Department of Medical Ultrasonics, The First Affiliated Hospital, Institute of Diagnostic and Interventional Ultrasound, Sun Yat-Sen University, Guangzhou, China.
Ren Fail. 2022 Dec;44(1):146-154. doi: 10.1080/0886022X.2022.2027784.
To analyze conventional ultrasound (CUS) and contrast-enhanced ultrasound (CEUS) features in patients with secondary hyperparathyroidism (SHPT) and to evaluate the clinical-ultrasonographic feature based model for predicting the severity of SHPT.
From February 2016 to March 2021, a total of 59 patients (age 51.3 ± 11.7 years, seCr 797.8 ± 431.7 μmol/L, iPTH 1535.1 ± 1063.9 ng/L) with SHPT (including 181 parathyroid glands (PTGs)) without the history of intact parathyroid hormone (iPTH)-reducing drugs using were enrolled. The patients were divided into the mild SHPT group (mSHPT, iPTH <800 ng/L) and the severe SHPT group (sSHPT, iPTH ≥ 800 ng/L) according to the serum iPTH level. The clinical test data of patients were collected and CUS and CEUS examinations were performed for every patient. Multivariable logistic regression model according to clinical-ultrasonographic features was adopted to establish a nomogram. We performed K-fold cross-validation on this nomogram model and nomogram performance was determined by its discrimination, calibration, and clinical usefulness.
There were 19 patients in the mSHPT group and 40 patients in the sSHPT group. Multivariable logistic regression indicated serum calcium, serum phosphorus and total volume of PTGs were independent predictors related with serum iPTH level. Even though CEUS score of wash-in and wash-out were showed related to severity of SHPT in univariate logistic regression analysis, they were not predictors of SHPT severity ( = 0.539, 0.474 respectively). The nomogram developed by clinical and ultrasonographic features showed good calibration and discrimination. The accuracy and the area under the curve (AUC), positive predictive value (PPV), negative predictive value (NPV) and accuracy of this model were 0.888, 92.5%, 63.2% and 83.1%, respectively. When applied to internal validation, the score revealed good discrimination with stratified fivefold cross-validation in the cohort (mean AUC = 0.833).
The clinical-ultrasonographic features model has good performance for predicting the severity of SHPT.
分析继发性甲状旁腺功能亢进症（SHPT）患者的常规超声（CUS）和超声造影（CEUS）特征，并评估基于临床-超声特征的模型预测 SHPT 严重程度的能力。
本研究纳入了 2016 年 2 月至 2021 年 3 月期间的 59 例 SHPT 患者（年龄 51.3±11.7 岁，血清肌酐 797.8±431.7 μmol/L，iPTH 1535.1±1063.9 ng/L），这些患者均未使用过降低 iPTH 的药物。根据血清 iPTH 水平，将患者分为轻度 SHPT 组（mSHPT，iPTH＜800 ng/L）和重度 SHPT 组（sSHPT，iPTH≥800 ng/L）。收集患者的临床检验数据，并对每位患者进行 CUS 和 CEUS 检查。采用基于临床-超声特征的多变量逻辑回归模型建立列线图。我们对该列线图模型进行了 K 折交叉验证，通过其区分度、校准度和临床实用性来确定模型的性能。
mSHPT 组 19 例，sSHPT 组 40 例。多变量逻辑回归分析表明，血清钙、磷和甲状旁腺总体积是与血清 iPTH 水平相关的独立预测因素。尽管单变量逻辑回归分析显示 CEUS 灌注和洗脱的评分与 SHPT 的严重程度相关，但它们不是 SHPT 严重程度的预测因素（ = 0.539，0.474）。基于临床和超声特征建立的列线图具有良好的校准度和区分度。该模型的准确性、曲线下面积（AUC）、阳性预测值（PPV）、阴性预测值（NPV）和准确率分别为 0.888、92.5%、63.2%和 83.1%。在内部验证中，该评分在分层五折交叉验证中的表现良好（平均 AUC=0.833）。
基于临床-超声特征的模型在预测 SHPT 的严重程度方面具有良好的性能。
