title: Predicting the need for blood transfusion in patients with hip fractures.
abstract: OBJECTIVE
  The need for perioperative blood management measures aimed at improving patient outcomes and reducing allogenic blood transfusion (ABT) is increasingly recognised. Our study aim is to create an algorithm to predict and manage the need for blood transfusion in patients with hip fractures.
  
  
  METHODS
  We retrospectively assessed 1,484 patients with hip fractures and analysed the probability of receiving an ABT within 72 hours of admission. Univariate, multiple logistic regression analysis and a probability algorithm for predicting the need for blood transfusion on the basis of independent multivariate predictors were used.
  
  
  RESULTS
  Significant predictors for ABT were: older age; lower haemoglobin on admission; female gender; type of surgical implant (cephalomedullary nail and dynamic hip screw more than hemiarthroplasty); and a shorter wait time from admission to surgery. A regression model algorithm correctly predicted the need for an ABT in 73 % of the cases.
  
  
  CONCLUSIONS
  An algorithm and a simple clinical tool were devised to predict and manage the need for a blood transfusion within 72 hours of admission in patients with hip fractures.
authors: Department of Orthopedics, Tel Aviv Sourasky Medical Center, affiliated with Sackler Faculty of Medicine, Tel Aviv University, 6 Weizmann Street, Tel Aviv, 64239, Israel. Assafkadar@hotmail.com
authors: Assaf
authors: A
authors: Kadar
authors: Ofir
authors: O
authors: Chechik
authors: Ely
authors: E
authors: Steinberg
authors: Evgeny
authors: E
authors: Reider
authors: Amir
authors: A
authors: Sternheim
mesh_terms: D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D016063:Blood Loss, Surgical / Q000517:prevention & control*; D001804:Blood Transfusion, Autologous; D001858:Bone Nails; D001863:Bone Screws; D005260:Female; D005593:Fracture Fixation, Internal / Q000295:instrumentation / Q000379:methods*; D006620:Hip Fractures / Q000601:surgery*; D006801:Humans; D008297:Male; D015233:Models, Statistical; D015999:Multivariate Analysis; D019990:Perioperative Care; D012189:Retrospective Studies; D012737:Sex Factors; D013997:Time Factors
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