Online prediction model for primary aldosteronism in patients with hypertension in Chinese population: A two-center retrospective study - Lin, Wenbin; Gan, Wenjia; Feng, Pinning; Zhong, Liangying; Yao, Zhenrong; Chen, Peisong; He, Wanbing; Yu, Nan (Frontiers in Endocrinology, 2022)
Background: The prevalence of primary aldosteronism (PA) varies from 5% to 20% in patient 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 which can expand screening for PA is highly desirable. Methods: Clinical characteristics and laboratory data of 1314 patients with hypertension were collected for modeling and randomly divided into training cohort (919 of 1314, 70%) and internal validation cohort (395 of 1314, 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 performances of the model. Results: 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 an AUC 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 predictive risk of the model and the actual risk. An online prediction model was developed to make the model more portable to use. Conclusion: The online prediction model we constructed using conventional clinical characteristics and laboratory tests is portable and reliable. It allowed widely used not only in hospital but also in community health service centers and may help to improve diagnostic efficiency of PA.