Department of Ultrasound, Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China.
J Nephrol. 2023 Apr;36(3):719-729. doi: 10.1007/s40620-022-01521-8.
Non-invasive evaluation of renal fibrosis is still challenging. This study aimed to establish a nomogram based on shear wave elastography (SWE) and clinical features for the assessment of the severity of renal fibrosis in patients with chronic kidney disease (CKD).
One hundred and sixty-two patients with CKD who underwent kidney biopsy and SWE examination were prospectively enrolled between April 2019 and December 2021. Patients were classified into mildly or moderately-severely impaired group based on pathology results. All patients were randomly divided into a training (n = 113) or validation cohort (n = 49). Least absolute shrinkage and selection operator (LASSO) algorithm was used for data dimensionality reduction and feature selection. Then, a diagnostic nomogram incorporating the selected features was constructed using multivariable logistic regression analysis. Nomogram performance was evaluated for discrimination, calibration, and clinical utility in training and validation cohorts.
The established SWE nomogram, which integrated SWE value, hypertension, and estimated glomerular filtration rate, showed fine calibration and discrimination in both training (area under the receiver operator characteristic curve (AUC) = 0.94; 95% confidence interval (CI) 0.89-0.98) and validation cohorts (AUC = 0.84; 95% CI 0.71-0.96). Significant improvement in net reclassification and integrated discrimination indicated that the SWE value is a valuable biomarker to assess moderate-severe renal impairment. Furthermore, decision curve analysis revealed that the SWE nomogram has clinical value.
The proposed SWE nomogram showed favorable performance in determining individualized risk of moderate-severe renal pathological impairment in patients with CKD, which will help to facilitate clinical decision-making.
肾纤维化的无创评估仍然具有挑战性。本研究旨在建立一种基于剪切波弹性成像(SWE)和临床特征的列线图,用于评估慢性肾脏病(CKD)患者肾纤维化的严重程度。
前瞻性纳入 2019 年 4 月至 2021 年 12 月期间接受肾活检和 SWE 检查的 162 例 CKD 患者。根据病理结果将患者分为轻度或中度-重度受损组。所有患者均随机分为训练组(n=113)和验证组(n=49)。采用最小绝对收缩和选择算子(LASSO)算法进行数据降维和特征选择。然后,使用多变量逻辑回归分析构建包含选定特征的诊断列线图。在训练和验证队列中评估列线图的性能,包括区分度、校准度和临床实用性。
建立的 SWE 列线图,整合了 SWE 值、高血压和估算肾小球滤过率,在训练组(受试者工作特征曲线下面积(AUC)=0.94;95%置信区间(CI)0.89-0.98)和验证组(AUC=0.84;95%CI 0.71-0.96)中均具有良好的校准度和区分度。净重新分类和综合判别改善的显著提高表明 SWE 值是评估中重度肾功能损害的有价值的生物标志物。此外,决策曲线分析表明 SWE 列线图具有临床价值。
所提出的 SWE 列线图在确定 CKD 患者中重度肾病理损伤的个体化风险方面表现出良好的性能,这将有助于促进临床决策。