title: Development and validation of a predictive score for perioperative transfusion in patients with hepatocellular carcinoma undergoing liver resection.
abstract: BACKGROUND
  Liver resection is a major surgery requiring perioperative blood transfusion. Predicting the need for blood transfusion for patients undergoing liver resection is of great importance. The present study aimed to develop and validate a model for predicting transfusion requirement in HBV-related hepatocellular carcinoma patients undergoing liver resection.
  
  
  METHODS
  A total of 1543 consecutive liver resections were included in the study. Randomly selected sample set of 1080 cases (70% of the study cohort) were used to develop a predictive score for transfusion requirement and the remaining 30% (n=463) was used to validate the score. Based on the preoperative and predictable intraoperative parameters, logistic regression was used to identify risk factors and to create an integer score for the prediction of transfusion requirement.
  
  
  RESULTS
  Extrahepatic procedure, major liver resection, hemoglobin level and platelets count were identified as independent predictors for transfusion requirement by logistic regression analysis. A score system integrating these 4 factors was stratified into three groups which could predict the risk of transfusion, with a rate of 11.4%, 24.7% and 57.4% for low, moderate and high risk, respectively. The prediction model appeared accurate with good discriminatory abilities, generating an area under the receiver operating characteristic curve of 0.736 in the development set and 0.709 in the validation set.
  
  
  CONCLUSIONS
  We have developed and validated an integer-based risk score to predict perioperative transfusion for patients undergoing liver resection in a high-volume surgical center. This score allows identifying patients at a high risk and may alter transfusion practices.
authors.affiliation: Department of Liver Surgery, West China Hospital, Sichuan University, Chengdu 610041, China. yanlunan_688@163.com.
authors.forename: Hai-Qing
authors.initials: HQ
authors.lastname: Wang
authors.forename: Jian
authors.initials: J
authors.lastname: Yang
authors.forename: Jia-Yin
authors.initials: JY
authors.lastname: Yang
authors.forename: Wen-Tao
authors.initials: WT
authors.lastname: Wang
authors.forename: Lu-Nan
authors.initials: LN
authors.lastname: Yan
mesh_terms: D000368:Aged; D019540:Area Under Curve; D016063:Blood Loss, Surgical / Q000517:prevention & control*; D006528:Carcinoma, Hepatocellular / Q000473:pathology / Q000601:surgery* / Q000821:virology; D016009:Chi-Square Distribution; D003661:Decision Support Techniques; D016002:Discriminant Analysis; D017707:Erythrocyte Transfusion; D005260:Female; D006498:Hepatectomy / Q000009:adverse effects*; D006509:Hepatitis B / Q000150:complications; D006801:Humans; D008113:Liver Neoplasms / Q000473:pathology / Q000601:surgery* / Q000821:virology; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D011237:Predictive Value of Tests; D012372:ROC Curve; D012042:Registries; D015203:Reproducibility of Results; D012189:Retrospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors; D016896:Treatment Outcome