Radiographic Indices Are Not Predictive of Clinical Outcomes Among 1735 Patients Indicated for Hip Arthroscopic Surgery: A Machine Learning Analysis

Abstract

Background: The relationship between the preoperative radiographic indices for femoroacetabular impingement syndrome (FAIS) and postoperative patient-reported outcome measure (PROM) scores continues to be under investigation, with inconsistent findings reported. Purpose: To apply a machine learning model to determine which preoperative radiographic indices, if any, among patients indicated for the arthroscopic correction of FAIS predict whether a patient will achieve the minimal clinically important difference (MCID) for 1- and 2-year PROM scores. Study Design: Cohort study; Level of evidence, 3. Methods: A total of 1735 consecutive patients undergoing primary hip arthroscopic surgery for FAIS were included from an institutional hip preservation registry. Patients underwent preoperative computed tomography of the hip, from which the following radiographic indices were calculated by a musculoskeletal radiologist: alpha angle, beta angle, sagittal center-edge angle, coronal center-edge angle, neck shaft angle, acetabular version angle, and femoral version angle. PROM scores were collected preoperatively, at 1 year postoperatively, and at 2 years postoperatively for the modified Harris Hip Score (mHHS), the Hip Outcome Score (HOS)–Activities of Daily Living (HOS-ADL) and –Sport Specific (HOS-SS), and the International Hip Outcome Tool (iHOT-33). Random forest models were created for each PROM at 1 and 2 years’ follow-up, with each PROM’s MCID used to establish clinical meaningfulness. Data inputted into the models included ethnicity, laterality, sex, age, body mass index, and radiographic indices. Comprehensive and separate models were built specifically to assess the association of the alpha angle, femoral version angle, coronal center-edge angle, McKibbin index, and hip impingement index with respect to each PROM. Results: As evidenced by poor area under the curves and P values >.05 for each model created, no combination of radiographic indices or isolated index (alpha angle, coronal center-edge angle, femoral version angle, McKibbin index, hip impingement index) was a significant predictor of a clinically meaningful improvement in scores on the mHHS, HOS-ADL, HOS-SS, or iHOT-33. The mean difference between 1- and 2-year PROM scores compared with preoperative values exceeded the respective MCIDs for the cohort. Conclusion: In patients appropriately indicated for FAIS corrective surgery, clinical improvements can be achieved, regardless of preoperative radiographic indices, such as the femoral version angle, coronal center-edge angle, and alpha angle. No specific radiographic parameter or combination of indices was found to be predictive of reaching the MCID for any of the 4 studied hip-specific PROMs at either 1 or 2 years’ follow-up.

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

“…17 This methodology has been utilized with increasing frequency in orthopaedics to develop models for predicting surgical outcomes. 13,27,29 Although patients who undergo revision hip arthroscopy have improved outcomes after their second procedure, their results remain inferior to patients who only undergo primary arthroscopy. 18 Several risk factors for revision hip arthroscopy have been reported in the literature, although there is variability.…”

“…15 The hip impingement index was calculated by subtracting the alpha angle from the femoral version angle. 27 All angles were analyzed as nonnormal continuous variables. Patients enrolled in the registry completed PROMs, with scores prospectively collected preoperatively and at 1and 2-year follow-up.…”

“…However, there is no consensus as to the importance of the femoral version, as other studies have demonstrated marked improvements in PROMs regardless of the femoral version. 10,11,14,27 Conversely, unaddressed acetabular dysplasia is a known risk factor for failure of hip arthroscopy and progression to PAO. 12,34,40 As patientreported outcomes are inferior among patients who undergo PAO after failed hip arthroscopy compared with those with PAO alone, it is important to preoperatively recognize patients unlikely to benefit from hip arthroscopy.…”

“…Machine learning has recently been applied in orthopaedic surgery to predict clinical outcomes, although no study to date has utilized this technique to comprehensively and simultaneously analyze preoperative demographic variables, radiographic parameters, and PROMs to the end of predicting subsequent hip surgery after primary hip arthroscopy. 22,23,[26][27][28] Machine learning, a subset of artificial intelligence, is capable of incorporating large data sets to predict future outcomes. 29 Machine learning is particularly powerful in its dynamic ability to incrementally ''learn'' when new data are provided, without the requirement of human input, to increase the accuracy of the output via self-regulation.…”

Hips 2021

“…Aside from examining general patient characteristics such as sex, global laxity, age, and BMI, many studies have been carried out to identify aspects of hip joint anatomy that correlate with surgical success or failure. 8,14-16,22 In another study in this month’s AJSM , Maldonado et al 14 examine the possible effect of preoperative acetabular overcoverage on the results of arthroscopic treatment for FAI. The authors compared the results in 45 patients whose preoperative LCEA was >40° with a similar number of patients matched for age, sex, BMI, and length of follow-up but whose LCEA measured between 25° and 40°.…”