The 2018 anatomic physiologic (AP) classification American Heart Association/American College of Cardiology (AHA/ACC) Guidelines for Adults with Congenital Heart Disease (ACHD) encompasses both ...native and post-operative anatomy and physiology to guide care management. As some physiologic conditions and post-operative states lack specific International Classification of Diseases (ICD) 9- Clinical Modification (CM) and 10-CM codes, an ICD code-based classification approximating the ACHD AP classification is needed for population-based studies.
A total of 232 individuals, aged ≥ 18 years at the time of a health encounter between January 1, 2010 and December 31, 2019 and identified with at least one of 87 ICD codes for a congenital heart defect were validated through medical chart review. Individuals were assigned one of 4 mutually exclusive modified AP classification categories: (1) severe AB, (2) severe CD, (3) non-severe AB, or (4) non-severe CD, based on native anatomy “severe” or “non-severe” and physiology AB (“none” or “mild”) or CD (“moderate” or “severe”) by two methods: (1) medical record review, and (2) ICD and Current Procedural Terminology (CPT) code-based classification. The composite outcome was defined as a combination of a death, emergency department (ED) visits, or any hospitalizations that occurred at least 6 months after the index date and was assessed by each modified AP classification method.
Of 232 cases (52.2% male, 71.1% White), 28.4% experienced a composite outcome a median of 1.6 years after the index encounter. No difference in prediction of the composite outcome was seen based on modified AP classification between chart review and ICD code-based methodology.
Modified AP classification by chart review and ICD codes are comparable in predicting the composite outcome at least 6 months after classification. Modified AP classification using ICD code-based classification of CHD native anatomy and physiology is an important tool for population-based ACHD surveillance using administrative data.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
3.
Memoriam: Jack P. Strong, MD Rodriguez, Fred H.
Laboratory investigation,
March 2020, 2020-Mar, 2020-03-00, 20200301, Volume:
100, Issue:
3
Journal Article
Peer reviewed
Open access
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Neurocognitive dysfunction (NCD) is a common comorbidity among children with congenital heart disease (CHD). However, it is unclear how underlying CHD and its sequelae combine with genetics and ...acquired cardiovascular and neurological disease to impact NCD and outcomes across the lifespan in adults with CHD.
The Multi-Institutional Neurocognitive Discovery Study in Adults with Congenital Heart Disease (MINDS-ACHD) is a partnership between the Pediatric Heart Network (PHN) and the Adult Alliance for Research in Congenital Cardiology (AARCC) that examines objective and subjective neurocognitive function and genetics in young ACHD. This multicenter cross-sectional pilot study is enrolling 500 young adults between 18 and 30 years with moderate or severe complexity CHD at 14 centers in North America. Enrollment includes 4 groups (125 participants each): (1) d-looped Transposition of the Great Arteries (d-TGA); (2) Tetralogy of Fallot (TOF); (3) single ventricle (SV) physiology; and (4) “other moderately or severely complex CHD.” Participants complete the standardized tests from the NIH Toolbox Cognitive Battery, the NeuroQoL, the Hospital Anxiety and Depression Scale, and the PROMIS Global QoL measure. Clinical and demographic variables are collected by interview and medical record review, and an optional biospecimen is collected for genetic analysis. Due to the COVID-19 pandemic, participation may be done remotely. Tests are reviewed by a Neurocognitive Core Laboratory.
MINDS-ACHD is the largest study to date characterizing NCD in young adults with moderate or severely complex CHD in North America. Its results will provide valuable data to inform screening and management strategies for NCD in ACHD and improve lifelong care.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Purpose
Direct oral anticoagulants (DOACs) are not recommended in adult Fontan patients (Level of Evidence C). We hypothesized that DOACs are comparable to warfarin and do not increase thrombotic and ...embolic complications (TEs) or clinically significant bleeds.
Methods
We reviewed the medical records of adult Fontan patients on DOACs or warfarin at three major medical centers. We identified 130 patients: 48 on DOACs and 107 on warfarin. In total, they were treated for 810 months on DOACs and 5637 months on warfarin.
Results
The incidence of TEs in patients on DOACs compared to those on warfarin was not increased in a statistically significant way (hazard ratio HR 1.7 and
p
value 0.431). Similarly, the incidence of nonmajor and major bleeds in patients on DOACs compared to those on warfarin was also not increased in a statistically significant way (HR for nonmajor bleeds in DOAC patients was 2.8 with a
p
value of 0.167 and the HR for major bleeds was 2.0 with a
p
value 0.267). In multivariate analysis, congestive heart failure (CHF) was a risk factor for TEs across both groups (odds ratio OR = 4.8, 95% confidence interval CI = 1.3–17.6) and bleed history was a risk factor for clinically significant bleeds (OR = 6.8, 95% CI = 2.7–17.2).
Conclusion
In this small, retrospective multicenter study, the use of DOACs did not increase the risk of TEs or clinically significant bleeds compared to warfarin in a statistically significant way.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Background Administrative data permit analysis of large cohorts but rely on
(
), and
(
) 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
/
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
/
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
/
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
/
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;
=0.0017). Conclusions CHD
/
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.
Socioeconomic factors may lead to a disproportionate impact on health care usage and death among individuals with congenital heart defects (CHD) by race, ethnicity, and socioeconomic factors. How ...neighborhood poverty affects racial and ethnic disparities in health care usage and death among individuals with CHD across the life span is not well described.
Individuals aged 1 to 64 years, with at least 1 CHD-related
(
) code were identified from health care encounters between January 1, 2011, and December 31, 2013, from 4 US sites. Residence was classified into lower- or higher-poverty neighborhoods on the basis of zip code tabulation area from the 2014 American Community Survey 5-year estimates. Multivariable logistic regression models, adjusting for site, sex, CHD anatomic severity, and insurance-evaluated associations between race and ethnicity, and health care usage and death, stratified by neighborhood poverty. Of 31 542 individuals, 22.2% were non-Hispanic Black and 17.0% Hispanic. In high-poverty neighborhoods, non-Hispanic Black (44.4%) and Hispanic (47.7%) individuals, respectively, were more likely to be hospitalized (adjusted odds ratio aOR, 1.2 95% CI, 1.1-1.3; and aOR, 1.3 95% CI, 1.2-1.5) and have emergency department visits (aOR, 1.3 95% CI, 1.2-1.5 and aOR, 1.8 95% CI, 1.5-2.0) compared with non-Hispanic White individuals. In high poverty neighborhoods, non-Hispanic Black individuals with CHD had 1.7 times the odds of death compared with non-Hispanic White individuals in high-poverty neighborhoods (95% CI, 1.1-2.7). Racial and ethnic disparities in health care usage were similar in low-poverty neighborhoods, but disparities in death were attenuated (aOR for non-Hispanic Black, 1.2 95% CI=0.9-1.7).
Racial and ethnic disparities in health care usage were found among individuals with CHD in low- and high-poverty neighborhoods, but mortality disparities were larger in high-poverty neighborhoods. Understanding individual- and community-level social determinants of health, including access to health care, may help address racial and ethnic inequities in health care usage and death among individuals with CHD.
Background The Fontan operation is associated with significant morbidity and premature mortality. Fontan cases cannot always be identified by
(
) 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
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
code-based classification on 20% of the held-out patient data using the
score metric. The
classification model, support vector machine, and RoBERTa achieved
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 (
<0.05), and both natural language processing models outperformed
code-based classification (
<0.05). The sliding window strategy improved performance over the base model (
<0.05) but did not outperform support vector machines.
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
codes, and the former demonstrated the possibility of further improvement.
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