Evidence map›Paper›PMID 35673215›Full record

ArticleStudies in health technology and informatics2022

Data Analytics of Electronic Health Records to Enhance Care of Coronary Artery Disease in Younger Women with Avoiding Possible Delay in Treatment.

Maryam Panahiazar, Andrew M Bishara, Yorick Chern, Roohallah Alizadehsani, Omar S Latif, Dexter Hadley, Ramin E Beygui

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Studies in health technology and informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Maryam PanahiazarDepartment of Surgery, Division of Cardiothoracic Surgery, School of Medicine, University of California San Francisco, San Francisco, CA, USA.
Andrew M BisharaDepartment of Anesthesia and Perioperative Care, University of California San Francisco, San Francisco, CA, USA.
Yorick ChernDepartment of Bioengineering, University of California Berkeley, Berkeley, CA, USA.
Roohallah AlizadehsaniInstitute for Intelligent Systems Research and Innovation, Deakin University, Australia.
Omar S LatifDepartment of Internal Medicine, Mayo Clinic, Rochester, MN, USA.
Dexter HadleyDepartment of Clinical Sciences, College of Medicine, University of Central Florida, Orlando, FL, USA.
Ramin E BeyguiDepartment of Surgery, Division of Cardiothoracic Surgery, School of Medicine, University of California San Francisco, San Francisco, CA, USA.

Funding

Crowd-Assisted Deep Learning (CrADLe) Digital Curation to Translate Big Data into Precision MedicineU01LM012675 · NLM · UNIVERSITY OF CENTRAL FLORIDA · PI HADLEY, DEXTER D · 2017 to 2020
$2.1M
NLM NIH HHS U01 LM012675
6 · The paper itself

Abstract

Early detection plays a key role to enhance the outcome for Coronary Artery Disease. We utilized a big data analytics platform on ∼32,000 patients to trace patients from the first encounter to CAD treatment. There are significant gender-based differences in patients younger than 60 from the time of the first encounter to Coronary Artery Bypass Grafting with a p-value=0.03. This recognition makes significant changes in outcome by avoiding delay in treatment.

Indexed as

Coronary Artery DiseaseCoronary Artery BypassData ScienceElectronic Health RecordsFemaleHumansRisk FactorsTime-to-TreatmentTreatment OutcomeCoronary Artery DiseaseData ScienceElectronic Health Records

Identifiers

PMID35673215
PMCPMC10342193

What OpenQuestion holds

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LicenceCC BY
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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.