Most blood units routinely cross-matched for patients undergoing Caesarean section (CS) in Nigeria are not used for transfusion. Over-ordering increases blood wastage, blood bank running costs, surgery costs and waiting times. A one-year review of all CS performed in the University of Ilorin Teaching Hospital (UITH), Nigeria, was thus conducted to evaluate blood reservation and utilisation practice. Efficiency of blood utilisation was evaluated using a cross-match to transfusion (C/T) ratio, transfusion probability (TP) and transfusion index (TI). The overall C/T ratio, TP and blood wastage were, respectively, 3.1, 24.6%, and 68%, indicative of inefficient blood utilisation. Establishing a Maximal Surgical Blood Order Schedule (MSBOS), which estimates the units of blood required for specific CS indications, is recommended to minimise blood over-ordering. Blood grouping alone should be done for patients at low risk for transfusion. For moderate risk patients, blood type and screen without cross-matching should be done, reserving cross-matching for high-risk patients.
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http://dx.doi.org/10.1177/00494755221123191 | DOI Listing |
J Orthop Surg Res
January 2025
Center of Medical Genetics, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, 610041, China.
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LaoLuxLab/Vaccine Preventable Diseases Laboratory, Institut Pasteur du Laos, Vientiane, Laos.
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Department of General, Visceral and Thoracic Surgery, German Armed Forces Central Hospital, Koblenz, Germany.
Liquid biomarkers are essential in trauma cases and critical care and offer valuable insights into the extent of injury, prognostic predictions, and treatment guidance. They can help assess the severity of organ damage (OD), assist in treatment decisions and forecast patient outcomes. Notably, small extracellular vesicles, particularly those involved in splenic trauma, have been overlooked.
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División de Terapia Intensiva, Hospital Juan A. Fernández, Buenos Aires, Argentina.
The advancements in cardiovascular imaging over the past two decades have been significant. The miniaturization of ultrasound devices has greatly contributed to their widespread adoption in operating rooms and intensive care units. The integration of AI-enabled tools has further transformed the field by simplifying echocardiographic evaluations and enhancing the reproducibility of hemodynamic measurements, even for less experienced operators.
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Background: Disease progression within 24 months (POD24) significantly impacts overall survival (OS) in patients with follicular lymphoma (FL). This study aimed to develop a robust predictive model, FLIPI-C, using a machine learning approach to identify FL patients at high risk of POD24.
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