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Development and Validation of a Prediction Model for Stroke, Cardiac, and Mortality Risk After Non-Cardiac Surgery
Sang H Woo1, Gregary D Marhefka2, Scott W Cowan3
1Division of Hospital Medicine Department of Medicine Thomas Jefferson University Philadelphia PA.
Insights
A new tool accurately predicts postoperative stroke, major cardiac complications, and mortality after non-cardiac surgery. This model uses preoperative patient data to improve risk assessment for surgical patients.
Area of Science:
- Cardiology
- Surgery
- Medical Informatics
Background:
- Existing cardiovascular risk calculators lack stroke prediction for non-cardiac surgery.
- Stroke is a significant postoperative complication with high morbidity and mortality.
Purpose of the Study:
- To develop and validate a cardiovascular risk prediction tool for stroke, major cardiac complications, and mortality after non-cardiac surgery.
Main Methods:
- Retrospective cohort study of 1,165,750 patients from the American College of Surgeons National Surgical Quality Improvement Program Database (2007-2010).
- Developed a predictive model using preoperative factors including age, medical history, lab values, and surgery type.
- Validated the model using multivariate logistic regression and assessed predictive accuracy via receiver operating characteristic curves.
Main Results:
- The model demonstrated high predictive accuracy for 30-day stroke (AUC 0.876), major cardiovascular events (AUC 0.868), and 30-day mortality (AUC 0.925) in the validation cohort.
- Identified surgery type, history of stroke, and coronary artery disease as significant risk factors.
- Postoperative stroke occurred in 0.25%, major cardiac complications in 0.66%, and 30-day mortality in 1.66% of patients.
Conclusions:
- A web-based predictive model accurately estimates the risk of postoperative stroke, major cardiac complications, and mortality.
- This tool can aid in preoperative risk assessment and patient management for non-cardiac surgery.
Abstract:
Background Commonly used cardiovascular risk calculators do not provide risk estimation of stroke, a major postoperative complication with high morbidity and mortality. We developed and validated an accurate cardiovascular risk prediction tool for stroke, major cardiac complications (myocardial infarction or cardiac arrest), and mortality after non-cardiac surgery. Methods and Results This retrospective cohort study included 1 165 750 surgical patients over a 4-year period (2007-2010) from the American College of Surgeons National Surgical Quality Improvement Program Database. A predictive model was developed with the following preoperative conditions: age, history of coronary artery disease, history of stroke, emergency surgery, preoperative serum sodium (≤130 mEq/L, >146 mEq/L), creatinine >1.8 mg/dL, hematocrit ≤27%, American Society of Anesthesiologists physical status class, and type of surgery. The model was trained using American College of Surgeons National Surgical Quality Improvement Program data from 2007 to 2009 (n=809 880) and tested using data from 2010 (n=355 870). Risk models were developed using multivariate logistic regression. The outcomes were postoperative 30-day stroke, major cardiovascular events (myocardial infarction, cardiac arrest, or stroke), and 30-day mortality. Major cardiac complications occurred in 0.66% (n=5332) of patients (myocardial infarction, 0.28%; cardiac arrest, 0.41%), postoperative stroke in 0.25% (n=2005); 30-day mortality was 1.66% (n=13 484). The risk prediction model had high predictive accuracy with area under the receiver operating characteristic curve for stroke (training cohort=0.869, validation cohort=0.876), major cardiovascular events (training cohort=0.871, validation cohort=0.868), and 30-day mortality (training cohort=0.922, validation cohort=0.925). Surgery types, history of stroke, and coronary artery disease are significant risk factors for stroke and major cardiac complications. Conclusions Postoperative stroke, major cardiac complications, and 30-day mortality can be predicted with high accuracy using this web-based predictive model.
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