What Are the Underlying Mental Health Constructs Associated With Level of Capability in People With Knee and Hip Osteoarthritis?
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.
(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?
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.
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 R 2 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 R 2 0.10; p < 0.001) had the strongest relationship, and mood was no longer associated.
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 III, prognostic study.
Jayakumar P
，Crijns TJ
，Misciagna W
，Manickas-Hill O
，Malay M
，Jiranek W
，Mather RC 3rd
，Lentz TA
... -
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Do the Revision Rates of Arthroplasty Surgeons Correlate With Postoperative Patient-reported Outcome Measure Scores? A Study From the Australian Orthopaedic Association National Joint Replacement Registry.
Patient-reported outcome measures (PROMs) are a pragmatic and efficient means to evaluate the functional quality of arthroplasty beyond revision rates, which are used by most joint replacement registries to judge success. The relationship between these two measures of quality-revision rates and PROMs-is unknown, and not every procedure with a poor functional result is revised. It is logical-although still untested-that higher cumulative revision rates correlate inversely with PROMs for individual surgeons; more revisions are associated with lower PROM scores.
We used data from a large national joint replacement registry to ask: (1) Does a surgeon's early THA cumulative percent revision (CPR) rate and (2) early TKA CPR rate correlate with the postoperative PROMs of patients undergoing primary THA and TKA, respectively, who have not undergone revision?
Elective primary THA and TKA procedures in patients with a primary diagnosis of osteoarthritis that were performed between August 2018 and December 2020 and registered in the Australian Orthopaedic Association National Joint Replacement Registry PROMs program were eligible. THAs and TKAs were eligible for inclusion in the primary analysis if 6-month postoperative PROMs were available, the operating surgeon was clearly identified, and the surgeon had performed at least 50 primary THAs or TKAs. Based on the inclusion criteria, 17,668 THAs were performed at eligible sites. We excluded 8878 procedures that were not matched to the PROMs program, leaving 8790 procedures. A further 790 were excluded because they were performed by unknown or ineligible surgeons or were revised, leaving 8000 procedures performed by 235 eligible surgeons, including 4256 (53%; 3744 cases of missing data) patients who had postoperative Oxford Hip Scores and 4242 (53%; 3758 cases of missing data) patients who had a postoperative EQ-VAS score recorded. Complete covariate data were available for 3939 procedures for the Oxford Hip Score and for 3941 procedures for the EQ-VAS. A total of 26,624 TKAs were performed at eligible sites. We excluded 12,685 procedures that were not matched to the PROMs program, leaving 13,939 procedures. A further 920 were excluded because they were performed by unknown or ineligible surgeons, or because they were revisions, leaving 13,019 procedures performed by 276 eligible surgeons, including 6730 (52%; 6289 cases of missing data) patients who had had postoperative Oxford Knee Scores and 6728 (52%; 6291 cases of missing data) patients who had a postoperative EQ-VAS score recorded. Complete covariate data were available for 6228 procedures for the Oxford Knee Score and for 6241 procedures for the EQ-VAS. The Spearman correlation between the operating surgeon's 2-year CPR and 6-month postoperative EQ-VAS Health and Oxford Hip or Oxford Knee Score was evaluated for THA and TKA procedures where a revision had not been performed. Associations between postoperative Oxford and EQ-VAS scores and a surgeon's 2-year CPR were estimated based on multivariate Tobit regressions and a cumulative link model with a probit link, adjusting for patient age, gender, ASA score, BMI category, preoperative PROMs, as well as surgical approach for THA. Missing data were accounted for using multiple imputation, with models assuming they were missing at random and a worst-case scenario.
Of the eligible THA procedures, postoperative Oxford Hip Score and surgeon 2-year CPR were correlated so weakly as to be clinically irrelevant (Spearman correlation ρ = -0.09; p < 0.001), and the correlation with postoperative EQ-VAS was close to zero (ρ = -0.02; p = 0.25). Of the eligible TKA procedures, postoperative Oxford Knee Score and EQ-VAS and surgeon 2-year CPR were correlated so weakly as to be clinically irrelevant (ρ = -0.04; p = 0.004 and ρ = 0.03; p = 0.006, respectively). All models accounting for missing data found the same result.
A surgeon's 2-year CPR did not exhibit a clinically relevant correlation with PROMs after THA or TKA, and all surgeons had similar postoperative Oxford scores. PROMs, revision rates, or both may be inaccurate or imperfect indicators of successful arthroplasty. Missing data may limit the findings of this study, although the results were consistent under a variety of different missing data scenarios. Innumerable factors contribute to arthroplasty results, including patient-related variables, differences in implant design, and the technical quality of the procedure. PROMs and revision rates may be analyzing two different facets of function after arthroplasty. Although surgeon variables are associated with revision rates, patient factors may exert a stronger influence on functional outcomes. Future research should identify variables that correlate with functional outcome. Additionally, given the gross level of function that Oxford scores record, outcome measures that can identify clinically meaningful functional differences are required. The use of Oxford scores in national arthroplasty registries may rightfully be questioned.
Level III, therapeutic study.
Hoskins W
，Bingham R
，Corfield S
，Harries D
，Harris IA
，Vince KG
... -
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Does Preoperative Pharmacogenomic Testing of Patients Undergoing TKA Improve Postoperative Pain? A Randomized Trial.
Pharmacogenomics is an emerging and affordable tool that may improve postoperative pain control. One challenge to successful pain control is the large interindividual variability among analgesics in their efficacy and adverse drug events. Whether preoperative pharmacogenomic testing is worthwhile for patients undergoing TKA is unclear.
(1) Are the results of preoperative pharmacogenetic testing associated with lower postoperative pain scores as measured by the Overall Benefit of Analgesic Score (OBAS)? (2) Do the results of preoperative pharmacogenomic testing lead to less total opioids given? (3) Do the results of preoperative pharmacogenomic testing lead to changes in opioid prescribing patterns?
Participants of this randomized trial were enrolled from September 2018 through December 2021 if they were aged 18 to 80 years and were undergoing primary TKA under general anesthesia. Patients were excluded if they had chronic kidney disease, a history of chronic pain or narcotic use before surgery, or if they were undergoing robotic surgery. Preoperatively, patients completed pharmacogenomic testing (RightMed, OneOME) and a questionnaire and were randomly assigned to the experimental group or control group. Of 99 patients screened, 23 were excluded, one before randomization; 11 allocated patients in each group did not receive their allocated interventions for reasons such as surgery canceled, patients ultimately undergoing spinal anesthesia, and change in surgery plan. Another four patients in each group were excluded from the analysis because they were missing an OBAS report. This left 30 patients for analysis in the control group and 38 patients in the experimental group. The control and experimental groups were similar in age, gender, and race. Pharmacogenomic test results for patients in the experimental group were reviewed before surgery by a pharmacist, who recommended perioperative medications to the clinical team. A pharmacist also assessed for clinically relevant drug-gene interactions and recommended drug and dose selection according to guidelines from the Clinical Pharmacogenomics Implementation Consortium for each patient enrolled in the study. Patients were unaware of their pharmacogenomic results. Pharmacogenomic test results for patients in the control group were not reviewed before surgery; instead, standard perioperative medications were administered in adherence to our institutional care pathways. The OBAS (maximum 28 points) was the primary outcome measure, recorded 24 hours postoperatively. A two-sample t-test was used to compare the mean OBAS between groups. Secondary measures were the mean 24-hour pain score, total morphine milligram equivalent, and frequency of opioid use. Postoperatively, patients were assessed for pain with a VAS (range 0 to 10). Opioid use was recorded preoperatively, intraoperatively, in the postanesthesia care unit, and 24 hours after discharge from the postanesthesia care unit. Changes in perioperative opioid use based on pharmacogenomic testing were recorded, as were changes in prescription patterns for postoperative pain control. Preoperative characteristics were also compared between patients with and without various phenotypes ascertained from pharmacogenomic test results.
The mean OBAS did not differ between groups (mean ± SD 4.7 ± 3.7 in the control group versus 4.2 ± 2.8 in the experimental group, mean difference 0.5 [95% CI -1.1 to 2.1]; p = 0.55). Total opioids given did not differ between groups or at any single perioperative timepoint (preoperative, intraoperative, or postoperative). We found no difference in opioid prescribing pattern. After adjusting for multiple comparisons, no difference was observed between the treatment and control groups in tramadol use (41% versus 71%, proportion difference 0.29 [95% CI 0.05 to 0.53]; nominal p = 0.02; adjusted p > 0.99).
Routine use of pharmacogenomic testing for patients undergoing TKA did not lead to better pain control or decreased opioid consumption. Future studies might focus on at-risk populations, such as patients with chronic pain or those undergoing complex, painful surgical procedures, to test whether pharmacogenomic results might be beneficial in certain circumstances.
Level I, therapeutic study.
Kraus MB
，Bingham JS
，Kekic A
，Erickson C
，Grilli CB
，Seamans DP
，Upjohn DP
，Hentz JG
，Clarke HD
，Spangehl MJ
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ResearchGate
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