Dataset Information

Reproductive factors and risk of total knee replacement due to severe knee osteoarthritis in women, the Singapore Chinese Health Study.

ABSTRACT: OBJECTIVES:Knee osteoarthritis (OA) is more common in women, and may be related to reproductive or hormonal factors. We evaluated these factors with the risk of total knee replacement (TKR) for severe knee OA among women. METHODS:The Singapore Chinese Health Study recruited 63,257 Chinese aged 45-74 years from 1993 to 1998, and among them, 35,298 were women. Information on height, weight, lifestyle factors, number of biological children, ages at menarche and menopause, and use of hormonal therapies was collected through interviews. Incident cases of TKR were identified via linkage with nationwide database. RESULTS:There were 1,645 women with TKR after mean follow-up of 14.8 years. Higher parity was associated with increased TKR risk in a stepwise manner (P for trend <0.001). Compared to nulliparous women, those with ?5 children had the highest risk [hazard ratio (HR) 2.01, 95% confidence intervals (CIs) 1.50-2.70]. The effect of parity on TKR risk was significantly stronger among lean women compared to heavier women; HRs (95% CIs) for highest parity was 4.86 (2.22-10.63) for women with body mass index (BMI) <23 kg/m2 and 1.57 (1.14-2.14) for those ?23 kg/m2 (P for interaction = 0.001). Earlier age at menarche and use of oral contraceptives were significantly associated with TKR in a stepwise manner (P for trend ?0.002). Age at menopause and use of hormonal therapy were not associated with TKR risk. CONCLUSION:Higher parity, earlier age of menarche and use of oral contraceptives were associated with increased risk of TKR for severe knee OA among women.

SUBMITTER: Leung YY

PROVIDER: S-EPMC6646081 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Reproductive factors and risk of total knee replacement due to severe knee osteoarthritis in women, the Singapore Chinese Health Study.

Leung Y-Y YY Talaei M M Ang L-W LW Yuan J-M JM Koh W-P WP

Osteoarthritis and cartilage 20190319 8

<h4>Objectives</h4>Knee osteoarthritis (OA) is more common in women, and may be related to reproductive or hormonal factors. We evaluated these factors with the risk of total knee replacement (TKR) for severe knee OA among women.<h4>Methods</h4>The Singapore Chinese Health Study recruited 63,257 Chinese aged 45-74 years from 1993 to 1998, and among them, 35,298 were women. Information on height, weight, lifestyle factors, number of biological children, ages at menarche and menopause, and use of ...[more]

PMID: 30902701

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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

Reproductive factors and risk of total knee replacement due to severe knee osteoarthritis in women, the Singapore Chinese Health Study.

Publications

Reproductive factors and risk of total knee replacement due to severe knee osteoarthritis in women, the Singapore Chinese Health Study.

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