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.

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

Burrello J.
First
;
Amongero M.;Buffolo F.;Sconfienza E.;Forestiero V.;Veglio F.;Williams T. A.;Monticone S.;Mulatero P.
Last
2021

Abstract

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.
106
4
e1708
e1716
aldosterone; confirmatory testing; machine learning; primary aldosteronism
Burrello J.; Amongero M.; Buffolo F.; Sconfienza E.; Forestiero V.; Burrello A.; Adolf C.; Handgriff L.; 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/2318/1796285
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