Hypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region; Xinjiang Hypertension Institute, National Health Committee Key Laboratory of Hypertension Clinical Research, No. 91 Tianchi Road Urumqi, 830001, Xinjiang, China.
Endocrine. 2021 Sep;73(3):682-692. doi: 10.1007/s12020-021-02745-7. Epub 2021 May 24.
Primary aldosteronism (PA) remains, to a large extent, an under-diagnosed disease. We aimed to develop and validate a novel clinical nomogram to predict PA based on routine biochemical variables including new ones, calcium-phosphorus product.
Records from 806 patients with hypertension were randomly divided into 70% (n = 564) as the training set and the remaining 30% (n = 242) as the validation set. Predictors for PA were extracted to construct a nomogram model based on regression analysis of the training set. An internal validation was performed to assess the nomogram model's discrimination and consistency using the area under the curve for receiver operating characteristic curves and calibration plots. The diagnostic accuracy was compared between nomogram and other known prediction models, using receiver operating characteristics (ROC) and decision curve analyses (DCA).
Female gender, serum potassium, serum calcium-phosphorus product, and urine pH were adopted as predictors in the nomogram. The nomogram resulted in an area under the curve of 0.73 (95% confidence interval: 0.68-0.78) in the training set and an area under the curve of 0.68 (0.59-0.75) in the validation set. Predicted probability and actual probability matched well in the nomogram (p > 0.05). Based on ROC and DCA, 21-70% threshold to predict PA in the nomogram model was clinically useful.
We have developed a novel nomogram to predict PA in hypertensive individuals based on routine biochemical variables. External validation is needed to further demonstrate its predictive ability in primary care settings.
原发性醛固酮增多症（PA）在很大程度上仍然是一种未被充分诊断的疾病。我们旨在开发和验证一种新的临床列线图，以预测 PA，该列线图基于包括钙磷产物在内的常规生化变量。
记录 806 例高血压患者的病历，随机分为 70%（n=564）作为训练集，其余 30%（n=242）作为验证集。从训练集中提取预测 PA 的预测因子，构建基于回归分析的列线图模型。使用接受者操作特征曲线和校准图的曲线下面积对内验证列线图模型的判别和一致性进行评估。使用接受者操作特征（ROC）和决策曲线分析（DCA）比较列线图与其他已知预测模型的诊断准确性。
女性性别、血清钾、血清钙磷产物和尿 pH 值被选为列线图中的预测因子。该列线图在训练集中的曲线下面积为 0.73（95%置信区间：0.68-0.78），在验证集中的曲线下面积为 0.68（0.59-0.75）。列线图中的预测概率和实际概率匹配良好（p>0.05）。基于 ROC 和 DCA，21-70%的阈值来预测列线图模型中的 PA 具有临床意义。
我们已经开发了一种新的列线图，用于预测高血压个体中的 PA，该列线图基于常规生化变量。需要进一步的外部验证来证明其在基层医疗环境中的预测能力。
