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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Purpose: This study aimed to identify the association between hyperchloremia at intensive care unit (ICU) admission and/or the increase of blood chloride levels and the incidence of major adverse kidney events within 30 days (MAKE30) in critically ill adults. Methods: We conducted a retrospective study to analyze the data of all adult patients admitted to the ICU of a tertiary academic hospital in China between April 2020 and April 2022. Patients were categorized based on their admission chloride levels (hyperchloremia ≥110 mmol/L and nonhyperchloremia <110 mmol/L) and stratified on the increased chloride levels 48 h after ICU admission (∆Cl ≥5 mmol/L and ∆Cl <5 mmol/L). ⋯ After adjusted for confounders, it was found that ΔCl ≥5 mmol/L (odds ratio [OR], 1.46; 95% confidence interval [CI], 1.096-1.93; P = 0.010), but not hyperchloremia (OR, 0.99; 95% CI, 0.77-1.28; P = 0.947), was associated with increased incidence of MAKE30. Conclusion: An increased chloride level in the first 48 h of ICU admission was an independent risk factor for MAKE30, whereas hyperchloremia at ICU admission was not associated with an increased incidence of MAKE30. Large-scale prospective studies are needed to verify our findings.
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The COVID-19 pandemic has been a challenge to propose efficient therapies. Because severe SARS-CoV2 infection is a viral sepsis eventually followed by an immunological autoinflammatory phenomenon, many approaches have been inspired by the previous attempts made in bacterial sepsis, while specific antiviral strategies (use of interferon or specific drugs) have been additionally investigated. We summarize our current thinking on the use of SARS-CoV-2 antivirals, corticosteroids, anti-IL-1, anti-IL-6, anti-C5a, as well as stem cell therapy in severe COVID-19. Patient stratification and appropriate time window will be important to be defined to guide successful treatment.
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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.
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Introduction: Septic patients with atrial fibrillation (AF) are common in the intensive care unit accompanied by high mortality. The early prediction of prognosis of these patients is critical for clinical intervention. This study aimed to develop a model by using machine learning (ML) algorithms to predict the risk of 28-day mortality in septic patients with AF. ⋯ Conclusion: We established the first ML model for predicting the 28-day mortality of septic patients with AF. Compared with conventional scoring systems, the AdaBoost model performed moderately. The model established will have the potential to improve the level of clinical practice.