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DOI 10.1016/j.wneu.2025.124293

Медицинская библиотека · World neurosurgery · 2025

Development of a Predictive Model for Kummell's Disease.

Song Yiliang, Zhao Houling, Liu Shisheng, Liu Qi, Qi Yucheng, Wei Gang, Song Hongliang

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Аннотация

BACKGROUND

Kümmell's disease (KD) is a recognized complication of osteoporotic thoracolumbar vertebral compression fractures (OVCFs), but the risk factors for progression from OVCFs to KD remain unclear.

METHODS

This retrospective study included 104 patients with OVCFs from a tertiary hospital between January 2020 and December 2023. Patients were divided into a case group (56 patients with KD) and a control group (48 patients without KD). Data on 24 variables, including demographics, fracture characteristics, and biochemical markers, were collected and analyzed using SPSS 25.0 software. Statistical methods included independent samples t-test, Mann-Whitney U test, chi-square test, Fisher's exact test, and binary logistic regression.

RESULTS

Univariate analysis identified 5 significant factors related to KD onset: age, vertebral length, intervertebral foramen width, alkaline phosphatase, and albumin. Binary logistic regression analysis revealed 3 independent risk factors for KD: vertebral length, alkaline phosphatase, and albumin.

CONCLUSIONS

When treating OVCF patients, clinicians should consider comprehensive factors. Patients with high alkaline phosphatase levels, longer fractured vertebrae, and low albumin levels should be closely monitored. Targeted interventions, such as early surgery, enhanced nutrition, and optimized antiosteoporosis plans, may reduce KD risk and improve patient outcomes.

Для цитирования: Song Yiliang, Zhao Houling, Liu Shisheng, Liu Qi, Qi Yucheng, Wei Gang, Song Hongliang. Development of a Predictive Model for Kummell's Disease.. World neurosurgery. 2025. DOI: 10.1016/j.wneu.2025.124293.

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