Pareek, Ayoosh

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: 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.

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.

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.

Background: Several factors are associated with an increased risk of anterior cruciate ligament (ACL) reconstruction revision. However, the ability to accurately translate these factors into a quantifiable risk of revision at a patient-specific level has remained elusive. We sought to determine if machine learning analysis of the Norwegian Knee Ligament Register (NKLR) can identify the most important risk factors associated with subsequent revision of primary ACL reconstruction and develop a clinically meaningful calculator for predicting revision of primary ACL reconstruction. Methods: Machine learning analysis was performed on the NKLR data set. The primary outcome was the probability of revision ACL reconstruction within 1, 2, and/or 5 years. Data were split randomly into training sets (75%) and test sets (25%). Four machine learning models were tested: Cox Lasso, survival random forest, generalized additive model, and gradient boosted regression. Concordance and calibration were calculated for all 4 models. Results: The data set included 24,935 patients, and 4.9% underwent a revision surgical procedure during a mean follow-up (and standard deviation) of 8.1 ± 4.1 years. All 4 models were well-calibrated, with moderate concordance (0.67 to 0.69). The Cox Lasso model required only 5 variables for outcome prediction. The other models either used more variables without an appreciable improvement in accuracy or had slightly lower accuracy overall. An in-clinic calculator was developed that can estimate the risk of ACL revision (Revision Risk Calculator). This calculator can quantify risk at a patient-specific level, with a plausible range from near 0% for low-risk patients to 20% for high-risk patients at 5 years. Conclusions: Machine learning analysis of a national knee ligament registry can predict the risk of ACL reconstruction revision with moderate accuracy. This algorithm supports the creation of an in-clinic calculator for point-of-care risk stratification based on the input of only 5 variables. Similar analysis using a larger or more comprehensive data set may improve the accuracy of risk prediction, and future studies incorporating patients who have experienced failure of ACL reconstruction but have not undergone subsequent revision may better predict the true risk of ACL reconstruction failure. Level of Evidence: Prognostic Level III . See Instructions for Authors for a complete description of levels of evidence.

Introduction The literature has consistently demonstrated utilization disparities in joint replacement procedures, though no studies have evaluated disparities in total shoulder arthroplasty with regard to operative volume. Methods We queried the New York (NY) Statewide Planning and Research Cooperative System (SPARCS) database for 32 410 total shoulder arthroplasties performed between 2009 and 2017. Patients were identified using Clinical Classifications Software code 154 for Non-Hip/Knee Arthroplasty and All Patient Refined-Diagnosis Related Group code 322 for Shoulder. Racial groups included Hispanic, non-Hispanic white, non-Hispanic black, and Other. High-volume centers were facilities that performed 2 standard deviations above the mean annual procedures. Utilization rates were calculated by dividing total shoulder arthroplasties per group by the 2010 NY Census population of that group. The Fisher exact test was used to determine significance. Results Total shoulder arthroplasty utilization increased from 43/100 000 to 73/100 000, two-thirds of which was driven by an increase in white resident utilization. More White residents per 100 000 underwent shoulder arthroplasty than Black, Hispanic, and Other residents per 100 000 residents of their respective race. White residents were 90% more likely than Hispanic residents to undergo total shoulder arthroplasty at high-volume centers ( P = .04). There were no differences in utilization rate regarding operative volume comparing Black or Other residents to White residents. More females underwent total shoulder arthroplasty than males, though there was no difference in utilization rate regarding operative volume. Conclusion Though total shoulder arthroplasty utilization nearly doubled, disparities persisted across gender and minority groups particularly in Hispanic utilization as White residents were 90% more likely than Hispanic residents to undergo shoulder arthroplasty at high-volume centers.

Abstract The application of artificial intelligence (AI) and machine learning to the field of orthopaedic surgery is rapidly increasing. While this represents an important step in the advancement of our specialty, the concept of AI is rich with statistical jargon and techniques unfamiliar to many clinicians. This knowledge gap may limit the impact and potential of these novel techniques. We aim to narrow this gap in a way that is accessible for all orthopaedic surgeons. With this manuscript, we introduce the concept of AI and machine learning and give examples of how it can impact clinical practice and patient care. Level of evidence VI.

Purpose To evaluate both the potential causes and resultant outcomes in patients in whom subchondral insufficiency fracture of the knee (SIFK) develops after arthroscopy. Methods We performed a retrospective review of all patients with a magnetic resonance imaging diagnosis of SIFK after arthroscopic meniscectomy and chondroplasty over a 12‐year period. Results A total of 28 patients were included, with a mean age of 61 years and mean follow‐up period of 5.7 years. SIFK showed a predilection for the medial compartment (n = 25, 89%), specifically the medial femoral condyle (n = 21, 75%). In 7 patients (25%), SIFK developed in both the femoral condyle and tibial plateau in the ipsilateral compartment. Fifteen patients (54%) went on to conversion to arthroplasty at a mean of 0.72 years. The rate of survival free of conversion to arthroplasty was 57%, 45%, and 40% at 1 year, 2 years, and 5 years, respectively. Furthermore, 63% of patients with a meniscal tear and SIFK in the same compartment went on to arthroplasty ( P = .04). There was an increased risk of conversion to arthroplasty if SIFK was present in both the femur and tibia in the same compartment ( P = .04). A higher Kellgren‐Lawrence grade at the time of the SIFK diagnosis increased the likelihood of eventual arthroplasty ( P = .03). The presence of SIFK in both the femur and tibia in the ipsilateral compartment, an increased Kellgren‐Lawrence grade, and a meniscal tear or prior meniscectomy in the same compartment as SIFK were associated with an increased risk of eventual arthroplasty. Conclusions Post‐arthroscopic SIFK most commonly occurs in the medial compartment, particularly in patients who underwent a prior meniscectomy. The presence of meniscal root and radial tears in these patients is notable (75%). Ultimately, there is a high rate of progression of arthrosis (33%) and eventual conversion to arthroplasty. Level of Evidence Level IV, case series.

AbstractMany different techniques with multiple graft types have been described for the reconstruction of the injured posterior cruciate ligament (PCL); autograft versus allograft, single- versus double-bundle, open inlay versus arthroscopic inlay versus arthroscopic transtibial, and recently described the arthroscopic “all-inside” socket technique. Reported clinical outcomes have demonstrated no significant difference in any of these PCL reconstruction techniques, likely because of the heterogeneity in injury characteristics and patient population. The ideal surgical technique should be safe, simple, and reproducible while allowing treatment of concomitant knee injuries resulting and return to function.