Blain Yamile, Alessandrino Francesco, Scortegagna Eduardo, Balcacer Patricia
Department of Radiology, University of Miami Health System, 1611 NW 12th Ave, West Wing 279, Miami, FL, 33136, USA.
Abdom Radiol (NY). 2023 Jun;48(6):2102-2110. doi: 10.1007/s00261-023-03872-7. Epub 2023 Mar 22.
To determine if ancillary sonographic and Doppler parameters can be used to predict transplant renal artery stenosis in patients with renal graft dysfunction.
IRB-approved, HIPAA-compliant retrospective study included 80 renal transplant patients who had renal US followed by renal angiogram between January 2018 and December 2019. A consensus read of two radiologists recorded these parameters: peak systolic velocity, persistence of elevated velocity, grayscale narrowing, parvus tardus, delayed systolic upstroke, angle of the systolic peak (SP angle), and aliasing. Univariate analysis using t-test or chi-square was performed to determine differences between patients with and without stenosis. P values under 0.05 were deemed statistically significant. We used machine learning algorithms to determine parameters that could better predict the presence of stenosis. The algorithms included logistic regression, random forest, imbalanced random forest, boosting, and CART. All 80 cases were split between training and testing using stratified sampling using a 75:25 split.
We found a statistically significant difference in grayscale narrowing (p = 0.0010), delayed systolic upstroke (p = 0.0002), SP angle (p = 0.0005), and aliasing (p = 0.0024) between the two groups. No significant difference was found for an elevated peak systolic velocity (p = 0.1684). The imbalanced random forest (IRF) model was selected for improved accuracy, sensitivity, and specificity. Specificity, sensitivity, AUC, and normalized Brier score for the IRF model using all parameters were 73%, 81%, 0.82, and 69 in the training set, and 78%, 58%, 0.78, and 80 in the testing set. VIMP assessment showed that the combination of variables that resulted in the most significant change of the training set performance was that of grayscale narrowing and SP angle.
Elevated peak systolic velocity did not discriminate between patients with and without TRAS. Adding ancillary parameters into the machine learning algorithm improved specificity and sensitivity similarly in the training and testing sets. The algorithm identified the combination of lumen narrowing coupled with the angle of the systolic peak as better predictor of TRAS. This model may improve the accuracy of ultrasound for transplant renal artery stenosis.
确定辅助超声和多普勒参数是否可用于预测肾移植功能障碍患者的移植肾动脉狭窄。
经机构审查委员会批准、符合健康保险流通与责任法案的回顾性研究纳入了80例肾移植患者，这些患者在2018年1月至2019年12月期间接受了肾脏超声检查，随后进行了肾血管造影。两位放射科医生通过共识阅读记录了这些参数：收缩期峰值流速、流速升高的持续时间、灰阶狭窄、小慢波、收缩期上升延迟、收缩期峰值角度（SP角）和混叠。使用t检验或卡方检验进行单因素分析，以确定有狭窄和无狭窄患者之间的差异。P值小于0.05被认为具有统计学意义。我们使用机器学习算法来确定能够更好地预测狭窄存在的参数。这些算法包括逻辑回归、随机森林、不平衡随机森林、提升算法和分类与回归树。使用分层抽样以75:25的比例将所有80例病例分为训练集和测试集。
我们发现两组之间在灰阶狭窄（p = 0.0010）、收缩期上升延迟（p = 0.0002）、SP角（p = 0.0005）和混叠（p = 0.0024）方面存在统计学显著差异。收缩期峰值流速升高方面未发现显著差异（p = 0.1684）。选择不平衡随机森林（IRF）模型以提高准确性、敏感性和特异性。使用所有参数的IRF模型在训练集的特异性、敏感性、AUC和标准化布里尔得分分别为73%、81%、0.82和69，在测试集分别为78%、58%、0.78和80。变量重要性评估表明，导致训练集性能最显著变化的变量组合是灰阶狭窄和SP角。
收缩期峰值流速升高并不能区分有或无移植肾动脉狭窄（TRAS）的患者。在机器学习算法中加入辅助参数在训练集和测试集中同样提高了特异性和敏感性。该算法确定管腔狭窄与收缩期峰值角度的组合是TRAS的更好预测指标。此模型可能提高超声诊断移植肾动脉狭窄的准确性。
