Red Blood Cell Transfusions and Iron Therapy for Patients Presenting with Acute Upper Gastrointestinal Bleeding: A Survey of Canadian Gastroenterologists and Hepatologists.
Kyle J FortinskyMyriam MartelRoshan RazikGillian SpiegleZane R GallingerSamir C GroverKaterina PavenskiAdam V WeizmanLukasz KwapiszSangeeta MehtaSarah GrayAlan N BarkunPublished in: Canadian journal of gastroenterology & hepatology (2016)
Introduction. There is limited data evaluating physician transfusion practices in patients with acute upper gastrointestinal bleeding (UGIB). Methods. A web-based survey was sent to 500 gastroenterologists and hepatologists across Canada. The survey included clinical vignettes where physicians were asked to choose transfusion thresholds. Results. The response rate was 41% (N = 203). The reported hemoglobin (Hgb) transfusion trigger differed by up to 50 g/L. Transfusions were more liberal in hemodynamically unstable patients compared to stable patients (mean Hgb of 86.7 g/L versus 71.0 g/L; p < 0.001). Many clinicians (24%) reported transfusing a hemodynamically unstable patient at a Hgb threshold of 100 g/L and the majority (57%) are transfusing two units of RBCs as initial management. Patients with coronary artery disease (mean Hgb of 84.0 g/L versus 71.0 g/L; p < 0.01) or cirrhosis (mean Hgb of 74.4 g/L versus 71.0 g/L; p < 0.01) were transfused more liberally than healthy patients. Fewer than 15% would prescribe iron to patients with UGIB who are anemic upon discharge. Conclusions. The transfusion practices of gastroenterologists in the management of UGIB vary widely and more high-quality evidence is needed to help assess the efficacy and safety of selected transfusion thresholds in varying patients presenting with UGIB.
Keyphrases
- end stage renal disease
- ejection fraction
- red blood cell
- newly diagnosed
- primary care
- chronic kidney disease
- cardiac surgery
- prognostic factors
- healthcare
- intensive care unit
- palliative care
- acute kidney injury
- cross sectional
- artificial intelligence
- deep learning
- acute respiratory distress syndrome
- aortic dissection