title: Clinical prediction rule to estimate the absolute 3-year risk of major cardiovascular events after carotid endarterectomy.
abstract: OBJECTIVE
  Prognosis after carotid endarterectomy is mainly determined by the occurrence of major adverse cardiovascular events (MACEs). Optimal medical treatment to reduce risk is the mainstay of MACE prevention. The level of risk of MACE may determine the initiation and aggressiveness of medical treatment, yet a prediction rule to assess that absolute MACE risk after carotid endarterectomy is currently unavailable.
  
  
  METHODS
  The prediction model was developed in a consecutive cohort of 1138 patients who underwent carotid endarterectomy between 2002 and 2009. Primary end point was MACE and encompassed myocardial infarction, stroke, and cardiovascular death. Fourteen potential clinical predictors were entered into a Cox proportional hazard model. After backward stepwise regression, internal validation was conducted with bootstrapping techniques to correct for overfitting. To provide an easily applicable clinical tool, a score chart was constructed, dividing patients into 4 risk groups. Model performance was assessed in terms of discrimination, calibration, and risk stratification.
  
  
  RESULTS
  During a mean follow-up of 2.28 (± 0.95) years, 148 events occurred, corresponding to a cumulative incidence of 13%. Clinical predictors in the final model were age, history of coronary or peripheral artery disease, smoking, systolic blood pressure, use of antihypertensive drugs, clinical presentation, presence of contralateral carotid stenosis, and serum creatinine levels. Discrimination of the final model, in terms of a C statistic, was 0.69 (0.64-0.73) and calibration showed a good overall fit (Gronnesby and Borgan, P=0.39). The observed incidence of MACE in the 4 risk groups was 6%, 9%, 19%, and 35%, respectively, indicating good overall risk stratification.
  
  
  CONCLUSIONS
  The clinical prediction model for MACE in the first 3 years after carotid endarterectomy may be used to identify high-risk patients to help optimize medical treatment and risk factor management as part of secondary prevention to increase life expectancy free from MACE. Despite our success in stratifying risk among these patients, even the lowest stratum remains at high risk and all of these patients should receive maximal secondary preventive measures.
authors.affiliation: University Medical Center Utrecht, Utrecht, The Netherlands.
authors.forename: Guus W
authors.initials: GW
authors.lastname: van Lammeren
authors.forename: Louise M
authors.initials: LM
authors.lastname: Catanzariti
authors.forename: Linda M
authors.initials: LM
authors.lastname: Peelen
authors.forename: Jean-Paul P M
authors.initials: JP
authors.lastname: de Vries
authors.forename: Dominique P V
authors.initials: DP
authors.lastname: de Kleijn
authors.forename: Frans L
authors.initials: FL
authors.lastname: Moll
authors.forename: Gerard
authors.initials: G
authors.lastname: Pasterkamp
authors.forename: Michiel L
authors.initials: ML
authors.lastname: Bots
mesh_terms: D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D002318:Cardiovascular Diseases / Q000401:mortality*; D016893:Carotid Stenosis / Q000601:surgery*; D015331:Cohort Studies; D016894:Endarterectomy, Carotid; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D006973:Hypertension / Q000150:complications; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D009203:Myocardial Infarction / Q000453:epidemiology*; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D012189:Retrospective Studies; D012307:Risk Factors; D012907:Smoking / Q000009:adverse effects; D020521:Stroke / Q000453:epidemiology*; D013997:Time Factors