Evidence map›Paper›PMID 35837616›Full record

ArticleFrontiers in artificial intelligence2022

Leveraging Open Electronic Health Record Data and Environmental Exposures Data to Derive Insights Into Rare Pulmonary Disease.

Karamarie Fecho, Stanley C Ahalt, Michael Knowles, Ashok Krishnamurthy, Margaret Leigh, Kenneth Morton, Emily Pfaff, Max Wang, Hong Yi

Open access · goldAbstract read
In one paragraph

Article in Frontiers in artificial intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 36% of its field
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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. 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

9 authors at 3 institutions in 1 country.

Karamarie FechoRenaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
Stanley C AhaltRenaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
Michael KnowlesSchool of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
Ashok KrishnamurthyRenaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
Margaret LeighSchool of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
Kenneth MortonCoVar Applied Technologies, Durham, NC, United States.
Emily PfaffNorth Carolina Clinical and Translational Sciences Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
Max WangCoVar Applied Technologies, Durham, NC, United States.
Hong YiRenaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
University of North Carolina at Chapel Hill · USRenaissance Computing Institute · USApplied Technologies (United States) · US

Funding

Re-Entry Supplement: Investigation of Oral Microbial Enzymes for the Detection and Treatment of Periodontal DiseaseUL1TR002489 · NCATS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI BUSE, JOHN BERNARD, SHAHEEN, NICHOLAS J · 2018 to 2022
$48.6M
6 · The paper itself

Abstract

Research on rare diseases has received increasing attention, in part due to the realized profitability of orphan drugs. Biomedical informatics holds promise in accelerating translational research on rare disease, yet challenges remain, including the lack of diagnostic codes for rare diseases and privacy concerns that prevent research access to electronic health records when few patients exist. The Integrated Clinical and Environmental Exposures Service (ICEES) provides regulatory-compliant open access to electronic health record data that have been integrated with environmental exposures data, as well as analytic tools to explore the integrated data. We describe a proof-of-concept application of ICEES to examine demographics, clinical characteristics, environmental exposures, and health outcomes among a cohort of patients enriched for phenotypes associated with cystic fibrosis (CF), idiopathic bronchiectasis (IB), and primary ciliary dyskinesia (PCD). We then focus on a subset of patients with CF, leveraging the availability of a diagnostic code for CF and serving as a benchmark for our development work. We use ICEES to examine select demographics, co-diagnoses, and environmental exposures that may contribute to poor health outcomes among patients with CF, defined as emergency department or inpatient visits for respiratory issues. We replicate current understanding of the pathogenesis and clinical manifestations of CF by identifying co-diagnoses of asthma, chronic nasal congestion, cough, middle ear disease, and pneumonia as factors that differentiate patients with poor health outcomes from those with better health outcomes. We conclude by discussing our preliminary findings in relation to other published work, the strengths and limitations of our approach, and our future directions.

Indexed as

application programming interfacebiomedical informaticsenvironmental exposuresenvironmental healthopen clinical datarare disease

Identifiers

PMID35837616
PMCPMC9274244
OpenAlexW4283661910

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.