Evidence map›Paper›PMID 36632328›Full record

ArticleJAMIA open2023

Integrating real-world data to assess cardiac ablation device outcomes in a multicenter study using the OMOP common data model for regulatory decisions: implementation and evaluation.

Yue Yu, Guoqian Jiang, Eric Brandt, Tom Forsyth, Sanket S Dhruva, Shumin Zhang, Jiajing Chen, Peter A Noseworthy, Amit A Doshi, Kimberly Collison-Farr and 4 more

Abstract read
In one paragraph

Article in JAMIA open, 2023. 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
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  5. Review
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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

14 authors.

Yue YuDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA.
Guoqian JiangDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA.
Eric BrandtMercy Research, Mercy, Chesterfield, Missouri, USA.
Tom ForsythMercy Research, Mercy, Chesterfield, Missouri, USA.
Sanket S DhruvaSchool of Medicine, University of California San Francisco, and Section of Cardiology, Department of Medicine, San Francisco Veterans Affairs Medical Center, San Francisco, California, USA.ORCID https://orcid.org/0000-0003-0674-2032
Shumin ZhangMedTech Epidemiology and Real-World Data Sciences, Office of the Chief Medical Officer, Johnson & Johnson, New Brunswick, New Jersey, USA.
Jiajing ChenMercy Research, Mercy, Chesterfield, Missouri, USA.
Peter A NoseworthyDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.
Amit A DoshiMercy Clinic, Mercy, St. Louis, Missouri, USA.
Kimberly Collison-FarrMercy Research, Mercy, Chesterfield, Missouri, USA.
Dure KimNational Evaluation System for Health Technology Coordinating Center (NESTcc), Medical Device Innovation Consortium, Arlington, Virginia, USA.
Joseph S RossDepartment of Internal Medicine, Yale School of Medicine, and the Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Connecticut, USA.
Paul M CoplanMedTech Epidemiology and Real-World Data Sciences, Office of the Chief Medical Officer, Johnson & Johnson, New Brunswick, New Jersey, USA.
Joseph P DrozdaMercy Research, Mercy, Chesterfield, Missouri, USA.

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
NCATS NIH HHS UL1 TR001863
6 · The paper itself

Abstract

The objective of this study is to describe application of the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to support medical device real-world evaluation in a National Evaluation System for health Technology Coordinating Center (NESTcc) Test-Case involving 2 healthcare systems, Mercy Health and Mayo Clinic. CDM implementation was coordinated across 2 healthcare systems with multiple hospitals to aggregate both medical device data from supply chain databases and patient outcomes and covariates from electronic health record data. Several data quality assurance (QA) analyses were implemented on the OMOP CDM to validate the data extraction, transformation, and load (ETL) process. OMOP CDM-based data of relevant patient encounters were successfully established to support studies for FDA regulatory submissions. QA analyses verified that the data transformation was robust between data sources and OMOP CDM. Our efforts provided useful insights in real-world data integration using OMOP CDM for medical device evaluation coordinated across multiple healthcare systems.

Indexed as

medical data standardizationmedical deviceOMOP CDMUDI

Identifiers

PMID36632328
PMCPMC9831049

What OpenQuestion holds

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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.