How Well Do ICD-9-CM Codes Predict True Congenital Heart Defects? A Centers for Disease Control and Prevention-Based Multisite Validation Project

• Authors: Fred Rodriguez III, Emory University; Fred H Rodriguez, Emory University; Cheryl Raskind-Hood, Emory University; Trenton Hoffman, Emory University; Sherry L Farr, Centers for Disease Control and Prevention, Atlanta; Jill Glidewell, Centers for Disease Control and Prevention, Atlanta; Jennifer S Li, Duke University; Alfred D'Ottavio, Duke University; Lorenzo Botto, University of Utah; Matthew R Reeder, University of Utah; Daphne Hsu, Albert Einstein College of Medicine; George K Lui, Stanford University; Anaclare M Sullivan, New York State Department of Health; Wendy Book, Emory University
• Journal Title: JOURNAL OF THE AMERICAN HEART ASSOCIATION
• Volume: Volume 11, Number 15
• Publisher: WILEY | 2022-08-02, Pages e024911-e024911
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1161/JAHA.121.024911
• Subjects: Health Sciences, Public Health
• Keywords: Science & Technology; Life Sciences & Biomedicine; Cardiac & Cardiovascular Systems; Cardiovascular System & Cardiology; birth defects; congenital heart defects; epidemiology; surveillance; validation; GENERAL-POPULATION; PREVALENCE

Abstract

BACKGROUND: The Centers for Disease Control and Prevention’s Surveillance of Congenital Heart Defects Across the Lifespan project uses large clinical and administrative databases at sites throughout the United States to understand population-based congenital heart defect (CHD) epidemiology and outcomes. These individual databases are also relied upon for accurate cod-ing of CHD to estimate population prevalence. METHODS AND RESULTS: This validation project assessed a sample of 774 cases from 4 surveillance sites to determine the positive predictive value (PPV) for identifying a true CHD case and classifying CHD anatomic group accurately based on 57 International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes. Chi-square tests assessed differences in PPV by CHD severity and age. Overall, PPV was 76.36% (591/774 [95% CI, 73.20–79.31]) for all sites and all CHD-related ICD-9-CM codes. Of patients with a code for complex CHD, 89.85% (177/197 [95% CI, 84.76– 93.69]) had CHD; corresponding PPV es-timates were 86.73% (170/196 [95% CI, 81.17– 91.15]) for shunt, 82.99% (161/194 [95% CI, 76.95– 87.99]) for valve, and 44.39% (83/187 [95% CI, 84.76– 93.69]) for “Other” CHD anatomic group (X2=142.16, P<0.0001). ICD-9-CM codes had higher PPVs for having CHD in the 3 younger age groups compared with those >64 years of age, (X2=4.23, P<0.0001). CONCLUSIONS: While CHD ICD-9-CM codes had acceptable PPV (86.54%) (508/587 [95% CI, 83.51– 89.20]) for identifying whether a patient has CHD when excluding patients with ICD-9-CM codes for “Other” CHD and code 745.5, further evaluation and algorithm development may help inform and improve accurate identification of CHD in data sets across the CHD ICD-9-CM code groups.