Radiographic parameter-driven decision tree reliably predicts aseptic mechanical failure of compressive osseointegration fixation

基于放射影像参数的决策树能够可靠地预测压缩性骨整合固定的无菌性机械失效

阅读:1

Abstract

Background and purpose - Compressive osseointegration fixation is an alternative to intramedullary fixation for endoprosthetic reconstruction. Mechanical failure of compressive osseointegration presents differently on radiographs than stemmed implants, therefore we aimed to develop a reliable radiographic method to determine stable integration.Patients and methods - 8 reviewers evaluated 11 radiographic parameters from 29 patients twice, 2 months apart. Interclass correlation coefficients (ICCs) were used to assess test-retest and inter-rater reliability. We constructed a fast and frugal decision tree using radiographic parameters with substantial test-retest agreement, and then tested using radiographs from a new cohort of 49 patients. The model's predictions were compared with clinical outcomes and a confusion matrix was generated.Results - 6 of 8 reviewers had non-significant intra-rater ICCs for ≥ one parameter; all inter-rater ICCs were highly reliable (p < 0.001). Change in length between the top of the spindle sleeve and bottom of the anchor plug (ICC 0.98), bone cortex hypertrophy (ICC 0.86), and bone pin hypertrophy (ICC 0.81) were used to create the decision tree. The sensitivity and specificity of the training cohort were 100% (95% CI 52-100) and 87% (CI 74-94) respectively. The decision tree demonstrated 100% (CI 40-100) sensitivity and 89% (CI 75-96) specificity with the test cohort.Interpretation - A stable spindle length and at least 3 cortices with bone hypertrophy at the implant interface predicts stable osseointegration; failure is predicted in the absence of bone hypertrophy at the implant interface if the pin sites show hypertrophy. Thus, our decision tree can guide clinicians as they follow patients with compressive osseo-integration implants.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。