A novel clinical nomogram to predict bilateral hyperaldosteronism in Chinese patients with primary aldosteronism
A novel clinical nomogram to predict bilateral hyperaldosteronism in Chinese patients with primary aldosteronism
复制标题
预测中国原发性醛固酮增多症患者双侧醛固酮增多症的新型临床列线图
DOI：
10.1111/cen.13962
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发表时间：
2019-06-01
影响因子：
3.2
通讯作者：
Wang, Weiqing
中科院分区：
文献类型：
--
作者：
Xiao, Libin;Jiang, Yiran;Wang, Weiqing
背景肾上腺静脉取样（AVS）被推荐为原发性醛固酮增多症（PA）亚型分类的金标准;然而，这种方法的可用性有限。目的建立一种新的基于常规变量的预测PA亚型的临床诺模图，从而减少AVS的候选人数。患者和方法患者被随机分为训练集（n = 185）和验证集（n = 79）。采用Logistic回归分析确定与醛固酮腺瘤（阿帕）不同的特发性醛固酮增多症（IHA）的危险因素。构建诺模图预测IHA的概率。应用受试者工作特征（ROC）曲线和校准图来评估预测值。然后，115例患者前瞻性入组，并使用列线图预测AVS前的亚型。结果诺模图采用体重指数（BMI）、血钾和CT检查结果。列线图显示训练集中的ROC下面积（AUC）为0.924（95% CI：0.875-0.957），灵敏度为86.59%，特异性为87.38%，验证集中的AUC为0.894（95% CI：0.804-0.952），灵敏度为82.86%，特异性为84.09%。诺模图中预测概率与实际概率吻合较好（Hosmer-Lemeshow检验：P > 0.05）。使用诺模图作为替代预测IHA在前瞻性设置前AVS，特异性达到100%，当我们增加阈值的概率为90%。结论我们已经开发出一种工具，能够预测PA患者的IHA，并可能避免AVS。
Context Adrenal venous sampling (AVS) is recommended as the gold standard for subtype classification in primary aldosteronism (PA); however, this approach has limited availability. Objective We aimed to develop a novel clinical nomogram to predict PA subtype based on routine variables, thereby reducing the number of candidates for AVS. Patients and method Patients were randomly divided into a training set (n = 185) and a validation set (n = 79). Risk factors for idiopathic hyperaldosteronism (IHA) differentiating from aldosterone-producing adenoma (APA) were identified using logistic regression analysis. A nomogram was constructed to predict the probability of IHA. A receiver operating characteristic (ROC) curve and a calibration plot were applied to assess the predictive value. Then, 115 patients were prospectively enrolled, and a nomogram was used to predict the subtypes before AVS. Results Body mass index (BMI), serum potassium and computed tomography (CT) finding were adopted in the nomogram. The nomogram presented an area under the ROC (AUC) of 0.924 (95% CI: 0.875-0.957), sensitivity of 86.59% and specificity of 87.38% in the training set and an AUC of 0.894 (95% CI: 0.804-0.952), sensitivity of 82.86% and specificity of 84.09% in the validation set. Predicted probability and actual probability matched well in the nomogram (Hosmer-Lemeshow test: P > 0.05). Using the nomogram as a surrogate to predict IHA in the prospective set before AVS, the specificity reached 100% when we increased the threshold to a probability of 90%. Conclusion We have developed a tool that is able to predict IHA in patients with PA and potentially avoid AVS.