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Machine learning approach to predict subtypes of primary aldosteronism is helpful to estimate indication of adrenal vein sampling

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Primary aldosteronism (PA) is a common disease. Especially in unilateral PA (UPA), the risk of cardiovascular disease is high and proper localization is important. Adrenal vein sampling (AVS) is commonly used to localize PA, but its availability is limited. Therefore, it is important to predict the unilateral or bilateral PA and to choose the appropriate cases for AVS or watchful observation.


The purpose of this study is to develop a model using machine learning to predict bilateral or unilateral PA to extract cases for AVS or watchful observation.


We retrospectively analyzed 154 patients diagnosed with PA and who underwent AVS at our hospital between January 2010 and June 2021. Based on machine learning, we determined predictors of PA subtypes diagnosis from the results of blood and loading tests.


The accuracy of the machine learning was 88% and the top predictors of the UPA were plasma aldosterone concentration after the saline infusion test, aldosterone to renin ratio after the captopril challenge test, serum potassium and aldosterone-to-renin ratio. By using these factors, the accuracy, sensitivity, specificity and the area under the curve (AUC) were 91%, 70%, 99% and 0.91, respectively. Furthermore, we examined the surgical outcomes of UPA and found that the group diagnosed as unilateral by the predictors showed improvement in clinical findings, while the group diagnosed as bilateral by the predictors showed no improvement.


Our predictive model based on machine learning can support to choose the performance of adrenal vein sampling or watchful observation.

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  1. Milliez P, Girerd X, Plouin PF, Blacher J, Safar ME, Mourad JJ. Evidence for an increased rate of cardiovascular events in patients with primary aldosteronism. J Am Coll Cardiol. 2005;45(8):1243–8.

    Article  CAS  Google Scholar 

  2. Stowasser M, Gordon RD, Gunasekera TG, Cowley DC, Ward G, Archibald C, et al. High rate of detection of primary aldosteronism, including surgically treatable forms, after “non-selective” screening of hypertensive patients. J Hypertens. 2003;21(11):2149–57.

    Article  CAS  Google Scholar 

  3. Calhoun DA. Resistant or difficult-to-treat hypertension. J Clin Hypertens (Greenwich). 2006;8(3):181–6.

    Article  Google Scholar 

  4. Douma S, Petidis K, Doumas M, Papaefthimiou P, Triantafyllou A, Kartali N, et al. Prevalence of primary hyperaldosteronism in resistant hypertension: a retrospective observational study. Lancet. 2008;371(9628):1921–6.

    Article  CAS  Google Scholar 

  5. Mulatero P, Rabbia F, Milan A, Paglieri C, Morello F, Chiandussi L, et al. Drug effects on aldosterone/plasma renin activity ratio in primary aldosteronism. Hypertension. 2002;40(6):897–902.

    Article  CAS  Google Scholar 

  6. Muiesan ML, Salvetti M, Rizzoni D, Paini A, Agabiti-Rosei C, Aggiusti C, et al. Resistant hypertension and target organ damage. Hypertens Res. 2013;36(6):485–91.

    Article  Google Scholar 

  7. Abad-Cardiel M, Alvarez-Álvarez B, Luque-Fernandez L, Fernández C, Fernández-Cruz A, Martell-Claros N. Hypertension caused by primary hyperaldosteronism: increased heart damage and cardiovascular risk. Rev Esp Cardiol (Engl Ed). 2013;66(1):47–52.

    Article  Google Scholar 

  8. Savard S, Amar L, Plouin PF, Steichen O. Cardiovascular complications associated with primary aldosteronism: a controlled cross-sectional study. Hypertension. 2013;62(2):331–6.

    Article  CAS  Google Scholar 

  9. Tanabe A, Naruse M, Naruse K, Hase M, Yoshimoto T, Tanaka M, et al. Left ventricular hypertrophy is more prominent in patients with primary aldosteronism than in patients with other types of secondary hypertension. Hypertens Res. 1997;20(2):85–90.

    Article  CAS  Google Scholar 

  10. Hundemer GL, Curhan GC, Yozamp N, Wang M, Vaidya A. Cardiometabolic outcomes and mortality in medically treated primary aldosteronism: a retrospective cohort study. Lancet Diabetes Endocrinol. 2018;6(1):51–9.

    Article  Google Scholar 

  11. Krug AW, Ehrhart-Bornstein M. Aldosterone and metabolic syndrome: is increased aldosterone in metabolic syndrome patients an additional risk factor? Hypertension. 2008;51(5):1252–8.

    Article  CAS  Google Scholar 

  12. Lethielleux G, Amar L, Raynaud A, Plouin PF, Steichen O. Influence of diagnostic criteria on the interpretation of adrenal vein sampling. Hypertension. 2015;65(4):849–54.

    Article  CAS  Google Scholar 

  13. Vonend O, Ockenfels N, Gao X, Allolio B, Lang K, Mai K, et al. Adrenal venous sampling: evaluation of the German Conn’s registry. Hypertension. 2011;57(5):990–5.

    Article  CAS  Google Scholar 

  14. Monticone S, Satoh F, Dietz AS, Goupil R, Lang K, Pizzolo F, et al. Clinical management and outcomes of adrenal hemorrhage following adrenal vein sampling in primary aldosteronism. Hypertension. 2016;67(1):146–52.

    Article  CAS  Google Scholar 

  15. Nishikawa T, Omura M, Satoh F, Shibata H, Takahashi K, Tamura N, et al. Guidelines for the diagnosis and treatment of primary aldosteronism–the Japan Endocrine Society 2009. Endocr J. 2011;58(9):711–21.

    Article  CAS  Google Scholar 

  16. Shimamoto K, Ando K, Fujita T, Hasebe N, Higaki J, Horiuchi M, et al. The Japanese Society of Hypertension Guidelines for the Management of Hypertension (JSH 2014). Hypertens Res. 2014;37(4):253–390.

    Article  Google Scholar 

  17. Funder JW, Carey RM, Fardella C, Gomez-Sanchez CE, Mantero F, Stowasser M, et al. Case detection, diagnosis, and treatment of patients with primary aldosteronism: an endocrine society clinical practice guideline. J Clin Endocrinol Metab. 2008;93(9):3266–81.

    Article  Google Scholar 

  18. Kempers MJ, Lenders JW, van Outheusden L, van der Wilt GJ, Schultze Kool LJ, Hermus AR, et al. Systematic review: diagnostic procedures to differentiate unilateral from bilateral adrenal abnormality in primary aldosteronism. Ann Intern Med. 2009;151(5):329–37.

    Article  Google Scholar 

  19. Rossi GP, Barisa M, Allolio B, Auchus RJ, Amar L, Cohen D, et al. The Adrenal Vein Sampling International Study (AVIS) for identifying the major subtypes of primary aldosteronism. J Clin Endocrinol Metab. 2012;97(5):1606–14.

    Article  CAS  Google Scholar 

  20. Stowasser M, Taylor PJ, Pimenta E, Ahmed AH, Gordon RD. Laboratory investigation of primary aldosteronism. Clin Biochem Rev. 2010;31(2):39–56.

    PubMed  PubMed Central  Google Scholar 

  21. Rossi GP, Auchus RJ, Brown M, Lenders JW, Naruse M, Plouin PF, et al. An expert consensus statement on use of adrenal vein sampling for the subtyping of primary aldosteronism. Hypertension. 2014;63(1):151–60.

    Article  CAS  Google Scholar 

  22. Li XF, Huang YZ, Tang JY, Li RC, Wang XQ. Development of a random forest model for hypotension prediction after anesthesia induction for cardiac surgery. World J Clin Cases. 2021;9(29):8729–39.

    Article  Google Scholar 

  23. Wang Z, Zhe S, Zimmerman J, Morrisey C, Tonna JE, Sharma V, et al. Development and validation of a machine learning method to predict intraoperative red blood cell transfusions in cardiothoracic surgery. Sci Rep. 2022;12(1):1355.

    Article  CAS  Google Scholar 

  24. Tsai IJ, Shen WC, Lee CL, Wang HD, Lin CY. Machine learning in prediction of bladder cancer on clinical laboratory data. Diagnostics (Basel). 2022;12(1):203.

    Article  Google Scholar 

  25. Garge NR, Bobashev G, Eggleston B. Random forest methodology for model-based recursive partitioning: the mobForest package for R. BMC Bioinformatics. 2013;14:125.

    Article  Google Scholar 

  26. Su X, Xu Y, Tan Z, Wang X, Yang P, Su Y, et al. Prediction for cardiovascular diseases based on laboratory data: an analysis of random forest model. J Clin Lab Anal. 2020;34(9): e23421.

    Article  CAS  Google Scholar 

  27. Zhang Y, Luo F, Fan P, Meng X, Yang K, Zhou X. Is primary aldosteronism a potential risk factor for aortic dissection? A case report and literature review. BMC Endocr Disord. 2020;20(1):115.

    Article  CAS  Google Scholar 

  28. Byun JM, Chon S, Kim SJ. A case of primary aldosteronism presenting as non-ST elevation myocardial infarction. Korean J Intern Med. 2013;28(6):739–42.

    Article  Google Scholar 

  29. Auchus RJ, Michaelis C, Wians FH, Dolmatch BL, Josephs SC, Trimmer CK, et al. Rapid cortisol assays improve the success rate of adrenal vein sampling for primary aldosteronism. Ann Surg. 2009;249(2):318–21.

    Article  Google Scholar 

  30. Daunt N. Adrenal vein sampling: how to make it quick, easy, and successful. Radiographics. 2005;25(Suppl 1):S143–58.

    Article  Google Scholar 

  31. Küpers EM, Amar L, Raynaud A, Plouin PF, Steichen O. A clinical prediction score to diagnose unilateral primary aldosteronism. J Clin Endocrinol Metab. 2012;97(10):3530–7.

    Article  Google Scholar 

  32. Nanba K, Tsuiki M, Nakao K, Nanba A, Usui T, Tagami T, et al. A subtype prediction score for primary aldosteronism. J Hum Hypertens. 2014;28(12):716–20.

    Article  CAS  Google Scholar 

  33. Kocjan T, Janez A, Stankovic M, Vidmar G, Jensterle M. A new clinical prediction criterion accurately determines a subset of patients with bilateral primary aldosteronism before adrenal venous sampling. Endocr Pract. 2016;22(5):587–94.

    Article  Google Scholar 

  34. Kobayashi H, Haketa A, Ueno T, Ikeda Y, Hatanaka Y, Tanaka S, et al. Scoring system for the diagnosis of bilateral primary aldosteronism in the outpatient setting before adrenal venous sampling. Clin Endocrinol (Oxf). 2017;86(4):467–72.

    Article  CAS  Google Scholar 

  35. Kobayashi H, Abe M, Soma M, Takeda Y, Kurihara I, Itoh H, et al. Development and validation of subtype prediction scores for the workup of primary aldosteronism. J Hypertens. 2018;36(11):2269–76.

    Article  CAS  Google Scholar 

  36. Umakoshi H, Tsuiki M, Takeda Y, Kurihara I, Itoh H, Katabami T, et al. Significance of computed tomography and serum potassium in predicting subtype diagnosis of primary aldosteronism. J Clin Endocrinol Metab. 2018;103(3):900–8.

    Article  Google Scholar 

  37. Burrello J, Burrello A, Pieroni J, Sconfienza E, Forestiero V, Rabbia P, et al. Development and validation of prediction models for subtype diagnosis of patients with primary aldosteronism. J Clin Endocrinol Metab. 2020;105(10):e3706–17.

    Article  Google Scholar 

  38. Kaneko H, Umakoshi H, Ogata M, Wada N, Iwahashi N, Fukumoto T, et al. Machine learning based models for prediction of subtype diagnosis of primary aldosteronism using blood test. Sci Rep. 2021;11(1):9140.

    Article  CAS  Google Scholar 

  39. Sechi LA, Colussi G, Di Fabio A, Catena C. Cardiovascular and renal damage in primary aldosteronism: outcomes after treatment. Am J Hypertens. 2010;23(12):1253–60.

    Article  CAS  Google Scholar 

  40. Zarnegar R, Young WF, Lee J, Sweet MP, Kebebew E, Farley DR, et al. The aldosteronoma resolution score: predicting complete resolution of hypertension after adrenalectomy for aldosteronoma. Ann Surg. 2008;247(3):511–8.

    Article  Google Scholar 

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Correspondence to Hirotsugu Suwanai.

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The authors did not receive support from any organization for the submitted work.

Conflict of interest

Authors have no conflicts of interest to declare that are relevant to the content of this article.

Ethics approval

This study was performed in accordance with the principles of the Declaration of Helsinki and was approved by the ethics committee of Tokyo Medical University Hospital (T2018-0017). The need for informed patient consent was waived owing to the study’s retrospective design. Of note, at the start of the study, we used an opt-out approach to notify patients and disclose information on the purpose and implementation of the research and to guarantee opportunities to opt-out as much as possible.

Data availability

Raw data were generated at Tokyo Medical University. Derived data supporting the findings of this study are available from the corresponding author on request.

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Tamaru, S., Suwanai, H., Abe, H. et al. Machine learning approach to predict subtypes of primary aldosteronism is helpful to estimate indication of adrenal vein sampling. High Blood Press Cardiovasc Prev 29, 375–383 (2022).

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