Shock : molecular, cellular, and systemic pathobiological aspects and therapeutic approaches : the official journal the Shock Society, the European Shock Society, the Brazilian Shock Society, the International Federation of Shock Societies
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Observational Study
Identification of Subphenotypes of Sepsis-Associated Liver Dysfunction Using Cluster Analysis.
Objectives: We attempted to identify and validate the subphenotypes of sepsis-associated liver dysfunction (SALD) using routine clinical information. Design: This article is a retrospective observational cohort study. Setting: We used the Medical Information Mart for Intensive Care IV database and the eICU Collaborative Research Database. ⋯ In addition, we were surprised to find that GGT levels in subphenotype δ were significantly higher than in other subphenotypes, showing a different pattern from bilirubin. Conclusions: We identified four subphenotypes of SALD that presented with different clinical features and outcomes. These results can provide a valuable reference for understanding the clinical characteristics and associated outcomes to improve the management of patients with SALD in the ICU.
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Multicenter Study
A Preventative Tool for Predicting Blood Stream Infections in Children with Burns.
Introduction: Despite significant advances in pediatric burn care, bloodstream infections (BSIs) remain a compelling challenge during recovery. A personalized medicine approach for accurate prediction of BSIs before they occur would contribute to prevention efforts and improve patient outcomes. Methods: We analyzed the blood transcriptome of severely burned (total burn surface area [TBSA] ≥20%) patients in the multicenter Inflammation and Host Response to Injury ("Glue Grant") cohort. ⋯ Conclusions: The multibiomarker panel model yielded a highly accurate prediction of BSIs before their onset. Knowing patients' risk profile early will guide clinicians to take rapid preventive measures for limiting infections, promote antibiotic stewardship that may aid in alleviating the current antibiotic resistance crisis, shorten hospital length of stay and burden on health care resources, reduce health care costs, and significantly improve patients' outcomes. In addition, the biomarkers' identity and molecular functions may contribute to developing novel preventive interventions.
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Introduction: Little is known regarding peripheral blood mononuclear cell telomere length (PBMC-TL) and response to traumatic injury. The objective of this study was to characterize the role of PBMC-TL in coagulation and clinical outcomes after injury. Methods: Plasma and buffy coats were prospectively collected from trauma patients and healthy volunteers. ⋯ Older patients in the bottom quartile of PBMC-TL had shorter lag time (2.78 min [2.33, 3.00] vs. 3.33 min [3.24, 3.89], P = 0.030) and were less likely to be discharged home (22% vs. 90%, P = 0.006) than those in the top quartile of PBMC-TL. Multivariable logistic regression models revealed both increased age and shorter PBMC-TL to be independent predictors of discharge disposition other than home. Conclusion: In older trauma patients, shorter PBMC-TL is associated with accelerated initiation of thrombin generation and lower likelihood of being discharged to home.
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Background: Acute kidney injury (AKI) is a prevalent and serious complication among patients with sepsis-associated acute respiratory distress syndrome (ARDS). Prompt and accurate prediction of AKI has an important role in timely intervention, ultimately improving the patients' survival rate. This study aimed to establish machine learning models to predict AKI via thorough analysis of data derived from electronic medical records. ⋯ In addition, a novel shiny application based on the XGBoost model was established to predict the probability of developing AKI among patients with sepsis-associated ARDS. Conclusions: Machine learning models could be used for predicting AKI in patients with sepsis-associated ARDS. Accordingly, a user-friendly shiny application based on the XGBoost model with reliable predictive performance was released online to predict the probability of developing AKI among patients with sepsis-associated ARDS.