Inflammatory burden index as a predictor of mortality in septic patients: a retrospective study using the MIMIC-IV database

BMC Infectious Diseases, Journal Year: 2025, Volume and Issue: 25(1)

Published: April 17, 2025

Previous studies have identified the Inflammatory Burden Index (IBI) as a potential predictor of mortality risk in inflammatory diseases. However, its relationship with rates specifically septic patients has not been thoroughly investigated. This study aimed to explore association between IBI and sepsis. We sourced clinical records 1,828 from Multiparameter Intelligent Monitoring Intensive Care IV (MIMIC-IV 3.0) dataset, covering period 2008 2022. The primary endpoint was within 28 days, secondary endpoints including during intensive care unit (ICU) stays throughout hospitalization. Patients were categorized into quartiles based on their log-transformed (LnIBI) levels. Binary logistic regression utilized examine independent influence outcomes, adjusting for confounders. Additionally, these outcomes explored using restricted cubic splines Kaplan-Meier analysis. Further comparison receiver operating characteristic (ROC) curves conducted investigate predictive performance. involved patients, 1,047 males. all-cause 17.78% (325/1828) 17.34% (317/1828) ICU stays, 18.22% (333/1828) over course In adjusted model, positive correlation found LnIBI at days (OR 1.093[1.014, 1.179], P = 0.021), stay 1.106[1.025, 1.195], 0.01), hospitalization 1.1[1.022, 1.187], 0.012). analysis showed linear risks. areas under curve (AUC) larger than that CRP (P < 0.05), there no significant differences Neutrophil counts or Lymphocyte > 0.05). plots revealed significantly lower survival highest quartile 0.001). Elevated values are linked higher risks ICU, patients.

Language: Английский

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Biomarker-Driven Pharmacokinetics and Efficacy of Polymyxin B in Critically Ill Patients with XDR-GN Pneumonia

Pharmaceuticals, Journal Year: 2025, Volume and Issue: 18(4), P. 586 - 586

Published: April 17, 2025

Background: Achieving pharmacokinetic/pharmacodynamic (PK/PD) targets is critical for improving treatment success, particularly in critically ill patients. This study investigates the role of inflammatory biomarkers and their influence on PK/PD characteristics polymyxin B (PMB) patients with extensively drug-resistant Gram-negative (XDR-GN) bacterial nosocomial pneumonia. Methods: Serial blood and/or bronchoalveolar lavage fluid (BALF) samples were collected at specified time points analyzed PMB biomarkers, including IL-6 IL-10. Clinical data also recorded, correlations PK parameters further analyzed. Results: Among 27 enrolled patients, 22 (81.5%) achieved success. The pharmacokinetic included a maximum plasma concentration (Cmax) 8.3 µg/mL, clearance (CL) 1.55 L/h, volume distribution (Vd) 30.44 L, half-life (t1/2) 19.56 h, steady-state area under concentration–time curve from 0 to 24 h (AUCss,0–24h) 110.08 h·µg/mL, protein-binding ratio 85.53%. AUCss,0–24h metric was identified as robust predictor clinical efficacy, an optimal cutoff value 77.27 h·µg/mL. Notably, 48.15% target range 50–100 76.95% these attaining Another exceeded this target, 92.31% subgroup demonstrated limited pulmonary penetration, epithelial lining (ELF)/plasma 15.69% [16.86, 18.15]. Furthermore, TNF-α IL-6/IL-10 significantly correlated parameters. Conclusions: Our others’ studies suggest heterogeneity majority or surpassed recommended attained success through intravenous administration simplified fixed dose. However, did not achieve satisfactory concentrations, suggesting that its efficacy may involve alternative mechanisms. modulation responses play pivotal severe infections, highlighting potential biomarker-guided therapeutic strategies.

Language: Английский

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Controlling Glycemic Variability in Non-diabetic Sepsis Patients: A Step toward Precision in Critical Care

Indian Journal of Critical Care Medicine, Journal Year: 2024, Volume and Issue: 29(1), P. 6 - 7

Published: Dec. 30, 2024

Language: Английский

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Inflammatory Burden Index as a predictor of mortality in septic patients: A retrospective study using the MIMIC-IV Database

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 4, 2024

Purpose Previous studies have identified the Inflammatory Burden Index (IBI) as a potential predictor of mortality risk in inflammatory diseases. However, its relationship with rates specifically septic patients has not been thoroughly investigated. This study aimed to explore association between IBI and sepsis. Patients methods: We sourced clinical records 1,828 from MIMIC-IV (3.0) dataset. The primary endpoint was within 28 days, secondary endpoints including during ICU stays throughout hospitalization. were categorized into quartiles based on their LnIBI levels. Binary logistic regression utilized examine independent influence outcomes, adjusting for confounders. Additionally, these outcomes explored using restricted cubic splines Kaplan-Meier analysis. Results involved patients, 1,047 males. all-cause 17.78% (325/1828) 17.34% (317/1828) stays, 18.22% (333/1828) over course In adjusted model, positive correlation found Ln at days (OR 1.093[1.014, 1.179], P = 0.021), stay 1.106[1.025, 1.195], 0.01), hospitalization 1.1[1.022, 1.187], 0.012). analysis showed linear risks. plots revealed significantly lower survival highest quartile (P < 0.001). Conclusion Elevated values are linked higher risks ICU, period patients.

Language: Английский

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The Role of Immune Semaphorins in Sepsis—A Prospective Cohort Study

Microorganisms, Journal Year: 2024, Volume and Issue: 12(12), P. 2563 - 2563

Published: Dec. 12, 2024

In sepsis, a balanced pro-inflammatory and anti-inflammatory response results in the bacterial clearance resolution of inflammation, promoting clinical recovery survival. Semaphorins, large family secreted membrane-bound glycoproteins, are newly recognized biomarkers therapeutic targets immunological neoplastic disorders. Although semaphorins might also be crucial part host defense responses to infection, their role sepsis is yet determined. This study aimed analyze association serum semaphorin concentrations with severity outcomes. Serum (SEMA3A, SEMA3C, SEMA3F, SEMA4D, SEMA7A) were measured 115 adult patients community-acquired 50 healthy controls. While SEMA3A was decreased, SEMA7A increased patients. All analyzed SEMA showed good accuracy identifying sepsis. kinetics related complications; SEMA3A, SEMA4D respiratory failure; SEMA3C acute kidney injury; SEMA3F septic shock. Importantly, associated 28-day mortality. conclusion, we provide evidence that course

Language: Английский

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Machine Learning Models in Sepsis Outcome Prediction for ICU Patients: Integrating Routine Laboratory Tests—A Systematic Review

Biomedicines, Journal Year: 2024, Volume and Issue: 12(12), P. 2892 - 2892

Published: Dec. 19, 2024

Background. Sepsis presents significant diagnostic and prognostic challenges, traditional scoring systems, such as SOFA APACHE, show limitations in predictive accuracy. Machine learning (ML)-based survival models can support risk assessment treatment decision-making the intensive care unit (ICU) by accounting for numerous complex factors that influence outcome septic patient. Methods. A systematic literature review of studies published from 2014 to 2024 was conducted using PubMed database. Eligible investigated development ML incorporating commonly available laboratory clinical data predicting outcomes adult ICU patients with sepsis. Study selection followed PRISMA guidelines relied on predefined inclusion criteria. All records were independently assessed two reviewers, conflicts resolved a third senior reviewer. Data related study design, methodology, results, interpretation results extracted grid. Results. Overall, 19 identified, encompassing primarily logistic regression, random forests, neural networks. Most used datasets US-based (MIMIC-III, MIMIC-IV, eICU-CRD). The most common variables model age, albumin levels, lactate ventilator. demonstrated superior performance metrics compared conventional methods systems. best-performing gradient boosting decision tree, an area under curve 0.992, accuracy 0.954, sensitivity 0.917. However, several critical should be carefully considered when interpreting population bias (i.e., single center studies), small sample sizes, limited external validation, interpretability. Conclusions. Through real-time integration routine data, ML-based tools assist enhance consistency quality sepsis management across various healthcare contexts, including ICUs resources.

Language: Английский

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Systemic immune-inflammation index and the short-term mortality of patients with sepsis: A meta-analysis

Biomolecules and Biomedicine, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 27, 2024

The systemic immune-inflammation index (SII) is a novel biomarker that reflects the balance between host immune response and inflammation, two key pathophysiological processes involved in sepsis. This meta-analysis aimed to evaluate relationship SII at admission short-term mortality patients with Literature searches were performed PubMed, Embase, Web of Science, CNKI, Wanfang up August 30, 2024, using relevant search terms. Observational studies reported association sepsis included. Risk ratios (RRs) 95% confidence intervals (CIs) comparing incidence within 90 days high versus low calculated. Nine cohort studies, total 25,626 patients, A was significantly associated an increased risk all-cause (RR: 1.51, CI: 1.31-1.67, P < 0.001), moderate heterogeneity (I² = 43%). Sensitivity analyses confirmed robustness these findings. Subgroup suggested stronger younger than 67 years compared those aged or older (P 0.04), but no significant differences observed based on sex, cutoff values, follow-up duration. In conclusion, this demonstrates elevated particularly individuals. Further research needed validate findings explore their clinical implications.

Language: Английский

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