Hiroki Kobayashi1, Masanori Abe1, Masayoshi Soma1, Yoshiyu Takeda2, Isao Kurihara3, Hiroshi Itoh3, Hironobu Umakoshi4, Mika Tsuiki4, Takuyuki Katabami5, Takamasa Ichijo6, Norio Wada7, Takanobu Yoshimoto8, Yoshihiro Ogawa8, Junji Kawashima9, Masakatsu Sone10, Nobuya Inagaki10, Katsutoshi Takahashi11, Minemori Watanabe12, Yuichi Matsuda13, Hirotaka Shibata14, Kohei Kamemura15, Toshihiko Yanase16, Michio Otsuki17, Yuichi Fujii18, Koichi Yamamoto19, Atsushi Ogo20, Kazutaka Nanba21, Akiyo Tanabe22, Tomoko Suzuki23, Mitsuhide Naruse4. 1. Division of Nephrology, Hypertension, and Endocrinology, Nihon University School of Medicine, Tokyo. 2. Department of Internal Medicine, Graduate School of Medical Science, Kanazawa University, Kanazawa. 3. Department of Endocrinology, Metabolism, and Nephrology, Keio University School of Medicine, Tokyo. 4. Department of Endocrinology and Metabolism, National Hospital Organization Kyoto Medical Center, Kyoto. 5. Division of Metabolism and Endocrinology, Department of Internal Medicine, St. Marianna University School of Medicine, Yokohama City Seibu Hospital. 6. Department of Endocrinology and Metabolism, Saiseikai Yokohamashi Tobu Hospital, Yokohama. 7. Department of Diabetes and Endocrinology, Sapporo City General Hospital, Sapporo. 8. Department of Molecular Endocrinology and Metabolism, Tokyo Medical and Dental University, Tokyo. 9. Department of Metabolic Medicine, Faculty of Life Science, Kumamoto University, Kumamoto. 10. Department of Diabetes, Endocrinology, and Nutrition, Kyoto University, Kyoto. 11. Division of Metabolism, Showa General Hospital, Tokyo. 12. Department of Endocrinology and Diabetes, Okazaki City Hospital, Okazaki. 13. Department of Cardiology, Sanda City Hospital, Sanda. 14. Department of Endocrinology, Metabolism, Rheumatology, and Nephrology, Faculty of Medicine, Oita University, Oita. 15. Department of Cardiology, Akashi Medical Center, Akashi. 16. Department of Endocrinology and Diabetes Mellitus, Fukuoka University, Fukuoka. 17. Department of Metabolic Medicine, Osaka University Graduate School of Medicine, Osaka. 18. Department of Cardiology, JR Hiroshima Hospital, Hiroshima. 19. Department of Geriatric and General Medicine, Osaka University Graduate School of Medicine, Osaka. 20. Clinical Research Institute, National Hospital Organization Kyusyu Medical Center, Fukuoka, Japan. 21. Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, Michigan, USA. 22. Division of Endocrinology, National Center for Global Health and Medicine, Tokyo. 23. Department of Public Health, International University of Health and Welfare School of Medicine, Chiba, Japan.
Abstract
OBJECTIVES: A subtype prediction score for primary aldosteronism has not yet been developed and validated using a large dataset. This study aimed to develop and validate a new subtype prediction score and to compare it with existing scores using a large multicenter database. METHODS: In total, 1936 patients with primary aldosteronism were randomly assigned to the development and validation datasets, constituting 1290 and 646 patients, respectively. Three prediction scores were generated with or without confirmatory tests, using logistic regression analysis. In the validation dataset, new and existing prediction scores were compared using receiver operating characteristic curve, net reclassification improvement, and integrated discrimination improvement analyses. RESULTS: The new prediction score is simply calculated using serum potassium levels [>3.9 mmol/l (four points); 3.5-3.9 mmol/l (three points)], the absence of adrenal nodules during computed tomography (three points), a baseline plasma aldosterone concentration of <210.0 pg/ml (two points), a baseline aldosterone/renin ratio of less than 620 (two points), and female sex (one point). Using the validation dataset, we found that a new subtype prediction score of at least 8 had a positive predictive value of 93.5% for bilateral hyperaldosteronism. The new prediction score for bilateral hyperaldosteronism was better than the existing prediction scores in the receiver operating characteristic curve and net reclassification improvement analyses. CONCLUSION: The new prediction score has clear advantages over the existing prediction scores in terms of diagnostic accuracy, feasibility, and the potential for generalization in a large population. These data will help healthcare professionals to better select patients who require adrenal venous sampling.
OBJECTIVES: A subtype prediction score for primary aldosteronism has not yet been developed and validated using a large dataset. This study aimed to develop and validate a new subtype prediction score and to compare it with existing scores using a large multicenter database. METHODS: In total, 1936 patients with primary aldosteronism were randomly assigned to the development and validation datasets, constituting 1290 and 646 patients, respectively. Three prediction scores were generated with or without confirmatory tests, using logistic regression analysis. In the validation dataset, new and existing prediction scores were compared using receiver operating characteristic curve, net reclassification improvement, and integrated discrimination improvement analyses. RESULTS: The new prediction score is simply calculated using serum potassium levels [>3.9 mmol/l (four points); 3.5-3.9 mmol/l (three points)], the absence of adrenal nodules during computed tomography (three points), a baseline plasma aldosterone concentration of <210.0 pg/ml (two points), a baseline aldosterone/renin ratio of less than 620 (two points), and female sex (one point). Using the validation dataset, we found that a new subtype prediction score of at least 8 had a positive predictive value of 93.5% for bilateral hyperaldosteronism. The new prediction score for bilateral hyperaldosteronism was better than the existing prediction scores in the receiver operating characteristic curve and net reclassification improvement analyses. CONCLUSION: The new prediction score has clear advantages over the existing prediction scores in terms of diagnostic accuracy, feasibility, and the potential for generalization in a large population. These data will help healthcare professionals to better select patients who require adrenal venous sampling.
Authors: Seung Hun Lee; Jong Woo Kim; Hyun-Ki Yoon; Jung-Min Koh; Chan Soo Shin; Sang Wan Kim; Jung Hee Kim Journal: Endocrinol Metab (Seoul) Date: 2021-03-31
Authors: Seung Hun Lee; Jong Woo Kim; Hyun-Ki Yoon; Jung-Min Koh; Chan Soo Shin; Sang Wan Kim; Jung Hee Kim Journal: Endocrinol Metab (Seoul) Date: 2021-08-27