Accuracy and Performance Characteristics of Administrative Codes for the Diagnosis of Autoinflammatory Syndromes: A Discovery and Validation Study.

Abstract

Objective: The aim of this study was to evaluate and validate the accuracy and performance characteristics of administrative codes in diagnosing autoinflammatory syndromes (AISs).

Methods: We identified potential AIS patients from the electronic medical records at the University of Iowa Hospital and Clinics and the Stead Family Children's Hospital using a screening filter based on the 10th edition of the International Classification of Diseases ( ICD-10 ) codes and interleukin-1 antagonists. Diagnostic criteria for adult-onset Still disease, systemic juvenile idiopathic arthritis, Behçet disease (BD), familial Mediterranean fever (FMF), cryopyrin-associated periodic syndrome (CAPS), and SAPHO (synovitis, acne, pustulosis, hyperostosis, and osteitis) syndrome and chronic nonbacterial osteomyelitis (SAPHO-CNO) were reviewed for each patient. Patients who did not meet the diagnostic criteria were categorized as non-AIS. In this cross-sectional study, we calculated the sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve for the ICD codes in diagnosing AIS.

Results: Out of the 502 patients with potential AIS, 338 patients (67%) had a true AIS diagnosis. Sensitivity ranged from 80% (SAPHO-CNO) to 100% (BD and FMF), and positive predictive value ranged from 15% (FMF) to 80% (SAPHO-CNO). Specificity ranged from 81% (FMF) to 99% (CAPS and SAPHO-CNO), whereas negative predictive value ranged from 98% (adult-onset Still disease) to 100% (systemic juvenile idiopathic arthritis, BD, FMF, and CAPS). All ICD codes or code combinations for the diagnosis of specific AIS subtypes showed high accuracy with areas under the receiver operating characteristic curve ≥0.89.

Conclusions: This study validated the accuracy of administrative codes for diagnosing AIS, supporting their use in constructing AIS cohorts for clinical outcomes research.