Injury patterns and healthcare utilisation by runners of the New York City Marathon.

Objectives: The purpose of this study was to describe injury patterns and healthcare utilisation of marathon runners. Methods: This was a previously reported 16-week prospective observational study of runners training for the New York City Marathon. Runners completed a baseline survey including demographics, running experience and marathon goal. Injury surveys were collected every 4 weeks during training, as well as 1 week before and 1 week after the race. Injury details collected included anatomic location, diagnosis, onset, and treatment received. Results: A total of 1049 runners were enrolled. Injuries were reported by 398 (38.4%) during training and 128 (14.1%) during the marathon. The overall prevalence of injury was 447/1049 (42.6%). Foot, knee and hip injuries were most common during training, whereas knee, thigh and foot injuries were most common during the race. The most frequent tissue type affected was the category of muscle, tendon/fascia and bursa. The prevalence of overuse injuries increased, while acute injuries remained constant throughout training. Hamstring injuries had the highest prevalence of diagnosis with 38/564 injuries (6.7%). Of the 447 runners who reported an injury, 224 (50.1%) received medical care. Physical therapy was the most common medical care received with 115/1037 (11.1%) runners during training and 44/907 (4.9%) postrace. Conclusion: Runners training and participating in a marathon commonly experience injuries, especially of the foot and knee, which often are overuse soft tissue injuries. Half of the injured runners sought out medical care for their injury. Understanding the patterns of injuries affecting marathon runners could help guide future injury prevention efforts.

Defining Patient-relevant Thresholds and Change Scores for the HOOS JR and KOOS JR Anchored on the Patient-acceptable Symptom State Question.

BACKGROUND: When evaluating the results of clinical research studies, readers need to know that patients perceive effect sizes, not p values. Knowing the minimum clinically important difference (MCID) and the patient-acceptable symptom state (PASS) threshold for patient-reported outcome measures helps us to ascertain whether our interventions result in improvements that are large enough for patients to care about, and whether our treatments alleviate patient symptoms sufficiently. Prior studies have developed the MCID and PASS threshold for the Hip Disability and Osteoarthritis Outcome Score for Joint Replacement (HOOS JR) and Knee Injury and Osteoarthritis Outcome Score for Joint Replacement (KOOS JR) anchored on satisfaction with surgery, but to our knowledge, neither the MCID nor the PASS thresholds for these instruments anchored on a single-item PASS question have been described. QUESTIONS/PURPOSES: (1) What are the MCID (defined here as the HOOS/KOOS JR change score associated with achieving PASS) and PASS threshold for the HOOS JR and KOOS JR anchored on patient responses to the single-item PASS instrument? (2) How do patient demographic factors such as age, gender, and BMI correlate with MCID and PASS thresholds using the single-item PASS instrument? METHODS: Between July 2020 and September 2021, a total of 10,970 patients underwent one primary unilateral THA or TKA and completed at least one of the three surveys (preoperative HOOS or KOOS JR, 1-year postoperative HOOS or KOOS JR, and 1-year postoperative single-item anchor) at one large, academic medical center. Of those, only patients with data for all three surveys were eligible, leaving 13% (1465 total; 783 THAs and 682 TKAs) for analysis. Despite this low percentage, the overall sample size was large, and there was little difference between completers and noncompleters in terms of demographics or baseline patient-reported outcome measure scores. Patients undergoing bilateral total joint arthroplasty or revision total joint arthroplasty and those without all three surveys at 1 year of follow-up were excluded. A receiver operating characteristic curve analysis, leveraging a 1-year, single-item PASS (that is, "Do you consider that your current state is satisfactory?" with possible answers of "yes" or "no") as the anchor was then used to establish the MCID and PASS thresholds among the 783 included patients who underwent primary unilateral THA and 682 patients who underwent primary unilateral TKA. We also explored the associations of age at the time of surgery (younger than 65 years or 65 years and older), gender (men or women), BMI (< 30 or ≥ 30 kg/m 2 ), and baseline Patient-Reported Outcome Measure Information System-10 physical and mental component scores (< 50 or ≥ 50) for each of the MCID and PASS thresholds through stratified analyses. RESULTS: For the HOOS JR, the MCID associated with the PASS was 23 (95% CI 18 to 31), with an area under the receiver operating characteristic curve of 0.75, and the PASS threshold was 81 (95% CI 77 to 85), with an area under the receiver operating characteristic curve of 0.81. For the KOOS JR, the MCID was 16 (95% CI 14 to 18), with an area under the receiver operating characteristic curve of 0.75, and the PASS threshold was 71 (95% CI 66 to 73) with an area under the receiver operating characteristic curve of 0.84. Stratified analyses indicated higher change scores and PASS threshold for younger men undergoing THA and higher PASS thresholds for older women undergoing TKA. CONCLUSION: Here, we demonstrated the utility of a single patient-centered anchor question, raising the question as to whether simply collecting a postoperative PASS is an easier way to measure success than collecting preoperative and postoperative patient-reported outcome measures and then calculating MCIDs and the substantial clinical benefit. LEVEL OF EVIDENCE: Level III, therapeutic study.

Arthritis Foundation/HSS Workshop on Hip Osteoarthritis, Part 4: Nonoperative Options, Machine Learning in Predicting Total Hip Arthroplasty, Robotics, and Phenotyping to Guide Precision Rehabilitation.

Far more publications are available for osteoarthritis of the knee than of the hip. Recognizing this research gap, the Arthritis Foundation (AF), in partnership with the Hospital for Special Surgery (HSS), convened an in-person meeting of thought leaders to review the state of the science of and clinical approaches to hip osteoarthritis. This article summarizes the recommendations gleaned from presentations given in the "late-stage osteoarthritis" session of the 2023 Hip Osteoarthritis Clinical Studies Conference, which took place on February 17 and 18, 2023, in New York City. It covers conservative treatment, decision-making in end-stage hip osteoarthritis, advancements in robotics, and the role of phenotyping in precision rehabilitation post-total hip arthroplasty (THA).

Machine Learning on Medicare Claims Poorly Predicts the Individual Risk of 30-Day Unplanned Readmission After Total Joint Arthroplasty, Yet Uncovers Interesting Population-level Associations With Annual Procedure Volumes.

BACKGROUND: Unplanned hospital readmissions after total joint arthroplasty (TJA) represent potentially serious adverse events and remain a critical measure of hospital quality. Predicting the risk of readmission after TJA may provide patients and clinicians with valuable information for preoperative decision-making. QUESTIONS/PURPOSES: (1) Can nonlinear machine-learning models integrating preoperatively available patient, surgeon, hospital, and county-level information predict 30-day unplanned hospital readmissions in a large cohort of nationwide Medicare beneficiaries undergoing TJA? (2) Which predictors are the most important in predicting 30-day unplanned hospital readmissions? (3) What specific information regarding population-level associations can we obtain from interpreting partial dependency plots (plots describing, given our modeling choice, the potentially nonlinear shape of associations between predictors and readmissions) of the most important predictors of 30-day readmission? METHODS: National Medicare claims data (chosen because this database represents a large proportion of patients undergoing TJA annually) were analyzed for patients undergoing inpatient TJA between October 2016 and September 2018. A total of 679,041 TJAs (239,391 THAs [61.3% women, 91.9% White, 52.6% between 70 and 79 years old] and 439,650 TKAs [63.3% women, 90% White, 55.2% between 70 and 79 years old]) were included. Model features included demographics, county-level social determinants of health, prior-year (365-day) hospital and surgeon TJA procedure volumes, and clinical classification software-refined diagnosis and procedure categories summarizing each patient's Medicare claims 365 days before TJA. Machine-learning models, namely generalized additive models with pairwise interactions (prediction models consisting of both univariate predictions and pairwise interaction terms that allow for nonlinear effects), were trained and evaluated for predictive performance using area under the receiver operating characteristic (AUROC; 1.0 = perfect discrimination, 0.5 = no better than random chance) and precision-recall curves (AUPRC; equivalent to the average positive predictive value, which does not give credit for guessing "no readmission" when this is true most of the time, interpretable relative to the base rate of readmissions) on two holdout samples. All admissions (except the last 2 months' worth) were collected and split randomly 80%/20%. The training cohort was formed with the random 80% sample, which was downsampled (so it included all readmissions and a random, equal number of nonreadmissions). The random 20% sample served as the first test cohort ("random holdout"). The last 2 months of admissions (originally held aside) served as the second test cohort ("2-month holdout"). Finally, feature importances (the degree to which each variable contributed to the predictions) and partial dependency plots were investigated to answer the second and third research questions. RESULTS: For the random holdout sample, model performance values in terms of AUROC and AUPRC were 0.65 and 0.087, respectively, for THA and 0.66 and 0.077, respectively, for TKA. For the 2-month holdout sample, these numbers were 0.66 and 0.087 and 0.65 and 0.075. Thus, our nonlinear models incorporating a wide variety of preoperative features from Medicare claims data could not well-predict the individual likelihood of readmissions (that is, the models performed poorly and are not appropriate for clinical use). The most predictive features (in terms of mean absolute scores) and their partial dependency graphs still confer information about population-level associations with increased risk of readmission, namely with older patient age, low prior 365-day surgeon and hospital TJA procedure volumes, being a man, patient history of cardiac diagnoses and lack of oncologic diagnoses, and higher county-level rates of hospitalizations for ambulatory-care sensitive conditions. Further inspection of partial dependency plots revealed nonlinear population-level associations specifically for surgeon and hospital procedure volumes. The readmission risk for THA and TKA decreased as surgeons performed more procedures in the prior 365 days, up to approximately 75 TJAs (odds ratio [OR] = 1.2 for TKA and 1.3 for THA), but no further risk reduction was observed for higher annual surgeon procedure volumes. For THA, the readmission risk decreased as hospitals performed more procedures, up to approximately 600 TJAs (OR = 1.2), but no further risk reduction was observed for higher annual hospital procedure volumes. CONCLUSION: A large dataset of Medicare claims and machine learning were inadequate to provide a clinically useful individual prediction model for 30-day unplanned readmissions after TKA or THA, suggesting that other factors that are not routinely collected in claims databases are needed for predicting readmissions. Nonlinear population-level associations between low surgeon and hospital procedure volumes and increased readmission risk were identified, including specific volume thresholds above which the readmission risk no longer decreases, which may still be indirectly clinically useful in guiding policy as well as patient decision-making when selecting a hospital or surgeon for treatment. LEVEL OF EVIDENCE: Level III, therapeutic study.

Key Thresholds and Relative Contributions of Knee Geometry, Anteroposterior Laxity, and Body Weight as Risk Factors for Noncontact ACL Injury.

Background: Limited data exist regarding the association of tibiofemoral bony and soft tissue geometry and knee laxity with risk of first-time noncontact anterior cruciate ligament (ACL) rupture. Purpose: To determine associations of tibiofemoral geometry and anteroposterior (AP) knee laxity with risk of first-time noncontact ACL injury in high school and collegiate athletes. Study Design: Cohort study; Level of evidence, 2. Methods: Over a 4-year period, noncontact ACL injury events were identified as they occurred in 86 high school and collegiate athletes (59 female, 27 male). Sex- and age-matched control participants were selected from the same team. AP laxity of the uninjured knee was measured using a KT-2000 arthrometer. Magnetic resonance imaging was taken on ipsilateral and contralateral knees, and articular geometries were measured. Sex-specific general additive models were implemented to investigate associations between injury risk and 6 features: ACL volume, meniscus-bone wedge angle in the lateral compartment of the tibia, articular cartilage slope at the middle region of the lateral compartment of the tibia, femoral notch width at the anterior outlet, body weight, and AP displacement of the tibia relative to the femur. Importance scores (in percentages) were calculated to rank the relative contribution of each variable. Results: In the female cohort, the 2 features with the highest importance scores were tibial cartilage slope (8.6%) and notch width (8.1%). In the male cohort, the 2 top-ranked features were AP laxity (5.6%) and tibial cartilage slope (4.8%). In female patients, injury risk increased by 25.5% with lateral middle cartilage slope becoming more posteroinferior from -6.2° to -2.0° and by 17.5% with lateral meniscus-bone wedge angle increasing from 27.3° to 28.2°. In males, an increase in AP displacement from 12.5 to 14.4 mm in response to a 133-N anterior-directed load was associated with a 16.7% increase in risk. Conclusion: Of the 6 variables studied, there was no single dominant geometric or laxity risk factor for ACL injury in either the female or male cohort. In males, AP laxity >13 to 14 mm was associated with sharply increased risk of noncontact ACL injury. In females, lateral meniscus-bone wedge angle >28° was associated with a sharply decreased risk of noncontact ACL injury.

An Interpretable Machine Learning Model for Predicting 10-Year Total Hip Arthroplasty Risk.

BACKGROUND: As the demand for total hip arthroplasty (THA) rises, a predictive model for THA risk may aid patients and clinicians in augmenting shared decision-making. We aimed to develop and validate a model predicting THA within 10 years in patients using demographic, clinical, and deep learning (DL)-automated radiographic measurements. METHODS: Patients enrolled in the osteoarthritis initiative were included. DL algorithms measuring osteoarthritis- and dysplasia-relevant parameters on baseline pelvis radiographs were developed. Demographic, clinical, and radiographic measurement variables were then used to train generalized additive models to predict THA within 10 years from baseline. A total of 4,796 patients were included [9,592 hips; 58% female; 230 THAs (2.4%)]. Model performance using 1) baseline demographic and clinical variables 2) radiographic variables, and 3) all variables was compared. RESULTS: Using 110 demographic and clinical variables, the model had a baseline area under the receiver operating curve (AUROC) of 0.68 and area under the precision recall curve (AUPRC) of 0.08. Using 26 DL-automated hip measurements, the AUROC was 0.77 and AUPRC was 0.22. Combining all variables, the model improved to an AUROC of 0.81 and AUPRC of 0.28. Three of the top five predictive features in the combined model were radiographic variables, including minimum joint space, along with hip pain and analgesic use. Partial dependency plots revealed predictive discontinuities for radiographic measurements consistent with literature thresholds of osteoarthritis progression and hip dysplasia. CONCLUSION: A machine learning model predicting 10-year THA performed more accurately with DL radiographic measurements. The model weighted predictive variables in concordance with clinical THA pathology assessments.

Standardized Fixation Zones and Cone Assessments for Revision Total Knee Arthroplasty Using Deep Learning.

BACKGROUND: Achieving adequate implant fixation is critical to optimize survivorship and postoperative outcomes after revision total knee arthroplasty (rTKA). Three anatomical zones (ie, epiphysis, metaphysis, and diaphysis) have been proposed to assess fixation, but are not well-defined. The purpose of the study was to develop a deep learning workflow capable of automatically delineating rTKA zones and cone placements in a standardized way on postoperative radiographs. METHODS: A total of 235 patients who underwent rTKA were randomly partitioned (6:2:2 training, validation, and testing split), and a U-Net segmentation workflow was developed to delineate rTKA fixation zones and assess revision cone placement on anteroposterior radiographs. Algorithm performance for zone delineation and cone placement were compared against ground truths from a fellowship-trained arthroplasty surgeon using the dice segmentation coefficient and accuracy metrics. RESULTS: On the testing cohort, the algorithm defined zones in 98% of images (8 seconds/image) using anatomical landmarks. The dice segmentation coefficient between the model and surgeon was 0.89 ± 0.08 (interquartile range [IQR]:0.88-0.94) for femoral zones, 0.91 ± 0.08 (IQR: 0.91-0.95) for tibial zones, and 0.90 ± 0.05 (IQR:0.88-0.94) for all zones. Cone identification and zonal cone placement accuracy were 98% and 96%, respectively, for the femur and 96% and 89%, respectively, for the tibia. CONCLUSION: A deep learning algorithm was developed to automatically delineate revision zones and cone placements on postoperative rTKA radiographs in an objective, standardized manner. The performance of the algorithm was validated against a trained surgeon, suggesting that the algorithm demonstrated excellent predictive capabilities in accordance with relevant anatomical landmarks used by arthroplasty surgeons in practice.

Training patterns associated with injury in New York City Marathon runners.

OBJECTIVE: Training patterns are commonly implicated in running injuries. The purpose of this study was to measure the incidence of injury and illness among marathon runners and the association of injuries with training patterns and workload. METHODS: Runners registered for the New York City Marathon were eligible to enrol and prospectively monitored during the 16 weeks before the marathon, divided into 4-week 'training quarters' (TQ) numbered TQ1-TQ4. Training runs were tracked using Strava, a web and mobile platform for tracking exercise. Runners were surveyed at the end of each TQ on injury and illness, and to verify all training runs were recorded. Acute:chronic workload ratio (ACWR) was calculated by dividing the running distance in the past 7 days by the running distance in the past 28 days and analysed using ratio thresholds of 1.3 and 1.5. RESULTS: A total of 735 runners participated, mean age 41.0 (SD 10.7) and 46.0% female. Runners tracked 49 195 training runs. The incidence of injury during training was 40.0% (294/735), and the incidence of injury during or immediately after the marathon was 16.0% (112/699). The incidence of illness during training was 27.2% (200/735). Those reporting an initial injury during TQ3 averaged less distance/week during TQ2 compared with uninjured runners, 27.7 vs 31.9 miles/week (p=0.018). Runners reporting an initial injury during TQ1 had more days when the ACWR during TQ1 was ≥1.5 compared with uninjured runners (injured IQR (0-3) days vs uninjured (0-1) days, p=0.009). Multivariable logistic regression for training injuries found an association with the number of days when the ACWR was ≥1.5 (OR 1.06, 95% CI (1.02 to 1.10), p=0.002). CONCLUSION: Increases in training volume ≥1.5 ACWR were associated with more injuries among runners training for a marathon. These findings can inform training recommendations and injury prevention programmes for distance runners.

Eliciting Activity Goals With a Self-Administered Survey Among Patients With Hip or Knee Osteoarthritis.

Background: Success of treatment for hip or knee osteoarthritis (OA) should be evaluated relative to patients' personal activity goals. Questions/Purposes: We sought to ascertain important principles for collecting such goals and developed a survey informed by those principles to facilitate better shared decision-making. Methods: From a series of 100 patient interviews inquiring about specific activity goals, we identified 6 principles for goal collection that are important to patients and physicians and could practically facilitate better shared decision-making (phase 1). Incorporating these principles, we designed a self-administered survey to measure specific pretreatment activity goals, piloting in 1 surgeon's office (phase 2). During office visits, the feasibility of achieving stated goals was discussed between the surgeon and the patient, and goal modifications were recorded. Results: The phase 2 survey was administered to 252 patients, among whom 130 were women (51.6%); 215 (85.3%), white; mean age, 58.5 years; mean body mass index, 30.2 kg/m2; and 92.9% had 1 or more goals, totaling 106 unique goals. Patient demographics were associated with having goals for walking, running, exercising, golfing, tennis, and stairs. Hip and knee patients could last perform their goal on average 21.7 and 38.6 months prior (P = .002). Patient and surgeon agreed to modify goals 19% of the time, more often among younger patients (P = .001) and for running (64% modified, P < .0001) and skiing (42%, P = .0026), but less often for walking (14%, P = .0430) and golf (0%, P = .0204). Conclusions: Patients' activity goals can be captured by a self-administered survey, collected before an office visit, and used to facilitate shared decision-making.

Computational pathology for musculoskeletal conditions using machine learning: advances, trends, and challenges.

Histopathology is widely used to analyze clinical biopsy specimens and tissues from pre-clinical models of a variety of musculoskeletal conditions. Histological assessment relies on scoring systems that require expertise, time, and resources, which can lead to an analysis bottleneck. Recent advancements in digital imaging and image processing provide an opportunity to automate histological analyses by implementing advanced statistical models such as machine learning and deep learning, which would greatly benefit the musculoskeletal field. This review provides a high-level overview of machine learning applications, a general pipeline of tissue collection to model selection, and highlights the development of image analysis methods, including some machine learning applications, to solve musculoskeletal problems. We discuss the optimization steps for tissue processing, sectioning, staining, and imaging that are critical for the successful generalizability of an automated image analysis model. We also commenting on the considerations that should be taken into account during model selection and the considerable advances in the field of computer vision outside of histopathology, which can be leveraged for image analysis. Finally, we provide a historic perspective of the previously used histopathological image analysis applications for musculoskeletal diseases, and we contrast it with the advantages of implementing state-of-the-art computational pathology approaches. While some deep learning approaches have been used, there is a significant opportunity to expand the use of such approaches to solve musculoskeletal problems.

Increased Incidence of Injury Among Runners With COVID-19.

BACKGROUND: Coronavirus disease 2019 (COVID-19) affects multiple organ systems. Whether and how COVID-19 affects the musculoskeletal system remains unknown. We aim to assess the association between COVID-19 and risk of injury. HYPOTHESIS: Runners who report having COVID-19 also report a higher incidence of injury. STUDY DESIGN: Cross-sectional study. LEVEL OF EVIDENCE: Level 4. METHODS: An electronic survey was distributed from July through September 2020, by New York Road Runners, ASICS North America, race medical directors, and through social media. Inclusion criteria were runners 18 years or older who had participated in ≥1 race (running or triathlon) in 2019. RESULTS: A total of 1947 runners participated and met inclusion criteria. Average age was 45.0 (SD, 12.2) years and 56.5% were women. A total of 123 (6.3%) runners self-reported having COVID-19; 100 (81%) reported their diagnosis was from a laboratory test (polymerase chain reaction or antibody) and 23 reported being diagnosed by a medical professional without confirmatory laboratory testing. Since March 2020, 427 (21.9%) reported an injury that prevented running for at least 1 week, including 38 of 123 (30.9%) who self-reported having COVID-19 and 389 of 1435 (21.3%) who did not report having COVID-19 (P = 0.01). After adjusting for age, sex, the number of races in 2019, and running patterns before March 2020, runners who self-reported a diagnosis of COVID-19 had a higher incidence of injury compared with those who did not (odds ratio, 1.66; 95% CI, 1.11-2.48; P = 0.01). CONCLUSION: Injuries were more often self-reported by runners with laboratory-confirmed or clinically diagnosed COVID-19 compared with those who did not report COVID-19. Given the limitations of the study, any direct role of COVID-19 in the pathophysiology of injuries among runners remains unclear. CLINICAL RELEVANCE: Direct and indirect musculoskeletal sequelae of COVID-19 should be further investigated, including the risk of exercise- and sports-related injury after COVID-19.

Patient and Surgeon Risk-Taking Regarding Total Joint Arthroplasty.

BACKGROUND: Decisions regarding care for osteoarthritis involve physicians helping patients understand likely benefits and harms of treatment. Little work has directly compared patient and surgeon risk-taking attitudes, which may help inform strategies for shared decision-making and improve patient satisfaction. METHODS: We surveyed patients contemplating total joint arthroplasty visiting a high-volume specialty hospital regarding general questions about risk-taking, as well as willingness to undergo surgery under hypothetical likelihoods of moderate improvement and complications. We compared responses from surgeons answering similar questions about willingness to recommend surgery. RESULTS: Altogether 82% (162/197) of patients responded, as did 65% (30/46) of joint replacement surgeons. Mean age among patients was 66.4 years; 58% were female. Surgeons averaged 399 surgeries in 2019. Responses were similar between groups for general, health, career, financial, and sports/leisure risk-taking (P > .20); surgeons were marginally more risk-taking in driving (P = .05). For willingness to have or recommend surgery, as the chance of benefit decreased, or the chance of harm increased, the percentage willing to have or recommend surgery decreased. Between a 70% and 95% chance of moderate improvement (for a 2% complication risk), as well as between a 90% and 95% chance of moderate improvement (for 4% and 6% complication risks), the percentage willing to have or recommend surgery was indistinguishable between patients and surgeons. However, for lower likelihoods of improvement, a higher percentage of patients were willing to undergo surgery than surgeons recommended. Patients were also more often indifferent between complication risks. CONCLUSION: Although patients and surgeons were often willing to have or recommend joint replacement surgery at similar rates, they diverged for lower-benefit higher-harm scenarios.

Defining the Patient Acceptable Symptom State for the HOOS JR and KOOS JR After Primary Total Joint Arthroplasty.

BACKGROUND: It is essential to quantify an acceptable outcome after total joint arthroplasty (TJA) in order to understand quality of care. The purpose of this study was to define patient acceptable symptom state (PASS) thresholds for the Knee injury and Osteoarthritis Outcome Score, Joint Replacement (KOOS JR) and the Hip disability and Osteoarthritis Outcome Score, Joint Replacement (HOOS JR) after TJA. METHODS: A receiver operating characteristic (ROC) curve analysis, leveraging 2-year satisfaction of "moderate improvement" or better as the anchor, was used to establish PASS thresholds among 5,216 patients who underwent primary total hip arthroplasty and 4,036 who underwent primary total knee arthroplasty from 2007 to 2012 with use of an institutional registry. Changes in PASS thresholds were explored by stratifying and recalculating these thresholds by age at the time of surgery (<70 or ≥70 years of age), sex (men or women), body mass index (BMI; <30 or ≥30 kg/m2), and baseline Short Form-36 (SF-36) physical and mental component scores (<50 or ≥50). RESULTS: The HOOS JR PASS threshold was 76.7 (area under the ROC curve [AUC] = 0.91), which was achieved by 4,334 patients (83.1%). The KOOS JR PASS threshold was 63.7 (AUC = 0.89), which was achieved by 3,461 patients (85.8%). Covariate stratification demonstrated that PASS thresholds were higher in men compared with women, and in those with higher preoperative SF-36 physical and mental scores (≥50) compared with lower SF-36 scores (<50). Results differed between instruments for BMI and age: higher BMI was associated with a lower PASS threshold for the HOOS JR but a higher PASS threshold for the KOOS JR. The HOOS JR PASS threshold was higher in patients who were <70 years of age compared with those who were ≥70 years of age, but was equivalent for the KOOS JR. CONCLUSIONS: The PASS thresholds for the HOOS JR and KOOS JR at 2 years after TJA were 76.7 and 63.7, respectively. The PASS thresholds were associated with certain preoperative covariates, suggesting that an acceptable symptom state after TJA is influenced by patient-specific factors. LEVEL OF EVIDENCE: Prognostic Level IV. See Instructions for Authors for a complete description of levels of evidence.

History of COVID-19 Was Not Associated With Length of Stay or In-Hospital Complications After Elective Lower Extremity Joint Replacement.

BACKGROUND: The impact of previous SARS-CoV-2 infection on the morbidity of elective total joint arthroplasty (TJA) is not fully understood. This study reports on the association between previous COVID-19 disease, hospital length of stay (LOS), and in-hospital complications after elective primary TJA. METHODS: Demographics, comorbidities, LOS, and in-hospital complications of consecutive 340 patients with a history of COVID-19 were compared with those of 5014 patients without a history of COVID-19 undergoing TJA. History of COVID-19 was defined as a positive IgG antibody test for SARS-CoV-2 before surgery. All patients were given both antibody and polymerase chain reaction tests before surgery. RESULTS: Patients with a history of COVID-19 were more likely to be obese (43.8% vs 32.4%, P < .001), Black (15.6% vs 6.8%, P < .001), or Hispanic (8.5% vs 5.4%, P = .028) than patients without a history of COVID-19. COVID-19 treatment was reported by 6.8% of patients with a history of COVID-19. Patients with a history of COVID-19 did not have a significantly longer median LOS after controlling for other factors (for hip replacements, median 2.9 h longer, 95% confidence interval = -2.0 to 7.8, P = .240; for knee replacements, median 4.1 h longer, 95% confidence interval = -2.4 to 10.5, P = .214), but a higher percentage were discharged to a post-acute care facility (4.7% vs 1.9%, P = .001). There was no significant difference in in-hospital complication rates between the 2 groups (0/340 = 0.0% vs 22/5014 = 0.44%, P = .221). CONCLUSIONS: We do not find differences in LOS or in-hospital complications between the 2 groups. However, more work is needed to confirm these findings, particularly for patients with a history of more severe COVID-19. LEVEL OF EVIDENCE: II.

Presenteeism and absenteeism before and after single-level lumbar spine surgery.

BACKGROUND CONTEXT: Health can impact work performance through absenteeism, time spent away from work, and presenteeism, inhibited at-work performance. Low back pain is common and costly, both in terms of direct medical expenditures and indirect reduced work performance. PURPOSE: Surgery for lumbar spinal pathology is an important part of treatment for patients who do not respond to nonsurgical management. While the indirect costs of return to work and absenteeism among employed patients undergoing lumbar spine surgery have been studied, little work has been done to quantify presenteeism before and after lumbar spine surgery. STUDY DESIGN/SETTING: Prospective cohort study at a single high-volume urban musculoskeletal specialty hospital. PATIENT SAMPLE: Patients undergoing single-level lumbar spinal fusion and/or decompression surgery. OUTCOME MEASURES: Presenteeism and absenteeism were measured using the World Health Organization's Health and Work Performance Questionnaire before surgery, as well as 6 weeks, 6 months, and 12 months after surgery. METHODS: Average presenteeism and absenteeism were evaluated at pre-surgical baseline and each follow-up timepoint. Monthly average time lost to presenteeism and absenteeism were calculated before surgery and 12 months after surgery. Study data were collected and managed using REDCap electronic data capture tools with support from Clinical and Translational Science Center grant, UL1TR002384. One author discloses royalties, private investments, consulting fees, speaking/teaching arrangements, travel, board of directorship, and scientific advisory board membership totaling >$300,000. RESULTS: We enrolled 134 employed surgical patients, among whom 115 (86%) responded at 6 weeks, 105 (78%) responded at 6 months, and 115 (86%) responded at 12 months. Preoperatively, mean age was 56.4 years (median 57.5), and 41.0% were women; 68 (50.7%) had only decompressions, while 66 (49.3%) had fusions. Among respondents at each time point, 98%, 92%, and 92% were still employed, among whom 76%, 96%, and 96% had resumed working, respectively (median 29 days). Average at-work performance among working patients (who responded at each pair of timepoints) moved from 75.4 to 78.7 between baseline and 6 weeks, 71.8 to 85.9 between baseline and 6 months, and 73.0 to 88.1 between baseline and 12 months. Gains were concentrated among the 52.0% of patients whose at-work performance was declining (and low) leading up to surgery. Average absenteeism was relatively unmoved between baseline and each follow-up. Before surgery, the monthly average time lost to presenteeism and absenteeism was 19.8% and 18.9%, respectively; 12 months after surgery, these numbers were 9.7% and 16.0%; changes represent a mitigated loss of 13.0 percentage points of average monthly value. CONCLUSIONS: Presenteeism and absenteeism contributed roughly evenly to preoperative average monthly lost time. Although average changes in absenteeism and 6-week at-work performance were small, average changes in at-work performance at 6 and 12 months were significant. Cost-benefit analyses of lumbar spine surgery should therefore consider improved presenteeism, which appears to offset some of the direct and indirect costs of surgical treatment.

Factors associated with injuries in first-time marathon runners from the New York City marathon.

OBJECTIVES: To determine how baseline characteristics of first-time marathon runners and training patterns are associated with risk of injuries during training and the race. METHODS: First-time adult marathon runners who were registered for the 2017 New York City Marathon were monitored starting 12 weeks prior to the race. Baseline data collection included demographics and running experience. Running frequency, distance, and injury occurrence were self-reported using online surveys every 2 weeks. RESULTS: A total of 720 runners participated of which 675 completed the study. There were 64/675 (9.5%) who had major injuries during training or the race that preventing starting or finishing the race. An additional 332 (49.2%) had minor injuries interfering with training and/or affecting race performance. Injury incidence was not significantly different based on age or sex. Runners who completed a half marathon prior to the study were less likely to report getting injured [multivariable odds ratio (OR) 0.40, (0.22, 0.76), p= 0.005]. Runners who averaged <4 training runs per week during the study were less likely to report getting injured compared to those who averaged ≥4 per week [relative risk 1.36, (1.13-1.63), p= 0.001]. Longest training run distance during the study was inversely associated with race-day injury incidence [OR 0.87 (0.81, 0.94), p< 0.001]. CONCLUSION: Injuries are common among first-time marathon runners. We found that risk of injury during training was associated with lack of half marathon experience and averaging ≥4 training runs per week. Longer training runs were associated with a lower incidence of race-day injuries. These results can inform the development of targeted injury-prevention interventions.

Running races during the COVID-19 pandemic: a 2020 survey of the running community.

OBJECTIVES: To survey runners and triathletes about their willingness to resume in-person racing during the COVID-19 pandemic, health concerns related to mass races and changes in running patterns since the start of the pandemic. DESIGN: An electronic survey was distributed from 15 July to 1 September 2020 to runners and triathletes by New York Road Runners, ASICS North America, and race medical directors, and through social media. PARTICIPANTS: Runners and triathletes 18 years of age or older who participated in at least one race in 2019. RESULTS: A total of 2278 surveys were received. Not all participants answered every question; the denominator represents the number of responses to each question. Most participants were from the USA (1620/1940, 83.5%), of which over half were from New York (812/1475, 55.1%). Regarding when respondents would feel comfortable returning to in-person racing, the most frequent response was 'Whenever local laws allow, but only if there are sufficient precautions' (954/2173, 43.9%), followed by 'Not until there is a vaccine' (540/2173, 24.9%). The most common concerns about in-person races were crowded starting corrals (1802/2084, 86.5%), the number of COVID-19 cases in the race location (1585/2084, 76.1%) and the number of participants (1517/2084, 72.8%). Comparing running patterns before the pandemic to Summer 2020, the mean weekly mileage decreased from 25.5 (SD 15.4) miles to 22.7 (16.2) miles (p<0.001). CONCLUSION: Most runners are willing to return to racing when local laws allow, though as of Summer 2020, many desired certain precautions to feel comfortable.

Causal Language in Observational Orthopaedic Research.

ABSTRACT: With the increasing availability of large clinical registries and administrative data sets, observational (i.e., nonexperimental) orthopaedic research is being performed with increased frequency. While this research substantially advances our field, there are fundamental limitations to what can be determined through a single observational study. Avoiding overstatements and misstatements is important for the sake of accuracy, particularly for ensuring that clinical care is not inadvertently swayed by how an observational study is written up and described. We have noticed that causal language is frequently misused in observational orthopaedic research-that is, language that says or implies that 1 variable definitively causes another, despite the fact that causation can generally only be determined with randomization. In this data-backed commentary, we examine the prevalence of causal language in a random sample of 400 observational orthopaedic studies; we found that causal language was misused in 60% of them. We discuss the implications of these results and how to report observational findings more accurately: the word "association" (and its derivatives) can almost always replace or reframe a causal phrase.

Genomic analysis of diet composition finds novel loci and associations with health and lifestyle.

We conducted genome-wide association studies (GWAS) of relative intake from the macronutrients fat, protein, carbohydrates, and sugar in over 235,000 individuals of European ancestries. We identified 21 unique, approximately independent lead SNPs. Fourteen lead SNPs are uniquely associated with one macronutrient at genome-wide significance (P < 5 × 10-8), while five of the 21 lead SNPs reach suggestive significance (P < 1 × 10-5) for at least one other macronutrient. While the phenotypes are genetically correlated, each phenotype carries a partially unique genetic architecture. Relative protein intake exhibits the strongest relationships with poor health, including positive genetic associations with obesity, type 2 diabetes, and heart disease (rg ≈ 0.15-0.5). In contrast, relative carbohydrate and sugar intake have negative genetic correlations with waist circumference, waist-hip ratio, and neighborhood deprivation (|rg| ≈ 0.1-0.3) and positive genetic correlations with physical activity (rg ≈ 0.1 and 0.2). Relative fat intake has no consistent pattern of genetic correlations with poor health but has a negative genetic correlation with educational attainment (rg ≈-0.1). Although our analyses do not allow us to draw causal conclusions, we find no evidence of negative health consequences associated with relative carbohydrate, sugar, or fat intake. However, our results are consistent with the hypothesis that relative protein intake plays a role in the etiology of metabolic dysfunction.