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Evaluation and analysis of risk factors for adverse events of the fractured vertebra post-percutaneous kyphoplasty: a retrospective cohort study using multiple machine learning models.
Zhao, YingLun; Bo, Li; Chen, XueMing; Wang, YanHui; Cui, LiBin; Xin, Yuan; Liang, Liu; Chao, Kong; Lu, ShiBao.
Afiliación
  • Zhao Y; Department of Orthopedics, Xuanwu Hospital, National Clinical Research Center for Geriatric Diseases, Capital Medical University, 45 Changchun Street, Xicheng District, Beijing, 100053, China.
  • Bo L; Department of Bone Center, Beijing Luhe Hospital Affiliated to Capital Medical University, Beijing, 101100, China.
  • Chen X; Department of Bone Center, Beijing Luhe Hospital Affiliated to Capital Medical University, Beijing, 101100, China.
  • Wang Y; Department of Bone Center, Beijing Luhe Hospital Affiliated to Capital Medical University, Beijing, 101100, China.
  • Cui L; Department of Bone Center, Beijing Luhe Hospital Affiliated to Capital Medical University, Beijing, 101100, China.
  • Xin Y; Department of Bone Center, Beijing Luhe Hospital Affiliated to Capital Medical University, Beijing, 101100, China.
  • Liang L; Department of Bone Center, Beijing Luhe Hospital Affiliated to Capital Medical University, Beijing, 101100, China.
  • Chao K; Department of Bone Center, Beijing Luhe Hospital Affiliated to Capital Medical University, Beijing, 101100, China.
  • Lu S; Department of Orthopedics, Xuanwu Hospital, National Clinical Research Center for Geriatric Diseases, Capital Medical University, 45 Changchun Street, Xicheng District, Beijing, 100053, China.
J Orthop Surg Res ; 19(1): 575, 2024 Sep 18.
Article en En | MEDLINE | ID: mdl-39289697
ABSTRACT

BACKGROUND:

Adverse events of the fractured vertebra (AEFV) post-percutaneous kyphoplasty (PKP) can lead to recurrent pain and neurological damage, which considerably affect the prognosis of patients and the quality of life. This study aimed to analyze the risk factors of AEFV and develop and select the optimal risk prediction model for AEFV to provide guidance for the prevention of this condition and enhancement of clinical outcomes.

METHODS:

This work included 383 patients with primary osteoporotic vertebral compression fracture (OVCF) who underwent PKP. The patients were grouped based on the occurrence of AEFV postsurgery, and data were collected. Group comparisons and correlation analysis were conducted to identify potential risk factors, which were then included in the five prediction models. The performance indicators served as basis for the selection of the best model.

RESULTS:

Multivariate logistic regression analysis revealed the following independent risk factors for AEFV kissing spine (odds ratio (OR) = 8.47, 95% confidence interval (CI) 1.46-49.02), high paravertebral muscle fat infiltration grade (OR = 29.19, 95% CI 4.83-176.04), vertebral body computed tomography value (OR = 0.02, 95% CI 0.003-0.13, P < 0.001), and large Cobb change (OR = 5.31, 95% CI 1.77-15.77). The support vector machine (SVM) model exhibited the best performance in the prediction of the risk of AEFV.

CONCLUSION:

Four independent risk factors were identified of AEFV, and five risk prediction models that can aid clinicians in the accurate identification of high-risk patients and prediction of the occurrence of AEFV were developed.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Complicaciones Posoperatorias / Fracturas de la Columna Vertebral / Fracturas Osteoporóticas / Cifoplastia / Aprendizaje Automático Límite: Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Revista: J Orthop Surg Res Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Complicaciones Posoperatorias / Fracturas de la Columna Vertebral / Fracturas Osteoporóticas / Cifoplastia / Aprendizaje Automático Límite: Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Revista: J Orthop Surg Res Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido