Determining the Key Predictive Factors for Non-Union in Fifth Metatarsal Fractures: A Machine Learning-Based Study
Authors: Tomlinson Emma , Flaherty Alexandra , Akhbari Bardiya , Weaver Bradley , Waryasz Gregory R. , Guss Daniel , Schwab Joseph , DiGiovanni Christopher W. , Ghaednia Hamid , Ashkani-Esfahani Soheil 
Published: November 2022

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<0.05 was considered statistically significant.

Results:

Overall, this cohort of patients demonstrated a non-union rate of 22.4%. When divided by fracture zone, Zone 2 fractures results in a statistically significant increased rate of delayed union (17.2%) and non-union (8.6%), when compared to Zone 1 (10.8% and 5.8%, respectively) and Zone 3 (9.7%, 2.3%). Analysis of correlation between demographics data and union rates found no correlation with age, gender, race, or BMI. Our machine learning algorithm outcomes showed a significant correlation between nonunion and seven chronic diseases: diabetes, thyroid disease, hypertension, gastroesophageal reflux disease (GERD), irritable bowel syndrome (IBS) obstructive sleep apnea (OSA), and glaucoma. In terms of medications, significant correlation with nonunion was demonstrated with the use of levothyroxine, lisinopril, aspirin, steroids, and acetaminophen.

Conclusion:

Zone 2 fifth metatarsal base fractures have a significantly higher rate of nonunion as compared to other zones. Comorbid conditions including diabetes, thyroid disease, OSA, and glaucoma as well as medications, may also play a role, though their mechanism or correlative precipitants are yet to be determined. Although our results demonstrated correlation, causality can only be assessed using studies with limited confounding factors, such as clinical trials. Our outcomes suggest that physicians should pay attention not only to the location of the fracture, but also past medical history and medication use when making treatment decisions and discussing prognosis with patients.