations
- Monocentric design with unspecified sample size; external validation and calibration are needed.
- Clinical decision-making was not guided by FcMBL results; thresholds and integration algorithms require development.
Future Directions: Multicenter studies to validate prognostic thresholds, evaluate additive value over SOFA/procalcitonin, and test FcMBL-guided management algorithms.
BACKGROUND: Various serum biomarkers and scoring systems are currently employed to manage septic critically ill patients. However, a paucity of biomarker evidence facilitates sepsis identification or prognosis. Mannose-binding lectin (MBL) is the main circulating protein in innate immunity. It acts as a broad-spectrum recognition molecule that binds most pathogens, along with their breakdown products and cell debris. We report results of an original approach dosing molecular patterns captured by FcMBL, an engineered version of MBL, in patients with septic shock. This study aimed at evaluating molecular patterns kinetics to assess their potential contribution to the clinical management of critically ill patients suffering from septic shock. RESULTS: This monocentric, prospective, observational study was conducted on adults admitted to the intensive care unit (ICU) for septic shock. Using magnetic microbeads coated with FcMBL, we quantified molecular patterns captured in blood and analyzed their kinetics for 5 days. Pathogen-associated molecular patterns (PAMP) levels were sampled at 6-h intervals over the first 24 h of ICU admission, then at 12 h intervals on Day 2, and then daily through Day 5. To align the data from the real time of admission to the ICU, the "Serial Measurements" module in MedCalc CONCLUSIONS: Molecular pattern levels captured by FcMBL during septic shock exhibited large inter-patient variability, suggesting values depend on numerous parameters. The signal's kinetics demonstrated predictive value and may contribute to clinical management. TRIAL REGISTRATION: clinicaltrials, NCT03457038, Registered 15 October 2017, https://clinicaltrials.gov/study/NCT03457038.