Trial reportEndocrine2021
Development and validation of a novel diagnostic nomogram model to predict primary aldosteronism in patients with hypertension.
Trial report in Endocrine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 9 citations in OpenAlex.
- Development of a machine-learning based diagnosis procedure to distinguish aortic dissection from non-ST-elevation myocardial infarction.BMC medical informatics and decision making · 2026Article
- Association between serum uric acid levels and primary aldosteronism.BMC nephrology · 2025Article
- Development and external validation of a nomogram prediction model based on quantitative coronary angiography for predicting ischemic lesions: a multi-centre study.Frontiers in cardiovascular medicine · 2025Article
- A comparative study of antihypertensive drugs prediction models for the elderly based on machine learning algorithms.Frontiers in cardiovascular medicine · 2022Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposePrimary 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.
methodsRecords 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).
resultsFemale 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.
conclusionsWe 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.
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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 OpenQuestion graph.