Development and Validation of Machine Learning Algorithms for Predicting 30-Day Mortality Following Carotid Endarterectomy: Carotid Endarterectomy Mortality Scoring System (MMS). (16th November 2020)

• Record Type: Journal Article
• Authors: Fatima, Nida; Shuaib, Ashfaq
• Is Part Of: Neurosurgery. Volume 67(2010)Supplement 1
• Publication Date: 2020-11-16
• Subjects: Nervous system -- Surgery -- Periodicals; 617.48005
• DOI: 10.1093/neuros/nyaa447_394
• Languages: English
• ISSNs: 0148-396X

Abstract

Abstract: INTRODUCTION: Pre-operative prognostication of 30-day mortality in patients with carotid endarterectomy can optimize surgical risk stratification and guide the decision-making process to improve survival. 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 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 uploaded on an open access web application. CONCLUSION: Machine learning algorithms show promising results for predicting 30-day mortality following carotid endarterectomy. These algorithms can be useful aids for counseling patients, assessing pre-operative medical risks, and predicting survival after surgery.