Gender Differences in the Correlation between Symptom and Radiographic Severity in Patients with Knee Osteoarthritis

Abstract Purpose The exact nature of sex and gender differences in knee osteoarthritis (OA) among patient candidates for total knee arthroplasty (TKA) remains unclear and requires better elucidation to guide clinical practice. The purpose of this investigation was to survey physician practices and perceptions about the influence of sex and gender on knee OA presentation, care, and outcomes after TKA. Methods The survey questions were elaborated by a multidisciplinary scientific board composed of 1 pain specialist, 4 orthopedic specialists, 2 physiatrists, and 1 expert in gender medicine. The survey included 5 demographic questions and 20 topic questions. Eligible physician respondents were those who treat patients during all phases of care (pain specialists, orthopedic specialists, and physiatrists). All survey responses were anonymized and handled via remote dispersed geographic participation. Results Fifty-six physicians (71% male) accepted the invitation to complete the survey. In general, healthcare professionals expressed that women presented worse symptomology, higher pain intensity, and lower pain tolerance and necessitated a different pharmacological approach compared to men. Pain and orthopedic specialists were more likely to indicate sex and gender differences in knee OA than physiatrists. Physicians expressed that the absence of sex and gender-specific instruments and indications is an important limitation on available studies. Conclusions Healthcare professionals perceive multiple sex and gender-related differences in patients with knee OA, especially in the pre- and perioperative phases of TKA. Sex and gender bias sensitivity training for physicians can potentially improve the objectivity of care for knee OA among TKA candidates.

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Soluble P selectin in synovial fluid level is correlated with the radiographic severity of knee osteoarthritis

Clinica Chimica Acta ◽

10.1016/j.cca.2010.05.009 ◽

2010 ◽

Vol 411 (19-20) ◽

pp. 1529-1531 ◽

Cited By ~ 9

Author(s):

Tao Cheng ◽

Feng-Feng Li ◽

Song Zhao ◽

Xiao-Chun Peng ◽

Xian-Long Zhang

Keyword(s):

Synovial Fluid ◽

Knee Osteoarthritis ◽

Fluid Level ◽

Synovial Fluid Level ◽

Radiographic Severity ◽

Soluble P

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Convolutional Neural Network-Based Automatic Measurement of Joint Space Width to Predict Radiographic Severity and Progression of Knee Osteoarthritis

10.21203/rs.3.rs-221004/v1 ◽

2021 ◽

Author(s):

James Chung Wai Cheung ◽

Yiu Chow TAM ◽

Lok Chun CHAN ◽

Ping Keung CHAN ◽

Chunyi WEN

Keyword(s):

Neural Network ◽

Knee Osteoarthritis ◽

Convolutional Neural Network ◽

Pearson Correlation ◽

Joint Space Width ◽

Joint Space ◽

Receiver Operating Curve ◽

Radiographic Severity ◽

X Ray ◽

Space Width

Abstract Objectives To develop a deep convolutional neural network (CNN) for the segmentation of femur and tibia on plain x-ray radiographs, hence enabling an automated measurement of joint space width (JSW) to predict the severity and progression of knee osteoarthritis (KOA). Methods A CNN with ResU-Net architecture was developed for knee X-ray imaging segmentation. The efficiency was evaluated by the Intersection over Union (IoU) score by comparing the outputs with the annotated contour of the distal femur and proximal tibia. By leveraging imaging segmentation, the minimal and multiple JSWs in the tibiofemoral joint were estimated and then validated by radiologists’ measurements in the Osteoarthritis Initiative (OAI) dataset using Pearson correlation and Bland–Altman plot. The estimated JSWs were deployed to predict the radiographic severity and progression of KOA defined by Kellgren-Lawrence (KL) grades using the XGBoost model. The classification performance was assessed using F1 and area under receiver operating curve (AUC). Results The network has attained a segmentation efficiency of 98.9% IoU. Meanwhile, the agreement between the CNN-based estimation and radiologist’s measurement of minimal JSW reached 0.7801 (p < 0.0001). Moreover, the 32-point multiple JSW obtained the highest AUC score of 0.656 to classify KL-grade of KOA. Whereas the 64-point multiple JSWs achieved the best performance in predicting KOA progression defined by KL grade change within 48 months, with AUC of 0.621. The multiple JSWs outperform the commonly used minimum JSW with 0.587 AUC in KL-grade classification and 0.554 AUC in disease progression prediction. Conclusion Fine-grained characterization of joint space width of KOA yields comparable performance to the radiologist in assessing disease severity and progression. We provide a fully automated and efficient radiographic assessment tool for KOA.

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