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.

Development and validation of prediction models for subtype diagnosis of patients with primary aldosteronism

Burrello J.
First
;
Pieroni J.;Sconfienza E.;Forestiero V.;Rabbia P.;Adolf C.;Veglio F.;Williams T. A.;Monticone S.
Co-last
;
Mulatero P.
2020

Abstract

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.
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adrenal venous sampling; aldosterone; machine learning; primary aldosteronism
Burrello J.; Burrello A.; Pieroni J.; Sconfienza E.; Forestiero V.; Rabbia P.; Adolf C.; Reincke M.; 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/2318/1742456
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