# Accelerating research to advance care for adults with congenital heart disease through development of validated scalable computational phenotypes

> **NIH NIH R01** · CINCINNATI CHILDRENS HOSP MED CTR · 2020 · $784,220

## Abstract

PROJECT SUMMARY
The advent of surgery to treat congenital heart disease (CHD) in the second half of the 20th century shifted the
care paradigm from palliation of disease fatal in infancy to management of lifelong chronic disease through
adulthood. There are now more than 1.5 million adults with CHD living in the United States. These patients
have a substantial burden of cardiovascular and other medical comorbidities, as well as markedly increased
risk for adverse outcomes such as arrhythmia, heart failure, cerebrovascular accident, and premature death.
The emergence of this population requires new clinical care models as well as the development of novel
research tools and infrastructures to address these patients' unique characteristics and healthcare needs.
Adult CHD is characterized by substantial complexity, era-dependent heterogeneity in treatment strategies,
and time-varying implications of lifelong disease. This burgeoning population is understudied, and the
pathophysiology of the component diseases remains incompletely understood. Billing and other administrative
codes available in the electronic medical record are neither sensitive nor specific for CHD diagnosis and do not
adequately describe many other salient clinical features. As a result, structured data in large administrative
databases are not well suited to studying adults with CHD, even when the goal is simply to identify a cohort of
patients with a given diagnosis. This constitutes a major impediment to research efforts and is the primary
barrier underlying the limited population-based research performed to date. Adult CHD investigation would
benefit immensely from methods to establish harmonized, large-scale, multi-center datasets.
While billing codes are inadequate, the information needed to accurately classify adults with CHD is already
available in the electronic medical record in the form of clinical notes, comprised mainly of unstructured (“free”)
text. Manual data extraction is laborious, resource intensive, and, therefore, not scalable. We propose to apply
cutting-edge natural language processing approaches to unstructured text in the electronic medical record to
develop computable classifiers for variables fundamental to the study of adults with CHD. We will use two
unique institutional data resources at Boston Children's Hospital and Brigham and Women's Hospital that are
already populated with expert-adjudicated labels to train classifiers for key phenotypes that are poorly defined
by administrative codes. These classifiers will be validated in an independent patient cohort at Vanderbilt
University Medical Center and tested in new disease-specific risk prediction models. This work promises to
accelerate CHD research by massively increasing the scale of the patient cohorts that can be studied and by
establishing a foundation for improved evidence-based decision support for this underserved population.

## Key facts

- **NIH application ID:** 9946462
- **Project number:** 1R01HL151604-01
- **Recipient organization:** CINCINNATI CHILDRENS HOSP MED CTR
- **Principal Investigator:** Alexander R. Opotowsky
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $784,220
- **Award type:** 1
- **Project period:** 2020-07-10 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9946462

## Citation

> US National Institutes of Health, RePORTER application 9946462, Accelerating research to advance care for adults with congenital heart disease through development of validated scalable computational phenotypes (1R01HL151604-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9946462. Licensed CC0.

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