Dataset Information

Musculoskeletal Symptomatic Areas After Total Knee Replacement for Osteoarthritis.

ABSTRACT: Objective:The objective of this study was to measure cumulative incidence and incidence rate and identify factors associated with new musculoskeletal (MSK) symptomatic areas after total knee replacement (TKR) for osteoarthritis (OA). Methods:Using data from a randomized controlled trial of patients undergoing elective TKR for OA, we assessed for MSK symptomatic areas by region (neck, hands/wrists/arms/shoulders, back, hips, nonindex knee, and ankles/feet) at baseline (pre-TKR), and at 3, 6, 12, 24, 36, and 48 months post-TKR. Cumulative incidence and incidence rates were calculated for each region. Factors associated with incident MSK symptomatic areas were identified using generalized linear mixed models. Time to incident symptomatic area was assessed using Cox proportional hazards regression. Results:Among 293 subjects, the cumulative incidence of any new MSK symptomatic area over 4 years was 45%; the incidence rate was 19.2 per 100 person-years. Body site-specific cumulative incidence and incidence rates were highest for nonindex knee and back. Predictors of incident MSK symptomatic areas included female sex (relative risk [RR] 1.64; 95% confidence interval [CI] 1.15-2.34), body mass index of 35 or higher (RR 1.27; 95% CI 0.88-1.85), Charlson Comorbidity Index 2 or more (RR 1.28; 95% CI 0.92-1.78), baseline index knee Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain score greater than 40 (RR 1.39; 95% CI 0.99-1.95), and anxiety/depression (measured by the five-item Mental Health Index) (RR 1.70; 95% CI 1.20-2.40). Conclusion:Incident MSK symptomatic areas occurred in roughly half of recipients of TKR in the 4 years after the operation. Further study is needed to examine the long-term impact of MSK symptomatic areas on postoperative pain, function, and quality of life.

SUBMITTER: Zhang M

PROVIDER: S-EPMC6858031 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Musculoskeletal Symptomatic Areas After Total Knee Replacement for Osteoarthritis.

Zhang MaryAnn M Selzer Faith F Losina Elena E Collins Jamie E JE Katz Jeffrey N JN

ACR open rheumatology 20190801 6

<h4>Objective</h4>The objective of this study was to measure cumulative incidence and incidence rate and identify factors associated with new musculoskeletal (MSK) symptomatic areas after total knee replacement (TKR) for osteoarthritis (OA).<h4>Methods</h4>Using data from a randomized controlled trial of patients undergoing elective TKR for OA, we assessed for MSK symptomatic areas by region (neck, hands/wrists/arms/shoulders, back, hips, nonindex knee, and ankles/feet) at baseline (pre-TKR), an ...[more]

PMID: 31777817

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Project description:ObjectiveTo examine impact of pre-existing and incident problematic musculoskeletal (MSK) areas after total knee replacement (TKR) on postoperative 60-month Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain/function scores.MethodsUsing data from a randomized controlled trial of subjects undergoing TKR for osteoarthritis, we assessed problematic MSK areas in six body regions before TKR and 12, 24, 36, and 48 months after TKR. We defined the following two variables: 1) density count (number of problematic MSK areas occurring after TKR; range 0-24) and 2) cumulative density count (problematic MSK areas both before and after TKR, categorized into four levels: no preoperative areas and density count of 0-1 [reference group]; no preoperative areas and density count of 2 or more; one or more preoperative areas and density count of 0-1; and one or more preoperative areas and density count of 2 or greater). We evaluated the associations between categorized 60-month WOMAC and cumulative density count by ordinal logistic regression.ResultsAmong 230 subjects, 24% reported one or more preoperative problematic MSK area. After TKR, 75% reported a density count of 0 to 1; 25% reported a density count of 2 or more. Compared with the reference group, each cumulative density count category was associated with an increased odds of having a higher category of 60-month WOMAC pain score, as follows: 2.97 (95% confidence interval [CI], 1.48-5.98) for no preoperative problematic areas and density count of 2 or greater, 3.31 (95% CI, 1.64-6.66) for one or more preoperative problematic areas and density count of 0 to 1, and 2.85 (95% CI, 0.97-8.39) for one or more preoperative problematic areas and density count of 2 or greater. Similar associations were observed with 60-month WOMAC function score.ConclusionIn TKR recipients, the presence of problematic musculoskeletal areas beyond the index knee-preoperatively and/or postoperatively-was associated with worse 60-month WOMAC pain/function score.

Project description:Reduced external knee adduction moments in the second half of stance after total hip replacement have been reported in hip osteoarthritis patients. This reduction is thought to shift the load from the medial to the lateral knee compartment and as such increase the risk for knee osteoarthritis. The knee adduction moment is a surrogate for the load distribution between the medial and lateral compartments of the knee and not a valid measure for the tibiofemoral contact forces which are the result of externally applied forces and muscle forces. The purpose of this study was to investigate whether the distribution of the tibiofemoral contact forces over the knee compartments in unilateral hip osteoarthritis patients 1 year after receiving a primary total hip replacement differs from healthy controls. Musculoskeletal modeling on gait was performed in OpenSim using the detailed knee model of Lerner et al. (2015) for 19 patients as well as for 15 healthy controls of similar age. Knee adduction moments were calculated by the inverse dynamics analysis, medial and lateral tibiofemoral contact forces with the joint reaction force analysis. Moments and contact forces of patients and controls were compared using Statistical Parametric Mapping two-sample t-tests. Knee adduction moments and medial tibiofemoral contact forces of both the ipsi- and contralateral leg were not significantly different compared to healthy controls. The contralateral leg showed 14% higher medial tibiofemoral contact forces compared to the ipsilateral (operated) leg during the second half of stance. During the first half of stance, the lateral tibiofemoral contact force of the contralateral leg was 39% lower and the ratio 32% lower compared to healthy controls. In contrast, during the second half of stance the forces were significantly higher (39 and 26%, respectively) compared to healthy controls. The higher ratio indicates a changed distribution whereas the increased lateral tibiofemoral contact forces indicate a higher lateral knee joint loading in the contralateral leg in OA patients after total hip replacement (THR). Musculoskeletal modeling using a detailed knee model can be useful to detect differences in the load distribution between the medial and lateral knee compartment which cannot be verified with the knee adduction moment.

Project description:Background The methods for assessing knee osteoarthritis (OA) do not provide enough comprehensive information to make robust and accurate outcome predictions. Purpose To develop a deep learning (DL) prediction model for risk of OA progression by using knee radiographs in patients who underwent total knee replacement (TKR) and matched control patients who did not undergo TKR. Materials and Methods In this retrospective analysis that used data from the OA Initiative, a DL model on knee radiographs was developed to predict both the likelihood of a patient undergoing TKR within 9 years and Kellgren-Lawrence (KL) grade. Study participants included a case-control matched subcohort between 45 and 79 years. Patients were matched to control patients according to age, sex, ethnicity, and body mass index. The proposed model used a transfer learning approach based on the ResNet34 architecture with sevenfold nested cross-validation. Receiver operating characteristic curve analysis and conditional logistic regression assessed model performance for predicting probability and risk of TKR compared with clinical observations and two binary outcome prediction models on the basis of radiographic readings: KL grade and OA Research Society International (OARSI) grade. Results Evaluated were 728 participants including 324 patients (mean age, 64 years ± 8 [standard deviation]; 222 women) and 324 control patients (mean age, 64 years ± 8; 222 women). The prediction model based on DL achieved an area under the receiver operating characteristic curve (AUC) of 0.87 (95% confidence interval [CI]: 0.85, 0.90), outperforming a baseline prediction model by using KL grade with an AUC of 0.74 (95% CI: 0.71, 0.77; P < .001). The risk for TKR increased with probability that a person will undergo TKR from the DL model (odds ratio [OR], 7.7; 95% CI: 2.3, 25; P < .001), KL grade (OR, 1.92; 95% CI: 1.17, 3.13; P = .009), and OARSI grade (OR, 1.20; 95% CI: 0.41, 3.50; P = .73). Conclusion The proposed deep learning model better predicted risk of total knee replacement in osteoarthritis than did binary outcome models by using standard grading systems. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Richardson in this issue.

Dataset Information

Musculoskeletal Symptomatic Areas After Total Knee Replacement for Osteoarthritis.

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Musculoskeletal Symptomatic Areas After Total Knee Replacement for Osteoarthritis.

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