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).

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.

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.

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.

Presenteeism and Absenteeism Before and After Total Hip and Knee Arthroplasty.

BACKGROUND: Absenteeism is costly, yet evidence suggests that presenteeism-illness-related reduced productivity at work-is costlier. We quantified employed patients' presenteeism and absenteeism before and after total joint arthroplasty (TJA). METHODS: We measured presenteeism (0-100 scale, 100 full performance) and absenteeism using the World Health Organization's Health and Work Performance Questionnaire before and after TJA among a convenience sample of employed patients. We captured detailed information about employment and job characteristics and evaluated how and among whom presenteeism and absenteeism improved. RESULTS: In total, 636 primary, unilateral TJA patients responded to an enrollment email, confirmed employment, and completed a preoperative survey (mean age: 62.1 years, 55.3% women). Full at-work performance was reported by 19.7%. Among 520 (81.8%) who responded to a 1-year follow-up, 473 (91.0%) were still employed, and 461 (88.7%) had resumed working. Among patients reporting at baseline and 1 year, average at-work performance improved from 80.7 to 89.4. A Wilcoxon signed-rank test indicated that postoperative performance was significantly higher than preoperative performance (P < .0001). The percentage of patients who reported full at-work performance increased from 20.9% to 36.8% (delta = 15.9%, 95% confidence interval = [10.0%, 21.9%], P < .0001). Presenteeism gains were concentrated among patients who reported declining work performance leading up to surgery. Average changes in absences were relatively small. Combined, the average monthly value lost by employers to presenteeism declined from 15.3% to 8.3% and to absenteeism from 16.9% to 15.5% (ie, mitigated loss of 8.4% of monthly value). CONCLUSION: Among employed patients before TJA, presenteeism and absenteeism were similarly costly. After, employed patients reported increased performance, concentrated among those with declining performance leading up to surgery.

Adipose tissue 12/15 lipoxygenase pathway in human obesity and diabetes.

CONTEXT: Visceral adipose tissue (VAT) is a key contributor to chronic inflammation in obesity. The 12/15-lipoxygenase pathway (ALOX) is present in adipose tissue (AT) and leads to inflammatory cascades that are causal for the onset of insulin resistance in rodent models of obesity. OBJECTIVE: The pathophysiology of the ALOX 12/15 pathway in human AT is unknown. We characterized the ALOX pathway in different AT depots in obese humans with or without type 2 diabetes (T2D). DESIGN: This study includes a cross-sectional cohort of 46 morbidly obese (body mass index >39 kg/m(2)) nondiabetic (n = 25) and T2D (n = 21) subjects. SETTING: This study was conducted at Eastern Virginia Medical School (Norfolk, Virginia) in collaboration with Sentara Metabolic and Weight Loss Surgery Center (Sentara Medical Group, Norfolk, Virginia). PATIENTS: Twenty-five obese (body mass index 44.8 ± 4.4 kg/m(2)) nondiabetic (hemoglobin A1c 5.83% ± 0.27%) and 21 obese (43.4 ± 4.1 kg/m(2)) and T2D (hemoglobin A1c 7.66% ± 1.22%) subjects were included in the study. The subjects were age matched and both groups had a bias toward female gender. MAIN OUTCOMES AND MEASURES: Expression of ALOX isoforms along with fatty acid substrates and downstream lipid metabolites were measured. Correlations with depot-specific inflammatory markers were also established. RESULTS: ALOX 12 expression and its metabolite 12(S)-hydroxyeicosatetraenoic acid were significantly increased in the VAT of T2D subjects. ALOX 15A was exclusively expressed in VAT in both groups. ALOX 12 expression positively correlated with expression of inflammatory genes IL-6, IL-12a, CXCL10, and lipocalin-2. CONCLUSIONS: ALOX 12 may have a critical role in regulation of inflammation in VAT in obesity and T2D. Selective ALOX 12 inhibitors may constitute a new approach to limit AT inflammation in human obesity.

The surgical treatment of metabolic disease and morbid obesity.

The disease of obesity has continued to increase in the United States. Obesity is defined as a body mass index (BMI) greater than 30 kg/m(2). In 1991, the National Institute of Health Consensus Panel on Gastric Surgery for Severe Obesity defined the population who would most likely benefit from bariatric surgery. These same criteria continue to be used today to determine which patients should undergo metabolic and weight loss surgery. These recommendations include patients who have a BMI greater than 35 kg/m(2) with significant comorbid conditions such as diabetes, hypertension, or obstructive sleep apnea; and patients who have a BMI greater than 40 kg/m(2) with or without any significant comorbid conditions because they have a significant increased risk for developing these conditions.