Worapaka Manosroi1,2,3, Tanyong Pipanmekaporn2,3,4, Jiraporn Khorana2,3,5, Pichitchai Atthakomol6, Mattabhorn Phimphilai1. 1. Division of Endocrinology, Department of Internal Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand. 2. Clinical Epidemiology Center, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand. 3. Clinical Statistic Center, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand. 4. Department of Anesthesiology, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand. 5. Division of Pediatric Surgery, Department of Surgery, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand. 6. Department of Orthopaedics, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand.
Abstract
Background: The diagnosis of adrenal insufficiency (AI) requires dynamic tests which may not be available in some institutions. This study aimed to develop a predictive risk score to help diagnose AI in outpatients with indeterminate serum cortisol levels. Methods: Five hundred and seven patients with intermediate serum cortisol levels (3-17.9 µg/dL) who had undergone ACTH (adrenocorticotropin) stimulation tests were included in the study. A predictive risk score was created using significant predictive factors identified by multivariable analysis using Poisson regression clustered by ACTH dose. Results: The seven predictive factors used in the development of a predictive model with their assigned scores are as follows: chronic kidney disease (9.0), Cushingoid appearance in exogenous steroid use (12.0), nausea and/or vomiting (6.0), fatigue (2.0), basal cortisol <9 µg/dL (12.5), cholesterol <150 mg/dL (2.5) and sodium <135 mEq/L (1.0). Predictive risk scores range from 0-50.0. A high risk level (scores of 19.5-50.0) indicates a higher possibility of having AI (positive likelihood ratio (LR+) = 11.75), while a low risk level (scores of <19.0) indicates a lower chance of having AI (LR+ = 0.09). The predictive performance of the scoring system was 0.82 based on the area under the curve. Conclusions: This predictive risk score can help to determine the probability of AI and can be used as a guide to determine which patients need treatment for AI and which require dynamic tests to confirm AI.
Background: The diagnosis of adrenal insufficiency (AI) requires dynamic tests which may not be available in some institutions. This study aimed to develop a predictive risk score to help diagnose AI in outpatients with indeterminate serum cortisol levels. Methods: Five hundred and seven patients with intermediate serum cortisol levels (3-17.9 µg/dL) who had undergone ACTH (adrenocorticotropin) stimulation tests were included in the study. A predictive risk score was created using significant predictive factors identified by multivariable analysis using Poisson regression clustered by ACTH dose. Results: The seven predictive factors used in the development of a predictive model with their assigned scores are as follows: chronic kidney disease (9.0), Cushingoid appearance in exogenous steroid use (12.0), nausea and/or vomiting (6.0), fatigue (2.0), basal cortisol <9 µg/dL (12.5), cholesterol <150 mg/dL (2.5) and sodium <135 mEq/L (1.0). Predictive risk scores range from 0-50.0. A high risk level (scores of 19.5-50.0) indicates a higher possibility of having AI (positive likelihood ratio (LR+) = 11.75), while a low risk level (scores of <19.0) indicates a lower chance of having AI (LR+ = 0.09). The predictive performance of the scoring system was 0.82 based on the area under the curve. Conclusions: This predictive risk score can help to determine the probability of AI and can be used as a guide to determine which patients need treatment for AI and which require dynamic tests to confirm AI.
Authors: M Clodi; M Riedl; S Schmaldienst; A Vychytil; H Kotzmann; A Kaider; C Bieglmayer; G Mayer; W Waldhäusl; A Luger Journal: Am J Kidney Dis Date: 1998-07 Impact factor: 8.860
Authors: Sang Hoon Park; Min Sun Joo; Byoung Hoon Kim; Ha Na Yoo; Sung Eun Kim; Jin Bae Kim; Myoung Kuk Jang; Dong Jun Kim; Myung Seok Lee Journal: Medicine (Baltimore) Date: 2018-06 Impact factor: 1.889
Authors: Stefan R Bornstein; Bruno Allolio; Wiebke Arlt; Andreas Barthel; Andrew Don-Wauchope; Gary D Hammer; Eystein S Husebye; Deborah P Merke; M Hassan Murad; Constantine A Stratakis; David J Torpy Journal: J Clin Endocrinol Metab Date: 2016-01-13 Impact factor: 5.958