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- Title
Establishment and validation of a nomogram model for aseptic loosening after tumor prosthetic replacement around the knee: a retrospective analysis.
- Authors
Zhang, Hao-ran; Wang, Feng; Yang, Xiong-gang; Xu, Ming-you; Qiao, Rui-qi; Li, Ji-kai; Zhao, Yun-long; Pang, Cheng-gang; Yu, Xiu-chun; Hu, Yong-cheng
- Abstract
Background: Aseptic loosening has become the main cause of prosthetic failure in medium- to long-term follow-up. The objective of this study was to establish and validate a nomogram model for aseptic loosening after tumor prosthetic replacement around knee. Methods: We collected data on patients who underwent tumor prosthetic replacements. The following risk factors were analyzed: tumor site, stem length, resection length, prosthetic motion mode, sex, age, extra-cortical grafting, custom or modular, stem diameter, stem material, tumor type, activity intensity, and BMI. We used univariate and multivariate Cox regression for analysis. Finally, the significant risk factors were used to establish the nomogram model. Results: The stem length, resection length, tumor site, and prosthetic motion mode showed a tendency to be related to aseptic loosening, according to the univariate analysis. Multivariate analysis showed that the tumor site, stem length, and prosthetic motion mode were independent risk factors. The internal validation indicated that the nomogram model had acceptable predictive accuracy. Conclusions: A nomogram model was developed for predicting the prosthetic survival rate without aseptic loosening. Patients with distal femoral tumors and those who are applied with fixed hinge and short-stem prostheses are more likely to be exposed to aseptic loosening.
- Subjects
RISK of prosthesis complications; AGE distribution; ARTIFICIAL joints; BONE tumors; KNEE; PATIENT-professional relations; MULTIVARIATE analysis; NURSING models; PROSTHETICS; RISK assessment; SEX distribution; STATISTICS; TOTAL knee replacement; BODY mass index; PREDICTIVE validity; TREATMENT effectiveness; RETROSPECTIVE studies; STATISTICAL models
- Publication
Journal of Orthopaedic Surgery & Research, 2019, Vol 14, Issue 1, p1
- ISSN
1749-799X
- Publication type
Article
- DOI
10.1186/s13018-019-1423-3