Determining the Key Predictive Factors for Non-Union in Fifth Metatarsal Fractures: A Machine Learning-Based Study

Category: Midfoot/Forefoot; Trauma; Other Introduction/Purpose: Metatarsal fractures account for over 35% of all foot fractures, and of these 68% specifically involve the fifth metatarsal [1],[2]. Subgroups of fractures affecting the fifth metatarsal base may be at higher risk of nonunion and therefore benefit from early surgical fixation, but traditional predictive models focus on the location of the fracture and little else. In this study, we aimed to determine predictive factors associated with non-union of fifth metatarsal fractures to assist surgeons and patients, alike, in identifying those at higher risk of nonunion. Methods: A retrospective machine learning-based analysis of 1,000 patients, >=18 y/o, diagnosed with a fifth metatarsal fracture at three tertiary medical centers was conducted. The fifth metatarsal base fracture was confirmed radiographically. We gathered imaging and narrative data including demographics (age, height, weight, BMI, gender, race, smoking habits, activity level), medications, chronic conditions, and fracture status (fracture zone, displacement, treatment method, healing status, and healing time). Non-union was described as failing to heal within 180 days of initial injury [3]. A machine learning analysis together with Pearson's correlation test and T-test were utilized where applicable. Five imputation methods were used to fill in missing datapoints. P