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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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Abstract

Introduction

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.

Aim

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.

Methods

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.

Results

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.

Conclusion

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

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Author information

Authors and Affiliations

  1. Department of Diabetes, Metabolism, and Endocrinology, Tokyo Medical University, 6-7-1 Nishishinjuku Shinjuku-ku, Tokyo, 160-0023, Japan

    Shinichi Tamaru, Hirotsugu Suwanai, Hironori Abe, Junko Sasaki, Keitaro Ishii, Hajime Iwasaki, Jumpei Shikuma, Rokuro Ito, Takashi Miwa, Masato Odawara & Ryo Suzuki

  2. Department of Obstetrics and Gynecology, Tokyo Medical University, 6-7-1 Nishishinjuku Shinjuku-ku, Tokyo, 160-0023, Japan

    Toru Sasaki

  3. Department of Preventive Medicine and Public Health, Tokyo Medical University, 6-1-1 Shinjuku Shinjuku-ku, Tokyo, 160-8402, Japan

    Tomoko Takamiya & Shigeru Inoue

  4. Department of Radiology, Tokyo Medical University, 6-7-1 Nishishinjuku Shinjuku-ku, Tokyo, 160-0023, Japan

    Kazuhiro Saito

  5. Department of Diabetes, Metabolism and Endocrinology, International University of Health and Welfare, 4-3 Kozunomori Narita-shi, Chiba, 286-8686, Japan

    Masato Odawara

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  1. Shinichi Tamaru
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Corresponding author

Correspondence to Hirotsugu Suwanai.

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Funding

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). https://doi.org/10.1007/s40292-022-00523-8

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  • DOI: https://doi.org/10.1007/s40292-022-00523-8

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