Screening of primary aldosteronism by clinical features and daily laboratory tests: combination of urine pH, sex, and serum K

Development of a risk-prediction model for primary aldosteronism in veterans with hypertension

BACKGROUND

Rates of screening for primary aldosteronism in patients who meet the criteria are exceedingly low (1%-3%). To help clinicians prioritize screening in patients most likely to benefit, we developed a risk-prediction model.

METHODS

Using national Veterans Health Administration data, we identified patients who met the criteria for primary aldosteronism screening between 2000 and 2019. We performed multivariable logistic regression to identify characteristics associated with positive primary aldosteronism testing before generating a risk-scoring system based on the coefficients (0< β < 0.5 = 1 pt, 0.5 ≤ β < 1 = 2 pts, 1 ≤ β < 1.5 = 3 pts) and then tested the system performance using an internal validation cohort.

RESULTS

We identified 502,190 patients who met primary aldosteronism screening criteria, of whom 1.6% were screened and 15% tested positive. Based on the regression model, we generated a risk-scoring system based on a total of 9 possible points in which age under 50, absence of smoking history, and resistant hypertension each scored 1 point; elevated serum sodium 2 points; and hypokalemia 3 points. Rates of positive screening increased with risk score, with 5.6% to 6.7% of those scoring 0 points testing positive; 7.9% to 9.0% 1 point; 8.6% to 10% 2 points; 13% to 14% 3 points; 21% 4 points; 22% to 38% 5 points; 27% to 38% 6 points; 42% to 49% 7 points; and 50% to 51% ≥8 points.

CONCLUSION

In hypertensive patients who meet the criteria for primary aldosteronism screening, rates of positive screening range from 5.6% to 51%. Use of our risk-predication model incorporating these factors can identify patients most likely to benefit from testing.

A novel clinical-imaging nomogram for predicting primary aldosteronism in patients with hypertension

This study aimed to develop and validate the accuracy of a clinical-imaging index nomogram in predicting primary aldosteronism (PA) in patients with hypertension. This case-control study enrolled 404 hypertension patients in the First Affiliated Hospital of Nanjing Medical University, China, from April 2017 to September 2021. The patients were randomly divided into the training set (n = 283, 70%) and the validation set (n = 121, 30%). Univariate and multivariate logistic regression analyses were performed to identify independent predictors of PA, which were then used construct a nomogram. The receiver operating characteristic (ROC) curve and calibration plot were drawn to assess the predictive value. The accuracies of our nomogram and other known prediction models were compared using decision curve analyses (DCA). Four significant variables (history of hypokalemia [OR = 2.684, 95% CI: 1.281-5.623, P < 0.001], typical imaging feature [OR = 2.316, 95% CI: 1.166-4.601, P = 0.003], 24 h urine potassium [OR = 0.956, 95% CI: 0.932-0.980, P < 0.001], plasma renin activity [PRA] [OR = 1.423, 95% CI: 1.161-1.744, P < 0.001]) in the multivariate logistic regression analysis were sifted out, and used to build the nomogram. The predictive nomogram yielded an AUC of 0.890 (95% CI, 0.853-0.927) in the training set and 0.860 (95% CI, 0.793-0.927) in the validation set. Predicted and actual probability of PA matched well in the nomogram. Moreover, the DCA showed that the nomogram gained a net benefit in clinical practice in predicting PA when the threshold value was set between 0.1 and 1.0. Our four-variable nomogram was accurate in predicting PA patients and might be introduced into clinical management.

Online prediction model for primary aldosteronism in patients with hypertension in Chinese population: A two-center retrospective study

BACKGROUND

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.

METHODS

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.

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 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.

CONCLUSION

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.

Clinical Score and Machine Learning-Based Model to Predict Diagnosis of Primary Aldosteronism in Arterial Hypertension

[Figure: see text].

Development and validation of a novel diagnostic nomogram model to predict primary aldosteronism in patients with hypertension

PURPOSE

Primary 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.

METHODS

Records 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).

RESULTS

Female 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.

CONCLUSIONS

We 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.

Prevalence, diagnosis and outcomes of treatment for primary aldosteronism

Primary aldosteronism (PA) is the most common potentially curable form of hypertension. The overproduction of aldosterone leads to an increased risk of cardiovascular and cerebrovascular events as well as adverse effects to the heart and kidney and psychological disorders. PA is mainly caused by unilateral aldosterone excess due to an aldosterone-producing adenoma or bilateral excess due to bilateral adrenocortical hyperplasia. The diagnostic work-up of PA comprises three steps: screening, confirmatory testing and differentiation of unilateral surgically-correctable forms from medically treated bilateral PA. These specific treatments can mitigate or reverse the increased risks associated with PA. Herein we summarise the prevalence, outcomes and current and future clinical approaches for the diagnosis of primary aldosteronism.