Predicting the Risk of Subsequent Hip Surgery Before Primary Hip Arthroscopy for Femoroacetabular Impingement Syndrome: A Machine Learning Analysis of Preoperative Risk Factors in Hip Preservation

Abstract

Background: The number of patients requiring reoperation has increased as the volume of hip arthroscopy for femoroacetabular impingement syndrome (FAIS) has increased. The factors most important in determining patients who are likely to require reoperation remain elusive. Purpose: To leverage machine learning to better characterize the complex relationship across various preoperative factors (patient characteristics, radiographic parameters, patient-reported outcome measures [PROMs]) for patients undergoing primary hip arthroscopy for FAIS to determine which features predict the need for future ipsilateral hip reoperation, namely, revision hip arthroscopy, total hip arthroplasty (THA), hip resurfacing arthroplasty (HRA), or periacetabular osteotomy (PAO). Study Design: Cohort study; Level of evidence, 3. Methods: A cohort of 3147 patients undergoing 3748 primary hip arthroscopy procedures were included from an institutional hip preservation registry. Preoperative computed tomography of the hip was obtained for each patient, from which the following parameters were calculated: the alpha angle; the coronal center-edge angle; the neck-shaft angle; the acetabular version angle at 1, 2, and 3 o’clock; and the femoral version angle. Preoperative PROMs included the modified Harris Hip Score (mHHS), the Hip Outcome Score (HOS)–Activities of Daily Living subscale (HOS-ADL) and the Sport Specific subscale, and the international Hip Outcome Tool (iHOT-33). Random forest models were created for revision hip arthroscopy, the THA, the HRA, and the PAO. Area under the curve (AUC) for the receiver operating characteristic curve and accuracy were calculated to evaluate each model. Results: A total of 171 patients (4.6%) underwent subsequent hip surgery after primary hip arthroscopy for FAIS. The AUC and accuracy, respectively, were 0.77 (fair) and 76% for revision hip arthroscopy (mean, 26.4-month follow-up); 0.80 (good) and 81% for THA (mean, 32.5-month follow-up); 0.62 (poor) and 69% for HRA (mean, 45.4-month follow-up); and 0.76 (fair) and 74% for PAO (mean, 30.4-month follow-up). The most important factors in predicting reoperation after primary hip arthroscopy were higher body mass index (BMI) and lower preoperative HOS-ADL for revision hip arthroscopy, greater age and lower preoperative iHOT-33 for THA, increased BMI for HRA, and larger neck-shaft angle and lower preoperative mHHS for PAO. Conclusion: Despite the low failure rate of hip arthroscopy for FAIS, our study demonstrated that machine learning has the capability to identify key preoperative risk factors that may predict subsequent ipsilateral hip surgery before the index hip arthroscopy. Knowledge of these demographic, radiographic, and patient-reported outcome data may aid in preoperative counseling and expectation management to better optimize hip preservation.

Predictors of Achieving the Maximal Outcome Improvement Threshold for Willingness to Undergo Revision Hip Arthroscopy

“…Previous studies have analyzed risk factors of revision hip arthroscopy. Haeberle et al 21 used machine learning to identify higher BMI and a lower preoperative Hip Outcome Score-Activities of Daily Living subscale as key preoperative risk factors for revision. In addition, their model predicted the need for revision before the index hip arthroscopy with an accuracy of 76%.…”

“…49 Consequently, the number of patients requiring revision hip arthroscopy has also increased. 21 The most common causes of primary failed hip arthroscopy are labral retear and inaccurate cam morphology correction. 46 Moreover, the treatment of labral retears during revision hip arthroscopy often requires technically advanced procedures such as labral reconstruction.…”

Predicting the Objective and Subjective Clinical Outcomes of Anterior Cruciate Ligament Reconstruction: A Machine Learning Analysis of 432 Patients

“…Several studies have revealed the performance of machine learning algorithms in predicting PROMs and the risk of reoperation. 10,17,22,29,30 Ramkumar et al 29,30 applied 7 machine learning models to predict whether patients would achieve the MCIDs of PROMs at 2 years after osteochondral allograft transplant, and the AUCs of the best-performing models ranged from 0.60 to 0.94. Haeberle et al 10 reported AUCs ranging from 0.62 to 0.80 for random forest models predicting the risk of subsequent hip surgeries before primary hip arthroscopy.…”

“…Recently, machine learning has been introduced in clinical scenarios as an advanced approach to predict the outcomes of some surgical techniques, such as osteochondral transplant, 29,30 hip arthroscopic surgery, 10,16 and ACLR. 17,22,23 As a branch of artificial intelligence, machine learning is characterized by using well-documented historical data to predict future clinical outcomes using computational algorithms, thus combining related indicators and eliminating potential confounding factors to improve the accuracy of predictions.…”

Limited clinical utility of a machine learning revision prediction model based on a national hip arthroscopy registry

“…Most have focused on patient reported outcome, with Kunze et al analysing single-surgeon data to predict multiple post-operative endpoints based on different outcome measuring tools [19][20][21][22]. The prediction of subsequent surgery following hip arthroscopy has also been performed by Haeberle et al based on another single-surgeon database of over 3000 patients [11]. With their study, Haeberle et al achieved an AUC of 0.77 ± 0.08 for predicting a patient's risk of subsequent revision hip arthroscopy.…”