Crijns, Tom J

Background Mental health characteristics such as negative mood, fear avoidance, unhelpful thoughts regarding pain, and low self-efficacy are associated with symptom intensity and capability among patients with hip and knee osteoarthritis (OA). Knowledge gaps remain regarding the conceptual and statistical overlap of these constructs and which of these are most strongly associated with capability in people with OA. Further study of these underlying factors can inform us which mental health assessments to prioritize and how to incorporate them into whole-person, psychologically informed care. Questions/purposes (1) What are the distinct underlying factors that can be identified using statistical grouping of responses to a multidimensional mental health survey administered to patients with OA? (2) What are the associations between these distinct underlying factors and capability in knee OA (measured using the Knee Injury and Osteoarthritis Outcome Score, Joint Replacement [KOOS JR]) and hip OA (measured using Hip Disability and Osteoarthritis Outcome Score, Joint Replacement [HOOS JR]), accounting for sociodemographic and clinical factors? Methods We performed a retrospective cross-sectional analysis of adult patients who were referred to our program with a primary complaint of hip or knee pain secondary to OA between October 2017 and December 2020. Of the 2006 patients in the database, 38% (760) were excluded because they did not have a diagnosis of primary osteoarthritis, and 23% (292 of 1246) were excluded owing to missing data, leaving 954 patients available for analysis. Seventy-three percent (697) were women, with a mean age of 61 ± 10 years; 65% (623) of patients were White, and 52% (498) were insured under a commercial plan or via their employer. We analyzed demographic data, patient-reported outcome measures, and a multidimensional mental health survey (the 10-item Optimal Screening for Prediction of Referral and Outcome-Yellow Flag [OSPRO-YF] assessment tool), which are routinely collected for all patients at their baseline new-patient visit. To answer our first question about identifying underlying mental health factors, we performed an exploratory factor analysis of the OSPRO-YF score estimates. This technique helped identify statistically distinct underlying factors for the entire cohort based on extracting the maximum common variance among the variables of the OSPRO-YF. The exploratory factor analysis established how strongly different mental health characteristics were intercorrelated. A scree plot technique was then applied to reduce these factor groupings (based on Eigenvalues above 1.0) into a set of distinct factors. Predicted factor scores of these latent variables were generated and were subsequently used as explanatory variables in the multivariable analysis that identified variables associated with HOOS JR and KOOS JR scores. Results Two underlying mental health factors were identified using exploratory factor analysis and the scree plot; we labeled them “pain coping” and “mood.” For patients with knee OA, after accounting for confounders, worse mood and worse pain coping were associated with greater levels of incapability (KOOS JR) in separate models but when analyzed in a combined model, pain coping (regression coefficient -4.3 [95% confidence interval -5.4 to -3.2], partial R2 0.076; p < 0.001) had the strongest relationship, and mood was no longer associated. Similarly, for hip OA, pain coping (regression coefficient -5.4 [95% CI -7.8 to -3.1], partial R2 0.10; p < 0.001) had the strongest relationship, and mood was no longer associated. Conclusion This study simplifies the multitude of mental health assessments into two underlying factors: cognition (pain coping) and feelings (mood). When considered together, the association between capability and pain coping was dominant, signaling the importance of a mental health assessment in orthopaedic care to go beyond focusing on unhelpful feelings and mood (assessment of depression and anxiety) alone to include measures of pain coping, such as the Pain Catastrophizing Scale or Tampa Scale for Kinesiophobia, both of which have been used extensively in patients with musculoskeletal conditions. Level of Evidence Level III, prognostic study.

Background: Shoulder and elbow function is essential to basic basketball actions. Outside of anterior shoulder instability, injuries in these joints are not well characterized in National Basketball Association (NBA) players. Purpose: To describe the epidemiology and associated risk factors of shoulder and elbow injuries in NBA players and identify factors that influence player performance upon return to play. Study Design: Descriptive epidemiology study. Methods: Historical injury data from the 2015-2020 NBA seasons were retrieved from Pro Sports Transactions, a public online database. An injury was defined as a health-related concern resulting in an absence of ≥1 NBA games. Primary measures included pre- and postinjury player efficiency rating (PER) and true shooting percentage (TS%) with interquartile ranges (IQRs), stratified by extremity dominance. Multivariate logistic regression analyses with stepwise regression were performed to identify risk factors associated with return-to-play performance. Results: A total of 192 shoulder and elbow injuries were sustained among 126 NBA athletes, with incidence rates of 1.11 per 1000 game exposures (GEs) and 0.30 per 1000 GEs, respectively. Sprain/strain and general soreness were the most common injury types in both the shoulder and the elbow. In the 2 years after injury, baseline PER was achieved in all groups, except for players with dominant shoulder injuries (baseline PER, 16 [IQR, 14-18] vs 2-year PER, 13 [IQR 11-16]; P = .012). Younger age was associated with quicker return to baseline PER (odds ratio, 0.77 [95% CI, 0.67-0.88]). Shoulder and elbow injuries did not negatively influence TS% upon return to play (baseline TS%, 0.55% [IQR, 0.51%-0.58%] vs 1-year TS%, 0.55% [IQR, 0.52%-0.58%]; P = .13). Conclusion: Dominant shoulder injuries negatively influenced PER during the first 2 seasons upon return to play in NBA players. Therefore, expectations that players with this type of injury immediately achieve baseline statistical production should be tempered. Shooting accuracy appears to remain unaffected after shoulder or elbow injury.

Background: Ankle injuries are more common in the National Basketball Association (NBA) compared with other professional sports. Purpose/Hypothesis: The purpose of this study was to report the incidence and associated risk factors of ankle injuries in NBA athletes. It was hypothesized that factors associated with an increased physiologic burden, such as minutes per game (MPG), usage rate, and associated lower extremity injury, would be associated with increased ankle injury risk and time loss. Study Design: Descriptive epidemiology study. Methods: Ankle injury data from the 2015-2016 through 2020-2021 NBA seasons were evaluated. The truncated 2019-2020 season due to the COVID-19 pandemic was omitted. The primary outcome was the incidence of ankle injuries, reported per 1000 game-exposures (GEs). Secondary analysis was performed to identify risk factors for ankle injuries through bivariate analysis and multivariable logistic regression of player demographic characteristics, performance statistics, injury characteristics, and previous lower extremity injuries. Factors influencing the time loss after injury were assessed via a negative binomial regression analysis. Results: A total of 554 ankle injuries (4.06 injuries per 1000 GEs) were sustained by NBA players over 5 NBA seasons, with sprain/strain the most common injury type (3.71 injuries per 1000 GEs). The majority of ankle injury events (55%) resulted in 2 to 10 game absences. The likelihood of sustaining an ankle injury was significantly associated with a greater number of games played ( P = .029) and previous injury to the hip, hamstring, or quadriceps ( P = .004). Increased length of absence due to ankle injury was associated with greater height ( P = .019), MPG ( P < .001), usage rate ( P = .025), points per game ( P = .011), and a prior history of foot ( P = .003), ankle ( P < .001), and knee injuries ( P < .001). Conclusion: The incidence of ankle injuries was 4.06 per 1000 GEs in professional basketball players. Games played and prior history of hip, hamstring, or quadriceps injuries were found to be risk factors for ankle injuries. Factors associated with physiologic burden such as MPG and usage rate were associated with an increased time loss after injury.