# Comprehensive Pediatric Phenotyping for Evidence-Based Diagnosis in Genetic Disease

> **NIH NIH K08** · CHILDREN'S HOSP OF PHILADELPHIA · 2024 · $148,824

## Abstract

To facilitate the diagnosis of among 7000 rare genetic diseases, clinicians have developed diagnostic
criteria that enumerate different elements that define disease. These include medical problems, physical exam
findings, laboratory test results, and imaging findings. However, most clinical diagnostic criteria have unknown
predictive value. Despite being critical for diagnosis and provision of genetic testing, they are typically proposed
without rigorous evidence or estimates of performance such as sensitivity or specificity. Suboptimal criteria may
cause faulty interpretations of genetic testing with variants of uncertain clinical significance or lead clinicians to
overlook diagnosis, depriving patients of prognostication, reproductive planning, or targeted molecular therapies.
Our previous work has delineated an approach to more evidence-based rare disease criteria. We developed
novel clinical criteria for nevoid basal cell carcinoma syndrome using survey data and statistical optimization,
and we estimate the novel criteria have improved sensitivity compared to the existing expert consensus criteria,
particularly at early ages (53% versus 13% at 7 years). My central hypothesis is that diagnosis of rare pediatric
genetic disease can be improved by utilizing evidence-based diagnostic approaches. Moreover, such
approaches may be one avenue to address inequities in the provision of genetic referral and testing among
individuals belonging to historically marginalized groups. Therefore, I will scale our previous work across the
spectrum of rare genetic diseases using comprehensive, clinician-validated phenotype information to establish
and test diagnostic methodologies.
 To address this hypothesis and progress towards my long-term career goal of becoming and independent
physician-scientist that advances accurate and timely diagnosis for all children with a rare genetic disease, I
have developed a comprehensive five-year career development plan. This plan delineates a strategy to gain
knowledge and experience with natural language processing and machine learning, human-centered design and
human factors, and electronic health record intervention. Using these new skills, I will create comprehensive,
chronological phenotype histories for over 37,000 children with suspected or confirmed genetic disease. I will
embed a tool in the clinical workflow that elicits clinician validation of these phenotypes. From these data, I will
implement a framework to develop and validate diagnostic criteria in genetic disease. I will initially focus on 10
specific diseases. I will also develop computationally tractable machine learning algorithms to aid in diagnosis at
scale. Next, I will develop a web-based user interface to empower other clinicians to develop and test their own
diagnostic criteria. Finally, I will apply the same phenotyping and machine learning approaches at the health
system level to predict which children are more likely to be diagnosed with a rare gen...

## Key facts

- **NIH application ID:** 10847496
- **Project number:** 5K08HD111688-02
- **Recipient organization:** CHILDREN'S HOSP OF PHILADELPHIA
- **Principal Investigator:** Ian Morgan Campbell
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $148,824
- **Award type:** 5
- **Project period:** 2023-06-01 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10847496

## Citation

> US National Institutes of Health, RePORTER application 10847496, Comprehensive Pediatric Phenotyping for Evidence-Based Diagnosis in Genetic Disease (5K08HD111688-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10847496. Licensed CC0.

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