Personalized Medicine Using Predictive Analytics: A Machine Learning-Based Prognostic Model for Patients Undergoing Hip Arthroscopy
Authors: Domb BG, Ouyang VW, Go CC, Gornbein JA, Shapira J, Meghpara MB, Maldonado DR, Lall AC, Rosinsky PJ
Journal: American Journal of Sports Medicine, June 2022
DOI: 10.1177/03635465221091847
Background
Predictive tools are increasingly important in orthopedics to inform surgical decision-making and personalize care. This study aimed to build a machine learning-based model to forecast outcomes after hip arthroscopy for FAIS.
Methods
- Data from 2,415 patients undergoing hip arthroscopy were analyzed.
- Machine learning techniques (Cox proportional hazards and Fine-Gray models) were used to predict:
- Long-term survivorship (arthroscopy-free).
- Risk of revision surgery or conversion to arthroplasty.
- A web-based calculator was created for clinical use.
Key Findings
- The model predicted survivorship with a Harrell C-statistic of 0.848, indicating strong discrimination.
- Predictive ability for repeat surgery was moderate (C-statistic 0.662).
- The calculator integrates preoperative variables like age, Tönnis grade, BMI, and symptom duration.
Conclusions
This machine learning model offers a validated method to estimate individual patient risk and long-term outcomes following hip arthroscopy, supporting personalized treatment planning.
What Does This Mean for Providers?
The prognostic tool allows surgeons to objectively stratify risk and guide preoperative counseling. It supports shared decision-making and may improve patient selection, optimize expectations, and reduce unnecessary revisions or conversions.