Frontiers in endocrinology · 2026
The nomogram prediction model for primary aldosteronism: the combination of serum and urine electrolytes and serum uric acid.
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Zha Xiaoyu, Ren Yanv, Wang Hao, Dong Zuoliang
Аннотация
PURPOSE: To construct and preliminarily validate a novel nomogram for primary aldosteronism (PA) risk estimation, with the goal of providing a potential reference tool for PA screening in clinical practice.
METHODS: Hypertensive inpatients who underwent PA screening at the Department of Endocrinology and Metabolism, Cardiology, and Urology of Tianjin Medical University General Hospital from October 2021 to July 2024 were included. Basic information and laboratory test results of the patients were collected, and were randomly divided into training set and validation set at a 7:3 ratio. Differential analysis, Lasso regression, univariate logistic regression, and multivariate logistic regression were used to screen the variables associated with PA. The model results were presented as a nomogram. Predictive ability was assessed by plotting receiver operating characteristic (ROC) curves, and the diagnostic performance of the model was compared using the DeLong test. The Hosmer-Lemeshow test was used to evaluate the goodness of fit of the regression model. The clinical utility of the model was evaluated by clinical decision curve analysis (DCA). Kappa test was used to evaluate the agreement between the model and ARR.
RESULTS: Six indicators including serum sodium-to-potassium ratio, anion gap, uric acid, urine sodium, 24-hour urine potassium and urine pH were used to construct a nomogram prediction model for PA. The training and validation sets achieved area under the curve (AUC) values of 0.860 (95% CI: 0.825-0.894) and 0.850 (95% CI: 0.800-0.899), respectively. The AUCs of the validation set for PFK score and ARR were 0.718 (95% CI: 0.656-0.781) and 0.965 (95% CI: 0.943-0.987), respectively. The model demonstrated good discrimination ability. The Hosmer-Lemeshow test indicated a good fit (training set: χ² = 7.897, P = 0.444;validation set: χ² = 6.208, P = 0.624). The DCA showed that the model had good clinical application value. The Kappa test showed good agreement between the model and ARR(Kappa = 0.364, P < 0.001).
CONCLUSION: The nomogram model based on serum sodium-to-potassium ratio, anion gap, uric acid, urine sodium, 24-hour urine potassium and urine pH can predict the risk of PA, facilitating PA screening in clinical practice.
METHODS: Hypertensive inpatients who underwent PA screening at the Department of Endocrinology and Metabolism, Cardiology, and Urology of Tianjin Medical University General Hospital from October 2021 to July 2024 were included. Basic information and laboratory test results of the patients were collected, and were randomly divided into training set and validation set at a 7:3 ratio. Differential analysis, Lasso regression, univariate logistic regression, and multivariate logistic regression were used to screen the variables associated with PA. The model results were presented as a nomogram. Predictive ability was assessed by plotting receiver operating characteristic (ROC) curves, and the diagnostic performance of the model was compared using the DeLong test. The Hosmer-Lemeshow test was used to evaluate the goodness of fit of the regression model. The clinical utility of the model was evaluated by clinical decision curve analysis (DCA). Kappa test was used to evaluate the agreement between the model and ARR.
RESULTS: Six indicators including serum sodium-to-potassium ratio, anion gap, uric acid, urine sodium, 24-hour urine potassium and urine pH were used to construct a nomogram prediction model for PA. The training and validation sets achieved area under the curve (AUC) values of 0.860 (95% CI: 0.825-0.894) and 0.850 (95% CI: 0.800-0.899), respectively. The AUCs of the validation set for PFK score and ARR were 0.718 (95% CI: 0.656-0.781) and 0.965 (95% CI: 0.943-0.987), respectively. The model demonstrated good discrimination ability. The Hosmer-Lemeshow test indicated a good fit (training set: χ² = 7.897, P = 0.444;validation set: χ² = 6.208, P = 0.624). The DCA showed that the model had good clinical application value. The Kappa test showed good agreement between the model and ARR(Kappa = 0.364, P < 0.001).
CONCLUSION: The nomogram model based on serum sodium-to-potassium ratio, anion gap, uric acid, urine sodium, 24-hour urine potassium and urine pH can predict the risk of PA, facilitating PA screening in clinical practice.