Development and validation of subtype prediction scores for the workup of primary aldosteronism.
Kobayashi Hiroki,Abe Masanori,Soma Masayoshi,Takeda Yoshiyu,Kurihara Isao,Itoh Hiroshi,Umakoshi Hironobu,Tsuiki Mika,Katabami Takuyuki,Ichijo Takamasa,Wada Norio,Yoshimoto Takanobu,Ogawa Yoshihiro,Kawashima Junji,Sone Masakatsu,Inagaki Nobuya,Takahashi Katsutoshi,Watanabe Minemori,Matsuda Yuichi,Shibata Hirotaka,Kamemura Kohei,Yanase Toshihiko,Otsuki Michio,Fujii Yuichi,Yamamoto Koichi,Ogo Atsushi,Nanba Kazutaka,Tanabe Akiyo,Suzuki Tomoko,Naruse Mitsuhide,
Journal of hypertension
OBJECTIVES:A subtype prediction score for primary aldosteronism has not yet been developed and validated using a large dataset. This study aimed to develop and validate a new subtype prediction score and to compare it with existing scores using a large multicenter database. METHODS:In total, 1936 patients with primary aldosteronism were randomly assigned to the development and validation datasets, constituting 1290 and 646 patients, respectively. Three prediction scores were generated with or without confirmatory tests, using logistic regression analysis. In the validation dataset, new and existing prediction scores were compared using receiver operating characteristic curve, net reclassification improvement, and integrated discrimination improvement analyses. RESULTS:The new prediction score is simply calculated using serum potassium levels [>3.9 mmol/l (four points); 3.5-3.9 mmol/l (three points)], the absence of adrenal nodules during computed tomography (three points), a baseline plasma aldosterone concentration of <210.0 pg/ml (two points), a baseline aldosterone/renin ratio of less than 620 (two points), and female sex (one point). Using the validation dataset, we found that a new subtype prediction score of at least 8 had a positive predictive value of 93.5% for bilateral hyperaldosteronism. The new prediction score for bilateral hyperaldosteronism was better than the existing prediction scores in the receiver operating characteristic curve and net reclassification improvement analyses. CONCLUSION:The new prediction score has clear advantages over the existing prediction scores in terms of diagnostic accuracy, feasibility, and the potential for generalization in a large population. These data will help healthcare professionals to better select patients who require adrenal venous sampling.
Development and Validation of Prediction Models for Subtype Diagnosis of Patients With Primary Aldosteronism.
Burrello Jacopo,Burrello Alessio,Pieroni Jacopo,Sconfienza Elisa,Forestiero Vittorio,Rabbia Paola,Adolf Christian,Reincke Martin,Veglio Franco,Williams Tracy Ann,Monticone Silvia,Mulatero Paolo
The Journal of clinical endocrinology and metabolism
CONTEXT:Primary aldosteronism (PA) comprises unilateral (lateralized [LPA]) and bilateral disease (BPA). The identification of LPA is important to recommend potentially curative adrenalectomy. Adrenal venous sampling (AVS) is considered the gold standard for PA subtyping, but the procedure is available in few referral centers. OBJECTIVE:To develop prediction models for subtype diagnosis of PA using patient clinical and biochemical characteristics. DESIGN, PATIENTS AND SETTING:Patients referred to a tertiary hypertension unit. Diagnostic algorithms were built and tested in a training (N = 150) and in an internal validation cohort (N = 65), respectively. The models were validated in an external independent cohort (N = 118). MAIN OUTCOME MEASURE:Regression analyses and supervised machine learning algorithms were used to develop and validate 2 diagnostic models and a 20-point score to classify patients with PA according to subtype diagnosis. RESULTS:Six parameters were associated with a diagnosis of LPA (aldosterone at screening and after confirmatory testing, lowest potassium value, presence/absence of nodules, nodule diameter, and computed tomography results) and were included in the diagnostic models. Machine learning algorithms displayed high accuracy at training and internal validation (79.1%-93%), whereas a 20-point score reached an area under the curve of 0.896, and a sensitivity/specificity of 91.7/79.3%. An integrated flowchart correctly addressed 96.3% of patients to surgery and would have avoided AVS in 43.7% of patients. The external validation on an independent cohort confirmed a similar diagnostic performance. CONCLUSIONS:Diagnostic modelling techniques can be used for subtype diagnosis and guide surgical decision in patients with PA in centers where AVS is unavailable.
A novel clinical nomogram to predict bilateral hyperaldosteronism in Chinese patients with primary aldosteronism.
Xiao Libin,Jiang Yiran,Zhang Cui,Jiang Lei,Zhou Weiwei,Su Tingwei,Ning Guang,Wang Weiqing
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.