- Adrenal Gland
- Outcome-Based Decision-Making Algorithm for Treating Patients with Primary Aldosteronism
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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
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Endocrinol Metab. 2022;37(2):369-382. Published online April 14, 2022
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DOI: https://doi.org/10.3803/EnM.2022.1391
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 Abstract
 PDF PubReader  ePub  Crossref - TDM
- 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.
- Clinical Study
- Diagnostic Accuracy of Computed Tomography in Predicting Primary Aldosteronism Subtype According to Age
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Seung Hun Lee, Jong Woo Kim, Hyun-Ki Yoon, Jung-Min Koh, Chan Soo Shin, Sang Wan Kim, Jung Hee Kim
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Endocrinol Metab. 2021;36(2):401-412. Published online March 31, 2021
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DOI: https://doi.org/10.3803/EnM.2020.901
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Abstract
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 Supplementary MaterialPubReader ePub Crossref - TDM
- Background
Guidelines by the Endocrine Society Guideline on bypassing adrenal vein sampling (AVS) in patients <35 years old with marked primary aldosteronism (PA) (hypokalemia and elevated plasma aldosterone concentration [PAC]) and a unilateral lesion on computed tomography (CT) are based on limited number of studies. We aimed to determine the accuracy of CT in PA patients according to age.
Methods
In this retrospective study, we investigated the concordance between CT and AVS in 466 PA patients from two tertiary centers who successfully underwent AVS.
Results
CT had an overall accuracy of 64.4% (300/466). In the group with unilateral lesion, patients with hypokalemia had higher concordance than those without hypokalemia (85.0% vs. 43.6%, P<0.001). In the group with marked PA (hypokalemia and PAC >15.9 ng/dL) and unilateral lesion, accuracy of CT was 84.6% (11/13) in patients aged <35 years; 100.0% (20/20), aged 35 to 39 years; 89.4% (59/66), aged 40 to 49 years; and 79.8% (79/99), aged ≥50 years. Cut-off age and PAC for concordance was <50 years and >29.6 ng/dL, respectively. The significant difference in accuracy of CT in 198 patients with marked PA and a unilateral lesion between the <50-year age group and ≥50-year age group (90.9% vs. 79.8%, P=0.044) disappeared in 139 of 198 patients with PAC > 30.0 ng/dL (91.9% vs. 87.7%, P=0.590).
Conclusion
Patients with hypokalemia, PAC >30.0 ng/dL, and unilateral lesion were at high risk of unilateral PA regardless of age.
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Citations
Citations to this article as recorded by  - Best Practices: Indications and Procedural Controversies of Adrenal Vein Sampling for Primary Aldosteronism
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American Journal of Roentgenology.2023; 220(2): 190. CrossRef - Accuracy of Gallium-68 Pentixafor Positron Emission Tomography–Computed Tomography for Subtyping Diagnosis of Primary Aldosteronism
Jinbo Hu, Tingting Xu, Hang Shen, Ying Song, Jun Yang, Aipin Zhang, Haoyuan Ding, Naiguo Xing, Zhuoyuan Li, Lin Qiu, Linqiang Ma, Yi Yang, Zhengping Feng, Zhipeng Du, Wenwen He, Yue Sun, Jun Cai, Qifu Li, Yue Chen, Shumin Yang, Mei Mei, Suxin Luo, Kangla
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The Journal of Clinical Endocrinology & Metabolism.2022; 107(3): e1315. CrossRef - Development and validation of model for sparing adrenal venous sampling in diagnosing unilateral primary aldosteronism
Ying Song, Jun Yang, Hang Shen, Elisabeth Ng, Peter J. Fuller, Zhengping Feng, Jinbo Hu, Linqiang Ma, Yi Yang, Zhipeng Du, Yue Wang, Ting Luo, Wenwen He, Qifu Li, Fei-Fei Wu, Shumin Yang
Journal of Hypertension.2022; 40(9): 1692. CrossRef - Validation of Existing Clinical Prediction Tools for Primary Aldosteronism Subtyping
Barbora Kološová, Petr Waldauf, Dan Wichterle, Jan Kvasnička, Tomáš Zelinka, Ondřej Petrák, Zuzana Krátká, Lubomíra Forejtová, Jan Kaván, Jiří Widimský, Robert Holaj
Diagnostics.2022; 12(11): 2806. CrossRef - Fully automatic volume measurement of the adrenal gland on CT using deep learning to classify adrenal hyperplasia
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European Radiology.2022; 33(6): 4292. CrossRef - Diagnostic Accuracy of Computed Tomography in Predicting Primary Aldosteronism Subtype According to Age (Endocrinol Metab 2021;36:401-12, Seung Hun Lee et al.)
Seung Hun Lee, Jong Woo Kim, Hyun-Ki Yoon, Jung-Min Koh, Chan Soo Shin, Sang Wan Kim, Jung Hee Kim
Endocrinology and Metabolism.2021; 36(4): 914. CrossRef - Diagnostic Accuracy of Computed Tomography in Predicting Primary Aldosteronism Subtype According to Age (Endocrinol Metab 2021;36:401-12, Seung Hun Lee et al.)
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