Prognostic model to predict the incidence of radiographic knee osteoarthritis

作者：Rocío Paz-González, Vanesa Balboa Barreiro, L. Lourido, Valentina Calamia, Patricia Fernández‐Puente, Natividad Oreiro, Cristina Ruíz‐Romero, Francisco Javier Blanco · 发表于：Annals of the Rheumatic Diseases · 年份：2024 · DOI：10.1136/ard-2023-225090 · 被引用次数：28 · 研究领域：Osteoarthritis Treatment and Mechanisms、Rheumatoid Arthritis Research and Therapies、Total Knee Arthroplasty Outcomes

OBJECTIVE: Early diagnosis of knee osteoarthritis (KOA) in asymptomatic stages is essential for the timely management of patients using preventative strategies. We develop and validate a prognostic model useful for predicting the incidence of radiographic KOA (rKOA) in non-radiographic osteoarthritic subjects and stratify individuals at high risk of developing the disease. METHODS: Subjects without radiographic signs of KOA according to the Kellgren and Lawrence (KL) classification scale (KL=0 in both knees) were enrolled in the OA initiative (OAI) cohort and the Prospective Cohort of A Coruña (PROCOAC). Prognostic models were developed to predict rKOA incidence during a 96-month follow-up period among OAI participants based on clinical variables and serum levels of the candidate protein biomarkers APOA1, APOA4, ZA2G and A2AP. The predictive capability of the biomarkers was assessed based on area under the curve (AUC), and internal validation was performed to correct for overfitting. A nomogram was plotted based on the regression parameters. Model performance was externally validated in the PROCOAC. RESULTS: 282 participants from the OAI were included in the development dataset. The model built with demographic, anthropometric and clinical data (age, sex, body mass index and WOMAC pain score) showed an AUC=0.702 for predicting rKOA incidence during the follow-up. The inclusion of ZA2G, A2AP and APOA1 data significantly improved the model's sensitivity and predictive performan...