Factors Predicting Failure of Internal Fixations of Fractures of the Lower Limbs: a Prospective Cohort Study [post]

Barbara Prediger, Thorsten Tjardes, Christian Probst, Anahieta Parvaresch, Angelina Glatt, Dominique Rodil Anjos, Bertil Bouillon, Tim Mathes
2021 unpublished
BackgroundWe assessed predictive factors of patients with fractures of the lower extremities caused by trauma. We examined which factors might increase failure rates. Furthermore, the predictive factors were set into context with other long-term outcomes, concrete pain and physical functioning.MethodsWe performed a prospective cohort study at a single level I trauma center. We enrolled patients with traumatic fractures of the lower extremities treated with internal fixation from April 2017 to
more » ... rom April 2017 to July 2018. We evaluated the following predictive factors: age, gender, diabetes, smoking status, obesity, open fractures and peripheral arterial diseases. The primary outcome was time to failure (nonunion, implant failure or reposition), secondary outcomes were pain and physical functioning measured at follow up 6 months after initial surgery. For the analysis of the primary outcome we used a multivariate stratified (according fracture location) Cox proportional hazard regression model.Results We included 204 patients. Overall, we observed a failure in 33 patients (16.2%). Most of the failures occurred within the first 3 months. Obesity and open fractures increased the risk of failure and decreased physical functioning. None of the predictors had an impact on pain. Age, female gender and smoking of more than ≥ 10 package years increased failure risk numerically but statistical uncertainty was high.Conclusion We found that obesity and open fractures strongly increased the risk of failure. These seem promising candidates to be included in a risk prediction model and can be considered as a good start for clinical decision making across different types of fractures in the lower limb. However, large heterogeneity in the other analyzed factors suggest that for a precise personalized risk estimation, computer-based models incorporating a variety of detailed information (e.g. pattern of injury, x-ray and clinical data) and their interrelation might be needed to increase precision of prediction significantly.Trial registrationNCT03091114
doi:10.21203/rs.3.rs-493067/v1 fatcat:cjpxy3vtijhbfmq3nrkrhrdqmi