Evidence map›Paper›PMID 32790674›Full record

ArticlePloS one2020

Time-series cardiovascular risk factors and receipt of screening for breast, cervical, and colon cancer: The Guideline Advantage.

Aixia Guo, Bettina F Drake, Yosef M Khan, James R Langabeer Ii, Randi E Foraker

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.4field-weighted citation impact, top 33% 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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. 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

5 authors at 3 institutions in 1 country.

Aixia GuoInstitute for Informatics (I2), Washington University School of Medicine, St. Louis, MO, United States of America.
Bettina F DrakeDivision of Public Health Sciences, Department of Surgery, Washington University in St. Louis School of Medicine, St. Louis, MO, United States of America.
Yosef M KhanHealth Informatics and Analytics, Centers for Health Metrics and Evaluation, American Heart Association, Dallas, TX, United States of America.
James R Langabeer IiSchool of Biomedical Informatics, Health Science Center at Houston, The University of Texas, Houston, TX, United States of America.
Randi E ForakerInstitute for Informatics (I2), Washington University School of Medicine, St. Louis, MO, United States of America.ORCID 0000-0001-9255-9394
Washington University in St. Louis · USAmerican Heart Association · USThe University of Texas Health Science Center at Houston · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCancer is the second leading cause of death in the United States. Cancer screenings can detect precancerous cells and allow for earlier diagnosis and treatment. Our purpose was to better understand risk factors for cancer screenings and assess the effect of cancer screenings on changes of Cardiovascular health (CVH) measures before and after cancer screenings among patients.

methodsWe used The Guideline Advantage (TGA)-American Heart Association ambulatory quality clinical data registry of electronic health record data (n = 362,533 patients) to investigate associations between time-series CVH measures and receipt of breast, cervical, and colon cancer screenings. Long short-term memory (LSTM) neural networks was employed to predict receipt of cancer screenings. We also compared the distributions of CVH factors between patients who received cancer screenings and those who did not. Finally, we examined and quantified changes in CVH measures among the screened and non-screened groups.

resultsModel performance was evaluated by the area under the receiver operator curve (AUROC): the average AUROC of 10 curves was 0.63 for breast, 0.70 for cervical, and 0.61 for colon cancer screening. Distribution comparison found that screened patients had a higher prevalence of poor CVH categories. CVH submetrics were improved for patients after cancer screenings.

conclusionDeep learning algorithm could be used to investigate the associations between time-series CVH measures and cancer screenings in an ambulatory population. Patients with more adverse CVH profiles tend to be screened for cancers, and cancer screening may also prompt favorable changes in CVH. Cancer screenings may increase patient CVH health, thus potentially decreasing burden of disease and costs for the health system (e.g., cardiovascular diseases and cancers).

Indexed as

Breast NeoplasmsCardiovascular DiseasesColonic NeoplasmsDeep LearningUterine Cervical NeoplasmsAdultArea Under CurveEarly Detection of CancerFemaleGuidelines as TopicHumansMiddle AgedRisk FactorsROC Curve

Identifiers

PMID32790674
PMCPMC7425922
OpenAlexW3049367427

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.