Developing and evaluating pediatric phecodes (Peds-Phecodes) for high-throughput phenotyping using electronic health records
© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissionsoup.com..
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 2051 distinct phecodes. Peds-Phecodes replicated more known pediatric genotype-phenotype associations than phecodes (248 vs 192 out of 687 SNPs, P < .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: | |
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:31 |
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| Enthalten in: |
Journal of the American Medical Informatics Association : JAMIA - 31(2024), 2 vom: 18. Jan., Seite 386-395 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Grabowska, Monika E [VerfasserIn] |
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| Links: |
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| Themen: |
Electronic health records (EHRs) |
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| Anmerkungen: |
Date Completed 23.01.2024 Date Revised 10.02.2024 published: Print UpdateOf: medRxiv. 2023 Aug 24;:. - PMID 37662278 Citation Status MEDLINE |
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| doi: |
10.1093/jamia/ocad233 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM365324639 |
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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 10.02.2024 | ||
| 500 | |a published: Print | ||
| 500 | |a UpdateOf: medRxiv. 2023 Aug 24;:. - PMID 37662278 | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissionsoup.com. | ||
| 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 2051 distinct phecodes. Peds-Phecodes replicated more known pediatric genotype-phenotype associations than phecodes (248 vs 192 out of 687 SNPs, P < .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 Journal Article | |
| 650 | 4 | |a electronic health records (EHRs) | |
| 650 | 4 | |a genomics | |
| 650 | 4 | |a pediatrics | |
| 650 | 4 | |a phecodes | |
| 650 | 4 | |a phenome-wide association study (PheWAS) | |
| 650 | 4 | |a phenotyping | |
| 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 H |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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