Developing and Evaluating Pediatric Phecodes (Peds-Phecodes) for High-Throughput Phenotyping Using Electronic Health Records
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.
| Errataetall: |
UpdateIn: J Am Med Inform Assoc. 2023 Dec 01;:. - PMID 38041473 |
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2023 |
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| Enthalten in: |
medRxiv : the preprint server for health sciences - (2023) vom: 24. Aug. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Grabowska, Monika E [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Revised 18.01.2024 published: Electronic UpdateIn: J Am Med Inform Assoc. 2023 Dec 01;:. - PMID 38041473 Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1101/2023.08.22.23294435 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM361599951 |
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| 100 | 1 | |a Grabowska, Monika E |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Developing and Evaluating Pediatric Phecodes (Peds-Phecodes) for High-Throughput Phenotyping Using Electronic Health Records |
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| 500 | |a Date Revised 18.01.2024 | ||
| 500 | |a published: Electronic | ||
| 500 | |a UpdateIn: J Am Med Inform Assoc. 2023 Dec 01;:. - PMID 38041473 | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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) | ||
| 520 | |a 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 | ||
| 520 | |a Conclusion: Peds-Phecodes capture higher-quality pediatric phenotypes and deliver superior PheWAS outcomes compared to phecodes | ||
| 650 | 4 | |a Preprint | |
| 700 | 1 | |a Van Driest, Sara L |e verfasserin |4 aut | |
| 700 | 1 | |a Robinson, Jamie R |e verfasserin |4 aut | |
| 700 | 1 | |a Patrick, Anna E |e verfasserin |4 aut | |
| 700 | 1 | |a Guardo, Chris |e verfasserin |4 aut | |
| 700 | 1 | |a Gangireddy, Srushti |e verfasserin |4 aut | |
| 700 | 1 | |a Ong, Henry |e verfasserin |4 aut | |
| 700 | 1 | |a Feng, QiPing |e verfasserin |4 aut | |
| 700 | 1 | |a Carroll, Robert |e verfasserin |4 aut | |
| 700 | 1 | |a Kannankeril, Prince J |e verfasserin |4 aut | |
| 700 | 1 | |a Wei, Wei-Qi |e verfasserin |4 aut | |
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