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Ann Thorac Surg 2003;76:436-443
© 2003 The Society of Thoracic Surgeons
a Department of Surgery, Catholic Medical Center, Manchester, New Hampshire, USA
b Departments of Medicine, Surgery, and Community and Family Medicine, and The Center For Evaluative Clinical Sciences, Dartmouth-Hitchcock Medical Center, Hanover, New Hampshire, USA
c Department of Surgery, Maine Medical Center, Portland, Maine, USA
d Department of Surgery, Fletcher Allen Health Care, Burlington, Vermont, USA
e Department of Surgery, Eastern Maine Medical Center, Bangor, Maine, USA
Accepted for publication March 10, 2003.
* Address reprint requests to Dr Charlesworth, Catholic Medical Center, 100 McGregor St, Manchester, NH 03102, USA
e-mail: charlesworth{at}nhheart.com
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 (
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.
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