Background Administrative data permit analysis of large cohorts but rely on International Classification of Diseases, Ninth Revision, Clinical Modification (ICD‐9‐CM), and International Classification of Diseases, Tenth Revision, Clinical Modification (ICD‐10‐CM) codes that may not reflect true congenital heart defects (CHDs). Methods and Results CHDs in 1497 cases with at least 1 encounter between January 1, 2010 and December 31, 2019 in 2 health care systems, identified by at least 1 of 87 ICD‐9‐CM/ICD‐10‐CM CHD codes were validated through medical record review for the presence of CHD and CHD native anatomy. Interobserver and intraobserver reliability averaged >95%. Positive predictive value (PPV) of ICD‐9‐CM/ICD‐10‐CM codes for CHD was 68.1% (1020/1497) overall, 94.6% (123/130) for cases identified in both health care systems, 95.8% (249/260) for severe codes, 52.6% (370/703) for shunt codes, 75.9% (243/320) for valve codes, 73.5% (119/162) for shunt and valve codes, and 75.0% (39/52) for “other CHD” (7 ICD‐9‐CM/ICD‐10‐CM codes). PPV for cases with >1 unique CHD code was 85.4% (503/589) versus 56.3% (498/884) for 1 CHD code. Of cases with secundum atrial septal defect ICD‐9‐CM/ICD‐10‐CM codes 745.5/Q21.1 in isolation, PPV was 30.9% (123/398). Patent foramen ovale was present in 66.2% (316/477) of false positives. True positives had younger mean age at first encounter with a CHD code than false positives (22.4 versus 26.3 years; P=0.0017). Conclusions CHD ICD‐9‐CM/ICD‐10‐CM codes have modest PPV and may not represent true CHD cases. PPV was improved by selecting certain features, but most true cases did not have these characteristics. The development of algorithms to improve accuracy may improve accuracy of electronic health records for CHD surveillance.

Improved treatment of congenital heart defects (CHDs) has resulted in women with CHDs living to childbearing age. However, no US population-based systems exist to estimate pregnancy frequency or complications among women with CHDs. Cases were identified in multiple data sources from 3 surveillance sites: Emory University (EU) whose catchment area included 5 metropolitan Atlanta counties; Massachusetts Department of Public Health (MA) whose catchment area was statewide; and New York State Department of Health (NY) whose catchment area included 11 counties. Cases were categorized into one of 5 mutually exclusive CHD severity groups collapsed to severe versus not severe; specific ICD-9-CM codes were used to capture pregnancy, gestational complications, and nongestational co-morbidities in women, age 11 to 50 years, with a CHD-related ICD-9-CM code. Pregnancy, CHD severity, demographics, gestational complications, co-morbidities, and insurance status were evaluated. ICD-9-CM codes identified 26,655 women with CHDs, of whom 5,672 (21.3%, range: 12.8% in NY to 22.5% in MA) had codes indicating a pregnancy. Over 3 years, age-adjusted proportion pregnancy rates among women with severe CHDs ranged from 10.0% to 24.6%, and 14.2% to 21.7% for women with nonsevere CHDs. Pregnant women with CHDs of any severity, compared with nonpregnant women with CHDs, reported more noncardiovascular co-morbidities. Insurance type varied by site and pregnancy status. These US population-based, multisite estimates of pregnancy among women with CHD indicate a substantial number of women with CHDs may be experiencing pregnancy and complications. In conclusion, given the growing adult population with CHDs, reproductive health of women with CHD is an important public health issue.

Objective: Transfer of congenital heart disease care from the pediatric to adult setting has been identified as a priority and is associated with better outcomes. Our objective is to determine what percentage of patients with congenital heart disease transferred to adult congenital cardiac care. Design: A retrospective cohort study. Setting: Referrals to a tertiary referral center for adult congenital heart disease patients from its pediatric referral base. Patients: This resulted in 1,514 patients age 16–30, seen at least once in three pediatric Georgia healthcare systems during 2008–2010. Interventions: We analyzed for protective factors associated with age-appropriate care, including distance from referral center, age, timing of transfer, gender, severity of adult congenital heart disease and comorbidities. Outcome Measures: We analyzed initial care by age among patients under pediatric care from 2008–2010 and if patients under pediatric care subsequently transferred to an adult congenital cardiologist in this separate pediatric and adult health system during 2008–2015. Results: Among 1,514 initial patients (39% severe complexity), 24% were beyond the recommended transfer age of 21 years. Overall, only 12.1% transferred care to the referral affiliated adult hospital. 90% of these adults that successfully transferred were seen by an adult congenital cardiologist, with an average of 33.9 months between last pediatric visit and first adult visit. Distance to referral center contributed to delayed transfer to adult care. Those with severe congenital heart disease were more likely to transfer (18.7% vs. 6.2% for not severe). Conclusion: Patients with severe disease are more likely to transfer to adult congenital heart disease care than non-severe disease. Most congenital heart disease patients do not transfer to adult congenital cardiology care with distance to referral center being a contributing factor. Both pediatric and adult care providers need to understand and address barriers in order to improve successful transfer.

BACKGROUND: The Fontan operation is associated with significant morbidity and premature mortality. Fontan cases cannot always be identified by International Classification of Diseases (ICD) codes, making it challenging to create large Fontan patient cohorts. We sought to develop natural language processing–based machine learning models to automatically detect Fontan cases from free texts in electronic health records, and compare their performances with ICD code–based classification. METHODS AND RESULTS: We included free-text notes of 10 935 manually validated patients, 778 (7.1%) Fontan and 10 157 (92.9%) non-Fontan, from 2 health care systems. Using 80% of the patient data, we trained and optimized multiple machine learning models, support vector machines and 2 versions of RoBERTa (a robustly optimized transformer-based model for language understanding), for automatically identifying Fontan cases based on notes. For RoBERTa, we implemented a novel sliding window strategy to overcome its length limit. We evaluated the machine learning models and ICD code–based classification on 20% of the held-out patient data using the F1 score metric. The ICD classification model, support vector machine, and RoBERTa achieved F1 scores of 0.81 (95% CI, 0.79–0.83), 0.95 (95% CI, 0.92–0.97), and 0.89 (95% CI, 0.88–0.85) for the positive (Fontan) class, respectively. Support vector machines obtained the best performance (P<0.05), and both natural language processing models outperformed ICD code–based classification (P<0.05). The sliding window strategy improved performance over the base model (P<0.05) but did not outperform support vector machines. ICD code–based classification produced more false positives. CONCLUSIONS: Natural language processing models can automatically detect Fontan patients based on clinical notes with higher accuracy than ICD codes, and the former demonstrated the possibility of further improvement.

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.

Background: In the United States, >1 million adults are living with congenital heart defects (CHDs), but gaps exist in understanding the health care needs of this growing population. Objectives: This study assessed the demographics, comorbidities, and health care use of adults ages 20 to 64 years with CHDs. Methods: Adults with International Classification of Disease-9th Revision-Clinical Modification CHD-coded health care encounters between January 1, 2008 (January 1, 2009 for Massachusetts) and December 31, 2010 were identified from multiple data sources at 3 U.S. sites: Emory University (EU) in Atlanta, Georgia (5 counties), Massachusetts Department of Public Health (statewide), and New York State Department of Health (11 counties). Demographics, insurance type, comorbidities, and encounter data were collected. CHDs were categorized as severe or not severe, excluding cases with isolated atrial septal defect and/or patent foramen ovale. Results: CHD severity and comorbidities varied across sites, with up to 20% of adults having severe CHD and >50% having ≥1 additional cardiovascular comorbidity. Most adults had ≥1 outpatient encounters (80% EU, 90% Massachusetts, and 53% New York). Insurance type differed across sites, with Massachusetts having a large proportion of Medicaid (75%) and EU and New York having large proportions of private insurance (44% EU, 67% New York). Estimated proportions of adults with CHD-coded health care encounters varied greatly by location, with 1.2 (EU), 10 (Massachusetts), and 0.6 (New York) per 1,000 adults based on 2010 census data. Conclusions: This was the first surveillance effort of adults with CHD-coded inpatient and outpatient health care encounters in 3 U.S. geographic locations using both administrative and clinical data sources. This information will provide a clearer understanding of health care use in this growing population.

Background: Many individuals born with congenital heart defects (CHD) survive to adulthood. However, population estimates of CHD beyond early childhood are limited in the U.S. Objectives: To estimate the percentage of individuals aged 1-to-64 years at five U.S. sites with CHD documented at a healthcare encounter during a three-year period and describe their characteristics. Methods: Sites conducted population-based surveillance of CHD among 1 to 10-year-olds (three sites) and 11 to 64-year-olds (all five sites) by linking healthcare data. Eligible cases resided in the population catchment areas and had one or more healthcare encounters during the surveillance period (January 1, 2011-December 31, 2013) with a CHD-related ICD-9-CM code. Site-specific population census estimates from the same age groups and time period were used to assess percentage of individuals in the catchment area with a CHD-related ICD-9-CM code documented at a healthcare encounter (hereafter referred to as CHD cases). Severe and non-severe CHD were based on an established mutually exclusive anatomic hierarchy. Results: Among 42,646 CHD cases, 23.7% had severe CHD and 51.5% were male. Percentage of CHD cases among 1 to 10-year-olds, was 6.36/1,000 (range: 4.33-9.96/1,000) but varied by CHD severity [severe: 1.56/1,000 (range: 1.04-2.64/1,000); non-severe: 4.80/1,000 (range: 3.28-7.32/1,000)]. Percentage of cases across all sites in 11 to 64-year-olds was 1.47/1,000 (range: 1.02-2.18/1,000) and varied by CHD severity [severe: 0.34/1,000 (range: 0.26-0.49/1,000); non-severe: 1.13/1,000 (range: 0.76-1.69/1,000)]. Percentage of CHD cases decreased with age until 20 to 44 years and, for non-severe CHD only, increased slightly for ages 45 to 64 years. Conclusion: CHD cases varied by site, CHD severity, and age. These findings will inform planning for the needs of this growing population.

With increasing survival trends for children and adolescents with congenital heart defects (CHD), there is a growing need to focus on transition from pediatric to adult specialty cardiac care. To better understand parental perspectives on the transition process, a survey was distributed to 451 parents of adolescents with CHD who had recent contact with the healthcare system in Georgia (GA) and New York (NY). Among respondents, 90.7% reported excellent, very good or good health-related quality of life (HRQoL) for their adolescent. While the majority of parents (77.8%) had been told by a provider about their adolescent’s need to transition to adult specialty cardiac care, most reported concerns about transitioning to adult care. Parents were most commonly concerned with replacing the strong relationship with pediatric providers (60.7%), locating an appropriate adult provider (48.7%), and accessing adult health insurance coverage (43.6%). These findings may offer insights into transition planning for adolescents with CHD.