Libin Xiao1, Yiran Jiang1, Cui Zhang1, Lei Jiang1, Weiwei Zhou1, Tingwei Su1, Guang Ning1,2, Weiqing Wang1,2. 1. Shanghai Key Laboratory for Endocrine Tumors, Shanghai Clinical Center for Endocrine and Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of Chinese Health Ministry, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. 2. Laboratory for Endocrine and Metabolic Diseases of Institute of Health Science, Shanghai Jiao Tong University School of Medicine and Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
Abstract
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.
RCT Entities:
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.