Articles: critical-illness.
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Pediatr Crit Care Me · Apr 2024
Sedation Research in Critically Ill Pediatric Patients: Proposals for Future Study Design From the Sedation Consortium on Endpoints and Procedures for Treatment, Education, and Research IV Workshop.
Sedation and analgesia for infants and children requiring mechanical ventilation in the PICU is uniquely challenging due to the wide spectrum of ages, developmental stages, and pathophysiological processes encountered. Studies evaluating the safety and efficacy of sedative and analgesic management in pediatric patients have used heterogeneous methodologies. The Sedation Consortium on Endpoints and Procedures for Treatment, Education, and Research (SCEPTER) IV hosted a series of multidisciplinary meetings to establish consensus statements for future clinical study design and implementation as a guide for investigators studying PICU sedation and analgesia. ⋯ These SCEPTER IV consensus statements are comprehensive and may assist investigators in the design, enrollment, implementation, and dissemination of studies involving sedation and analgesia of PICU patients requiring mechanical ventilation. Implementation may strengthen the rigor and reproducibility of research studies on PICU sedation and analgesia and facilitate the synthesis of evidence across studies to improve the safety and quality of care for PICU patients.
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Journal of critical care · Apr 2024
Effects of education, income and employment on ICU and post-ICU survival - A nationwide Swedish cohort study of individual-level data with 1-year follow up.
The aim of this study was to examine relationships between education, income, and employment (socioeconomic status, SES) and intensive care unit (ICU) survival and survival 1 year after discharge from ICU (Post-ICU survival). ⋯ Significant relationships between low SES in the critically ill and increased risk of death indicate that it is important to identify and support patients with low SES to improve survival after intensive care. Studies of survival after critical illness need to account for participants SES.
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J Clin Monit Comput · Apr 2024
Early prediction of mortality at sepsis diagnosis time in critically ill patients by using interpretable machine learning.
This study applied machine learning for the early prediction of 30-day mortality at sepsis diagnosis time in critically ill patients. Retrospective study using data collected from the Medical Information Mart for Intensive Care IV database. The data of the patient cohort was divided on the basis of the year of hospitalization, into training (2008-2013), validation (2014-2016), and testing (2017-2019) datasets. 24,377 patients with the sepsis diagnosis time < 24 h after intensive care unit (ICU) admission were included. ⋯ The calibration plot for the model revealed a slope of 1.03 (95% CI 0.94-1.12) and intercept of 0.14 (95% CI 0.04-0.25). The SHAP revealed the top three most significant features, namely age, increased red blood cell distribution width, and respiratory rate. Our study demonstrated the feasibility of using the interpretable machine learning model to predict mortality at sepsis diagnosis time.
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Critical care medicine · Apr 2024
Randomized Controlled Trial Multicenter StudyA Comparison of High and Usual Protein Dosing in Critically Ill Patients With Obesity: A Post Hoc Analysis of an International, Pragmatic, Single-Blinded, Randomized, Clinical Trial.
Across guidelines, protein dosing for critically ill patients with obesity varies considerably. The objective of this analysis was to evaluate whether this population would benefit from higher doses of protein. ⋯ In critically ill patients with obesity, higher protein doses did not improve clinical outcomes, including those with higher nutritional and frailty risk.
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Pediatr Crit Care Me · Apr 2024
ReviewThe Pediatric Data Science and Analytics Subgroup of the Pediatric Acute Lung Injury and Sepsis Investigators Network: Use of Supervised Machine Learning Applications in Pediatric Critical Care Medicine Research.
Perform a scoping review of supervised machine learning in pediatric critical care to identify published applications, methodologies, and implementation frequency to inform best practices for the development, validation, and reporting of predictive models in pediatric critical care. ⋯ Publication of supervised machine learning models to address clinical challenges in pediatric critical care medicine has increased dramatically in the last 5 years. While these approaches have the potential to benefit children with critical illness, the literature demonstrates incomplete reporting, absence of external validation, and infrequent clinical implementation.