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利用考坟/曼森疲劳模型预测髋关节假体股骨部件因无菌性松动导致的耐用性的方法。

A Method for Prediction of Femoral Component of Hip Prosthesis Durability due to Aseptic Loosening by Using Coffin/Manson Fatigue Model.

机构信息

Institute for Orthopedic Surgery and Traumatology "Banjica", Mihajla Avramovica 28, 11000 Belgrade, Serbia.

Medical School, University of Belgrade, Serbia.

出版信息

Biomed Res Int. 2018 May 23;2018:9263134. doi: 10.1155/2018/9263134. eCollection 2018.

Abstract

The purpose of this work is to develop a new model estimate of the fatigue life of a hip prosthesis due to aseptic loosening as a multifactorial phenomenon. The formula developed here is a three-parameter model based on Basquin's law for fatigue, eccentric compression formula for the compressive stress and torsion in the prosthesis due to the horizontal components of the contact force. With our model, we can accurately predict the durability of a hip prosthesis due to the following four parameters: body weight, femoral offset, duration, and intensity of daily physical activities of a patient. The agreement of the prediction with the real life of the prosthesis, observed on 15 patients, is found to be adequate. Based on the formula derived for a particular implant, there was a high degree of concurrence between the model-predicted and actual values of aseptic loosening (durability) proved by the Mann-Whitney test. By virtue of the validated model, it is possible to predict, quantitatively, the influence of various factors on the hip life. For example, we can conclude that a 10% decrease of a patient's body mass, with all other conditions being the same, causes 5% increase of the hip fatigue life.

摘要

这项工作的目的是开发一种新的模型,用于估计由于无菌性松动导致的髋关节假体疲劳寿命,这是一种多因素现象。这里开发的公式是基于巴斯昆定律的疲劳、由于接触力的水平分量在假体中产生的压缩应力和扭转的偏心压缩公式的三参数模型。通过我们的模型,我们可以根据以下四个参数准确预测髋关节假体的耐用性:体重、股骨偏心距、持续时间和患者日常体力活动的强度。在对 15 名患者的假体实际使用情况进行观察后,发现预测与实际结果吻合良好。基于为特定植入物推导的公式,通过曼-惠特尼检验证明,模型预测的无菌性松动(耐用性)与实际值之间具有高度一致性。通过验证后的模型,可以定量预测各种因素对髋关节寿命的影响。例如,我们可以得出结论,在其他条件相同的情况下,如果患者的体重减轻 10%,则髋关节疲劳寿命会增加 5%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b18/5989294/639710726894/BMRI2018-9263134.001.jpg

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