Division of Internal Medicine and Hypertension, Department of Medical Sciences, University of Torino, Torino, Italy.
Department of Mathematical Sciences G. L. Lagrange, Polytechnic University of Torino, Torino, Italy.
J Clin Endocrinol Metab. 2021 Mar 25;106(4):e1708-e1716. doi: 10.1210/clinem/dgaa974.
The diagnostic work-up of primary aldosteronism (PA) includes screening and confirmation steps. Case confirmation is time-consuming, expensive, and there is no consensus on tests and thresholds to be used. Diagnostic algorithms to avoid confirmatory testing may be useful for the management of patients with PA.
Development and validation of diagnostic models to confirm or exclude PA diagnosis in patients with a positive screening test.
DESIGN, PATIENTS, AND SETTING: We evaluated 1024 patients who underwent confirmatory testing for PA. The diagnostic models were developed in a training cohort (n = 522), and then tested on an internal validation cohort (n = 174) and on an independent external prospective cohort (n = 328).
Different diagnostic models and a 16-point score were developed by machine learning and regression analysis to discriminate patients with a confirmed diagnosis of PA.
Male sex, antihypertensive medication, plasma renin activity, aldosterone, potassium levels, and the presence of organ damage were associated with a confirmed diagnosis of PA. Machine learning-based models displayed an accuracy of 72.9%-83.9%. The Primary Aldosteronism Confirmatory Testing (PACT) score correctly classified 84.1% at training and 83.9% or 81.1% at internal and external validation, respectively. A flow chart employing the PACT score to select patients for confirmatory testing correctly managed all patients and resulted in a 22.8% reduction in the number of confirmatory tests.
The integration of diagnostic modeling algorithms in clinical practice may improve the management of patients with PA by circumventing unnecessary confirmatory testing.
原发性醛固酮增多症（PA）的诊断工作包括筛查和确认步骤。病例确认既费时又费钱，并且对于要使用的检查和阈值尚无共识。避免进行确认性检查的诊断算法可能有助于 PA 患者的管理。
开发和验证用于在筛查试验阳性的患者中确认或排除 PA 诊断的诊断模型。
设计、患者和设置：我们评估了 1024 名接受 PA 确认性检查的患者。诊断模型在训练队列（n = 522）中进行了评估，然后在内部验证队列（n = 174）和独立的外部前瞻性队列（n = 328）中进行了测试。
通过机器学习和回归分析开发了不同的诊断模型和 16 分评分，以区分确诊为 PA 的患者。
男性、抗高血压药物、血浆肾素活性、醛固酮、钾水平和器官损伤的存在与确诊为 PA 有关。基于机器学习的模型的准确性为 72.9%-83.9%。原发性醛固酮增多症确认性测试（PACT）评分在训练时正确分类 84.1%，在内部和外部验证时分别正确分类 83.9%或 81.1%。使用 PACT 评分选择进行确认性检查的患者的流程图正确管理了所有患者，并使确认性检查的数量减少了 22.8%。
将诊断模型算法纳入临床实践可能通过避免不必要的确认性检查来改善 PA 患者的管理。
