Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Department of Clinical Laboratory, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
Front Endocrinol (Lausanne). 2022 Aug 2;13:882148. doi: 10.3389/fendo.2022.882148. eCollection 2022.
The prevalence of primary aldosteronism (PA) varies from 5% to 20% in patients with hypertension but is largely underdiagnosed. Expanding screening for PA to all patients with hypertension to improve diagnostic efficiency is needed. A novel and portable prediction tool that can expand screening for PA is highly desirable.
Clinical characteristics and laboratory data of 1,314 patients with hypertension were collected for modeling and randomly divided into a training cohort (919 of 1,314, 70%) and an internal validation cohort (395 of 1,314, 30%). Additionally, an external dataset (n = 285) was used for model validation. Machine learning algorithms were applied to develop a discriminant model. Sensitivity, specificity, and accuracy were used to evaluate the performance of the model.
Seven independent risk factors for predicting PA were identified, including age, sex, hypokalemia, serum sodium, serum sodium-to-potassium ratio, anion gap, and alkaline urine. The prediction model showed sufficient predictive accuracy, with area under the curve (AUC) values of 0.839 (95% CI: 0.81-0.87), 0.814 (95% CI: 0.77-0.86), and 0.839 (95% CI: 0.79-0.89) in the training set, internal validation, and external validation set, respectively. The calibration curves exhibited good agreement between the predictive risk of the model and the actual risk. An online prediction model was developed to make the model more portable to use.
The online prediction model we constructed using conventional clinical characteristics and laboratory tests is portable and reliable. This allowed it to be widely used not only in the hospital but also in community health service centers and may help to improve the diagnostic efficiency of PA.
原发性醛固酮增多症（PA）在高血压患者中的患病率为 5%至 20%，但很大程度上被漏诊。需要扩大对所有高血压患者的 PA 筛查，以提高诊断效率。因此，非常需要一种新的、便携的预测工具来扩大 PA 的筛查范围。
收集了 1314 例高血压患者的临床特征和实验室数据进行建模，并将其随机分为训练队列（1314 例中的 919 例，70%）和内部验证队列（1314 例中的 395 例，30%）。此外，还使用外部数据集（n=285）进行模型验证。应用机器学习算法开发了一个判别模型。使用灵敏度、特异性和准确性来评估模型的性能。
确定了预测 PA 的七个独立危险因素，包括年龄、性别、低血钾、血清钠、血清钠钾比、阴离子间隙和碱性尿。该预测模型具有足够的预测准确性，在训练集、内部验证集和外部验证集中的曲线下面积（AUC）值分别为 0.839（95%置信区间：0.81-0.87）、0.814（95%置信区间：0.77-0.86）和 0.839（95%置信区间：0.79-0.89）。校准曲线显示模型预测风险与实际风险之间具有良好的一致性。我们开发了一个在线预测模型，使模型更便于携带使用。
我们使用常规临床特征和实验室检查构建的在线预测模型便携且可靠。这使得它不仅可以在医院中广泛使用，还可以在社区卫生服务中心使用，有助于提高 PA 的诊断效率。
