Citations

ABSTRACT Hip dysplasia is a common cause of hip pain and a known risk factor for hip osteoarthritis (OA) and early total hip arthroplasty (THA). Unfortunately, little is known about the specific factors associated with an increased risk of OA. The purpose was (i) to report the overall rate of symptomatic hip OA and THA and (ii) to identify radiographic features and patient characteristics associated with the development of symptomatic hip OA. A geographic database was used to identify all patients aged 14–50 years old diagnosed with symptomatic hip dysplasia between 2000 and 2016. Kaplan–Meier analysis was used to determine the rate of symptomatic hip OA, defined as a Tönnis grade of ≥1 on hip radiograph. Univariate and multivariate proportional hazard regression models were performed to determine risk factors for OA. One hundred and fifty-nine hips (144 patients) with hip dysplasia (52 F:107 M) out of 1893 patients with hip pain were included. Of these, 45 (28%) had severe hip dysplasia with a lateral center-edge angle ≤18°. Mean age at time of presentation was 26.1 (±10.1) years. Mean follow-up time was 8.2 (±5) years. The rate of OA was 20%. THA was performed in 11% of patients. Body mass index >29 (P = 0.03) and increased age (P < 0.01) were risk factors for OA. Patients with symptomatic hip dysplasia are at significant risk of developing hip OA. Body mass index >29 and age ≥35 years at the time of presentation with hip pain were risk factors for hip OA.

Purpose To develop a machine learning algorithm to predict total charges after ambulatory hip arthroscopy and create a risk‐adjusted payment model based on patient comorbidities. Methods A retrospective review of the New York State Ambulatory Surgery and Services database was performed to identify patients who underwent elective hip arthroscopy between 2015 and 2016. Features included in initial models consisted of patient characteristics, medical comorbidities, and procedure‐specific variables. Models were generated to predict total charges using 5 algorithms. Model performance was assessed by the root‐mean‐square error, root‐mean‐square logarithmic error, and coefficient of determination. Global variable importance and partial dependence curves were constructed to show the impact of each input feature on total charges. For performance benchmarking, the best candidate model was compared with a multivariate linear regression using the same input features. Results A total of 5,121 patients were included. The median cost after hip arthroscopy was $19,720 (interquartile range, $12,399‐$26,439). The gradient‐boosted ensemble model showed the best performance (root‐mean‐square error, $3,800 [95% confidence interval, $3,700‐$3,900]; logarithmic root‐mean‐square error, 0.249 [95% confidence interval, 0.24‐0.26]; R 2 = 0.73). Major cost drivers included total hours in facility less than 12 or more than 15, longer procedure time, performance of a labral repair, age younger than 30 years, Elixhauser Comorbidity Index (ECI) of 1 or greater, African American race, residence in extreme urban and rural areas, and higher household and neighborhood income. Conclusions The gradient‐boosted ensemble model effectively predicted total charges after hip arthroscopy. Few modifiable variables were identified other than anesthesia type; nonmodifiable drivers of total charges included duration of care less than 12 hours or more than 15 hours, operating room time more than 100 minutes, age younger than 30 years, performance of a labral repair, and ECI greater than 0. Stratification of patients based on the ECI highlighted the increased financial risk borne by physicians via flat reimbursement schedules given variable degrees of comorbidities. Level of Evidence Level III, retrospective cohort study.

Background: Osteochondritis dissecans (OCD) occurs most commonly in the knees of young individuals. This condition is known to cause pain and discomfort in the knee and can lead to disability and early knee osteoarthritis. The cause is not well understood, and treatment plans are not well delineated. The Research in Osteochondritis Dissecans of the Knee (ROCK) group established a multicenter, prospective cohort to better understand this disease. Purpose: To provide a baseline report of the ROCK multicenter prospective cohort and present a descriptive analysis of baseline data for patient characteristics, lesion characteristics, and clinical findings of the first 1000 cases enrolled into the prospective cohort. Study Design: Cross-sectional study; Level of evidence, 3. Methods: Patients were recruited from centers throughout the United States. Baseline data were obtained for patient characteristics, sports participation, patient-reported measures of functional capabilities and limitations, physical examination, diagnostic imaging results, and initial treatment plan. Descriptive statistics were completed for all outcomes of interest. Results: As of November 2020, a total of 27 orthopaedic surgeons from 17 institutions had enrolled 1004 knees with OCD, representing 903 patients (68.9% males; median age, 13.1 years; range, 6.3-25.4 years), into the prospective cohort. Lesions were located on the medial femoral condyle (66.2%), lateral femoral condyle (18.1%), trochlea (9.5%), patella (6.0%), and tibial plateau (0.2%). Most cases involved multisport athletes (68.1%), with the most common primary sport being basketball for males (27.3% of cases) and soccer for females (27.6% of cases). The median Pediatric International Knee Documentation Committee (Pedi-IKCD) score was 59.9 (IQR, 45.6-73.9), and the median Pediatric Functional Activity Brief Scale (Pedi-FABS) score was 21.0 (IQR, 5.0-28.0). Initial treatments were surgical intervention (55.4%) and activity restriction (44.0%). When surgery was performed, surgeons deemed the lesion to be stable at intraoperative assessment in 48.1% of cases. Conclusion: The multicenter ROCK group has been able to enroll the largest knee OCD cohort to date. This information is being used to further understand the pathology of OCD, including its cause, associated comorbidities, and initial presentation and symptoms. The cohort having been established is now being followed longitudinally to better define and elucidate the best treatment algorithms based on these presenting signs and symptoms.

Background: Management of anterior shoulder instability (ASI) aims to reduce risk of future recurrence and prevent complications via nonoperative and surgical management. Machine learning may be able to reliably provide predictions to improve decision making for this condition. Purpose: To develop and internally validate a machine-learning model to predict the following outcomes after ASI: (1) recurrent instability, (2) progression to surgery, and (3) the development of symptomatic osteoarthritis (OA) over long-term follow-up. Study Design: Cohort study (prognosis); Level of evidence, 2. Methods: An established geographic database of >500,000 patients was used to identify 654 patients aged <40 years with an initial diagnosis of ASI between 1994 and 2016; the mean follow-up was 11.1 years. Medical records were reviewed to obtain patient information, and models were generated to predict the outcomes of interest. Five candidate algorithms were trained in the development of each of the models, as well as an additional ensemble of the algorithms. Performance of the algorithms was assessed using discrimination, calibration, and decision curve analysis. Results: Of the 654 included patients, 443 (67.7%) experienced multiple instability events, 228 (34.9%) underwent surgery, and 39 (5.9%) developed symptomatic OA. The ensemble gradient-boosted machines achieved the best performances based on discrimination (via area under the receiver operating characteristic curve [AUC]: AUC recurrence = 0.86), AUC surgery = 0.76, AUC OA = 0.78), calibration, decision curve analysis, and Brier score (Brier recurrence = 0.138, Brier surgery = 0.185, Brier OA = 0.05). For demonstration purposes, models were integrated into a single web-based open-access application able to provide predictions and explanations for practitioners and researchers. Conclusion: After identification of key features, including time from initial instability, age at initial instability, sports involvement, and radiographic findings, machine-learning models were developed that effectively and reliably predicted recurrent instability, progression to surgery, and the development of OA in patients with ASI. After careful external validation, these models can be incorporated into open-access digital applications to inform patients, clinicians, and researchers regarding quantifiable risks of relevant outcomes in the clinic.

➤ Mesenchymal stromal cells (MSCs) are a subset of progenitor cells that help to promote tissue healing and regeneration through the secretion of various cytokines and growth factors. Although technically pluripotent, MSCs in vivo rarely repair damaged tissue through direct differentiation and engraftment. ➤ Augmentation of traditional marrow stimulation techniques with MSCs has been theorized to improve repair tissue quality and defect fill. Clinical trials evaluating this technique are limited but have shown modest improvements compared with isolated marrow stimulation. ➤ Various scaffolds also have been utilized in combination with MSCs to treat focal chondral defects. Although the techniques described are heterogeneous, many have shown promising early clinical outcomes. ➤ Newer techniques involving 3-dimensional bioprinted scaffolds seeded with MSCs allow for the recreation of complex architecture, more closely resembling articular cartilage. These techniques are evolving and have not yet been studied in human clinical trials.

Background: Basic pitcher statistics have been used to assess performance in pitchers after injury or surgery without being validated. Even among healthy pitchers, the normal variability of these parameters has not yet been established. Purpose: To determine (1) the normal variability of basic and advanced pitcher statistics in healthy professional baseball pitchers and (2) the minimum pitches needed to predict these parameters. Study Design: Cross-sectional study; Level of evidence, 3. Methods: Publicly available data from the MLB Statcast and PITCHf/x databases were used to analyze MLB pitchers during the 2015 and 2016 seasons who recorded a minimum of 100 innings without injury. Basic and advanced baseball pitcher statistics were analyzed. The variability of each parameter was assessed by computing the coefficient of variation (CV) between individual pitchers and across all pitchers. A CV <10 was indicative of a relatively constant parameter, and parameters with a CV >10 were generally considered inconsistent and unreliable. The minimum number of pitches needed to be followed for each variable was also analyzed. Results: A total of 118 pitchers, 55 baseball-specific statistical metrics (38 basic and 17 advanced), and 7.5 million pitches were included and analyzed. Of the 38 basic pitcher statistics, only fastball velocity demonstrated a CV <10 (CV = 1.5), while 6 of 17 (35%) advanced metrics demonstrated acceptable consistency (CV <10). Release position from plate and velocity from the plate were the 2 most consistent advanced parameters. When separated by pitch type, these 2 parameters were the most constant (lowest CV) across every pitch type. Conclusion: We recommend against utilizing nonvalidated statistical measures to assess performance after injury, as they demonstrated unacceptably high variability even among healthy, noninjured professional baseball pitchers. It is our hope that this study will serve as the foundation for the identification and implementation of validated pitcher-dependent statistical measures that can be used to assess return-to-play performance after injury in the future.

Background: Athletes of all sports often have shoulder instability, most commonly as anterior shoulder instability (ASI). For overhead athletes (OHAs) and those participating in throwing sports, clinical and surgical decision making can be difficult owing to a lack of long-term outcome studies in this population of athletes. Purpose/Hypothesis: To report presentation characteristics, pathology, treatment strategies, and outcomes of ASI in OHAs and throwers in a geographic cohort. We hypothesized that OHAs and throwers would have similar presenting characteristics, management strategies, and clinical outcomes but lower rates of return to play (RTP) when compared with non-OHAs (NOHAs) and nonthrowers, respectively. Study Design: Cohort study; Level of evidence, 3. Methods: An established geographic medical record system was used to identify OHAs diagnosed with ASI in the dominant shoulder. An overall 57 OHAs with ASI were matched 1:2 with 114 NOHAs with ASI. Of the OHAs, 40 were throwers. Sports considered overhead were volleyball, swimming, racquet sports, baseball, and softball, while baseball and softball composed the thrower subgroup. Records were reviewed for patient characteristics, type of sport, imaging findings, treatment strategies, and surgical details. Patients were contacted to collect Western Ontario Shoulder Instability index (WOSI) scores and RTP data. Statistical analysis compared throwers with nonthrowers and OHAs with NOHAs. Results: Four patients, 3 NOHAs and 1 thrower, were lost to follow-up at 6 months. Clinical follow-up for the remaining 167 patients (98%) was 11.9 ± 7.2 years (mean ± SD). Of the 171 patients included, an overall 41 (36%) NOHAs, 29 (51%) OHAs, and 22 (55%) throwers were able to be contacted for WOSI scores and RTP data. OHAs were more likely to initially present with subluxations (56%; P = .030). NOHAs were more likely to have dislocations (80%; P = .018). The number of instability events at presentation was similar. OHAs were more likely to undergo initial operative management. Differences in rates of recurrent instability were not significant after initial nonoperative management (NOHAs, 37.1% vs OHAs, 28.6% [ P = .331] and throwers, 21.2% [ P = .094]) and surgery (NOHAs, 20.5% vs OHAs, 13.0% [ P = .516] and throwers, 9.1% [ P = .662]). Rates of revision surgery were similar (NOHAs, 18.0% vs OHAs, 8.7% [ P = .464] and throwers, 18.2% [ P > .999]). RTP rates were 80.5% in NOHAs, as compared with 71.4% in OHAs ( P = .381) and 63.6% in throwers ( P = .143). Median WOSI scores were 40 for NOHAs, as compared with 28 in OHAs ( P = .425) and 28 in throwers ( P = .615). Conclusion: In a 1:2 matched comparison of general population athletes, throwers and OHAs were more likely to have more subtle instability, as evidenced by higher rates of subluxations rather than frank dislocations, when compared with NOHAs. Despite differences in presentation and the unique sport demands of OHAs, rates of recurrent instability and revision surgery were similar across groups. Similar outcomes in terms of RTP, level of RTP, and WOSI scores were achieved for OHAs and NOHAs, but these results must be interpreted with caution given the limited sample size.

Purpose The purposes of this study were to identify patient characteristics and risk factors for overnight admission following outpatient hip arthroscopy and to develop a machine learning algorithm that can effectively identify patients requiring admission following elective hip arthroscopy. Methods A retrospective review of a prospectively collected national surgical outcomes database was performed to identify patients who underwent elective outpatient hip arthroscopy from 2006 to 2018. Patients admitted overnight postoperatively were identified as those with length of stay of 1 or more days. Models were generated using random forest (RF), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), elastic net penalized logistic regression, and an additional model was produced as a weighted ensemble of the four final algorithms. Results Overall, 1,276 patients were included. The median age was 43 years, and 64.2% (819) were female. Of the included patients, 109 (8.5%) required an overnight stay following elective outpatient hip arthroscopy. The most important factors for inpatient admission were increasing operative time, general anesthesia, age extremes, male gender, greater body mass index (BMI), American Society of Anesthesiologists classification >1, and the following preoperative lab values outside of normal ranges: sodium, platelet count, hematocrit, and leukocyte count. The ensemble model achieved the best performance based on discrimination assessed via internal validation (area under the curve = .71), calibration, and decision curve analysis. The model was integrated into a Web‐based open‐access application able to provide both personalized predictions and explanations. Conclusion A machine learning algorithm developed based on preoperative features identified increasing operative time, age extremes, greater BMI, sodium, hematocrit, platelets, and leukocyte count as the most important variables associated with inpatient admission with fair validity.

Background: Studies evaluating the natural history of femoroacetabular impingement (FAI) are limited. Purpose: To stratify the risk of progression to osteoarthritis (OA) in patients with FAI using an unsupervised machine-learning algorithm, compare the characteristics of each subgroup, and validate the reproducibility of staging. Study Design: Cohort study (prognosis); Level of evidence, 2. Methods: A geographic database from the Rochester Epidemiology Project was used to identify patients with hip pain between 2000 and 2016. Medical charts were reviewed to obtain characteristic information, physical examination findings, and imaging details. The patient data were randomly split into 2 mutually exclusive sets: train set (70%) for model development and test set (30%) for validation. The data were transformed via Uniform Manifold Approximation and Projection and were clustered using Hierarchical Density-based Spatial Clustering of Applications with Noise. Results: The study included 1071 patients with a mean follow-up period of 24.7 ± 12.5 years. The patients were clustered into 5 subgroups based on train set results: patients in cluster 1 were in their early 20s (20.9 ± 9.6 years), female dominant (84%), with low body mass index (<19 ); patients in cluster 2 were in their early 20s (22.9 ± 6.7 years), female dominant (95%), and pincer-type FAI (100%) dominant; patients in cluster 3 were in their mid 20s (26.4 ± 9.7) and were mixed-type FAI dominant (92%); patients in cluster 4 were in their early 30s (32.7 ± 7.8), with high body mass index (≥29 ), and diabetes (17%); and patients in cluster 5 were in their early 30s (30.0 ± 9.1), with a higher percentage of males (43%) compared with the other clusters and with limited internal rotation (14%). Mean survival for clusters 1 to 5 was 17.9 ± 0.6, 18.7 ± 0.3, 17.1 ± 0.4, 15.0 ± 0.5, and 15.6 ± 0.5 years, respectively, in the train set. The survival difference was significant between clusters 1 and 4 ( P = .02), 2 and 4 ( P < .005), 2 and 5 ( P = .01), and 3 and 4 ( P < .005) in the train set and between clusters 2 and 5 ( P = .03) and 3 and 4 ( P = .01) in the test set. Cluster characteristics and prognosis was well reproduced in the test set. Conclusion: Using the clustering algorithm, it was possible to determine the prognosis for OA progression in patients with FAI in the presence of conflicting risk factors acting in combination.