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Author Notes:

Fred H. Rodriguez III, MD, 2835 Brandywine Rd, Suite 400, Atlanta, GA 30341. Email: fred.rodriguez@emory.edu

We thank the Pedigree and Population Resource of Huntsman Cancer Institute, University of Utah (funded in part by the Huntsman Cancer Foundation) for its role in the ongoing collection, maintenance, and support of the Utah Population Database. We also acknowledge partial support for the Utah Population Database through grant P30 CA2014 from the National Cancer Institute, University of Utah, and from the University of Utah's program in Personalized Health and Center for Clinical and Translational Science. We thank the University of Utah Center for Clinical and Translational Science (funded by National Institutes of Health Clinical and Translational Science Awards), the Pedigree and Population Resource, University of Utah Information Technology Services and Biomedical Informatics Core for establishing the Master Subject Index between the Utah Population Database, the University of Utah Health Sciences Center and Intermountain Healthcare. This analysis has undergone replication by Anaclare Sullivan.

Disclosures None.


Research Funding:

This work was supported by the CDC Cooperative Agreement, Surveillance of Congenital Heart Defects Across the Lifespan; Funding Opportunity Announcements #DD15‐1506. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the CDC.


  • Science & Technology
  • Life Sciences & Biomedicine
  • Cardiac & Cardiovascular Systems
  • Cardiovascular System & Cardiology
  • birth defects
  • congenital heart defects
  • epidemiology
  • surveillance
  • validation

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

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Journal Title:



Volume 11, Number 15


, Pages e024911-e024911

Type of Work:

Article | Final Publisher PDF


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.

Copyright information:

© 2022 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

This is an Open Access work distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/rdf).
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