Abstract WP230: Interpretable Machine Learning Models For Predicting 30-Day All-Cause Readmission Following Carotid Endarterectomy Among Acute Ischemic Stroke Cases: A NSQIP Study (2014 - 2017)

Abstract

Background: Acute ischemic stroke (AIS) patients show a good prognosis with Carotid endarterectomy (CEA). However, lowering short-term readmissions after CEA for AIS cases remains a significant challenge. Several machine learning (ML) models predicting readmissions fail to explain the predictors. Hence the utility of such ML models in clinical settings is limited. This study used interpretable machine learning (ML) methods to identify the predictors associated with 30-day readmission. Methods: We utilized the National Surgical Quality Improvement Program registry (2014-2017) for this study. Patients aged >18 years and who underwent CEA for AIS were included. ICD-9, ICD-10, and CPT codes were used to identify AIS and CEA cases. Decision Trees and Random Forest classification techniques were utilized to identify predictors of 30-day readmission. Results: A total of 22,373 AIS patients underwent CEA during the study period. The mean (SD) age of the patients was 70.7 (9.4) years, and the majority (61%) were males. About 80% were Non-Hispanic White, followed by non-Hispanic Black (4.6%). About 7% of AIS patients who underwent CEA had 30-day readmission. Random Forest classification and Decision Tree were able to provide clinically relevant predictors and cut-off values. For example, one of the top predictors was pre-operative Hematocrit, and its cut-off value of ≤33% with Diabetes showed a higher risk for readmission. Conclusion: Our study showed that interpretable ML models could be helpful for clinicians to stratify patients based on their pre-operative risk for a 30-day readmission and could help optimize management strategies for improving patient outcomes. Incorporating these cut-off values in EMR could help clinical decision-making and plan interventions to reduce readmissions by managing risk factors. This would improve the quality of care and save high healthcare costs.