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A hyperaldosteronism subtypes predictive model using ensemble learning.
Karashima, Shigehiro; Kawakami, Masaki; Nambo, Hidetaka; Kometani, Mitsuhiro; Kurihara, Isao; Ichijo, Takamasa; Katabami, Takuyuki; Tsuiki, Mika; Wada, Norio; Oki, Kenji; Ogawa, Yoshihiro; Okamoto, Ryuji; Tamura, Kouichi; Inagaki, Nobuya; Yoshimoto, Takanobu; Kobayashi, Hiroki; Kakutani, Miki; Fujita, Megumi; Izawa, Shoichiro; Suwa, Tetsuya; Kamemura, Kohei; Yamada, Masanobu; Tanabe, Akiyo; Naruse, Mitsuhide; Yoneda, Takashi.
Afiliação
  • Karashima S; Institute of Liberal Arts and Science, Kanazawa University, Kanazawa, Japan.
  • Kawakami M; School of Electrical Information Communication Engineering, College of Science and Engineering, Kanazawa University, Kanazawa, Japan.
  • Nambo H; School of Electrical Information Communication Engineering, College of Science and Engineering, Kanazawa University, Kanazawa, Japan.
  • Kometani M; Department of Endocrinology and Metabolism, Kanazawa University Graduate School of Medicine, Kanazawa, Japan.
  • Kurihara I; Department of Medical Education, National Defense Medical College, Tokorozawa, Japan.
  • Ichijo T; Department of Endocrinology, Metabolism and Nephrology, Keio University School of Medicine, Tokyo, Japan.
  • Katabami T; Department of Diabetes and Endocrinology, Saiseikai Yokohamashi Tobu Hospital, Yokohama, Japan.
  • Tsuiki M; Division of Metabolism and Endocrinology, Department of Internal Medicine, St. Marianna University Yokohama City Seibu Hospital, Yokohama, Japan.
  • Wada N; Department of Endocrinology and Metabolism, National Hospital Organization Kyoto Medical Center, Kyoto, Japan.
  • Oki K; Department of Diabetes and Endocrinology, Sapporo City General Hospital, Sapporo, Japan.
  • Ogawa Y; Department of Molecular and Internal Medicine, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
  • Okamoto R; Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
  • Tamura K; Department of Cardiology and Nephrology, Mie University Graduate School of Medicine, Tsu, Japan.
  • Inagaki N; Department of Medical Science and Cardiorenal Medicine, Yokohama City University Graduate School of Medicine, Yokohama, Japan.
  • Yoshimoto T; Division of Nephrology and Hypertension, Yokohama City University Medical Center, Yokohama, Japan.
  • Kobayashi H; Department of Diabetes, Endocrinology, and Nutrition, Kyoto University, Kyoto, Japan.
  • Kakutani M; Department of Molecular Endocrinology and Metabolism, Tokyo Medical and Dental University, Tokyo, Japan.
  • Fujita M; Division of Nephrology, Hypertension, and Endocrinology, Nihon University School of Medicine, Tokyo, Japan.
  • Izawa S; Division of Diabetes, Endocrinology, and Clinical Immunology, Department of Internal Medicine, Hyogo College of Medicine, Hyogo, Japan.
  • Suwa T; Division of Nephrology and Endocrinology, The University of Tokyo, Tokyo, Japan.
  • Kamemura K; Division of Endocrinology and Metabolism, Faculty of Medicine, Tottori University, Yonago, Japan.
  • Yamada M; Department of Diabetes and Endocrinology, Graduate School of Medicine, Gifu University, Gifu, Japan.
  • Tanabe A; Department of Cardiology, Shinko Hospital, Hyogo, Japan.
  • Naruse M; Department of Medicine and Molecular Science, Gunma University Graduate School of Medicine, Maebashi, 371-8511, Japan.
  • Yoneda T; Division of Endocrinology, National Center for Global Health and Medicine, Tokyo, Japan.
Sci Rep ; 13(1): 3043, 2023 02 21.
Article em En | MEDLINE | ID: mdl-36810868
This study aimed to develop a machine-learning algorithm to diagnose aldosterone-producing adenoma (APA) for predicting APA probabilities. A retrospective cross-sectional analysis of the Japan Rare/Intractable Adrenal Diseases Study dataset was performed using the nationwide PA registry in Japan comprised of 41 centers. Patients treated between January 2006 and December 2019 were included. Forty-six features at screening and 13 features at confirmatory test were used for model development to calculate APA probability. Seven machine-learning programs were combined to develop the ensemble-learning model (ELM), which was externally validated. The strongest predictive factors for APA were serum potassium (s-K) at first visit, s-K after medication, plasma aldosterone concentration, aldosterone-to-renin ratio, and potassium supplementation dose. The average performance of the screening model had an AUC of 0.899; the confirmatory test model had an AUC of 0.913. In the external validation, the AUC was 0.964 in the screening model using an APA probability of 0.17. The clinical findings at screening predicted the diagnosis of APA with high accuracy. This novel algorithm can support the PA practice in primary care settings and prevent potentially curable APA patients from falling outside the PA diagnostic flowchart.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Adenoma / Hiperaldosteronismo / Hipertensão Tipo de estudo: Diagnostic_studies / Observational_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Adenoma / Hiperaldosteronismo / Hipertensão Tipo de estudo: Diagnostic_studies / Observational_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article