title: Long-Term Post-CABG Survival: Performance of Clinical Risk Models Versus Actuarial Predictions.
abstract: OBJECTIVE
  Clinical risk models are commonly used to predict short-term coronary artery bypass grafting (CABG) mortality but are less commonly used to predict long-term mortality. The added value of long-term mortality clinical risk models over traditional actuarial models has not been evaluated. To address this, the predictive performance of a long-term clinical risk model was compared with that of an actuarial model to identify the clinical variable(s) most responsible for any differences observed.
  
  
  METHODS
  Long-term mortality for 1028 CABG patients was estimated using the Hannan New York State clinical risk model and an actuarial model (based on age, gender, and race/ethnicity). Vital status was assessed using the Social Security Death Index. Observed/expected (O/E) ratios were calculated, and the models' predictive performances were compared using a nested c-index approach. Linear regression analyses identified the subgroup of risk factors driving the differences observed.
  
  
  RESULTS
  Mortality rates were 3%, 9%, and 17% at one-, three-, and five years, respectively (median follow-up: five years). The clinical risk model provided more accurate predictions. Greater divergence between model estimates occurred with increasing long-term mortality risk, with baseline renal dysfunction identified as a particularly important driver of these differences.
  
  
  CONCLUSIONS
  Long-term mortality clinical risk models provide enhanced predictive power compared to actuarial models. Using the Hannan risk model, a patient's long-term mortality risk can be accurately assessed and subgroups of higher-risk patients can be identified for enhanced follow-up care. More research appears warranted to refine long-term CABG clinical risk models.
authors.affiliation: Department of Surgery, Stony Brook Medicine, Stony Brook University, Stony Brook, New York.
authors.forename: Brendan M
authors.initials: BM
authors.lastname: Carr
authors.affiliation: Department of Surgery, Stony Brook Medicine, Stony Brook University, Stony Brook, New York.
authors.forename: Jamie
authors.initials: J
authors.lastname: Romeiser
authors.affiliation: Department of Applied Mathematics and Statistics, College of Engineering and Applied Sciences, Stony Brook University, Stony Brook, New York.
authors.forename: Joyce
authors.initials: J
authors.lastname: Ruan
authors.affiliation: Department of Surgery, Stony Brook Medicine, Stony Brook University, Stony Brook, New York.
authors.forename: Sandeep
authors.initials: S
authors.lastname: Gupta
authors.affiliation: Department of Surgery, Stony Brook Medicine, Stony Brook University, Stony Brook, New York.
authors.forename: Frank C
authors.initials: FC
authors.lastname: Seifert
authors.affiliation: Department of Applied Mathematics and Statistics, College of Engineering and Applied Sciences, Stony Brook University, Stony Brook, New York.
authors.forename: Wei
authors.initials: W
authors.lastname: Zhu
authors.affiliation: Department of Surgery, Stony Brook Medicine, Stony Brook University, Stony Brook, New York.
authors.forename: A Laurie
authors.initials: AL
authors.lastname: Shroyer
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D001026:Coronary Artery Bypass / Q000401:mortality*; D005260:Female; D005544:Forecasting; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D012044:Regression Analysis; D012306:Risk; D012307:Risk Factors; D015996:Survival Rate; D013997:Time Factors