Development and validation of a prediction model for glucocorticoid-associated osteonecrosis of the femoral head by targeted sequencing. (12th May 2021)

• Record Type: Journal Article
• Authors: Jiang, Chang; Wang, Xinyuan; Huang, Kai; Chen, Limeng; Ji, Zongfei; Hua, Bingxuan; Qi, Guobin; Yuan, Hengfeng; Cao, Yuanwu; Jiang, Lindi; Peng, David Haixiang; Yan, Zuoqin
• Is Part Of: Rheumatology. Volume 61:Number 2(2022)
• Page Start: 846
• Page End: 855
• Publication Date: 2021-05-12
• Subjects: osteonecrosis of the femoral head -- glucocorticoid -- prediction model -- targeted sequencing -- single nucleotide polymorphism; Rheumatism -- Periodicals; Rheumatology -- Periodicals; 616.723005
• DOI: 10.1093/rheumatology/keab394
• Languages: English
• ISSNs: 1462-0324

Abstract

Abstract: Objective: To develop and validate a prediction model based on targeted sequencing for glucocorticoid (GC)-associated osteonecrosis of the femoral head (GA-ONFH) in GC-treated adults. Methods: This two-centre retrospective study was conducted between July 2015 and April 2019 at Zhongshan Hospital (training set) and the Sixth People's Hospital (test set) in Shanghai, China. All patients had a history of GC therapy, with a dose exceeding 2000 mg equivalent prednisone within 6 weeks. Patients were divided into two groups according to whether they were diagnosed with GA-ONFH within 2 years after GC initiation. Blood or saliva samples were collected for targeted sequencing of 358 single nucleotide polymorphisms and genetic risk score (GRS) calculating for developing GA-ONFH prediction model. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were performed to evaluate and validate the model. Results: . The training set comprised 117 patients, while the test set comprised 30 patients for external validation. Logistic regression analysis showed that GRS was significantly associated with GA-ONFH (OR 1.87, 95% CI: 1.48, 2.37). The ROC and DCA curves showed that the multivariate model considering GRS, age at GC initial, sex and underlying diseases had a discrimination with area under the ROC curve (AUC) of 0.98 (95% CI: 0.96, 1.00). This model was further externally validated using the test set with an AUC of 0.91 (95% CI: 0.81, 1.00). Conclusion: Our prediction model comprising GRS, age, sex and underlying diseases yields valid predictions of GA-ONFH incidence. It may facilitate effective screening and prevention strategies of GA-ONFH.