Evidence map›Paper›PMID 32994452›Full record

SynthesisScientific reports2020

Machine learning prediction in cardiovascular diseases: a meta-analysis.

Chayakrit Krittanawong, Hafeez Ul Hassan Virk, Sripal Bangalore, Zhen Wang, Kipp W Johnson, Rachel Pinotti, HongJu Zhang, Scott Kaplin, Bharat Narasimhan, Takeshi Kitai and 3 more

Abstract readMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 164 papers, 11 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
164citing papers in PubMed, 11 pooled it
–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

164 citing papers in PubMed, 11 syntheses or guidelines pooled it.

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104 more citing papers are in PubMed but not listed here.

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

13 authors.

Chayakrit KrittanawongSection of Cardiology, Baylor College of Medicine, Houston, TX, USA. Chayakrit.Krittanawong@bcm.edu.
Hafeez Ul Hassan VirkHarrington Heart & Vascular Institute, Case Western Reserve University, University Hospitals Cleveland Medical Center, Cleveland, OH, USA.
Sripal BangaloreDepartment of Cardiovascular Diseases, New York University School of Medicine, New York, NY, USA.
Zhen WangRobert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Rochester, MN, USA.
Kipp W JohnsonDepartment of Genetics and Genomic Sciences, Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Rachel PinottiLevy Library, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
HongJu ZhangDivision of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA.
Scott KaplinDepartment of Cardiovascular Diseases, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, Mount Sinai Heart, New York, NY, USA.
Bharat NarasimhanDepartment of Cardiovascular Diseases, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, Mount Sinai Heart, New York, NY, USA.
Takeshi KitaiDepartment of Cardiovascular Medicine, Heart and Vascular Institute, Cleveland Clinic, Cleveland, OH, USA.
Usman BaberDepartment of Cardiovascular Diseases, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, Mount Sinai Heart, New York, NY, USA.
Jonathan L HalperinDepartment of Cardiovascular Diseases, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, Mount Sinai Heart, New York, NY, USA.
W H Wilson TangDepartment of Cardiovascular Medicine, Heart and Vascular Institute, Cleveland Clinic, Cleveland, OH, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Several machine learning (ML) algorithms have been increasingly utilized for cardiovascular disease prediction. We aim to assess and summarize the overall predictive ability of ML algorithms in cardiovascular diseases. A comprehensive search strategy was designed and executed within the MEDLINE, Embase, and Scopus databases from database inception through March 15, 2019. The primary outcome was a composite of the predictive ability of ML algorithms of coronary artery disease, heart failure, stroke, and cardiac arrhythmias. Of 344 total studies identified, 103 cohorts, with a total of 3,377,318 individuals, met our inclusion criteria. For the prediction of coronary artery disease, boosting algorithms had a pooled area under the curve (AUC) of 0.88 (95% CI 0.84-0.91), and custom-built algorithms had a pooled AUC of 0.93 (95% CI 0.85-0.97). For the prediction of stroke, support vector machine (SVM) algorithms had a pooled AUC of 0.92 (95% CI 0.81-0.97), boosting algorithms had a pooled AUC of 0.91 (95% CI 0.81-0.96), and convolutional neural network (CNN) algorithms had a pooled AUC of 0.90 (95% CI 0.83-0.95). Although inadequate studies for each algorithm for meta-analytic methodology for both heart failure and cardiac arrhythmias because the confidence intervals overlap between different methods, showing no difference, SVM may outperform other algorithms in these areas. The predictive ability of ML algorithms in cardiovascular diseases is promising, particularly SVM and boosting algorithms. However, there is heterogeneity among ML algorithms in terms of multiple parameters. This information may assist clinicians in how to interpret data and implement optimal algorithms for their dataset.

Indexed as

AlgorithmsArea Under CurveCardiovascular DiseasesComputational BiologyCoronary Artery DiseaseDatabases, FactualForecastingHumansMachine LearningNeural Networks, ComputerROC CurveStrokeSupport Vector Machine

Identifiers

PMID32994452
PMCPMC7525515

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.