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PheMap: a multi-resource knowledge base for high-throughput phenotyping within electronic health records
Zheng NS, Feng Q, Kerchberger VE, Zhao J, Edwards TL, Cox NJ, Stein CM, Roden DM et al.. Journal of the American Medical Informatics Association : JAMIA, 2020-11-01
Topic: Genetics and APOE
Objective Developing algorithms to extract phenotypes from electronic health records (EHRs) can be challenging and time-consuming. We developed PheMap, a high-throughput phenotyping approach that leverages multiple independent, online resources to streamline the phenotyping process within EHRs. Materials and methods PheMap is a knowledge base of medical concepts with quantified relationships to phenotypes that have been extracted by natural language processing from publicly available resources. PheMap searches EHRs for each phenotype's quantified concepts and uses them to calculate an individual's probability of having this phenotype. We compared PheMap to clinician-validated phenotyping algorithms from the Electronic Medical Records and Genomics (eMERGE) network for type 2 diabetes mellitus (T2DM), dementia, and hypothyroidism using 84 821 individuals from Vanderbilt Univeresity Medical Center's BioVU DNA Biobank. We implemented PheMap-based phenotypes for genome-wide association studies (GWAS) for T2DM, dementia, and hypothyroidism, and phenome-wide association studies (PheWAS) for variants in FTO, HLA-DRB1, and TCF7L2. Results In this initial iteration, the PheMap knowledge base contains quantified concepts for 841 disease phenotypes. For T2DM, dementia, and hypothyroidism, the accuracy of the PheMap phenotypes were >97% using a 50% threshold and eMERGE case-control status as a reference standard. In the GWAS analyses, PheMap-derived phenotype probabilities replicated 43 of 51 previously reported disease-associated variants for the 3 phenotypes. For 9 of the 11 top associations, PheMap provided an equivalent or more significant P value than eMERGE-based phenotypes. The PheMap-based PheWAS showed comparable or better performance to a traditional phecode-based PheWAS. PheMap is publicly available online. Conclusions PheMap significantly streamlines the process of extracting research-quality phenotype information from EHRs, with comparable or better performance to current phenotyping approaches.
Key findings
- Results In this initial iteration, the PheMap knowledge base contains quantified concepts for 841 disease phenotypes.
- For 9 of the 11 top associations, PheMap provided an equivalent or more significant P value than eMERGE-based phenotypes.
- Conclusions PheMap significantly streamlines the process of extracting research-quality phenotype information from EHRs, with comparable or better performance to current phenotyping approaches.
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Study details
- Study designcase-control study
- Participants821
- Evidence strengthModerate
How this study scored 64/100
Published in a peer-reviewed journal, and it is a case-control study. Scored zero for effect reported and randomised / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 37 of the 58 points we could judge it on, which is 64 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Journal of the American Medical Informatics Association : JAMIA) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 6 / 12 | A case-control study |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 2 / 5 | 2020 — within 10 year(s) of the newest study here |
| Sample size | 2 / 4 | 821 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 32974638) |
| Free to read | 2 / 2 | Free to read |
| Points | 37 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 37 of the 58 points we could judge it on is 64 out of 100. |
| Total | 64 / 100 | — |
What each of these 20 components measures, and what this score is not →
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APA 7th
Zheng, N. S., Feng, Q., Kerchberger, V. E., Zhao, J., Edwards, T. L., Cox, N. J., et al. (2020). PheMap: a multi-resource knowledge base for high-throughput phenotyping within electronic health records. Journal of the American Medical Informatics Association : JAMIA. https://doi.org/10.1093/jamia/ocaa104
Vancouver
Zheng NS, Feng Q, Kerchberger VE, Zhao J, Edwards TL, Cox NJ, et al. PheMap: a multi-resource knowledge base for high-throughput phenotyping within electronic health records. Journal of the American Medical Informatics Association : JAMIA. 2020. doi:10.1093/jamia/ocaa104. PMID: 32974638.
BibTeX
@article{zheng2020-32974638,
author = {Zheng, N. S. and Feng, Q. and Kerchberger, V. E. and Zhao, J. and Edwards, T. L. and Cox, N. J. and Stein, C. M. and Roden, D. M. and others},
title = {{PheMap: a multi-resource knowledge base for high-throughput phenotyping within electronic health records}},
journal = {Journal of the American Medical Informatics Association : JAMIA},
year = {2020},
doi = {10.1093/jamia/ocaa104},
pmid = {32974638},
url = {https://doi.org/10.1093/jamia/ocaa104}
}