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ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2026

A nomogram for predicting fracture of the sandwich vertebrae after percutaneous vertebral augmentation: a multicenter study with 1-year follow-up.

Si Chen, Hanming Li, Wenjie Wu, Li Huo, Boxin Wang, Chuanqi Zou, Junxiong Cao

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Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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7 authors.

Si ChenAffiliated Banan Hospital of Chongqing Medical University, Chongqing, China.
Hanming LiAffiliated Hospital of Southwest Medical University, Luzhou, China.
Wenjie WuAffiliated Banan Hospital of Chongqing Medical University, Chongqing, China.
Li HuoAffiliated Banan Hospital of Chongqing Medical University, Chongqing, China.
Boxin WangAffiliated Banan Hospital of Chongqing Medical University, Chongqing, China.
Chuanqi ZouAffiliated Banan Hospital of Chongqing Medical University, Chongqing, China.
Junxiong CaoAffiliated Banan Hospital of Chongqing Medical University, Chongqing, China. cjx8839@126.com.

Funding

Science and Technology research project of the Chongqing Education Commission KJQN202300474
6 · The paper itself

Abstract

purposeThis work aimed to investigate the risk factors for fractures in sandwich vertebrae (SDV; an unfractured vertebra located between 2 cemented vertebrae) formed after percutaneous vertebral augmentation (PVA) and to construct a predictive model from this.

methodsThis study retrospectively analyzed patients who underwent PVA with the formation of SDV from July 2018 to July 2023 at Affiliated Banan Hospital of Chongqing Medical University. Patients were divided into a fracture group and a control group according to the presence or absence of fracture of the SDV during the 1-year postoperative follow-up period. Independent predictors were confirmed using the least absolute shrinkage and selection operator (LASSO) method, and the nomogram was constructed and transformed into an online calculator. The discrimination, calibration, and clinical applicability of the model were assessed by Area under the receiver operating characteristic curve (AUC), calibration curve analysis, and Decision curve analysis (DCA). Finally, the model was externally validated using data from another centre and internally validated using Bootstrap.

resultsA total of 259 patients were enrolled in this study, and 36 patients had fractures of SDV within one year. Multifactorial analyses showed that low bone mineral density (BMD) (OR = 4.264, 95% CI: 2.245-8.098, P < 0.001), number of PVA > 3 (OR = 3.703, 95% CI: 1.399-9.801, P = 0.008), lack of anti-osteoporosis (OR = 4.051, 95% CI: 1.573-10.430, P = 0.004), postoperative kyphosis angle of sandwich fracture segments (PKASFS) > 10° (OR = 8.273, 95% CI: 2.991-22.881, P < 0.001), and lumbar lordosis minus thoracic kyphosis (LL-TK) < 0° (OR = 3.701, 95% CI: 1.523-8.994, P = 0.004) were screened as independent risk factors. The AUC of the model constructed based on this was 0.881 (95% CI: 0.829-0.933). The calibration curves and DCA verified that the model had satisfactory practical consistency and clinical applicability. The externally validated AUC was 0.859 (95% CI: 0.788-0.930), validating the stability of the model.

conclusionsBMD, number of PVA, anti-osteoporosis, PKASFS, and LL-TK are independent influencing factors for fractures in SDV within one year, and a model based on this had excellent predictive efficacy.

Indexed as

Dynamic nomogramFracturePercutaneous vertebral augmentationRisk factorsSandwich vertebrae

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.