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.

Prediction of hyperaldosteronism subtypes when adrenal vein sampling is unilaterally successful

Burrello J.
First
;
Pieroni J.;Sconfienza E.;Forestiero V.;Amongero M.;Rossato D.;Veglio F.;Williams T. A.;Monticone S.
Co-last
;
Mulatero P.
2020

Abstract

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.
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Adrenal Glands; Adult; Aldosterone; Blood Specimen Collection; Diagnosis, Differential; Female; Humans; Hyperaldosteronism; Machine Learning; Male; Middle Aged; Predictive Value of Tests; Regression Analysis; Reproducibility of Results; Retrospective Studies; Sensitivity and Specificity; Veins
Burrello J.; Burrello A.; Pieroni J.; Sconfienza E.; Forestiero V.; Amongero M.; Rossato D.; Veglio F.; Williams T.A.; Monticone S.; Mulatero P.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/1764443
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