• Alireza Mirahmadi, Emad Kouhestani, Mehrdad Farrokhi, Seyed Morteza Kazemi, and Reza Minaei Noshahr.
• Department of Orthopedic Surgery, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
• Medicine (Baltimore). 2024 Jul 12; 103 (28): e38888e38888.

AbstractMalalignment is one of the most critical risk factors for knee osteoarthritis (KOA). Biomechanical factors such as knee varus or valgus, hip-knee-ankle angle, and femoral anteversion affect KOA severity. In this study, we aimed to investigate KOA severity predictive factors based on hip and pelvic radiographic geometry. In this cross-sectional study, 125 patients with idiopathic KOA were enrolled. Two investigators evaluated the knee and pelvic radiographs of 125 patients, and 16 radiological parameters were measured separately. KOA severity was categorized based on the medial tibiofemoral joint space widths (JSW). Based on JSW measurements, 16% (n = 40), 8.8% (n = 22), 16.4% (n = 41), and 56.8% (n = 147) were defined as grades 0, 1, 2, 3, respectively. There were significant differences between the JSW groups with respect to hip axis length, femoral neck-axis length, acetabular width, neck shaft angle (NSA), outer pelvic diameter, midpelvis-caput distance, acetabular-acetabular distance, and femoral head to femoral head length (P < .05). Two different functions were obtained using machine learning classification and logistic regression, and the accuracy of predicting was 74.4% by using 1 and 89.6% by using both functions. Our findings revealed that some hip and pelvic geometry measurements could affect the severity of KOA. Furthermore, logistic functions using predictive factors of hip and pelvic geometry can predict the severity of KOA with acceptable accuracy, and it could be used in clinical decisions.Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.

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