Cleaning Up the MESS: Can Machine Learning Be Used to Predict Lower Extremity Amputation after Trauma-Associated Arterial Injury?

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Thirty years after the Mangled Extremity Severity Score was developed, advances in vascular, trauma, and orthopaedic surgery have rendered the sensitivity of this score obsolete. A significant number of patients receive amputation during subsequent admissions, which are often missed in the analysis of amputation at the index admission. We aimed to identify risk factors for and predict amputation on initial admission or within 30 days of discharge (peritraumatic amputation [PTA]). The Nationwide Readmission Database for 2016 and 2017 was used in our analysis. Factors associated with PTA were identified. We used XGBoost, random forest, and logistic regression methods to develop a framework for machine learning-based prediction models for PTA. We identified 1,098 adult patients with traumatic lower extremity fracture and arterial injuries; 206 underwent amputation. One hundred and seventy-six patients (85.4%) underwent amputation during the index admission and 30 (14.6%) underwent amputation within a 30-day readmission period. After identifying factors associated with PTA, we constructed machine learning models based on random forest, XGBoost, and logistic regression to predict PTA. We discovered that logistic regression had the most robust predictive ability, with an accuracy of 0.88, sensitivity of 0.47, and specificity of 0.98. We then built on the logistic regression by the NearMiss algorithm, increasing sensitivity to 0.71, but decreasing accuracy to 0.74 and specificity to 0.75. Machine learning-based prediction models combined with sampling algorithms (such as the NearMiss algorithm in this study), can help identify patients with traumatic arterial injuries at high risk for amputation and guide targeted intervention in the modern age of vascular surgery.