Prediction of hyperaldosteronism subtypes when adrenal vein sampling is unilaterally successful
Autor: | Alessio Burrello, Denis Rossato, Franco Veglio, Silvia Monticone, Martina Amongero, Tracy Ann Williams, Vittorio Forestiero, Paolo Mulatero, Jacopo Pieroni, Jacopo Burrello, Elisa Sconfienza |
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Rok vydání: | 2020 |
Předmět: |
Adult
Male medicine.medical_specialty Endocrinology Diabetes and Metabolism 030209 endocrinology & metabolism Sensitivity and Specificity Veins Diagnosis Differential Machine Learning 03 medical and health sciences chemistry.chemical_compound 0302 clinical medicine Endocrinology Text mining Primary aldosteronism Predictive Value of Tests Internal medicine Diagnosis Adrenal Glands Hyperaldosteronism medicine Humans Aldosterone Retrospective Studies Blood Specimen Collection business.industry Reproducibility of Results Retrospective cohort study General Medicine Gold standard (test) Middle Aged medicine.disease chemistry 030220 oncology & carcinogenesis Predictive value of tests Differential Female Regression Analysis Radiology Differential diagnosis business |
Zdroj: | European journal of endocrinology. 183(6) |
ISSN: | 1479-683X |
Popis: | Objective Adrenal venous sampling (AVS) is the gold standard to discriminate patients with unilateral primary aldosteronism (UPA) from bilateral disease (BPA). AVS is technically demanding and in cases of unsuccessful cannulation of adrenal veins, the results may not always be interpreted. The aim of our study was to develop diagnostic models to distinguish UPA from BPA, in cases of unilateral successful AVS and the presence of contralateral suppression of aldosterone secretion. Design Retrospective evaluation of 158 patients referred to a tertiary hypertension unit who underwent AVS. We randomly assigned 110 patients to a training cohort and 48 patients to a validation cohort to develop and test the diagnostic models. Methods Supervised machine learning algorithms and regression models were used to develop and validate two prediction models and a simple 19-point score system to stratify patients according to their subtype diagnosis. Results Aldosterone levels at screening and after confirmatory testing, lowest potassium, ipsilateral and contralateral imaging findings at CT scanning, and contralateral ratio at AVS, were associated with a diagnosis of UPA and were included in the diagnostic models. Machine learning algorithms correctly classified the majority of patients both at training and validation (accuracy: 82.9–95.7%). The score system displayed a sensitivity/specificity of 95.2/96.9%, with an AUC of 0.971. A flow-chart integrating our score correctly managed all patients except 3 (98.1% accuracy), avoiding the potential repetition of 77.2% of AVS procedures. Conclusions Our score could be integrated in clinical practice and guide surgical decision-making in patients with unilateral successful AVS and contralateral suppression. |
Databáze: | OpenAIRE |
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