Abstract

Introduction: Administrative data permit analysis of large cohorts but rely on ICD-9-CM and ICD-10-CM billing codes that may not accurately identify CHD cases. Variables that may improve accuracy are unknown, yet improved accuracy will improve CHD surveillance. Methods: We validated 1500 cases with an encounter between 1/1/2010 - 12/31/2019 identified by at least one of 90 CHD codes (41 ICD-9-CM, 49 ICD-10-CM) in 2 healthcare systems (1 adult, 1 pediatric), through medical record review and chart abstraction for presence of a CHD. Inter- and intra-observer reliability exceeded 93%. Results: Positive predictive value (PPV) of ICD codes for CHD (Figure 1) was 68.0% (1020/1500) overall, 95.7% (247/258) for severe codes, 52.8% (371/703) for shunt codes, 75.2% (243/323) for valve codes, 73.2% (120/164) for shunt and valve codes, and 75.0% (39/52) for a select group of 7 codes in ‘other’ category. PPV for cases with > 1 unique CHD code was 73.1% (920/1259) vs. 41.5% (100/241) for cases with only 1 unique CHD code. Characteristics of cases with and without CHD are in Table 1. ICD code 745.5/Q21.1 in isolation was present in 2.2% of cases with confirmed CHD vs. 19.4% of cases without CHD (p< 0.0001). Median number of encounters with a CHD code was higher in cases with CHD (6) vs. without CHD (2), p< 0.0001. Patent foramen ovale was present in 65.8% of false positives (316/480). Conclusion: There is significant variability in the PPV of individual and groups of CHD codes for detection of CHD. The presence of a code for severe CHD is associated with high PPV for true CHD. Use of administrative data for CHD surveillance may require the development of algorithms to improve the accuracy of case detection.

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