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Clinical characteristics of adrenal crisis in adult population with and without predisposing chronic adrenal insufficiency: a retrospective cohort study

Masahiro Iwasaku, Maki Shinzawa, Shiro Tanaka and 2 more

BMC Endocrine Disorders · 2017

doi:10.1186/s12902-017-0208-0 (opens in a new tab)

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Abstract

Abstract Background Adrenal crisis (AC) occurs in various clinical conditions but previous epidemiological studies in AC are limited to chronic adrenal insufficiency (AI) and sepsis. The aim of this study was to investigate characteristics of AC patients, including predisposing diseases and to describe candidate risk factors for AC such as comorbidities and glucocorticoid (GC) therapy. Methods We conducted a retrospective cohort study using a claims database on 7.4 million patients from 145 acute care hospitals between January 1, 2003 and April 30, 2014. We identified AC patients who met the following criteria: 1) disease name with ICD-10 corresponded with AI; 2) therapeutic GC administration (hydrocortisone equivalent dose ≥100 mg/day); 3) admission; and 4) age ≥18 years. Results We identified 504 patients with AC (median age, 71 years; interquartile range, 59 to 80; 50.6% male). As predisposing conditions, primary AI and central AI accounted for 23 (4.6%) and 136 patients (27.0%), respectively. In the remaining AC patients (68.5%), comorbidities such as cancer, autoimmune diseases, and renal failure were frequent. The most frequent indication for hospitalization was AC (16.3%), followed by pituitary disease (14.7%), cancer (14.7%), AI-related clinical symptoms (11.5%), and infection (11.1%). Admission under oral GC treatment was reported in 104 patients (20.6%). Twenty-six patients were admitted within 14 days after GC cessation (5.2%). Conclusions These findings present an overview of patients with AC in general practice settings, clarifying that predisposing factors for AC were complicated and that patients other than those with chronic AI were older and had more comorbid conditions than those with primary and central AI.

Abstract licence: CC BY-NC-ND

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