Evidence map›Paper›PMID 32477698›Full record

ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2020

Developing a FHIR-based Framework for Phenome Wide Association Studies: A Case Study with A Pan-Cancer Cohort.

Nansu Zong, Deepak K Sharma, Yue Yu, Jan B Egan, Jaime I Davila, Chen Wang, Guoqian Jiang

Abstract read
In one paragraph

Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. HL7 FHIR-based tools and initiatives to support clinical research: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2022
    Article
  5. Review
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Nansu ZongDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
Deepak K SharmaDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
Yue YuDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
Jan B EganCenter for Individualized Medicine, Mayo Clinic, Rochester, MN.
Jaime I DavilaDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
Chen WangDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
Guoqian JiangDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.

Funding

Tools for standardizing clinical research metadata using HL7 FHIRU01HG009450 · NHGRI · MAYO CLINIC ROCHESTER · PI JIANG, GUOQIAN · 2016 to 2018
$1.4M
NHGRI NIH HHS U01 HG009450
6 · The paper itself

Abstract

Phenome Wide Association Studies (PheWAS) enables phenome-wide scans to discover novel associations between genotype and clinical phenotypes via linking available genomic reports and large-scale Electronic Health Record (EHR). Data heterogeneity from different EHR systems and genetic reports has been a critical challenge that hinders meaningful validation. To address this, we propose an FHIR-based framework to model the PheWAS study in a standard manner. We developed an FHIR-based data model profile to enable the standard representation of data elements from genetic reports and EHR data that are used in the PheWAS study. As a proof-of-concept, we implemented the proposed method using a cohort of 1,595 pan-cancer patients with genetic reports from Foundation Medicine as well as the corresponding lab tests and diagnosis from Mayo EHRs. A PheWAS study is conducted and 81 significant genotype-phenotype associations are identified, in which 36 significant associations for cancers are validated based on a literature review.

Identifiers

PMID32477698
PMCPMC7233075

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.