title: Development and validation of an all-cause mortality risk score in type 2 diabetes.
abstract: BACKGROUND
  Diabetes reduces life expectancy by 10 to 12 years, but whether death can be predicted in type 2 diabetes mellitus remains uncertain.
  
  
  METHODS
  A prospective cohort of 7583 type 2 diabetic patients enrolled since 1995 were censored on July 30, 2005, or after 6 years of follow-up, whichever came first. A restricted cubic spline model was used to check data linearity and to develop linear-transforming formulas. Data were randomly assigned to a training data set and to a test data set. A Cox model was used to develop risk scores in the test data set. Calibration and discrimination were assessed in the test data set.
  
  
  RESULTS
  A total of 619 patients died during a median follow-up period of 5.51 years, resulting in a mortality rate of 18.69 per 1000 person-years. Age, sex, peripheral arterial disease, cancer history, insulin use, blood hemoglobin levels, linear-transformed body mass index, random spot urinary albumin-creatinine ratio, and estimated glomerular filtration rate at enrollment were predictors of all-cause death. A risk score for all-cause mortality was developed using these predictors. The predicted and observed death rates in the test data set were similar (P > .70). The area under the receiver operating characteristic curve was 0.85 for 5 years of follow-up. Using the risk score in ranking cause-specific deaths, the area under the receiver operating characteristic curve was 0.95 for genitourinary death, 0.85 for circulatory death, 0.85 for respiratory death, and 0.71 for neoplasm death.
  
  
  CONCLUSIONS
  Death in type 2 diabetes mellitus can be predicted using a risk score consisting of commonly measured clinical and biochemical variables. Further validation is needed before clinical use.
authors.affiliation: Department of Medicine and Therapeutics, Prince of Wales Hospital, Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.
authors.forename: Xilin
authors.initials: X
authors.lastname: Yang
authors.forename: Wing Yee
authors.initials: WY
authors.lastname: So
authors.forename: Peter C Y
authors.initials: PC
authors.lastname: Tong
authors.forename: Ronald C W
authors.initials: RC
authors.lastname: Ma
authors.forename: Alice P S
authors.initials: AP
authors.lastname: Kong
authors.forename: Christopher W K
authors.initials: CW
authors.lastname: Lam
authors.forename: Chung Shun
authors.initials: CS
authors.lastname: Ho
authors.forename: Clive S
authors.initials: CS
authors.lastname: Cockram
authors.forename: Gary T C
authors.initials: GT
authors.lastname: Ko
authors.forename: Chun-Chung
authors.initials: CC
authors.lastname: Chow
authors.forename: Vivian C W
authors.initials: VC
authors.lastname: Wong
authors.forename: Juliana C N
authors.initials: JC
authors.lastname: Chan
mesh_terms: D000368:Aged; D002138:Calibration; D003924:Diabetes Mellitus, Type 2 / Q000401:mortality*; D005260:Female; D006723:Hong Kong / Q000453:epidemiology; D006801:Humans; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D011446:Prospective Studies; D012372:ROC Curve; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors