通过逻辑回归建立预测经皮椎体后凸成形术后骨折的提名图

IF 2.5 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Aiqi Zhang, Hongye Fu, Junjie Wang, Zhe Chen, Jiajun Fan
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引用次数: 0

摘要

引言已有多项研究探讨了经皮椎体后凸成形术(PKP)后再骨折的风险因素,并开发了许多临床预后模型。然而,使用随机森林(RF)模型预测新的椎体压缩性骨折(NVCF)的发生尚无研究。因此,本研究旨在调查PKP术后骨折发生的风险因素,比较逻辑回归模型和RF模型在预测PKP术后骨折中的预测性能,并对逻辑回归模型进行可视化分析。方法我们收集了2018年1月至2021年12月在我院接受PKP治疗的349例患者的临床数据。采用Lasso回归法筛选出与NVCF发生相关的风险因素。随后,建立了逻辑回归模型和 RF 模型,并比较了它们的预测能力。结果利用 Lasso 回归筛选出的变量,包括骨密度、骨水泥分布、椎体骨折位置、术前椎体高度和椎体高度恢复率,均被纳入逻辑回归和 RF 模型。在训练集中,逻辑回归模型和射频模型的曲线下面积分别为 0.868 和 0.786,在验证集中分别为 0.786 和 0.599。此外,逻辑回归模型的校准曲线也优于 RF 模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Establishing a nomogram to predict refracture after percutaneous kyphoplasty by logistic regression
Introduction

Several studies have examined the risk factors for post-percutaneous kyphoplasty (PKP) refractures and developed many clinical prognostic models. However, no prior research exists using the Random Forest (RF) model, a favored tool for model development, to predict the occurrence of new vertebral compression fractures (NVCFs). Therefore, this study aimed to investigate the risk factors for the occurrence of post-PKP fractures, compare the predictive performance of logistic regression and RF models in forecasting post-PKP fractures, and visualize the logistic regression model.

Methods

We collected clinical data from 349 patients who underwent PKP treatment at our institution from January 2018 to December 2021. Lasso regression was employed to select risk factors associated with the occurrence of NVCFs. Subsequently, logistic regression and RF models were established, and their predictive capabilities were compared. Finally, a nomogram was created.

Results

The variables selected using Lasso regression, including bone density, cement distribution, vertebral fracture location, preoperative vertebral height, and vertebral height restoration rate, were included in both the logistic regression and RF models. The area under the curves of the logistic regression and RF models were 0.868 and 0.786, respectively, in the training set and 0.786 and 0.599, respectively, in the validation set. Furthermore, the calibration curve of the logistic regression model also outperformed that of the RF model.

Conclusion

The logistic regression model provided better predictive capabilities for identifying patients at risk for post-PKP vertebral fractures than the RF model.

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来源期刊
Frontiers in Neuroinformatics
Frontiers in Neuroinformatics MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
4.80
自引率
5.70%
发文量
132
审稿时长
14 weeks
期刊介绍: Frontiers in Neuroinformatics publishes rigorously peer-reviewed research on the development and implementation of numerical/computational models and analytical tools used to share, integrate and analyze experimental data and advance theories of the nervous system functions. Specialty Chief Editors Jan G. Bjaalie at the University of Oslo and Sean L. Hill at the École Polytechnique Fédérale de Lausanne are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neuroscience is being propelled into the information age as the volume of information explodes, demanding organization and synthesis. Novel synthesis approaches are opening up a new dimension for the exploration of the components of brain elements and systems and the vast number of variables that underlie their functions. Neural data is highly heterogeneous with complex inter-relations across multiple levels, driving the need for innovative organizing and synthesizing approaches from genes to cognition, and covering a range of species and disease states. Frontiers in Neuroinformatics therefore welcomes submissions on existing neuroscience databases, development of data and knowledge bases for all levels of neuroscience, applications and technologies that can facilitate data sharing (interoperability, formats, terminologies, and ontologies), and novel tools for data acquisition, analyses, visualization, and dissemination of nervous system data. Our journal welcomes submissions on new tools (software and hardware) that support brain modeling, and the merging of neuroscience databases with brain models used for simulation and visualization.
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