External Validation of Clinical Prediction Models in Unilateral Primary Aldosteronism

Time to Benefit of Surgery vs Targeted Medical Therapy for Patients With Primary Aldosteronism: A Meta-analysis

CONTEXT

Primary aldosteronism (PA) is one of the most common causes of secondary hypertension, but the comparative outcomes of targeted treatment remain unclear.

OBJECTIVE

To compare the clinical outcomes in patients treated for primary aldosteronism over time.

METHODS

Medline and EMBASE were searched. Original studies reporting the incidence of mortality, major adverse cardiovascular outcomes (MACE), progression to chronic kidney disease, or diabetes following adrenalectomy vs medical therapy were selected. Two reviewers independently abstracted data and assessed study quality. Standard meta-analyses were conducted using random-effects models to estimate relative differences. Time to benefit meta-analyses were conducted by fitting Weibull survival curves to estimate absolute risk differences and pooled using random-effects models.

RESULTS

15 541 patients (16 studies) with PA were included. Surgery was consistently associated with an overall lower risk of death (hazard ratio [HR] 0.34, 95% CI 0.22-0.54) and MACE (HR 0.55, 95% CI 0.36-0.84) compared with medical therapy. Surgery was associated with a significantly lower risk of hospitalization for heart failure (HR 0.48 95% CI 0.34-0.70) and progression to chronic kidney disease (HR 0.62 95% CI 0.39-0.98), and nonsignificant reductions in myocardial infarction and stroke. In absolute terms, 200 patients would need to be treated with surgery instead of medical therapy to prevent 1 death after 12.3 (95% CI 3.1-48.7) months.

CONCLUSION

Surgery is associated with lower all-cause mortality and MACE than medical therapy for PA. For most patients, the long-term surgical benefits outweigh the short-term perioperative risks.

Predicting Bilateral Subtypes of Primary Aldosteronism Without Adrenal Vein Sampling: A Systematic Review and Meta-analysis

CONTEXT

Primary aldosteronism (PA) is the most common endocrine cause of hypertension. The final diagnostic step involves subtyping, using adrenal vein sampling (AVS), to determine if PA is unilateral or bilateral. The complete PA diagnostic process is time and resource intensive, which can impact rates of diagnosis and treatment. Previous studies have developed tools to predict bilateral PA before AVS.

OBJECTIVE

Evaluate the sensitivity and specificity of published tools that aim to identify bilateral subtypes of PA.

METHODS

Medline and Embase databases were searched to identify published models that sought to subtype PA, and algorithms to predict bilateral PA are reported. Meta-analysis and meta-regression were then performed.

RESULTS

There were 35 studies included, evaluating 55 unique algorithms to predict bilateral PA. The algorithms were grouped into 6 categories: those combining biochemical, radiological, and demographic characteristics (A); confirmatory testing alone or combined with biochemical, radiological, and demographic characteristics (B); biochemistry results alone (C); adrenocorticotropic hormone stimulation testing (D); anatomical imaging (E); and functional imaging (F). Across the identified algorithms, sensitivity and specificity ranged from 5% to 100% and 36% to 100%, respectively. Meta-analysis of 30 unique predictive tools from 32 studies showed that the group A algorithms had the highest specificity for predicting bilateral PA, while group F had the highest sensitivity.

CONCLUSIONS

Despite the variability in published predictive algorithms, they are likely important for decision-making regarding the value of AVS. Prospective validation may enable medical treatment upfront for people with a high likelihood of bilateral PA without the need for an invasive and resource-intensive test.

Primary Aldosteronism: State-of-the-Art Review

We are witnessing a revolution in our understanding of primary aldosteronism (PA). In the past 2 decades, we have learned that PA is a highly prevalent syndrome that is largely attributable to pathogenic somatic mutations, that contributes to cardiovascular, metabolic, and kidney disease, and that when recognized, can be adequately treated with widely available mineralocorticoid receptor antagonists and/or surgical adrenalectomy. Unfortunately, PA is rarely diagnosed, or adequately treated, mainly because of a lack of awareness and education. Most clinicians still possess an outdated understanding of PA; from primary care physicians to hypertension specialists, there is an urgent need to redefine and reintroduce PA to clinicians with a modern and practical approach. In this state-of-the-art review, we provide readers with the most updated knowledge on the pathogenesis, prevalence, diagnosis, and treatment of PA. In particular, we underscore the public health importance of promptly recognizing and treating PA and provide pragmatic solutions to modify clinical practices to achieve this.

Primary aldosteronism - a multidimensional syndrome

Primary aldosteronism is a common cause of hypertension and is a risk factor for cardiovascular and renal morbidity and mortality, via mechanisms mediated by both hypertension and direct insults to target organs. Despite its high prevalence and associated complications, primary aldosteronism remains largely under-recognized, with less than 2% of people in at-risk populations ever tested. Fundamental progress made over the past decade has transformed our understanding of the pathogenesis of primary aldosteronism and of its clinical phenotypes. The dichotomous paradigm of primary aldosteronism diagnosis and subtyping is being redefined into a multidimensional spectrum of disease, which spans subclinical stages to florid primary aldosteronism, and from single-focal or multifocal to diffuse aldosterone-producing areas, which can affect one or both adrenal glands. This Review discusses how redefining the primary aldosteronism syndrome as a multidimensional spectrum will affect the approach to the diagnosis and subtyping of primary aldosteronism.

Personalized Treatment of Patients With Primary Aldosteronism

Primary aldosteronism (PA) is a highly prevalent yet underdiagnosed secondary cause of hypertension. PA is associated with increased cardiovascular and renal morbidity compared with patients with primary hypertension. Thus, prompt identification and targeted therapy of PA are essential to reduce cardiovascular and renal morbidity and mortality in a large population with hypertension. Unilateral adrenalectomy is preferred for lateralized PA as the only potentially curative therapy. Surgery also mitigates the risk of cardiovascular and renal complications associated with PA. Targeted medical therapy, commonly including a mineralocorticoid receptor antagonist, is offered to patients with bilateral PA and those who are not surgical candidates. Novel therapies, including nonsteroidal mineralocorticoid receptor antagonists and aldosterone synthase inhibitors, are being developed as alternative options for PA treatment. In this review article, we discuss how to best individualize therapy for patients with PA.

A new machine learning-based prediction model for subtype diagnosis in primary aldosteronism

INTRODUCTION

Unilateral primary aldosteronism (UPA) and bilateral primary aldosteronism (BPA) are the two subtypes of PA. Discriminating UPA from BPA is of great significance. Although adrenal venous sampling (AVS) is the gold standard for diagnosis, it has shortcomings. Thus, improved methods are needed.

METHODS

The original data were extracted from the public database "Dryad". Ten parameters were included to develop prediction models for PA subtype diagnosis using machine learning technology. Moreover, the optimal model was chose and validated in an external dataset.

RESULTS

In the modeling dataset, 165 patients (71 UPA, 94 BPA) were included, while in the external dataset, 43 consecutive patients (20 UPA, 23 BPA) were included. The ten parameters utilized in the prediction model include age, sex, systolic and diastolic blood pressure, aldosterone to renin ratio (ARR), serum potassium, ARR after 50 mg captopril challenge test (CCT), primary aldosterone concentration (PAC) after saline infusion test (SIT), PAC reduction rate after SIT, and number of types of antihypertensive agents at diagnosis. The accuracy, sensitivity, specificity, F1 score, and AUC for the optimal model using the random forest classifier were 90.0%, 81.8%, 96.4%, 0.878, and 0.938, respectively, in the testing dataset and 81.4%, 90.0%, 73.9%, 0.818 and 0.887, respectively, in the validating external dataset. The most important variables contributing to the prediction model were PAC after SIT, ARR, and ARR after CCT.

DISCUSSION

We developed a machine learning-based predictive model for PA subtype diagnosis based on ten clinical parameters without CT imaging. In the future, artificial intelligence-based prediction models might become a robust prediction tool for PA subtype diagnosis, thereby, might reducing at least some of the requests for CT or AVS and assisting clinical decision-making.