Context Primary aldosteronism (PA) comprises unilateral (lateralized, LPA) and bilateral disease (BPA). The identification of LPA is important to recommend potentially curative adrenalectomy. Adrenal venous sampling (AVS) is considered the gold standard for PA subtyping, but the procedure is available in few referral centers. Objective To develop prediction models for subtype diagnosis of PA using patient clinical and biochemical characteristics. Design, Patients and Setting Patients referred to a tertiary hypertension unit. Diagnostic algorithms were built and tested in a training (N=150) and in an internal validation cohort (N=65), respectively. The models were validated in an external independent cohort (N=118). Main outcome measure Regression analyses and supervised machine learning algorithms were used to develop and validate two diagnostic models and a 20-point score to classify patients with PA according to subtype diagnosis. Results Six parameters were associated with a diagnosis of LPA (aldosterone at screening and after confirmatory testing, lowest potassium value, presence/absence of nodules, nodule diameter, and computed tomography results) and were included in the diagnostic models. Machine learning algorithms displayed high accuracy at training and internal validation (79.1% to 93%), whereas a 20-point score reached an AUC of 0.896, and a sensitivity/specificity of 91.7/79.3%. An integrated flow-chart correctly addressed 96.3% of patients to surgery and would have avoided AVS in 43.7% of patients. The external validation on an independent cohort confirmed a similar diagnostic performance. Conclusions Diagnostic modelling techniques can be used for subtype diagnosis and guide surgical decision in patients with PA in centers where AVS is unavailable.

Objectives. In experimental animal models, exogenous aldosterone excess has been linked to the progression of renal disease. However, the evidence of an increased risk of renal damage in patients affected by primary aldosteronism (PA) remains controversial. We aimed at evaluating the association between PA and renal damage through a meta-analysis. Methods.We performed a quantitative review of studies evaluating parameters of renal function in patients affected by PA compared with patients affected by non-PA arterial hypertension and in patients affected by PA before and after specific treatment. We searched MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials from January 1960 up to August 2017. Results. 44 studies including 4,467 patients with PA and 8,234 patients affected by non-PA arterial hypertension were included. After 8.5 years from hypertension diagnosis, patients with PA had an increased glomerular filtration (GFR) rate compared with non-PA hypertensive patients (by 3.93ml/min IQR [0.60; 7.26]) and a more severe albuminuria (Std. mean difference 0.57 [0.11-1.03]), resulting into a significant association with microalbuminuria (OR 2.15 [1.21; 3.84]).Following specific PA treatment, after a median follow-up of 12 months, a significant reduction in GFR was observed (by -10.91ml/min [-13.61; -8.21]) that was consistent in both patients surgically treated and patients treated with medical therapy. Similarly, a reduction in albumin excretion and an increase in serum creatinine were observed after treatment.Conclusions. Patients affected by PA, compared with patients affected by non-PA arterial hypertension, display a more pronounced target organ damage, which can be mitigated by the specific treatment.

Context The diagnostic work-up of primary aldosteronism (PA) includes screening and confirmation steps. Case confirmation is time-consuming, expensive, and there is no consensus on tests and thresholds to be used. Diagnostic algorithms to avoid confirmatory testing may be useful for the management of patients with PA. Objective Development and validation of diagnostic models to confirm or exclude PA diagnosis in patients with a positive screening test. Design, Patients and Setting We evaluated 1,024 patients who underwent confirmatory testing for PA. The diagnostic models were developed in a training cohort (n=522), and then tested on an internal validation cohort (n=174) and on an independent external prospective cohort (n=328). Main outcome measure Different diagnostic models and a 16-point score were developed by machine learning and regression analysis to discriminate patients with a confirmed diagnosis of PA. Results Male sex, antihypertensive medication, plasma renin activity, aldosterone, potassium levels and presence of organ damage were associated with a confirmed diagnosis of PA. Machine learning based models displayed an accuracy of 72.9-83.9%. The Primary Aldosteronism Confirmatory Testing (PACT) score correctly classified 84.1% at training and 83.9% or 81.1% at internal and external validation, respectively. A flow chart employing the PACT score to select patients for confirmatory testing, correctly managed all patients, and resulted in a 22.8% reduction in the number of confirmatory tests. Conclusions The integration of diagnostic modelling algorithms in clinical practice may improve the management of patients with PA by circumventing unnecessary confirmatory testing.

The coexistence of aldosterone oversecretion and obstructive sleep apnea is frequently observed, especially in patients with resistant hypertension, obesity, and metabolic syndrome. Since aldosterone excess and sleep apnea are both independently associated with an increased risk of cardiovascular disease, to investigate whether their coexistence might be attributed to common predisposing conditions, such as metabolic disorders, or to an actual pathophysiological interconnection appears of great importance. Fluid overload and metabolic abnormalities relating to aldosterone oversecretion may be implicated in obstructive sleep apnea development. Nocturnal intermittent hypoxia may in turn exacerbate renin-angiotensin-aldosterone system activity, thus leading to hyperaldosteronism. Furthermore, fat tissue excess and adipocyte secretory products might predispose to both sleep apnea and aldosterone oversecretion in subjects with obesity. Consistent with these evidences, obstructive sleep apnea frequently affects patients with primary aldosteronism. Conversely, whether primary aldosteronism is more prevalent in individuals affected by obstructive sleep apnea compared to the general population remains controversial.

Primary aldosteronism (PA) is the cause of arterial hypertension in 4% to 6% of patients, and 30% of patients with PA are affected by unilateral and surgically curable forms. Current guidelines recommend screening for PA ≈50% of patients with hypertension on the basis of individual factors, while some experts suggest screening all patients with hypertension. To define the risk of PA and tailor the diagnostic workup to the individual risk of each patient, we developed a conventional scoring system and supervised machine learning algorithms using a retrospective cohort of 4059 patients with hypertension. On the basis of 6 widely available parameters, we developed a numerical score and 308 machine learning-based models, selecting the one with the highest diagnostic performance. After validation, we obtained high predictive performance with our score (optimized sensitivity of 90.7% for PA and 92.3% for unilateral PA [UPA]). The machine learning-based model provided the highest performance, with an area under the curve of 0.834 for PA and 0.905 for diagnosis of UPA, with optimized sensitivity of 96.6% for PA, and 100.0% for UPA, at validation. The application of the predicting tools allowed the identification of a subgroup of patients with very low risk of PA (0.6% for both models) and null probability of having UPA. In conclusion, this score and the machine learning algorithm can accurately predict the individual pretest probability of PA in patients with hypertension and circumvent screening in up to 32.7% of patients using a machine learning-based model, without omitting patients with surgically curable UPA.