Abstract
In primary aldosteronism (PA) the differentiation of unilateral aldosterone-producing adenomas (APA) from bilateral adrenal hyperplasia (BAH) is usually performed by adrenal venous sampling (AVS) and/or computed tomography (CT). CT alone often lacks the sensitivity to identify micro-APAs. Our objectives were to establish if steroid profiling could be useful for the identification of patients with micro-APAs and for the development of an online tool to differentiate micro-APAs, macro-APAs and BAH. The study included patients with PA (n = 197) from Munich (n = 124) and Torino (n = 73) and comprised 33 patients with micro-APAs, 95 with macro-APAs, and 69 with BAH. Subtype differentiation was by AVS, and micro- and macro-APAs were selected according to pathology reports. Steroid concentrations in peripheral venous plasma were measured by liquid chromatography-tandem mass spectrometry. An online tool using a random forest model was built for the classification of micro-APA, macro-APA and BAH. Micro-APA were classified with low specificity (33%) but macro-APA and BAH were correctly classified with high specificity (93%). Improved classification of micro-APAs was achieved using a diagnostic algorithm integrating steroid profiling, CT scanning and AVS procedures limited to patients with discordant steroid and CT results. This would have increased the correct classification of micro-APAs to 68% and improved the overall classification to 92%. Such an approach could be useful to select patients with CT-undetectable micro-APAs in whom AVS should be considered mandatory.
Item Type: | Journal article |
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EU Funded Grant Agreement Number: | 694913 |
EU Projects: | Horizon 2020 > ERC Grants > ERC Advanced Grant > ERC Grant 694913: PAPA - Pathophysiology of Primary Aldosteronism |
Faculties: | Medicine > Medical Center of the University of Munich > Medical Clinic and Outpatient Clinic IV (Endocrinology, nephrology, other sections) |
Subjects: | 600 Technology > 610 Medicine and health |
URN: | urn:nbn:de:bvb:19-epub-61673-2 |
ISSN: | 0960-0760 |
Annotation: | Epub ahead of print |
Language: | English |
Item ID: | 61673 |
Date Deposited: | 16. Apr 2019, 14:00 |
Last Modified: | 04. Nov 2020, 13:39 |