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DOI 10.1186/s42836-023-00218-y

Медицинская библиотека · Arthroplasty (London, England) · 2023

Prediction of gap balancing based on 2-D radiography in total knee arthroplasty for knee osteoarthritis patients.

Zhang Zhuo, Luo Yang, Zhang Chong, Wang Xin, Zhang Tianwei, Zhang Guoqiang

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Аннотация

BACKGROUND

To investigate the influence of osteophytes on postoperative gap balancing, and to work out a predictive model of the relationship between osteophyte size and gap gaining in primary total knee replacement.

METHODS

One hundred and ten patients were enrolled in the study. Pre- and postoperative radiographs were collected and analyzed. They were assigned to the training dataset and test dataset randomly at a ratio of 9:1 by using the statistical package R (version 4.0.5). Size and marginal distances of osteophytes, planned bone cut planes, predicted bone cuts and joint gaps were labeled on the preoperative standing anteroposterior and lateral views, while actual bone cuts and joint gaps were recorded on the postoperative plain films, respectively. Statistical analysis was performed.

RESULTS

Actual joint gaps were significantly related to the distances of medial and lateral predictive bone cutting lines, bone cut thickness on tibial side and posterior condylar, as well as size and marginal distances of osteophytes (P < 0.05). A predictive equation was generated, with a root mean square error (RMSE) of 3.4761 in validation. A 2-D planning system with adjustable input parameters and dim predictive outputs on joint gap was developed. The equation is [Formula: see text]

CONCLUSION

Postoperative joint gap can be predicted on the basis of preoperative measurements on 2-D plain films. Larger sample size may help improve the effectiveness and accuracy of the predictive equation.

Для цитирования: Zhang Zhuo, Luo Yang, Zhang Chong, Wang Xin, Zhang Tianwei, Zhang Guoqiang. Prediction of gap balancing based on 2-D radiography in total knee arthroplasty for knee osteoarthritis patients.. Arthroplasty (London, England). 2023. DOI: 10.1186/s42836-023-00218-y.
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