Development of Machine Learning Algorithms to Predict Being Lost to Follow-up After Hip Arthroscopy for Femoroacetabular Impingement Syndrome.

Purpose: To determine factors predictive of patients who are at risk for being lost to follow-up after hip arthroscopy for femoroacetabular impingement syndrome (FAIS).

Methods: A prospective clinical repository was queried between January 2012 and October 2017 and all patients who underwent hip arthroscopy for primary or revision FAIS with minimum 2-year follow-up were included. A total of 27 potential risk factors for loss to follow-up were available and tested for predictive value.

Travel Distance Does Not Affect Outcomes in Hip Preservation Surgery: A Case for Centers of Excellence.

Background: Previous studies have evaluated the effect of distance to high-volume centers on outcomes after joint replacement. However, there is limited evidence on whether this distance has an effect on outcomes after undergoing hip arthroscopic surgery for femoroacetabular impingement syndrome (FAIS).

Purpose: To determine whether increased distance from a patient's home to his or her primary orthopaedic clinic has an influence on the ability to achieve the minimal clinically important difference (MCID) on outcome measures after surgery for FAIS.

Application of Machine Learning for Predicting Clinically Meaningful Outcome After Arthroscopic Femoroacetabular Impingement Surgery.

Background: Hip arthroscopy has become an important tool for surgical treatment of intra-articular hip pathology. Predictive models for clinically meaningful outcomes in patients undergoing hip arthroscopy for femoroacetabular impingement syndrome (FAIS) are unknown.

Purpose: To apply a machine learning model to determine preoperative variables predictive for achieving the minimal clinically important difference (MCID) at 2 years after hip arthroscopy for FAIS.