Lu, Yining

Background: Femoroacetabular impingement (FAI) is a common cause of hip pain in young, active athletes. American football players place high demands on the hip due to the sport's requirements for pivoting, cutting, and loading in deep flexion. Although arthroscopic treatment of FAI in professional National Football League (NFL) players has shown favorable outcomes, football-specific outcomes in non-NFL athletes remain limited. Purpose: To report the return-to-sport (RTS) rates and functional outcomes at a minimum 2-year follow-up for active American football players competing at multiple levels after primary hip arthroscopy for FAI. Study Design: Case series; Level of evidence, 4. Methods: An institutional database was queried to identify all active American football players who underwent primary hip arthroscopy with labral repair between 2010 and 2023. Demographic characteristics, radiographic parameters, procedure details, complications, and reoperations were collected. Follow-up was completed at a minimum of 2 years to obtain updated patient-reported outcome measures (PROMs) as well as football-specific data, including current participation and RTS status. PROMs were reported as mean ± standard deviation, and categorical variables, including RTS, were reported as frequencies. Pre- versus postoperative PROMs were compared using t tests or Mann-Whitney U tests to assess outcomes. Results: In total, 49 hips in 38 male football players (age, 18.1 years; range, 14-26 years) were included. Most athletes played at the high school (74%) or college level (21%). A total of 51% played as linemen. Labral repair was performed in all cases. At a mean 7.6 years, mean modified Harris Hip Score was 90 ± 13, Hip Outcome Score–Activities of Daily Living was 95 ± 7, and Hip Outcome Score–Sport was 89 ± 17. Overall mean surgery satisfaction was 8.7 ± 1.7 (out of 10). Significant postoperative improvements were observed in all PROMs ( P ≤ .002). Twelve players (32%) did not attempt RTS due to other nonmedical factors and were excluded from return to sport analysis. A total of 23 (88%) players achieved RTS, and 3 players (12%) did not. Two players (8%) cited other injuries, and 1 player (4%) continued to have hip limitations. Four hips (8%) underwent reoperation at a mean 4.9 years postoperative. Conclusion: Amateur American football players undergoing primary hip arthroscopy for FAI demonstrated excellent patient-reported outcomes and a high RTS rate of 88%, when excluding those who did not return for nonmedical reasons, at a mean 7.6-year follow-up. Notable positional differences were observed across multiple levels of play. Although outcomes were generally favorable, both medical and nonmedical factors could limit return to sport, particularly among high school and college athletes.

Background: Treatment decisions in patients with anterior cruciate ligament (ACL) injuries are influenced by multiple factors, such as the desire to return to sports or symptomatic instability. Identifying the differential treatment effect of ACL reconstruction (ACLR) compared with nonoperative management on a patient-specific level can inform surgical decision-making. Hypothesis: Unsupervised machine learning can identify distinct patient subgroups based on outcome achievement after ACL injury, and ACLR will exert a protective effect on the development of posttraumatic osteoarthritis (PTOA) over nonoperative management. Study Design: Cohort study; Level of evidence, 3. Methods: A longitudinal populational registry identified patients with ACL injuries from 1990 to 2016 with a minimum 7.5-year follow-up. An unsupervised random forest algorithm was utilized to develop and validate patient subgroups. Treatment effects of ACLR on outcomes were analyzed using a machine learning causal inference estimator. Patient subgroup membership was incorporated into a multivariable logistic regression to identify factors predictive of optimal outcomes. Results: A total of 923 patients (785 primary ACLR, 138 nonoperative) were included. The random forest algorithm arrived at an optimal partition of 2 subgroups, with 653 patients in the optimal outcome subgroup (368 male [56.4%]; mean age, 26.0 ± 10.2 years; mean body mass index [BMI], 26.5 ± 4.30) and 270 patients in the suboptimal outcome subgroup (152 male [56.3%]; mean age, 35.0 ± 10.1 years; mean BMI, 30.5 ± 5.54). The latter group demonstrated significantly increased rates of secondary meniscal injury, development of symptomatic PTOA, and progression to total knee arthroplasty (TKA) at the final follow-up (all P < .01). In the optimal outcome subgroup, ACLR had significantly protective treatment effects on the risk of secondary meniscal injury (average treatment effect [ATE], 61%), contralateral ACL injury (ATE, 8%), symptomatic PTOA (ATE, 16%), and progression to TKA (ATE, 6%) (all P < .01). Conversely, in the suboptimal outcome subgroup, ACLR only protected against symptomatic PTOA (ATE, 11%) and progression to TKA (ATE, 8%) (both P < .01). Conclusion: Two clinically meaningful subgroups were identified from retrospectively collected data and found to experience differential treatment responses after ACL injuries. ACLR decreased the rate of development of PTOA and TKA in both subgroups but was not as effective in preventing secondary meniscal injuries or contralateral ACL injuries in patients who were older, heavier, or had concomitant medial meniscus injuries.

Background: Nonoperative and operative management techniques after anterior cruciate ligament (ACL) injury are both appropriate treatment options for selected patients. However, the subsequent development of posttraumatic knee osteoarthritis (PTOA) remains an area of active study. Purpose: To compare the risk of PTOA between patients treated without surgery and with ACL reconstruction (ACLR) after primary ACL disruption using a machine learning causal inference model. Study Design: Cohort study; Level of evidence, 3. Methods: A geographic database identified patients undergoing ACLR between 1990 and 2016 with minimum 7.5-year follow-up. Variables collected include age, sex, body mass index, activity level, occupation, relevant comorbid diagnoses, radiographic findings, injury characteristics, and clinical course. Treatment effects of ACLR on the development of PTOA and progression to total knee arthroplasty (TKA) were analyzed with machine learning models (MLMs) in a causal inference estimator (targeted maximum likelihood estimation, TMLE), while controlling for confounders. Results: The study included 1194 patients with a minimum follow-up of 7.5 years, among whom 974 underwent primary reconstruction and 220 underwent nonoperative treatment. A total of 215 (22%) patients developed symptomatic PTOA in the ACLR group compared with 140 (64%) in the nonoperative treatment group ( P < .001), whereas 25 (3%) patients underwent TKA in the ACLR group compared with 50 (23%) in the nonoperative treatment group ( P < .001). Patients in the ACLR group had delayed TKA compared with patients in the nonoperative treatment group (193.4 vs 166.0 months, respectively; P = .02). TMLE evaluation revealed that reconstruction decreased the risk of PTOA by 11% (95% CI, 8%-13%; P < .001) compared with nonoperative treatment but did not demonstrate a significant effect on the rate of progression to TKA. Survival analysis with random forest algorithm demonstrated significant delay to the onset of PTOA as well as time to progression of TKA in patients undergoing ACLR. Additional risk factors for the development of PTOA, irrespective of treatment, included older age at injury, greater body mass index, total number of arthroscopic knee surgeries, and residual laxity at follow-up. Conclusion: MLMs in a causal inference estimator found ACLR to exert a significant treatment effect in reducing the rate of development of PTOA by 11% compared with nonoperative treatment. ACLR also delayed the onset of PTOA and progression to TKA.

Abstract Purpose Developing large‐scale, standardized radiographic registries for anterior cruciate ligament (ACL) injuries with artificial intelligence (AI) tools can enhance personalized orthopaedics. We propose deploying Artificial Intelligence for Knee Imaging Registration and Analysis (AKIRA), a trio of deep learning (DL) algorithms, to automatically classify and annotate radiographs. We hypothesize that algorithms can efficiently organize radiographs based on laterality, projection, identify implants and classify osteoarthritis (OA) grade. Methods A collection of 20,836 knee radiographs from all time points of treatment (mean orthopaedic follow‐up 70.7 months; interquartile range [IQR]: 6.8–172 months) were aggregated from 1628 ACL‐injured patients (median age 26 years [IQR: 19–42], 57% male). Three DL algorithms (EfficientNet, YOLO [You Only Look Once] and Residual Network) were employed. Radiograph laterality and projection (anterior‐posterior [AP], lateral, sunrise, posterior‐anterior, hip–knee–ankle and Camp‐Coventry intercondylar [notch]) were labelled by a DL model. Manually provided labels of metal fixation implants were used to develop a DL object detection algorithm. The degree of OA, both as measured by specific Kellgren–Lawrence (KL) grades, as well as based on a binarized label of OA (defined as KL Grade ≥2), on standing AP radiographs were classified using a DL algorithm. Individual model performances were evaluated on a subset of images prior to the deployment of AKIRA to registry construction using all ACL radiographs. Results The classification algorithms showed excellent performance in classifying radiographic laterality (F1 score: 0.962–0.975) and projection (F1 score: 0.941–1.0). The object detection algorithm achieved high precision–recall (area under the precision‐recall curve: 0.695–0.992) for identifying various metal fixations. The KL classifier reached concordances of 0.39–0.40, improving to 0.81–0.82 for binary OA labels. Sequential deployment of AKIRA following internal validation processed and labelled all 20,836 images with the appropriate views, implants, and the presence of OA within 88 min. Conclusion AKIRA effectively automated the classification and object detection in a large radiograph cohort of ACL injuries, creating an AI‐enabled radiographic registry with comprehensive details on laterality, projection, implants and OA. Study Design Cross‐sectional study. Level of Evidence Level IV.

Background: Anterior cruciate ligament (ACL) reconstruction (ACLR) can be successful in restoring knee stability. However, secondary ACL injury, either through graft failure or contralateral injury, is a known complication and can significantly impact the ability of a patient to return successfully to previous activities. Purpose: To develop and internally validate an interpretable machine learning model to quantify the risk of graft failure and contralateral ACL injury in a longitudinal cohort treated with ACLR. Study Design: Case-control study; Level of evidence, 3. Methods: An established geographic database of >600,000 patients was used to identify patients with a diagnosis of ACL rupture between 1990 and 2016 with a minimum 2-year follow-up. Medical records were reviewed for relevant patient information and 4 candidate machine learning algorithms were evaluated for prediction of graft failure and contralateral ACL injury in patients after ACLR as identified either on magnetic resonance imaging or via arthroscopy. Performance of the algorithms was assessed through discrimination, calibration, and decision curve analysis. Model interpretability was enhanced utilizing global variable importance plots and partial dependence curves. Results: A total of 1497 patients met inclusion criteria. Among them, 140 (9.4%) had graft failure and 128 (8.6%) had a contralateral ACL injury after index surgery at a median follow-up of 140.7 months (interquartile range, 77.2-219.2 months). The best performing models achieved an area under the receiver operating characteristics curve of 0.70 for prediction of graft failure and 0.67 for prediction of contralateral ACL injury, outperforming a logistic regression fitted on the identical feature set. Notable predictors for increased risk of graft failure included younger age at injury, body mass index (BMI) <30, return to sports <13 months, initial time to surgery >75 days, utilization of allograft, femoral/tibial fixation with suspension/expansion devices, concomitant collateral ligament injury, and active or former smoking history. Predictors of contralateral ACL injury included greater preoperative pain, younger age at initial injury, BMI <30, active smoking history, initial time to surgery >75 days, history of contralateral knee arthroscopies, and involvement in contact sports. Conclusion: Less than 18% of all patients who undergo ACLR should be expected to sustain either a graft failure or contralateral ACL injury. Machine learning models outperformed logistic regression and identified greater preoperative pain, younger age, BMI <30, earlier return to higher activity, and time to surgical intervention >75 days as common risk factors for both graft failure as well as contralateral ACL injury after ACLR. Surgeon-modifiable risk factors for graft failure included allograft and femoral/tibial fixation with a suspension/expansion combination.

Background: Arthroscopic diagnosis and treatment of femoroacetabular pathology has experienced significant growth in the last 30 years; nevertheless, reduced utilization of orthopaedic procedures has been observed among the underrepresented population. Purpose/Hypothesis: The purpose of this study was to examine racial differences in case incidence rates, outcomes, and complications in patients undergoing hip arthroscopy. It was hypothesized that racial and ethnic minority patients would undergo hip arthroscopy at a decreased rate compared with their White counterparts but that there would be no differences in clinical outcomes. Study Design: Cross-sectional study. Methods: The State Ambulatory Surgery and Services Database and the State Emergency Department Database of New York were queried for patients undergoing hip arthroscopy between 2011 and 2017. Patients were stratified into White and racial and ethnic minority races, and intergroup comparisons were performed for utilization over time, total charges billed per encounter, 90-day emergency department (ED) visits, and revision hip arthroscopy. Temporal trends in the utilization of hip arthroscopy were identified, and racial differences in secondary outcomes were analyzed with a semiparametric method known as targeted maximum likelihood estimation (TMLE) backed by a library of machine learning algorithms. Results: A total of 9745 patients underwent hip arthroscopy during the study period, with 1081 patients of minority race (11.1%). White patients underwent hip arthroscopy at 5.68 (95% CI, 4.98-6.48) times the incidence rate of racial and ethnic minority patients; these incidence rates grew annually at a ratio of 1.11 in White patients compared with 1.03 in racial and ethnic minority patients ( P < .001). Based on the TMLE, racial and ethnic minority patients were significantly more likely to incur higher costs ( P < .001) and visit the ED within 90 days ( P = .049) but had negligible differences in reoperation rates at a 2-year follow-up ( P = .53). Subgroup analysis identified that higher likelihood for 90-day ED admissions among racial and ethnic minority patients compared with White patients was associated with Medicare insurance ( P = .002), median income in the lowest quartile ( P = .012), and residence in low-income neighborhoods ( P = .006). Conclusion: Irrespective of insurance status, racial and ethnic minority patients undergo hip arthroscopy at a lower incidence and incur higher costs per surgical encounter.

Purpose To compile and analyze structural and clinical outcomes after meniscus root tear treatment as currently described in the literature. Methods A review was conducted to identify studies published since 2011 on efficacy of repair, meniscectomy, and nonoperative management in the treatment of meniscus root tears. Patient cohorts were grouped into treatment categories, with medial and lateral root tears analyzed separately; data were collected on patient demographics, structural outcomes including joint space width, degree of medial meniscal extrusion, progression to total knee arthroplasty, and patient‐reported outcome measures. Risk of bias was assessed using the MINORS (methodological index for non‐randomized studies) criteria. Heterogeneity was measured using the I‐statistic, and outcomes were summarized using forest plots without pooled means. Results The 56 included studies comprised a total of 3,191 patients. Mean age among the included studies ranged from 24.6 to 65.6 years, whereas mean follow‐up ranged from 12 to 125.9 months. Heterogeneity analysis identified significant differences between studies. Change in joint space width ranged from –2.4 to –0.6 mm (i.e., decreased space) after meniscectomy (n = 186) and –0.9 to –0.1 mm after root repair (n = 209); change in medial meniscal extrusion ranged from –0.6 to 6.5 mm after root repair (n = 521) and 0.2 to 4.2 mm after meniscectomy (n = 66); and event rate for total knee arthroplasty ranged from 0.00 to 0.22 after root repair (n = 205), 0.35 to 0.60 after meniscectomy (n = 53), and 0.27 to 0.35 after nonoperative treatment (n = 93). Root repair produced the greatest numerical increase in International Knee Documentation Committee and Lysholm scores of the 3 treatment arms. In addition, root repair improvements in Knee Injury and Osteoarthritis Outcome Score Pain (range: 22‐32), Sports and Recreational Activities (range: 23‐36), Quality of Life (range: 22‐42), and Symptoms subscales (range: 10‐19), in studies with low risk of bias. Conclusions The literature reporting on the treatment of meniscus root tears is heterogenous and largely limited to Level III and IV studies. Current evidence suggests root repair may be the most effective treatment strategy in lessening joint space narrowing of the knee and producing improvements in patient‐reported outcomes. Level of Evidence Level IV, systematic review of Level II‐IV studies.

Background: An increased posterior tibial slope (PTS) corresponds with an increased risk of graft failure after anterior cruciate ligament (ACL) reconstruction (ACLR). Validated methods of manual PTS measurements are subject to potential interobserver variability and can be inefficient on large datasets. Purpose/Hypothesis: To develop a deep learning artificial intelligence technique for automated PTS measurement from standard lateral knee radiographs. It was hypothesized that this deep learning tool would be able to measure the PTS on a high volume of radiographs expeditiously and that these measurements would be similar to previously validated manual measurements. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: A deep learning U-Net model was developed on a cohort of 300 postoperative short-leg lateral radiographs from patients who underwent ACLR to segment the tibial shaft, tibial joint surface, and tibial tuberosity. The model was trained via a random split after an 80 to 20 train-validation scheme. Masks for training images were manually segmented, and the model was trained for 400 epochs. An image processing pipeline was then deployed to annotate and measure the PTS using the predicted segmentation masks. Finally, the performance of this combined pipeline was compared with human measurements performed by 2 study personnel using a previously validated manual technique for measuring the PTS on short-leg lateral radiographs on an independent test set consisting of both pre- and postoperative images. Results: The U-Net semantic segmentation model achieved a mean Dice similarity coefficient of 0.885 on the validation cohort. The mean difference between the human-made and computer-vision measurements was 1.92° (σ = 2.81° [ P = .24]). Extreme disagreements between the human and machine measurements, as defined by ≥5° differences, occurred <5% of the time. The model was incorporated into a web-based digital application front-end for demonstration purposes, which can measure a single uploaded image in Portable Network Graphics format in a mean time of 5 seconds. Conclusion: We developed an efficient and reliable deep learning computer vision algorithm to automate the PTS measurement on short-leg lateral knee radiographs. This tool, which demonstrated good agreement with human annotations, represents an effective clinical adjunct for measuring the PTS as part of the preoperative assessment of patients with ACL injuries.

Purpose To compare 3 separate blood flow restriction (BFR) systems in their capacity to reduce repetitions to failure, impact perceptual responses, and cause adverse events during a low‐load free‐flow exercise. Methods The study included healthy subjects aged 18 years or older who presented to an ambulatory‐care sports medicine clinic. On day 1, participants’ demographic characteristics and anthropomorphic measurements were recorded. Each participant performed dumbbell biceps curl repetitions to failure using 20% of his or her 1‐repetition maximum weight with each arm. Participants were exposed to 3 different tourniquet systems for familiarization. On day 2, each participant’s arm was randomized to a cuff system, and the participant performed 2 sets of biceps curl repetitions to failure with the cuff inflated. Repetitions to failure, rating of perceived effort (RPE), rating of perceived discomfort, and pulse oxygenation levels were recorded after each set. On day 3, participants completed a survey of their perceived delayed‐onset muscle soreness. Results The final analysis was performed on 42 arms, with 14 limbs per system. The study population had a mean age of 28.7 ± 2.4 years and a mean body mass index of 24.9 ± 4.3. All 3 systems successfully reduced repetitions to failure compared with unrestricted low‐load exercise from baseline to BFR set 1 and from baseline to BFR set 2. There were no significant between‐group differences among BFR systems regarding the number of repetitions to failure performed at baseline versus BFR set 1 or BFR set 2. The Delfi Personalized Tourniquet System (PTS) cohort had the greatest reductions in repetitions to failure from BFR set 1 to BFR set 2 ( P = .002) and reported the highest RPE after set 2 ( P = .025). Conclusions The Delfi PTS, SmartCuffs Pro, and BStrong BFR systems were each safe and were able to significantly reduce repetitions to failure compared with a low‐load free‐flow condition when used in a BFR exercise protocol. The Delfi PTS system may produce a higher RPE with prolonged use in comparison to the other systems. Level of Evidence Level II, prospective cohort study.

Objectives: Background: Arthroscopic diagnosis and treatment of femoroacetabular pathology has been increasingly used in the past thirty years with interventions resulting in improved hip function and ultimate delay of hip arthroplasty in a minimally invasive manner. Unfortunately, previous investigations have observed decreased rates of access, utilization of, and outcomes following orthopedic interventions such as hip arthroplasty in underrepresented patients. The purpose of this study is to examine racial differences in procedural rates, outcomes, and complications in patients undergoing hip arthroscopy. Methods: The State Ambulatory Surgery and Services Database (SASD) and State Emergency Department Database (SEDD) of New York were queried for patients undergoing hip arthroscopy from 2011 to 2017. The primary outcomes investigated were utilization over time, total charges billed per encounter, 90-day emergency department visits, and revision hip arthroscopy. Patients were stratified into White and non-White race, and intergroup differences were evaluated with descriptive statistics. Subgroup analysis was performed with linear mixed-effects models to identify significant interactions between race and individual variables that contributed to any differences in the outcomes of interest. Temporal trends in utilization of hip arthroscopy and concomitant procedures between the two groups were analyzed with Poisson regression modeling. Finally, targeted maximum likelihood estimation (TMLE) was performed to provide nonparametric estimates of the specific differences in the outcomes studied using machine learning ensembles while controlling for patient risk factors. Results: A total of 9,745 patients underwent hip arthroscopy during the study period, with 1,081 patients of non-White race (11.9%). Results of Poisson regression demonstrated an annual increase of 1.11 in the incidence rate of hip arthroscopy among White patients, compared to 1.03 for non-White patients (p<0.001), with this disparity projected to increase by 2040. Based on TMLE utilizing an ensemble of machine learning models, non-White patients were significantly more likely to incur higher costs (OR: 1.30, 95% CI: 1.24-1.37, p<0.001) and visit the emergency department within 90-days (OR: 1.09, 95% CI: 1.01, 1.18, p=0.05), but had negligible differences in reoperation rates at 90 days to 2 years (OR: 1.13, 95% CI: 0.78-1.63, p=0.53). Subgroup analysis identified higher likelihood for 90-day emergency department admissions among non-White patients compared to White patients, which were significantly compounded by Medicare insurance (OR: 2.95, 95% CI 1.46-5.95, p=0.002), median income in the lowest quartile (OR: 1.84, 95% CI: 1.2-2.61, p=0.012), and residence in low-income neighborhoods (OR: 2.05, 95% CI: 1.31-3.2, p=0.006). Subgroup analysis for charges billed and reoperation did not identify significant findings. Conclusions: Hip arthroscopy remains an increasingly utilized surgical technique for the treatment of a myriad of hip disorders. Unfortunately, racial disparities exist and are worsening over time. Irrespective of insurance status, non-white patients undergo hip arthroscopy at a lower rate, incur higher costs, and more frequently experience unexpected returns to the emergency department. Improved initiatives to improve the disparity in access to and outcomes following hip arthroscopy must be addressed to further its utility for all patients.