title: Predicting outcome following colorectal cancer surgery using a colorectal biochemical and haematological outcome model (Colorectal BHOM).
abstract: OBJECTIVE
  To present a new biochemistry and haematology outcome model which uses a minimum dataset to model outcome following colorectal cancer surgery, a concept previously shown to be feasible with arterial operations.
  
  
  METHODS
  Predictive binary logistic regression models (a mortality and morbidity model) were developed for 704 patients who underwent colorectal cancer surgery over a 6-year period in one hospital. The variables measured included 30-day mortality and morbidity. Hosmer-Lemeshow goodness of fit statistics and frequency tables compared the predicted vs the reported number of deaths. Discrimination was quantified using the c-index.
  
  
  RESULTS
  There were 573 elective and 131 nonelective interventional cases. The overall mean predicted risk of death was 7.79% (50 patients). The actual number of reported deaths was also 50 patients (χ(2) = 1.331, df = 4, P-value = 0.856; no evidence of lack of fit). For the mortality model, the predictive c-index was = 0.810. The morbidity model had less discriminative power but there was no evidence of lack of fit (χ(2) = 4.198, df = 4, P-value = 0.380, c-index = 0.697).
  
  
  CONCLUSIONS
  The Colorectal Biochemistry and Haematology Outcome mortality model suggests good discrimination (c-index > 0.8) and uses only a minimal number of variables. However, it needs to be tested on independent datasets in different geographical locations.
authors.affiliation: Department of General Surgery, West Suffolk Hospital, Bury St Edmunds, Suffolk, UK.
authors.forename: N
authors.initials: N
authors.lastname: Farooq
authors.forename: A J
authors.initials: AJ
authors.lastname: Patterson
authors.forename: S R
authors.initials: SR
authors.lastname: Walsh
authors.forename: D R
authors.initials: DR
authors.lastname: Prytherch
authors.forename: T A
authors.initials: TA
authors.lastname: Justin
authors.forename: T Y
authors.initials: TY
authors.lastname: Tang
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D015179:Colorectal Neoplasms / Q000401:mortality* / Q000601:surgery*; D005260:Female; D005544:Forecasting / Q000379:methods; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D011183:Postoperative Complications / Q000453:epidemiology*; D012372:ROC Curve; D012709:Serum Albumin; D012964:Sodium / Q000097:blood; D016896:Treatment Outcome; D014508:Urea / Q000097:blood
chemical_list: D012709:Serum Albumin; D014508:Urea; D012964:Sodium