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Developing and Evaluating Pediatric Phecodes (Peds-Phecodes) for High-Throughput Phenotyping Using Electronic Health
Insights
We developed specialized pediatric phecodes (Peds-Phecodes) to improve the analysis of pediatric diseases using electronic health records. Peds-Phecodes offer higher quality phenotypes and better results in phenome-wide association studies for children.
Area of Science:
- Pediatric Genomics and Phenomics
- Biomedical Informatics
- Clinical Data Analysis
Background:
- Existing phecodes inadequately represent the unique spectrum of pediatric diseases and outcomes.
- There is a need for specialized tools to facilitate large-scale phenotypic analyses in pediatric populations.
Approach:
- Developed Pediatric-Specific Phecodes (Peds-Phecodes) by modifying existing phecodes using electronic health records and genetic data.
- Employed a hybrid data- and knowledge-driven approach, comparing pediatric and adult disease prevalence.
- Removed irrelevant phecodes and created new ones for pediatric-specific conditions.
Key Points:
- Peds-Phecodes aggregate 15,533 ICD-9-CM and 82,949 ICD-10-CM codes into 2,051 distinct phecodes.
- Peds-Phecodes demonstrated superior replication of known pediatric genotype-phenotype associations compared to standard phecodes (248 vs. 192).
Conclusions:
- Peds-Phecodes are a validated, high-throughput tool for pediatric phenotyping.
- This tool enhances phenome-wide association studies (PheWAS) in children and may identify novel genotype-phenotype associations.
- Peds-Phecodes are expected to significantly advance large-scale genomic and phenomic research in pediatric populations.
Objective:
Pediatric patients have different diseases and outcomes than adults; however, existing phecodes do not capture the distinctive pediatric spectrum of disease. We aim to develop specialized pediatric phecodes (Peds-Phecodes) to enable efficient, large-scale phenotypic analyses of pediatric patients.
Materials And Methods:
We adopted a hybrid data- and knowledge-driven approach leveraging electronic health records (EHRs) and genetic data from Vanderbilt University Medical Center to modify the most recent version of phecodes to better capture pediatric phenotypes. First, we compared the prevalence of patient diagnoses in pediatric and adult populations to identify disease phenotypes differentially affecting children and adults. We then used clinical domain knowledge to remove phecodes representing phenotypes unlikely to affect pediatric patients and create new phecodes for phenotypes relevant to the pediatric population. We further compared phenome-wide association study (PheWAS) outcomes replicating known pediatric genotype-phenotype associations between Peds-Phecodes and phecodes.
Results:
The Peds-Phecodes aggregate 15,533 ICD-9-CM codes and 82,949 ICD-10-CM codes into 2,051 distinct phecodes. Peds-Phecodes replicated more known pediatric genotype-phenotype associations than phecodes (248 versus 192 out of 687 SNPs, p<0.001).
Discussion:
We introduce Peds-Phecodes, a high-throughput EHR phenotyping tool tailored for use in pediatric populations. We successfully validated the Peds-Phecodes using genetic replication studies. Our findings also reveal the potential use of Peds-Phecodes in detecting novel genotype-phenotype associations for pediatric conditions. We expect that Peds-Phecodes will facilitate large-scale phenomic and genomic analyses in pediatric populations.
Conclusion:
Peds-Phecodes capture higher-quality pediatric phenotypes and deliver superior PheWAS outcomes compared to phecodes.
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