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Predictors of 30-day mortality using machine learning approach following carotid endarterectomy
Ahmed Mohamed1, Ashfaq Shuaib2, Ayman Z Ahmed3
1Department of Biology (Physiology), McMaster University, Hamilton, ON, Canada.
Insights
Predicting 30-day mortality after carotid endarterectomy (CEA) is crucial. A machine learning model using nine factors accurately forecasts survival, aiding surgical decision-making.
Area of Science:
- Vascular Surgery
- Predictive Analytics
- Medical Informatics
Background:
- Preoperative risk stratification for carotid endarterectomy (CEA) is essential for patient survival.
- Accurate prognostication of 30-day mortality can optimize surgical decision-making and improve outcomes.
Purpose of the Study:
- To develop and validate predictive variables for 30-day mortality following CEA.
- To create a reliable tool for assessing preoperative risks and patient survival post-CEA.
Main Methods:
- Utilized the American College of Surgeons National Surgical Quality Improvement Program database (2005-2016).
- Employed logistic regression and LASSO methods for variable selection, resulting in 28 candidate models.
- Selected the final model based on clinical relevance and statistical performance, including discrimination, calibration, and Brier score.
Main Results:
- Analyzed 65,807 patients, with a 30-day mortality rate of 0.7% (466 patients).
- A 9-factor model (age, BMI, functional status, ASA grade, COPD, albumin, hematocrit, creatinine, platelets) demonstrated superior performance.
- Machine learning algorithms, particularly logistic regression, outperformed LASSO in predictive accuracy (AUCs).
Conclusions:
- Machine learning algorithms show significant promise in predicting 30-day mortality after CEA.
- The developed predictive model can serve as a valuable tool for patient counseling and preoperative risk assessment.
- This approach aids in enhancing survival predictions for patients undergoing carotid endarterectomy.
Background:
Preoperative prognostication of 30-day mortality in patients with carotid endarterectomy (CEA) can optimize surgical risk stratification and guide the decision-making process to improve survival. This study aims to develop and validate a set of predictive variables of 30-day mortality following CEA.
Methods:
The patient cohort was identified from the American College of Surgeons National Surgical Quality Improvement Program (2005-2016). We performed logistic regression (enter, stepwise, and forward) and least absolute shrinkage and selection operator (LASSO) method for the selection of variables, which resulted in 28-candidate models. The final model was selected based upon clinical knowledge and numerical results.
Results:
Statistical analysis included 65,807 patients with 30-day mortality in 0.7% (n = 466) patients. The median age of our cohort was 71.0 years (range, 16-89 years). The model with 9 predictive factors which included age, body mass index, functional health status, American Society of Anesthesiologist grade, chronic obstructive pulmonary disorder, preoperative serum albumin, preoperative hematocrit, preoperative serum creatinine, and preoperative platelet count-performed best on discrimination, calibration, Brier score, and decision analysis to develop a machine learning algorithm. Logistic regression showed higher AUCs than LASSO across these different models. The predictive probability derived from the best model was converted into an open-accessible scoring system.
Conclusion:
Machine learning algorithms show promising results for predicting 30-day mortality following CEA. These algorithms can be useful aids for counseling patients, assessing preoperative medical risks, and predicting survival after surgery.
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