Endocrine and Metabolism Unit, Internal Medicine Department, Faculty of Medicine, Chiang Mai University, Muang Chiang Mai, Chiang Mai, Thailand.
Internal Medicine Department, Lee Hospital, Lamphun, Thailand.
PLoS One. 2022 Aug 5;17(8):e0272049. doi: 10.1371/journal.pone.0272049. eCollection 2022.
Primary aldosteronism (PA) is the most common cause of secondary hypertension. The diagnosis of PA currently requires multiple complicated measures. The aims of this study were to identify easy-to-obtain clinical and biochemical predictors, and to create predictive model to facilitate the identification of a patient at high risk of having PA.
This 2-year retrospective cohort study was conducted at a tertiary care medical center. A total of 305 patients who had been tested for plasma aldosterone concentration (PAC) and plasma renin activity (PRA) were identified. Patients with incomplete results of PAC and PRA and those who had an established diagnosis of Cushing's syndrome or pheochromocytoma were excluded. Logistic regression analysis was used to identify significant predictors and to create predictive model of PA.
PA was diagnosed in 128 of the patients (41.96%). Significant predictive factors for PA were age >60 years (OR 2.12, p = 0.045), female (OR 1.65, p<0.001), smoking (OR 2.79, p<0.001), coronary artery disease (OR 2.29, p<0.001), obstructive sleep apnea (OR 1.50, p = 0.017), systolic blood pressure >160 mmHg (OR 1.15, P<0.001), serum potassium <3 mEq/L (OR 3.72, p = 0.030), fasting blood glucose >126 mg/dL (OR 0.48, p = 0.001) and estimated glomerular filtration rate (eGFR) <60 mL/min/1.73m2 (OR 1.79, p = 0.001). Predictive model was created with a total score ranged from 0 to 42. A score above 7.5 indicated a higher probability of having PA with a sensitivity of 72% and a specificity of 70%. The diagnostic performance of the predictive model based on area under the curve was 71%.
The clinical and biochemical predictive factors including predictive model identified in this study can be employed as an additional tool to help identify patients at risk of having PA and could help reduce the number of screening and confirmation tests required for PA.
原发性醛固酮增多症（PA）是继发性高血压最常见的原因。目前，PA 的诊断需要多种复杂的措施。本研究旨在确定易于获得的临床和生化预测因子，并建立预测模型，以方便识别患有 PA 风险较高的患者。
这是一项在三级医疗中心进行的为期 2 年的回顾性队列研究。共确定了 305 名接受血浆醛固酮浓度（PAC）和血浆肾素活性（PRA）检测的患者。排除了 PAC 和 PRA 结果不完整以及已确诊库欣综合征或嗜铬细胞瘤的患者。采用 logistic 回归分析确定 PA 的显著预测因子，并建立 PA 的预测模型。
在 128 名患者（41.96%）中诊断出 PA。PA 的显著预测因素包括年龄>60 岁（OR 2.12，p=0.045）、女性（OR 1.65，p<0.001）、吸烟（OR 2.79，p<0.001）、冠状动脉疾病（OR 2.29，p<0.001）、阻塞性睡眠呼吸暂停（OR 1.50，p=0.017）、收缩压>160mmHg（OR 1.15，P<0.001）、血清钾<3mEq/L（OR 3.72，p=0.030）、空腹血糖>126mg/dL（OR 0.48，p=0.001）和估算肾小球滤过率（eGFR）<60mL/min/1.73m2（OR 1.79，p=0.001）。该研究创建了一个总分范围为 0 至 42 的预测模型。评分高于 7.5 分表明患有 PA 的可能性较高，敏感性为 72%，特异性为 70%。基于曲线下面积的预测模型的诊断性能为 71%。
本研究确定的临床和生化预测因子（包括预测模型）可作为识别患有 PA 风险较高的患者的附加工具，并有助于减少 PA 所需的筛查和确认试验数量。
