Therapeutic Prediction of Osteoporotic Vertebral Compression Fracture Using the AO Spine-DGOU Osteoporotic Fracture Classification and Classification-Based Score: A Single-Center Retrospective Observational Study

应用AO Spine-DGOU骨质疏松性骨折分类和基于分类的评分对骨质疏松性椎体压缩性骨折进行治疗预测:一项单中心回顾性观察研究

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Abstract

OBJECTIVE: The treatment of osteoporotic vertebral compression fractures (OVCFs) is based on their severity; however, an efficient prediction tool is lacking. We aimed to evaluate the validity of the osteoporotic fracture classification (OF classification) and scoring system (OF score) in predicting the treatment strategy for patients with OVCF, defined according to the Japanese criteria. METHODS: We retrospectively investigated 487 consecutive patients diagnosed with vertebral body fractures between January 2018 and December 2022. Only patients with their fresh vertebral fracture episode during the study period were included. Patients were classified into 3 groups: conservative treatment, balloon kyphoplasty (BKP), and open surgery. OF classification and OF scores were assessed for each patient. RESULTS: A total of 237 patients with OVCF were included. There were 127, 81, and 29 patients in the conservative, BKP, and open surgery groups, respectively. The OF score was significantly higher in the BKP and open surgery groups than in the conservative group (p < 0.001). Multivariate logistic regression analysis showed that antiosteoporotic drug use, OF classification, progressive deformity, neurological symptoms and mobilization were independent risk factors for operative treatment (all p < 0.001). Receiver operating characteristic analysis showed that the cutoff OF score for operative indication was 5.5, with a sensitivity of 91.9%, specificity of 56.5%, and area under the curve of 0.820 (95% confidence interval, 0.769-0.871). CONCLUSION: The OF score identified patients who required operative treatment with a high degree of accuracy. This is especially important for ruling out patients who definitely require operative treatment.

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