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Title: Clinical and biochemical factors to predict biochemical adrenal insufficiency in hospitalized patients with indeterminate cortisol levels: A retrospective study
Authors: Worapaka Manosroi
Natapong Kosachunhanan
Pichitchai Atthakomol
Keywords: Medicine
Issue Date: 19-Feb-2020
Abstract: © 2020 The Author(s). Background: Adrenal insufficiency (AI) in hospitalized patients is a fatal condition if left undiagnosed. Most patients may require an adrenocorticotropic hormone (ACTH) stimulation test to facilitate AI diagnosis. We aim to identify simple biochemical and clinical factors and derive a predictive model to help identify hospitalized patients with biochemical AI who have indeterminate 0800 h serum cortisol levels. Methods: A seven-year retrospective study was performed in a tertiary care medical center. We identified 128 inpatients who had undergone low-dose or high-dose ACTH stimulation tests. The association between biochemical AI and other factors was evaluated using a logistic regression model clustering by ACTH dose. Stepwise regression analysis was used to demonstrate the predictive model. Diagnostic performance was evaluated using ROC analysis. Results: Of the 128 patients, 28.1% had biochemical AI. The factors associated with biochemical AI were serum random cortisol < 10 μg/dL (OR = 8.69, p < 0.001), cholesterol < 150 mg/dL (OR = 2.64, p = 0.003), sodium < 140 mmol/L (OR = 1.73, p = 0.004)). Among clinical factors, cirrhosis (OR = 9.05, p < 0.001), Cushingoid appearance in those with exogenous steroid use (OR = 8.56, p < 0.001), and chronic kidney disease (OR = 2.21, p < 0.001) were significantly linked to biochemical AI. The AUC-ROC of the final model incorporating all factors was 83%. Conclusions: These easy-to-perform biochemical tests and easy-to-assess clinical factors could help predict biochemical AI in hospitalized patients with high accuracy. The physician should therefore have a high index of suspicion to perform dynamic tests for AI diagnosis in those who meet the proposed model criteria.
ISSN: 14726823
Appears in Collections:CMUL: Journal Articles

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