Evidence map›Paper›PMID 42835246›Full record

ArticleFrontiers in endocrinology2026

The nomogram prediction model for primary aldosteronism: the combination of serum and urine electrolytes and serum uric acid.

Xiaoyu Zha, Yanv Ren, Hao Wang, Zuoliang Dong

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Article in Frontiers in endocrinology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Xiaoyu ZhaDepartment of Clinical Laboratory, Tianjin Medical University General Hospital, Tianjin, China.
Yanv RenDepartment of Clinical Laboratory, Tianjin Medical University General Hospital, Tianjin, China.
Hao WangDepartment of Endocrinology and Metabolism, Tianjin Medical University General Hospital, Tianjin, China.
Zuoliang DongDepartment of Clinical Laboratory, Tianjin Medical University General Hospital, Tianjin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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). 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: 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.

Indexed as

ElectrolytesHyperaldosteronismNomogramsUric AcidAdultFemaleHumansMaleMiddle AgedPotassiumROC CurveElectrolytesPotassiumUric Acidessential hypertensionnomogramprediction modelprimary aldosteronismsecondary hypertension

Identifiers

PMID42835246
PMCPMC13635100

What Socratic holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.