12.3.7 - Natural History of Femoroacetabular Impingement: Using Machine Learning to Evaluate Risk Factors For Osteoarthritis
- A. Pareek (Rochester, US)
- A. Pareek (Rochester, US)
- S. Ko (Seoul, KP)
- H. Melugin (Rochester, US)
- C. Jo (Seoul, KP)
- D. Ro (Seoul, KR)
- A. Krych (Rochester, US)
Abstract
Purpose
The purpose of this study was to use machine learning to develop a predictive model of risk factors that influence progression to osteoarthritis (OA) in patients with FAI that did not have surgical intervention.
Methods and Materials
Between 2000 and 2016, medical records of all patients diagnosed with FAI in the Rochester Epidemiology Project (REP) were reviewed. Patient demographics, physical exam, and imaging characteristics (ex: cam lesion, alpha angle, Tonnis grade) were included for model creation. For the initial prediction method, a Gradient Boosting Machine algorithm was selected due to its predictive power and efficiency with 10-fold cross-validation. The primary outcome for progression was radiographic progression of symptomatic hip osteoarthritis via Tonnis Grade.
Results
1045 patients with a mean age 28.5 years, alpha angle of 61 degrees, Tonnis angle of 4.4 degrees were included. The mean follow-up was 24.9 years. A machine learning model was created using two steps. First model was built using only imaging parameters. Second model was build using both imaging parameters in addition to patient (age, BMI, etc) and physical exam (FAI impingement signs, groin pain, etc) parameters. The overall area under the curve (AUC) of the first model was 72.5% which was significantly improved to 81.9%. This model’s top two of the three features in order of importance were demographic related (age at diagnosis, BMI, Figure 1). The mean survival for the high-risk group was lower (121.9 months) than the low-risk group (201.9 months) for OA progression with survival of 90.4% vs 56.7% at 10-years (p<0.001).
Conclusion
In this long-term follow-up of a large geographic cohort treated nonoperatively, machine learning was successful in accurately predicting osteoarthritis progression given preoperative imaging, patient, and physical exam parameters. In addition, age, BMI, and Tonnis grade at initial presentation appear to be the most important three factors affecting osteoarthritis progression.