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Published on: March 27, 2018
Development and validation of a prediction model for strokes after coronary artery bypass grafting
David C Charlesworth1, Donald S Likosky, Charles A S Marrin
1Department of Surgery, Catholic Medical Center, Manchester, New Hampshire 03102, USA. charlesworth@nhheart.com
Insights
A new stroke risk model for coronary artery bypass graft (CABG) surgery patients uses seven preoperative factors. This tool helps clinicians quickly estimate a patient's stroke risk before the procedure.
Area of Science:
- Cardiovascular Surgery
- Neurology
- Medical Informatics
Background:
- Perioperative stroke is a significant risk for patients undergoing coronary artery bypass graft (CABG) surgery.
- Identifying predictive factors for stroke is crucial for patient management and surgical planning.
Purpose of the Study:
- To identify patient and disease factors associated with perioperative stroke development in CABG patients.
- To develop and validate a preoperative risk prediction model for stroke in this population.
Main Methods:
- A regional observational study included 33,062 patients undergoing isolated CABG surgery (1992-2001).
- Logistic regression analysis was used to develop a preoperative stroke risk prediction model.
- Bootstrap resampling techniques validated the model's fit, discrimination, and stability.
Main Results:
- The final model identified seven key predictors: age, gender, diabetes, vascular disease, renal failure (creatinine ≥ 2 mg/dL), low ejection fraction (<40%), and urgent/emergency surgery status.
- The model demonstrated significant predictive power (chi(2) = 258.72, p < 0.0001) with strong correlation between observed and expected strokes (0.99).
- The model showed good discrimination (AUC = 0.70) and acceptable internal validity and stability.
Conclusions:
- A robust risk prediction model for perioperative stroke in CABG patients was developed using seven readily available preoperative variables.
- The model accurately estimates a patient's preoperative stroke risk, aiding clinical decision-making.
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.
