title: Development and validation of a prediction model for strokes after coronary artery bypass grafting.
abstract: BACKGROUND
  A prospective study of patients undergoing coronary artery bypass graft surgery (CABG) was conducted to identify patient and disease factors related to the development of a perioperative stroke. A preoperative risk prediction model was developed and validated based on regionally collected data.
  
  
  METHODS
  We performed a regional observational study of 33,062 consecutive patients undergoing isolated CABG surgery in northern New England between 1992 and 2001. The regional stroke rate was 1.61% (532 strokes). We developed a preoperative stroke risk prediction model using logistic regression analysis, and validated the model using bootstrap resampling techniques. We assessed the model's fit, discrimination, and stability.
  
  
  RESULTS
  The final regression model included the following variables: age, gender, presence of diabetes, presence of vascular disease, renal failure or creatinine greater than or equal to 2 mg/dL, ejection fraction less than 40%, and urgent or emergency. The model significantly predicted (chi(2) [14 d.f.] = 258.72, p < 0.0001) the occurrence of stroke. The correlation between the observed and expected strokes was 0.99. The risk prediction model discriminated well, with an area under the relative operating characteristic curve of 0.70 (95% CI, 0.67 to 0.72). In addition, the model had acceptable internal validity and stability as seen by bootstrap techniques.
  
  
  CONCLUSIONS
  We developed a robust risk prediction model for stroke using seven readily obtainable preoperative variables. The risk prediction model performs well, and enables a clinician to estimate rapidly and accurately a CABG patient's preoperative risk of stroke.
authors.affiliation: Department of Surgery, Catholic Medical Center, Manchester, New Hampshire 03102, USA. charlesworth@nhheart.com
authors.forename: David C
authors.initials: DC
authors.lastname: Charlesworth
authors.forename: Donald S
authors.initials: DS
authors.lastname: Likosky
authors.forename: Charles A S
authors.initials: CA
authors.lastname: Marrin
authors.forename: Christopher T
authors.initials: CT
authors.lastname: Maloney
authors.forename: Hebe B
authors.initials: HB
authors.lastname: Quinton
authors.forename: Jeremy R
authors.initials: JR
authors.lastname: Morton
authors.forename: Bruce J
authors.initials: BJ
authors.lastname: Leavitt
authors.forename: Robert A
authors.initials: RA
authors.lastname: Clough
authors.forename: Gerald T
authors.initials: GT
authors.lastname: O'Connor
mesh_terms: D000328:Adult; D017677:Age Distribution; D000368:Aged; D000369:Aged, 80 and over; D000704:Analysis of Variance; D015331:Cohort Studies; D001026:Coronary Artery Bypass / Q000009:adverse effects* / Q000379:methods; D003327:Coronary Disease / Q000453:epidemiology / Q000601:surgery*; D005260:Female; D006801:Humans; D015994:Incidence; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D009511:New England / Q000453:epidemiology; D016017:Odds Ratio; D011183:Postoperative Complications / Q000175:diagnosis / Q000453:epidemiology*; D011237:Predictive Value of Tests; D011336:Probability; D012372:ROC Curve; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D017678:Sex Distribution; D020521:Stroke / Q000175:diagnosis / Q000453:epidemiology*; D016019:Survival Analysis