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Original Article
- Adrenal Gland
- Outcome-Based Decision-Making Algorithm for Treating Patients with Primary Aldosteronism
- Jung Hee Kim, Chang Ho Ahn, Su Jin Kim, Kyu Eun Lee, Jong Woo Kim, Hyun-Ki Yoon, Yu-Mi Lee, Tae-Yon Sung, Sang Wan Kim, Chan Soo Shin, Jung-Min Koh, Seung Hun Lee
- Endocrinol Metab. 2022;37(2):369-382. Published online April 14, 2022
- DOI: https://doi.org/10.3803/EnM.2022.1391
- 3,790 View
- 159 Download
- 4 Web of Science
- 4 Crossref
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Abstract
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ePub - Background
Optimal management of primary aldosteronism (PA) is crucial due to the increased risk of cardiovascular and cerebrovascular diseases. Adrenal venous sampling (AVS) is the gold standard method for determining subtype but is technically challenging and invasive. Some PA patients do not benefit clinically from surgery. We sought to develop an algorithm to improve decision- making before engaging in AVS and surgery in clinical practice.
Methods
We conducted the ongoing Korean Primary Aldosteronism Study at two tertiary centers. Study A involved PA patients with successful catheterization and a unilateral nodule on computed tomography and aimed to predict unilateral aldosterone-producing adenoma (n=367). Study B involved similar patients who underwent adrenalectomy and aimed to predict postoperative outcome (n=330). In study A, we implemented important feature selection using the least absolute shrinkage and selection operator regression.
Results
We developed a unilateral PA prediction model using logistic regression analysis: lowest serum potassium level ≤3.4 mEq/L, aldosterone-to-renin ratio ≥150, plasma aldosterone concentration ≥30 ng/mL, and body mass index <25 kg/m2 (area under the curve, 0.819; 95% confidence interval, 0.774 to 0.865; sensitivity, 97.6%; specificity, 25.5%). In study B, we identified female, hypertension duration <5 years, anti-hypertension medication <2.5 daily defined dose, and the absence of coronary artery disease as predictors of clinical success, using stepwise logistic regression models (sensitivity, 94.2%; specificity, 49.3%). We validated our algorithm in the independent validation dataset (n=53).
Conclusion
We propose this new outcome-driven diagnostic algorithm, simultaneously considering unilateral aldosterone excess and clinical surgical benefits in PA patients. -
Citations
Citations to this article as recorded by
- Subtype-specific Body Composition and Metabolic Risk in Patients With Primary Aldosteronism
Seung Shin Park, Chang Ho Ahn, Sang Wan Kim, Ji Won Yoon, Jung Hee Kim
The Journal of Clinical Endocrinology & Metabolism.2024; 109(2): e788. CrossRef - Prognostic models to predict complete resolution of hypertension after adrenalectomy in primary aldosteronism: A systematic review and meta‐analysis
Luigi Marzano, Amir Kazory, Faeq Husain‐Syed, Claudio Ronco
Clinical Endocrinology.2023; 99(1): 17. CrossRef - 2023 Korean Endocrine Society Consensus Guidelines for the Diagnosis and Management of Primary Aldosteronism
Jeonghoon Ha, Jung Hwan Park, Kyoung Jin Kim, Jung Hee Kim, Kyong Yeun Jung, Jeongmin Lee, Jong Han Choi, Seung Hun Lee, Namki Hong, Jung Soo Lim, Byung Kwan Park, Jung-Han Kim, Kyeong Cheon Jung, Jooyoung Cho, Mi-kyung Kim, Choon Hee Chung
Endocrinology and Metabolism.2023; 38(6): 597. CrossRef - Correlation of Histopathologic Subtypes of Primary Aldosteronism with Clinical Phenotypes and Postsurgical Outcomes
Chang Ho Ahn, You-Bin Lee, Jae Hyeon Kim, Young Lyun Oh, Jung Hee Kim, Kyeong Cheon Jung
The Journal of Clinical Endocrinology & Metabolism.2023;[Epub] CrossRef
- Subtype-specific Body Composition and Metabolic Risk in Patients With Primary Aldosteronism
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