Differences in total shoulder arthroplasty utilization and 30-day outcomes among White, Black, and Hispanic patients: do disparities exist in the outpatient setting?

BACKGROUND: In the United States, efforts to improve efficiency and reduce healthcare costs are shifting more total shoulder arthroplasty (TSA) surgeries to the outpatient setting. However, whether racial and ethnic disparities in access to high-quality outpatient TSA care exist remains to be elucidated. The purpose of this study was to assess racial/ethnic differences in relative outpatient TSA utilization and perioperative outcomes using a large national surgical database. METHODS: White, Black, and Hispanic patients who underwent TSA between 2017 and 2021 were identified from the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) database. Baseline demographic and clinical characteristics were collected, and rates of outpatient utilization, adverse events, readmission, reoperation, nonhome discharge, and mortality within 30 days of surgery were compared between racial/ethnic groups. Race/ethnicity-specific trends in utilization of outpatient TSA were assessed, and multivariable logistic regression was used to adjust for baseline demographic factors and comorbidities. RESULTS: A total of 21,186 patients were included, consisting of 19,135 (90.3%) White, 1093 (5.2%) Black, and 958 (4.5%) Hispanic patients and representing 17,649 (83.3%) inpatient and 3537 (16.7%) outpatient procedures. Black and Hispanic patients were generally younger and less healthy than White patients, yet incidences of complications, nonhome discharge, readmission, reoperation, and death within 30 days were similar across groups following outpatient TSA (P > .050 for all). Relative utilization of outpatient TSA increased by 28.7% among White patients, 29.5% among Black patients, and 38.6% among Hispanic patients (ptrend<0.001 for all). Hispanic patients were 64% more likely than White patients to undergo TSA as an outpatient procedure across the study period (OR: 1.64, 95% CI 1.40-1.92, P < .001), whereas odds did not differ between Black and White patients (OR: 1.04, 95% CI 0.87-1.23, P = .673). CONCLUSION: Relative utilization of outpatient TSA remains highest among Hispanic patients but has been significantly increasing across all racial and ethnic groups, now accounting for more than one-third of all TSA procedures. Considering outpatient TSA is associated with fewer complications and lower costs, increasing utilization may represent a promising avenue for reducing disparities in orthopedic shoulder surgery.

Hip Arthroscopy With Bone Marrow Aspirate Injection for Patients With Symptomatic Labral Tears and Early Degenerative Changes Shows Similar Improvement Compared With Patients Undergoing Hip Arthroscopy With Symptomatic Labral Tears Without Arthritis.

PURPOSE: To define the clinical effect of intra-articular injection of iliac crest-derived bone marrow aspirate concentrate (BMAC) at the time of hip arthroscopy in patients with symptomatic labral tears and early radiographic degenerative changes. METHODS: A retrospective review of a prospectively collected hip registry database was performed. Patients with symptomatic labral tears and Tönnis grade 1 or 2 degenerative changes who underwent labrum-preserving hip arthroscopy with BMAC injection were included and were matched with patients who underwent hip arthroscopy without BMAC injection. Patient-reported outcomes (PROs) collected preoperatively and up to 2 years postoperatively included the modified Harris Hip Score, Hip Outcome Score-Activities of Daily Living, Hip Outcome Score-Sport, and International Hip Outcome Tool 33 score. Clinical relevance was measured with the minimal clinically important difference, patient acceptable symptom state, and substantial clinical benefit for each outcome score. RESULTS: A total of 35 patients underwent labrum-preserving hip arthroscopy with BMAC injection and were matched with 35 control patients. There were no differences in demographic characteristics between the groups (P > .05). The BMAC group consisted of 22 patients (62.9%) with Tönnis grade 1 changes and 13 (37.1%) with Tönnis grade 2 changes, whereas all 35 control patients had Tönnis grade 0 hips. All PROs were significantly improved in both groups at 2 years, with no difference in improvement. The rate of failure requiring conversion to total hip arthroplasty was 14.3% (mean, 1.6 years postoperatively) in the BMAC group and 5.7% (mean, 7 years postoperatively) in the control group (P = .09). The difference in the frequency of patients achieving the minimal clinically important difference, patient acceptable symptom state, and substantial clinical benefit was not statistically significant between cohorts. CONCLUSIONS: In a challenging group of patients with symptomatic labral tears and early radiographic degenerative changes, hip arthroscopy with BMAC injection results in statistically and clinically significant improvement in PROs comparable to a group of patients with nonarthritic hips undergoing hip arthroscopy at short-term follow-up. LEVEL OF EVIDENCE: Level III, retrospective comparative therapeutic trial.

Multivariable regression: understanding one of medicine's most fundamental statistical tools.

Multivariable regression is a fundamental tool that drives observational research in orthopaedic surgery. However, regression analyses are not always implemented correctly. This study presents a basic overview of regression analyses and reviews frequent points of confusion. Topics include linear, logistic, and time-to-event regressions, causal inference, confounders, overfitting, missing data, multicollinearity, interactions, and key differences between multivariable versus multivariate regression. The goal is to provide clarity regarding the use and interpretation of multivariable analyses for those attempting to increase their statistical literacy in orthopaedic research.

Unsupervised machine learning methods and emerging applications in healthcare.

Unsupervised machine learning methods are important analytical tools that can facilitate the analysis and interpretation of high-dimensional data. Unsupervised machine learning methods identify latent patterns and hidden structures in high-dimensional data and can help simplify complex datasets. This article provides an overview of key unsupervised machine learning techniques including K-means clustering, hierarchical clustering, principal component analysis, and factor analysis. With a deeper understanding of these analytical tools, unsupervised machine learning methods can be incorporated into health sciences research to identify novel risk factors, improve prevention strategies, and facilitate delivery of personalized therapies and targeted patient care.Level of evidence: I.

Choosing the appropriate measure of central tendency: mean, median, or mode?

Mean, median, and mode are among the most basic and consistently used measures of central tendency in statistical analysis and are crucial for simplifying data sets to a single value. However, there is a lack of understanding of when to use each metric and how various factors can impact these values. The aim of this article is to clarify some of the confusion related to each measure and explain how to select the appropriate metric for a given data set. The authors present this work as an educational resource, ensuring that these common statistical concepts are better understood throughout the Orthopedic research community.

A guide to appropriately planning and conducting meta-analyses: part 2-effect size estimation, heterogeneity and analytic approaches.

Meta-analyses by definition are a subtype of systematic review intended to quantitatively assess the strength of evidence present on an intervention or treatment. Such analyses may use individual-level data or aggregate data to produce a point estimate of an effect, also known as the combined effect, and measure precision of the calculated estimate. The current article will review several important considerations during the analytic phase of a meta-analysis, including selection of effect estimators, heterogeneity and various sub-types of meta-analytic approaches.

Natural language processing: using artificial intelligence to understand human language in orthopedics.

Natural language processing (NLP) describes the broad field of artificial intelligence by which computers are trained to understand and generate human language. Within healthcare research, NLP is commonly used for variable extraction and classification/cohort identification tasks. While these tools are becoming increasingly popular and available as both open-source and commercial products, there is a paucity of the literature within the orthopedic space describing the key tasks within these powerful pipelines. Curation and navigation of the electronic medical record are becoming increasingly onerous, and it is important for physicians and other healthcare professionals to understand potential methods of harnessing this large data resource. The purpose of this study is to provide an overview of the tasks required to develop an NLP pipeline for orthopedic research and present recent examples of successful implementations.

A practical guide to the development and deployment of deep learning models for the Orthopedic surgeon: part I.

Deep learning has a profound impact on daily life. As Orthopedics makes use of this rapid escalation in technology, Orthopedic surgeons will need to take leadership roles on deep learning projects. Moreover, surgeons must possess an understanding of what is necessary to design and implement deep learning-based project pipelines. This review provides a practical guide for the Orthopedic surgeon to understand the steps needed to design, develop, and deploy a deep learning pipeline for clinical applications. A detailed description of the processes involved in defining the problem, building the team, acquiring and curating the data, labeling the data, establishing the ground truth, pre-processing and augmenting the data, and selecting the required hardware is provided. In addition, an overview of unique considerations involved in the training and evaluation of deep learning models is provided. This review strives to provide surgeons with the groundwork needed to identify gaps in the clinical landscape that deep learning models may be able to fill and equips them with the knowledge needed to lead an interdisciplinary team through the process of creating novel deep-learning-based solutions to fill those gaps.

Supervised machine learning and associated algorithms: applications in orthopedic surgery.

Supervised learning is the most common form of machine learning utilized in medical research. It is used to predict outcomes of interest or classify positive and/or negative cases with a known ground truth. Supervised learning describes a spectrum of techniques, ranging from traditional regression modeling to more complex tree boosting, which are becoming increasingly prevalent as the focus on "big data" develops. While these tools are becoming increasingly popular and powerful, there is a paucity of literature available that describe the strengths and limitations of these different modeling techniques. Typically, there is no formal training for health care professionals in the use of machine learning models. As machine learning applications throughout medicine increase, it is important that physicians and other health care professionals better understand the processes underlying application of these techniques. The purpose of this study is to provide an overview of commonly used supervised learning techniques with recent case examples within the orthopedic literature. An additional goal is to address disparities in the understanding of these methods to improve communication within and between research teams.

Editorial Commentary: Clinically Depressed Patients Show Worse Outcomes After Anterior Cruciate Ligament Reconstruction Than Situationally Depressed Patients and Patients Without Mood Issues.

The impact of psychological factors on outcome after orthopaedic surgery is increasingly well reported. Specific to anterior cruciate ligament reconstruction, prior authors have reported that psychological factors influence return-to-sport outcome. Recent research shows that there is a difference between clinical depression and situational depression. Situationally depressed patients score low on psychological screens but do not have a pre-injury clinical diagnosis for depression. Clinically depressed patients score worse on outcome measures after anterior cruciate ligament reconstruction than situationally depressed patients and patients without any mood issues. Furthermore 90.5% of situationally depressed patients had postoperative resolution of their depressive symptoms. Although there appears to be a strong link between clinical depression and limited postoperative outcome, it is still unclear whether depression is a modifier of outcome or whether poor outcome is predictive of persistent depression.

Machine Learning Algorithms Predict Achievement of Clinically Significant Outcomes After Orthopaedic Surgery: A Systematic Review.

PURPOSE: To determine what subspecialties have applied machine learning (ML) to predict clinically significant outcomes (CSOs) within orthopaedic surgery and to determine whether the performance of these models was acceptable through assessing discrimination and other ML metrics where reported. METHODS: The PubMed, EMBASE, and Cochrane Central Register of Controlled Trials databases were queried for articles that used ML to predict achievement of the minimal clinically important difference (MCID), patient acceptable symptomatic state (PASS), or substantial clinical benefit (SCB) after orthopaedic surgical procedures. Data pertaining to demographic characteristics, subspecialty, specific ML algorithms, and algorithm performance were analyzed. RESULTS: Eighteen articles met the inclusion criteria. Seventeen studies developed novel algorithms, whereas one study externally validated an established algorithm. All studies used ML to predict MCID achievement, whereas 3 (16.7%) predicted SCB achievement and none predicted PASS achievement. Of the studies, 7 (38.9%) concerned outcomes after spine surgery; 6 (33.3%), after sports medicine surgery; 3 (16.7%), after total joint arthroplasty (TJA); and 2 (11.1%), after shoulder arthroplasty. No studies were found regarding trauma, hand, elbow, pediatric, or foot and ankle surgery. In spine surgery, concordance statistics (C-statistics) ranged from 0.65 to 0.92; in hip arthroscopy, 0.51 to 0.94; in TJA, 0.63 to 0.89; and in shoulder arthroplasty, 0.70 to 0.95. Most studies reported C-statistics at the upper end of these ranges, although populations were heterogeneous. CONCLUSIONS: Currently available ML algorithms can discriminate the propensity to achieve CSOs using the MCID after spine, TJA, sports medicine, and shoulder surgery with a fair to good performance as evidenced by C-statistics ranging from 0.6 to 0.95 in most analyses. Less evidence is available on the ability of ML to predict achievement of SCB, and no evidence is available for achievement of the PASS. Such algorithms may augment shared decision-making practices and allow clinicians to provide more appropriate patient expectations using individualized risk assessments. However, these studies remain limited by variable reporting of performance metrics, CSO quantification methods, and adherence to predictive modeling guidelines, as well as limited external validation. LEVEL OF EVIDENCE: Level III, systematic review of Level III studies.

Preoperative Magnetic Resonance Imaging Offers Questionable Clinical Utility, Delays Time to Hip Arthroscopy, and Lacks Cost-Effectiveness in Patients Aged ≤40 Years With Femoroacetabular Impingement Syndrome: A Retrospective 5-Year Analysis.

PURPOSE: To assess the clinical utility of preoperative magnetic resonance imaging (MRI) and quantify the delay in surgical care for patients aged ≤40 years undergoing primary hip arthroscopy with history, physical examination, and radiographs concordant with femoroacetabular impingement syndrome (FAIS). METHODS: From August 2015 to December 2020, 1,786 consecutive patients were reviewed from the practice of 1 fellowship-trained hip arthroscopist. Inclusion criteria were FAIS, primary surgery, and age ≤40 years. Exclusion criteria were MRI contraindication, reattempt of conservative management, or concomitant periacetabular osteotomy. After nonoperative treatment options were exhausted and a surgical plan was established, patients were stratified by those who presented with versus without MRI. Those without existing MRI received one, and any deviations from the surgical plan were noted. All preoperative MRIs were compared with office evaluation and intraoperative findings to assess agreement. Demographic data, Hip Disability and Osteoarthritis Outcome Score (HOOS)-Pain, and time from office to MRI or arthroscopy were recorded. RESULTS: Of the patients indicated by history, physical examination, and radiographs alone (70% female, body mass index 24.8 kg/m2, age 25.9 years), 198 patients presented without MRI and 934 with MRI. None of the 198 had surgical plans altered after MRI. Patients in both groups had MRI findings demonstrating anterosuperior labral tears that were visualized and repaired intraoperatively. Mean time from office to arthroscopy for patients without MRI versus those with was 107.0 ± 67 and 85.0 ± 53 days, respectively (P < .001). Time to MRI was 22.8 days. No difference between groups was observed among the 85% of patients who surpassed the HOOS-Pain minimal clinically important difference (MCID). CONCLUSION: Once indicated for surgery based on history, physical examination, and radiographs, preoperative MRI did not alter the surgical plan for patients aged ≤40 years with FAIS undergoing primary hip arthroscopy. Moreover, preoperative MRI delayed time to arthroscopy. The necessity of routine preoperative MRI in the young primary FAIS population should be challenged.

Particulated Juvenile Articular Cartilage and Matrix-Induced Autologous Chondrocyte Implantation Are Cost-Effective for Patellar Chondral Lesions.

PURPOSE: To compare the cost-effectiveness of nonoperative management, particulated juvenile allograft cartilage (PJAC), and matrix-induced autologous chondrocyte implantation (MACI) in the management of patellar chondral lesions. METHODS: A Markov model was used to evaluate the cost-effectiveness of three strategies for symptomatic patellar chondral lesions: 1) nonoperative management, 2) PJAC, and 3) MACI. Model inputs (transition probabilities, utilities, and costs) were derived from literature review and an institutional cohort of 67 patients treated with PJAC for patellar chondral defects (mean age 26 years, mean lesion size 2.7 cm2). Societal and payer perspectives over a 15-year time horizon were evaluated. The principal outcome measure was the incremental cost-effectiveness ratio (ICER) using a $100,000/quality-adjusted life year (QALY) willingness-to-pay threshold. Sensitivity analyses were performed to assess the robustness of the model and the relative effects of variable estimates on base case conclusions. RESULTS: From a societal perspective, nonoperative management, PJAC, and MACI cost $4,140, $52,683, and $83,073 and were associated with 5.28, 7.22, and 6.92 QALYs gained, respectively. PJAC and MACI were cost-effective relative to nonoperative management (ICERs $25,010/QALY and $48,344/QALY, respectively). PJAC dominated MACI in the base case analysis by being cheaper and more effective, but this was sensitive to the estimated effectiveness of both strategies. PJAC remained cost-effective if PJAC and MACI were considered equally effective. CONCLUSIONS: In the management of symptomatic patellar cartilage defects, PJAC and MACI were both cost-effective compared to nonoperative management. Because of the need for one surgery instead of two, and less costly graft material, PJAC was cheaper than MACI. Consequently, when PJAC and MACI were considered equally effective, PJAC was more cost-effective than MACI. Sensitivity analyses accounting for the lack of robust long-term data for PJAC or MACI demonstrated that the cost-effectiveness of PJAC versus MACI depended heavily on the relative probabilities of yielding similar clinical results. LEVEL OF EVIDENCE: III, economic and decision analysis.

Dynamic Assessment of Femoroacetabular Impingement Syndrome Hips.

PURPOSE: The purpose of our study was to compare lower extremity rotational kinematics and kinetics (angles, torques, and powers) and hip muscle electromyography (EMG) activity between cam-type femoroacetabular impingement syndrome (FAIS) and age- and sex-matched controls during walking, fast walking, stair ascent, stair descent, and sit-to-stand. METHODS: This study included 10 males with unilateral FAIS and 10 control males with no FAIS. We measured kinematics, kinetics, and electromyographic signals during stair ascent/descent, sit-to-stand, self-selected walk, and fast walk. Peak signal differences between groups were compared with independent t-tests with statistical significance when P < .05. RESULTS: FAIS hips showed significant differences compared to controls, including increased hip flexion during walking (+4.9°, P = .048) and stair ascent (+7.8°, P =.003); diminished trunk rotation during stair ascent (-3.4°; P = .015), increased knee flexion during self-selected walking (+5.1°, P = .009), stair ascent (+7.4°, P = .001), and descent (+5.3°, P = .038); and increased knee valgus during fast walking (+4.7°, P = .038). gMed and MedHam showed significantly decreased activation in FAIS during walking (gMed: -12.9%, P = .002; MedHam: -7.4%, P = .028) and stair ascent (gMed: -16.7%, P = .036; MedHam: -13.0%, P = .041); decreased gMed activation during sit-to-stand (-8.8%, P = .004) and decreased MedHam activation during stair descent (-8.0%, P = .039). CONCLUSIONS: Three-dimensional motion analysis and EMG evaluation of functional kinematics and kinetics in subjects with symptomatic unilateral cam-type FAIS across a spectrum of provocative tasks demonstrated significant differences compared to controls in hip flexion, trunk rotation, knee flexion, and valgus. FAIS hips had significantly decreased gMed and MedHam activity. These findings may explain altered torso-pelvic, hip, and knee mechanics in FAIS patients and suggest that evaluation of FAIS should include the patient's hip, knee, and torso-pelvic relationships and muscle function. CLINICAL RELEVANCE: The clinical and functional manifestation of FAIS hip pathomechanics is not entirely understood, and previous literature to date has not clearly described the alterations in gait and functional movements seen in patients with cam-type FAIS. The current study used 3D motion analysis and EMG evaluation of functional kinematics and kinetics to identify a number of differences between FAIS and control hips, which help us better understand the lower extremity kinematics and kinetics and muscle activation in FAIS.

Fragility Part I: a guide to understanding statistical power.

The aim of this paper is to close the knowledge-to-practice gap around statistical power. We demonstrate how four factors affect power: p value, effect size, sample size, and variance. This article further delves into the advantages and disadvantages of a priori versus post hoc power analyses, though we believe only understanding of the former is essential to addressing the present-day issue of reproducibility in research. Upon reading this paper, physician-scientists should have expanded their arsenal of statistical tools and have the necessary context to understand statistical fragility.

Picking apart p values: common problems and points of confusion.

Due to its frequent misuse, the p value has become a point of contention in the research community. In this editorial, we seek to clarify some of the common misconceptions about p values and the hazardous implications associated with misunderstanding this commonly used statistical concept. This article will discuss issues related to p value interpretation in addition to problems such as p-hacking and statistical fragility; we will also offer some thoughts on addressing these issues. The aim of this editorial is to provide clarity around the concept of statistical significance for those attempting to increase their statistical literacy in Orthopedic research.

The development and deployment of machine learning models.

Applications of artificial intelligence, specifically machine learning, are becoming increasingly popular in Orthopaedic Surgery, and medicine as a whole. This growing interest is shared by data scientists and physicians alike. However, there is an asymmetry of understanding of the developmental process and potential applications of machine learning. As new technology will undoubtedly affect clinical practice in the coming years, it is important for physicians to understand how these processes work. The purpose of this paper is to provide clarity and a general framework for building and assessing machine learning models.

The performance of PROMIS computer adaptive testing for patient-reported outcomes in hip fracture surgery: a pilot study.

INTRODUCTION: Patient-reported outcome measures (PROMs) are essential to patient-centered care in orthopaedics. PROMIS measures have demonstrated reliability, validity, responsiveness, and minimal floor and ceiling effects in various populations of patients receiving orthopaedic care but have not yet been examined in hip fracture patients. This pilot study sought to evaluate the psychometric performance of the PROMIS Physical Function (PROMIS PF) and Pain Interference (PROMIS PI) computer adaptive tests and compare these instruments with legacy outcome measures in hip fracture patients. METHODS: This study included 67 patients who were 27-96 years old (median 76) and underwent osteosynthesis for a proximal femoral fracture. At 3, 6, and/or 12 months follow-up, patients completed both legacy (mHHS, SF-36-PCS, and VAS for pain) and PROMIS questionnaires (PROMIS PF and PROMIS PI). Respondent burden and floor/ceiling effects were calculated for each outcome measure. Correlation was calculated to determine concurrent validity between related constructs. RESULTS: A strong correlation was found between PROMIS PF and mHHS (rho = 0.715, p < 0.001) and moderately strong correlation between PROMIS PF and SF-36 PCS (rho = 0.697, p < 0.001). There was also a moderately strong correlation between the VAS and the PROMIS PI (rho = 0.641, p < 0.001). Patients who completed PROMIS PF were required to answer significantly fewer questions as compared with legacy PROMs (mHHS, SF-36). For the PROMIS measures, 1% of patients completing PROMIS PF achieved the highest allowable score while 34% of patients completing PROMIS PI achieved the lowest allowable score. Of the legacy outcome measures, 31% of patients completing the VAS for pain achieved the lowest allowable score and 7% of patients completing the mHHS achieved the highest allowable score. CONCLUSIONS: The results of this study support the validity of PROMIS CATs for use in hip fracture patients. The PROMIS PF was significantly correlated with SF-36 PCS and mHHS while requiring fewer question items per patient relative to the legacy outcome measures.

Editorial Commentary: Maximal Outcome Improvement: Another Arrow in the Quiver.

Clinically important outcome assessment has been a point of increasing emphasis in the orthopaedic literature. The minimal clinically important difference, patient acceptable symptom state, and substantial clinical benefit are the most reported in the hip preservation literature. Maximal outcome improvement (MOI) is now also being reported; however, its relation to patients undergoing hip preservation surgery is not well understood. The threshold values that represented satisfaction with surgery were 54.8%, 52.5%, 55.5%, and 55.8% of the MOI for the modified Harris Hip Score, Nonarthritic Hip Score, visual analog scale score for pain, and International Hip Outcome Tool-12 score, respectively. Although the MOI is helpful for characterizing outcome improvement, established measures such as substantial clinical benefit may be better used to grade outcomes in patients with high preoperative function.

Development and Internal Validation of Supervised Machine Learning Algorithms for Predicting Clinically Significant Functional Improvement in a Mixed Population of Primary Hip Arthroscopy.

PURPOSE: To (1) develop and validate a machine learning algorithm to predict clinically significant functional improvements after hip arthroscopy for femoroacetabular impingement syndrome and to (2) develop a digital application capable of providing patients with individual risk profiles to determine their propensity to gain clinically significant improvements in function. METHODS: A retrospective review of consecutive hip arthroscopy patients who underwent cam/pincer correction, labral preservation, and capsular closure between January 2012 and 2017 from 1 large academic and 3 community hospitals operated on by a single high-volume hip arthroscopist was performed. The primary outcome was the minimal clinically important difference (MCID) for the Hip Outcome Score (HOS)-Activities of Daily Living (ADL) at 2 years postoperatively, which was calculated using a distribution-based method. A total of 21 demographic, radiographic, and patient-reported outcome measures were considered as potential covariates. An 80:20 random split was used to create training and testing sets from the patient cohort. Five supervised machine learning algorithms were developed using 3 iterations of 10-fold cross-validation on the training set and assessed by discrimination, calibration, Brier score, and decision curve analysis on an independent testing set of patients. RESULTS: A total of 818 patients with a median (interquartile range) age of 32.0 (22.0-42.0) and 69.2% female were included, of whom 74.3% achieved the MCID for the HOS-ADL. The best-performing algorithm was the stochastic gradient boosting model (c-statistic = 0.84, calibration intercept = 0.20, calibration slope = 0.83, and Brier score = 0.13). Of the initial 21 candidate variables, the 8 most important features for predicting the MCID for the HOS-ADL included in model training were body mass index, age, preoperative HOS-ADL score, preoperative pain level, sex, Tönnis grade, symptom duration, and drug allergies. The algorithm was subsequently transformed into a digital application using local explanations to provide customized risk assessment: https://orthoapps.shinyapps.io/HPRG_ADL/. CONCLUSIONS: The stochastic boosting gradient model conferred excellent predictive ability for propensity to gain clinically significant improvements in function after hip arthroscopy. An open-access digital application was created, which may augment shared decision-making and allow for preoperative risk stratification. External validation of this model is warranted to confirm the performance of these algorithms, as the generalizability is currently unknown. LEVEL OF EVIDENCE: IV, Case series.