title: Efficacy of VBHOM to predict outcome following major lower limb amputation.
abstract: OBJECTIVE
  This study tests an existing Vascular Biochemistry and Haematology Outcome Model (VBHOM) on independent data and presents further refinements to the model.
  
  
  METHODS
  Data from 306 patients who underwent lower limb amputation over a 4-year period were collated. Urea, creatinine, sodium, potassium, hemoglobin, white cell count, albumin, age, gender, mode-of-admission, and short-term mortality events were extracted from the database. This study tests an existing model and trains a new model for predicting mortality using forward stepwise logistic regression.
  
  
  RESULTS
  The existing model suggests a significant lack of fit (c-index = 0.665, P = .04). For the exception of gender and mode-of-admission, all predictor variables had significant univariate associations with short-term mortality (P < .05). The refined model included age, sodium, potassium, creatinine, and albumin and had good discriminatory power (c-index = 0.8, no evidence of lack of fit, P = .616).
  
  
  CONCLUSIONS
  Our simplified model had good predictive ability and suggests redundancy in input variables used by the existing models.
authors.affiliation: MRIS Unit & University Department of Radiology, Cambridge University Hospitals NHS Foundation Trust, Hills Road, Cambridge, UK. andrew.patterson@addenbrookes.nhs.uk
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Patterson
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Degnan
authors.forename: Stewart R
authors.initials: SR
authors.lastname: Walsh
authors.forename: Mohammed
authors.initials: M
authors.lastname: Eltayeb
authors.forename: Earl F
authors.initials: EF
authors.lastname: Scout
authors.forename: James M F
authors.initials: JM
authors.lastname: Clarke
authors.forename: Yvonne G
authors.initials: YG
authors.lastname: Wilson
authors.forename: Tjun Y
authors.initials: TY
authors.lastname: Tang
mesh_terms: D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000671:Amputation, Surgical / Q000009:adverse effects / Q000401:mortality*; D015415:Biomarkers / Q000097:blood; D003404:Creatinine / Q000097:blood; D016002:Discriminant Analysis; D004739:England; D005260:Female; D006801:Humans; D016015:Logistic Models; D035002:Lower Extremity / Q000098:blood supply*; D008297:Male; D015999:Multivariate Analysis; D018579:Patient Selection; D016491:Peripheral Vascular Diseases / Q000097:blood / Q000401:mortality / Q000601:surgery*; D011188:Potassium / Q000097:blood; D018570:Risk Assessment; D012307:Risk Factors; D012709:Serum Albumin / Q000032:analysis; D012964:Sodium / Q000097:blood; D013997:Time Factors; D016896:Treatment Outcome
chemical_list: D015415:Biomarkers; D012709:Serum Albumin; D012964:Sodium; D003404:Creatinine; D011188:Potassium