Supervised and unsupervised learning to define the cardiovascular risk of patients according to an extracellular vesicle molecular signature.

Cardiovascular (CV) disease represents the most common cause of death in developed countries. Risk assessment is highly relevant to intervene at individual level and implement prevention strategies. Circulating extracellular vesicles (EVs) are involved in the development and progression of CV diseases and are considered promising biomarkers. We aimed at identifying an EV signature to improve the stratification of patients according to CV risk and likelihood to develop fatal CV events. EVs were characterized by nanoparticle tracking analysis and flow cytometry for a standardized panel of 37 surface antigens in a cross-sectional multicenter cohort (n = 486). CV profile was defined by presence of different indicators (age, sex, body mass index, hypertension, hyperlipidemia, diabetes, coronary artery disease, cardiac heart failure, chronic kidney disease, smoking habit, organ damage) and according to the 10-year risk of fatal CV events estimated using SCORE charts of European Society of Cardiology. By combining expression levels of EV antigens using unsupervised learning, patients were classified into 3 clusters: Cluster-I (n = 288), Cluster-II (n = 83), Cluster-III (n = 30). A separate analysis was conducted on patients displaying acute CV events (n = 82). Prevalence of hypertension, diabetes, chronic heart failure, and organ damage (defined as left ventricular hypertrophy and/or microalbuminuria) increased progressively from Cluster-I to Cluster-III. Several EV antigens, including markers for platelets (CD41b-CD42a-CD62P), leukocytes (CD1c-CD2-CD3-CD4-CD8-CD14-CD19-CD20-CD25-CD40-CD45-CD69-CD86), and endothelium (CD31-CD105) were independently associated with CV risk indicators and correlated to age, blood pressure, glucometabolic profile, renal function, and SCORE risk. EV profiling, obtained from minimally invasive blood sampling, allows accurate patient stratification according to CV risk profile.

Quality of life in primary aldosteronism: A prospective observational study.

BACKGROUND: Previous studies suggested that patients affected by primary aldosteronism (PA) have impaired quality of life (QOL) compared to the general population, but a direct comparison with patients affected by essential hypertension (EH) has never been performed. The aim of the study was to compare the QOL of patients affected by PA to the QOL of patients affected by EH. MATERIAL AND METHODS: We designed a prospective observational study comparing the QOL of patients with PA and carefully matched patients with EH before and after treatment. We recruited 70 patients with PA and 70 patients with EH, matched for age, sex, blood pressure levels and intensity of antihypertensive treatment. We assessed QOL at baseline and after specific treatment for PA or after optimization of medical therapy for patients with EH. RESULTS: Patients with PA displayed impaired QOL compared with the general healthy population, but similar to patients with EH. Both laparoscopic adrenalectomy and treatment with mineralocorticoid receptor antagonist allowed an improvement of QOL in patients with PA, that was more pronounced after surgical treatment. Optimization of blood pressure control by implementation of antihypertensive treatment (without MR antagonists) allowed a minimal improvement in only one of eight domains in patients with EH. CONCLUSIONS: Patients with PA have impaired QOL, which is likely caused by uncontrolled hypertension and the effects of intensive antihypertensive treatment. Surgical and medical treatment of PA allows a significant improvement of QOL, by amelioration of blood pressure control and, after surgical treatment, by reduction of antihypertensive treatment.

Development of a Prediction Score to Avoid Confirmatory Testing in Patients With Suspected Primary Aldosteronism.

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 1024 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 the 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 modeling algorithms in clinical practice may improve the management of patients with PA by circumventing unnecessary confirmatory testing.