In the realm of medical advancements, hip preservation surgery stands at the forefront of minimally invasive techniques focused on maintaining hip joint integrity and delaying the onset of arthritis. As the prevalence of femoroacetabular impingement syndrome (FAIS) continues to rise, arthroscopic hip preservation surgery is increasingly being explored as a viable option to mitigate the onset of this degenerative joint condition. On January 14, 2024, an influential editorial in ‘Arthroscopy: The Journal of Arthroscopic & Related Surgery’ emphasized the growing significance of using large clinical registry data and machine learning to assess the effectiveness of hip arthroscopy for FAIS. This article explores the critical insights from the editorial, authored by Joshua D. Harris, and examines the transformative impact that artificial intelligence (AI) holds in advancing hip preservation surgery analysis.
Assessing Hip Arthroscopy: Beyond Anecdotal Evidence
Traditionally, original research publications from experienced high-volume surgeons have addressed whether a certain medical technique is capable of producing successful outcomes under ideal conditions – essentially answering the question, “Can it work?” However, real-world efficacy – “Does it work?” – is where large high-quality registries step in, particularly for hip preservation surgery. Such registries offer a panoramic view of how treatments perform across a diverse array of real-life scenarios, taking into account the heterogeneity among doctors, patients, interventions, and outcomes.
The Power of AI in Big Data Analysis
The application of AI, specifically machine learning techniques, stands out as an ideal method for sifting through and making sense of the vast pools of data accrued before, during, and after surgeries recorded in registries. Understanding the effectiveness of hip arthroscopy, especially given the numerous patient- and hip-specific factors that impact outcomes, requires immense computing power. Harris in the editorial posits that a deep learning model with data from tens of thousands of subjects would be necessary for this type of medium-scale analysis.
Raising the Bar on Clinical Relevance
In reflecting on the measures of outcome used in such studies, Harris espouses that solely relying on the minimal clinically important difference (MCID) is insufficient. MCID serves as the basic threshold for clinical relevance, yet it often falls short of patient expectations, which are considerably higher. Therefore, other metrics such as substantial clinical benefit (SCB), patient acceptable symptom state (PASS), and maximal outcome improvement (MOI) should be incorporated. Hip preservation registries ought to embrace validated, responsive patient-reported outcome scores like the International Hip Outcome Tool (iHOT-12) and routinely assess measures that meet or exceed patient expectations.
Case Studies in Hip Preservation Research
The United Kingdom’s Non-Arthroplasty Hip Registry (NAHR) and Denmark’s Danish Hip Arthroscopy Registry (DHAR) epitomize two of the largest geographic-based registries in hip preservation research, both with 11 years of data and patient enrollment. These registries not only illustrate the capacity to follow long-term patient outcomes but also provide a foundation for a global hip preservation surgery registry.
Ethical Disclosures and the Path Ahead
Acknowledging potential conflicts of interests is essential in the spirit of transparency. Harris reveals consultancies and advisory relationships with medical companies such as Smith and Nephew Inc. and engagements with professional boards and editorial memberships. Despite these disclosures, he underscores the importance of pursuing a registry that could foster the meaningful use of AI in large-scale clinical data analysis for hip preservation surgery.
Conclusion
The editorial in ‘Arthroscopy’ represents a bold call to action for the medical community to leverage the power of AI and clinical registries in the quest to affirm and refine the effectiveness of hip arthroscopy. It underscores the need for a vast dataset and sophisticated analytical tools to extract actionable insights that ultimately benefit patient care. With the pioneering efforts of registries such as NAHR and DHAR, and the continued push for data integration and AI application, the potential to advance our understanding and treatment of FAIS seems boundless.
DOI and References
DOI: 10.1016/j.arthro.2023.10.035
1. Harris, J.D. (2023). Editorial Commentary: Artificial Intelligence Analysis of Biomedical, Large, Clinical Registry Data Using Machine Learning Requires Tens of Thousands of Subjects and a Focus on Substantial Clinical Benefit: MCID Is too Low a Bar. Arthroscopy. Elsevier Inc.
2. Griffin, D. R., Dickenson, E. J., O’Donnell, J., et al. (2016). The Warwick Agreement on femoroacetabular impingement syndrome (FAIS): an international consensus statement. British Journal of Sports Medicine, 50(19), 1169-1176.
3. Khan, M., Oduwole, K. O., Razdan, P., et al. (2017). Current Concepts in the Diagnosis and Management of Femoroacetabular Impingement. International Orthopaedics, 41(11), 2213-2223.
4. Ayeni, O. R., Wong, I., Chien, T., et al. (2015). Surgical Treatment of Femoroacetabular Impingement: A Systematic Review of the Literature. British Medical Bulletin, 112(1), 105-129.
5. Byrd, J. W. T., & Jones, K. S. (2014). Hip Arthroscopy in Athletes: 10-Year Follow-Up. The American Journal of Sports Medicine, 42(10), 2496-2504.
Keywords
1. Hip Arthroscopy Effectiveness
2. Machine Learning Healthcare
3. AI in Orthopedic Surgery
4. Hip Preservation Registry
5. FAIS Surgical Outcomes