Objectives: To present the consensus statements with supporting literature for plasma and platelet transfusions in critically ill neonates and children with malignancy, acute liver disease and/or following liver transplantation, and sepsis and/or disseminated intravascular coagulation from the Transfusion and Anemia EXpertise Initiative-Control/Avoidance of Bleeding.
Design: Systematic review and consensus conference of international, multidisciplinary experts in platelet and plasma transfusion management of critically ill children.
Setting: Not applicable.
Patients: Critically ill neonates and children with malignancy, acute liver disease and/or following liver transplantation, and sepsis and/or disseminated intravascular coagulation.
Interventions: None.
Measurements And Main Results: A panel of 13 experts developed evidence-based and, when evidence was insufficient, expert-based statements for plasma and platelet transfusions in critically ill neonates and children with malignancy, acute liver disease and/or following liver transplantation, and sepsis and/or disseminated intravascular coagulation. These statements were reviewed and ratified by the 29 Transfusion and Anemia EXpertise Initiative-Control/Avoidance of Bleeding experts. A systematic review was conducted using MEDLINE, EMBASE, and Cochrane Library databases, from inception to December 2020. Consensus was obtained using the Research and Development/University of California, Los Angeles Appropriateness Method. Results were summarized using the Grading of Recommendations Assessment, Development, and Evaluation method. We developed 12 expert consensus statements.
Conclusions: In the Transfusion and Anemia EXpertise Initiative-Control/Avoidance of Bleeding program, the current absence of evidence for use of plasma and/or platelet transfusion in critically ill children with malignancy, acute liver disease and/or following liver transplantation, and sepsis means that only expert consensus statements are possible for these areas of practice.
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http://dx.doi.org/10.1097/PCC.0000000000002857 | DOI Listing |
Am J Respir Crit Care Med
January 2025
Hosp Sabadell, critical care, sabadell, Spain;
Pediatr Crit Care Med
January 2025
Department of Pediatrics, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA.
Objectives: To report the feasibility of a fluid management practice bundle and describe the pre- vs. post-implementation prevalence and odds of cumulative fluid balance greater than 10% in critically ill pediatric patients with respiratory failure.
Design: Retrospective cohort from May 2022 to December 2022.
Am J Respir Crit Care Med
January 2025
Radbound Univeristy Medical Center, Nijmegen, Netherlands;
Rationale: In critically ill patients receiving invasive mechanical ventilation, switching from controlled to assisted ventilation is a crucial milestone towards ventilator liberation. The optimal timing for switching to assisted ventilation has not been studied.
Objectives: Our objective was to determine whether a strategy of early as compared to delayed switching affects the duration of invasive mechanical ventilation, ICU length of stay, and mortality.
Infect Drug Resist
January 2025
Department of Public Health, School of Allied Health Sciences, Kampala International University, Western Campus, Uganda.
Introduction: Coronavirus Disease 19 (COVID-19), caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), and Human Immunodeficiency Virus (HIV) are significant 21st-century pandemics with distinct virological and clinical characteristics. COVID-19 primarily presents as an acute respiratory illness, while HIV leads to chronic immune suppression. Understanding their differences can enhance public health strategies and treatment approaches.
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January 2025
Department of General Surgery, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China.
Background: Gastroparesis following complete mesocolic excision (CME) can precipitate a cascade of severe complications, which may significantly hinder postoperative recovery and diminish the patient's quality of life. In the present study, four advanced machine learning algorithms-Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), and -nearest neighbor (KNN)-were employed to develop predictive models. The clinical data of critically ill patients transferred to the intensive care unit (ICU) post-CME were meticulously analyzed to identify key risk factors associated with the development of gastroparesis.
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