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Title: Predictive factors of adrenal insufficiency in outpatients with indeterminate serum cortisol levels: A retrospective study
Authors: Worapaka Manosroi
Mattabhorn Phimphilai
Jiraporn Khorana
Pichitchai Atthakomol
Tanyong Pipanmekaporn
Keywords: Medicine
Issue Date: 1-Jan-2020
Abstract: © 2020 by the authors. Licensee MDPI, Basel, Switzerland. Background and Objectives: To diagnose adrenal insufficiency (AI), adrenocorticotropic hormone (ACTH) stimulation tests may need to be performed, but those tests may not be available in some institutions. In addition, they may not be necessary for some patients. The objective of this study was to identify clinical and biochemical factors that could facilitate AI diagnosis in outpatient departments and decrease the number of unnecessary dynamic tests. Materials and Methods: This seven‐year retrospective study was performed in a tertiary care medical center. A total of 517 patients who had undergone ACTH stimulation tests in the outpatient department were identified. AI was described as a peak serum cortisol level of <18 μg/dL at 30 or 60 min after stimulation. The associations between clinical factors, biochemical factors, and AI were analyzed using the Poisson regression model and reported by the risk ratio (RR). Results: AI was identified in 128 patients (24.7%). Significant predictive factors for the diagnosis of AI were chronic kidney disease (RR = 2.52, p < 0.001), Cushingoid appearance (RR = 3.44, p < 0.001), nausea and/or vomiting (RR = 1.84, p = 0.003), fatigue (RR = 1.23, p < 0.001), serum basal cortisol <9 μg/dL (RR = 3.36, p < 0.001), serum cholesterol <150 mg/dL (RR = 1.26, p < 0.001), and serum sodium <135 mEq/L (RR = 1.09, p = 0.001). The predictive ability of the model was 83% based on the area under the curve. Conclusion: The easy-to‐obtain clinical and biochemical factors identified may facilitate AI diagnosis and help identify patients with suspected AI. Using these factors in clinical practice may also reduce the number of nonessential dynamic tests for AI.
ISSN: 1010660X
Appears in Collections:CMUL: Journal Articles

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