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Updated: Aug 14, 2026

A Novel Method: Super-selective Adrenal Venous Sampling
Published on: September 15, 2017
Validity of discharge ICD-10 codes in detecting the etiologies of endogenous Cushing's syndrome
Jingya Zhou1,2, Meng Zhang1,2, Lin Lu3,4
1Department of Medical Records, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Objective:
To investigate the validity of discharge ICD-10 codes in detecting the etiology of endogenous Cushing's syndrome (CS) in hospitalized patients.
Methods:
We evaluated the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of CS etiology-related ICD-10 codes or code combinations by comparing hospital discharge administrative data (DAD) with established diagnoses from medical records.
Results:
Coding for patients with adrenocortical adenoma (ACA) and those with bilateral macronodular adrenal hyperplasia (BMAH) demonstrated disappointingly low sensitivity at 78.8% (95% CI: 70.1-85.6%) and 83.9% (95% CI: 65.5-93.9%), respectively. BMAH had the lowest PPV of 74.3% (95% CI: 56.4-86.9%). In confirmed ACA patients, the sensitivity for ACA code combinations was higher in patients initially admitted to the Department of Endocrinology before surgery than that in patients directly admitted to the Department of Urology (90.0 vs 73.1%, P = 0.033). The same phenomenon was observed in the PPV for the BMAH code (100.0 vs 60.9%, P = 0.012). Misinterpreted or confusing situations caused by coders (68.1%) and by the omission or denormalized documentation of symptomatic diagnosis by clinicians (26.1%) accounted for the main source of coding errors.
Conclusions:
Hospital DAD is an effective data source for evaluating the etiology of CS but not ACA and BMAH. Improving surgeons' documentation, especially in the delineation of symptomatic and locative diagnoses in discharge abstracts; department- or disease-specific training for coders and more multidisciplinary collaboration are ways to enhance the applicability of administrative data for CS etiologies.
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