Evidence map›Paper›PMID 27107435›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2016

An in silico framework for integrating epidemiologic and genetic evidence with health care applications: ventilation-related pneumothorax as a case illustration.

Yelizaveta Torosyan, Yuzhi Hu, Sarah Hoffman, Qianlai Luo, Bruce Carleton, Danica Marinac-Dabic

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Precision medicine informatics.Journal of the American Medical Informatics Association : JAMIA · 2016
    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

6 authors.

Yelizaveta TorosyanDivision of Epidemiology, Center for Devices and Radiological Health, CDRH/FDA, Silver Spring, MD, USA yelizaveta.torosyan@fda.hhs.gov.
Yuzhi HuDivision of Epidemiology, Center for Devices and Radiological Health, CDRH/FDA, Silver Spring, MD, USA Columbia University Mailman School of Public Health, New York, NY, USA.
Sarah HoffmanDivision of Epidemiology, Center for Devices and Radiological Health, CDRH/FDA, Silver Spring, MD, USA Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Qianlai LuoDivision of Epidemiology, Center for Devices and Radiological Health, CDRH/FDA, Silver Spring, MD, USA Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Bruce CarletonPharmaceutical Outcomes Programme, BC Children's Hospital; Division of Translational Therapeutics, Department of Pediatrics, Child and Family Research Institute, University of British Columbia, Vancouver, Canada.
Danica Marinac-DabicDivision of Epidemiology, Center for Devices and Radiological Health, CDRH/FDA, Silver Spring, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo illustrate an in silico integration of epidemiologic and genetic evidence that is being developed at the Center for Devices and Radiological Health/US Food and Drug Administration as part of regulatory research on postmarket device performance. In addition to using conventional epidemiologic evidence from registries, this innovative approach explores the vast potential of open-access omics databases for identifying genetic evidence pertaining to devices. MATERIAL AND

methodsA retrospective analysis of Agency for Healthcare Research and Quality (AHRQ)/Healthcare Cost and Utilization Project (HCUPNet) data (2002-2011) was focused on the ventilation-related iatrogenic pneumothorax (Vent-IP) outcome in discharges with mechanical ventilation (MV) and continuous positive airway pressure (CPAP). The derived epidemiologic evidence was analyzed in conjunction with pre-existing genomic data from Gene Expression Omnibus/National Center for Biotechnology Information and other databases.

resultsAHRQ/HCUPNet epidemiologic evidence showed that annual occurrence of Vent-IP did not decrease over a decade. While the Vent-IP risk associated with noninvasive CPAP comprised about 0.5%, the Vent-IP risk due to longer-term MV reached 2%. Along with MV posing an independent risk for Vent-IP, female sex and white race were found to be effect modifiers, resulting in the highest Vent-IP risk among mechanically ventilated white females. The Vent-IP risk was also potentiated by comorbidities associated with spontaneous pneumothorax (SP) and fibrosis. Consistent with the epidemiologic evidence, expression profiling in a number of animal models showed that the expression of several collagens and other SP/fibrosis-related genes was modified by ventilation settings.

conclusionIntegration of complementary genetic evidence into epidemiologic analysis can lead to a cost- and time-efficient discovery of the risk predictors and markers and thus can facilitate more efficient marker-based evaluation of medical product performance.

Indexed as

Computer SimulationEpidemiologic MethodsEthnicityFemaleGene Expression ProfilingHumansMalePilot ProjectsPneumothoraxRespiration, ArtificialRetrospective StudiesIn silico evidence integration using big data approachesMedical device safety biomarkersRepurposing and re-utilization of pre-existing genetic dataTranslational Epidemiology

Identifiers

PMID27107435
PMCPMC9397514

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.