Evidence map›Paper›PMID 34169259›Full record

ArticleCJC open2021

Machine Learning Methods for Identifying Atrial Fibrillation Cases and Their Predictors in Patients With Hypertrophic Cardiomyopathy: The HCM-AF-Risk Model.

Moumita Bhattacharya, Dai-Yin Lu, Ioannis Ventoulis, Gabriela V Greenland, Hulya Yalcin, Yufan Guan, Joseph E Marine, Jeffrey E Olgin, Stefan L Zimmerman, Theodore P Abraham and 2 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in CJC open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
1.4field-weighted citation impact, top 18% 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

15 citing papers in PubMed, 18 citations in OpenAlex.

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  14. Hagit Shatkay-Reshef 1965-2022.Bioinformatics advances · 2022
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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

12 authors at 4 institutions in 2 countries.

Moumita BhattacharyaComputational Biomedicine and Machine Learning Lab, Department of Computer and Information Sciences, University of Delaware, Newark, Delaware, USA.
Dai-Yin LuHypertrophic Cardiomyopathy Center of Excellence, Johns Hopkins University, Baltimore, Maryland, USA.
Ioannis VentoulisHypertrophic Cardiomyopathy Center of Excellence, Johns Hopkins University, Baltimore, Maryland, USA.
Gabriela V GreenlandHypertrophic Cardiomyopathy Center of Excellence, Johns Hopkins University, Baltimore, Maryland, USA.
Hulya YalcinHypertrophic Cardiomyopathy Center of Excellence, Johns Hopkins University, Baltimore, Maryland, USA.
Yufan GuanHypertrophic Cardiomyopathy Center of Excellence, Johns Hopkins University, Baltimore, Maryland, USA.
Joseph E MarineHypertrophic Cardiomyopathy Center of Excellence, Johns Hopkins University, Baltimore, Maryland, USA.
Jeffrey E OlginHypertrophic Cardiomyopathy Center of Excellence, Division of Cardiology, University of California San Francisco, San Francisco, California, USA.
Stefan L ZimmermanDepartment of Radiology, Johns Hopkins University, Baltimore, Maryland, USA.
Theodore P AbrahamHypertrophic Cardiomyopathy Center of Excellence, Johns Hopkins University, Baltimore, Maryland, USA.
M Roselle AbrahamHypertrophic Cardiomyopathy Center of Excellence, Johns Hopkins University, Baltimore, Maryland, USA.
Hagit ShatkayComputational Biomedicine and Machine Learning Lab, Department of Computer and Information Sciences, University of Delaware, Newark, Delaware, USA.
Johns Hopkins University · USUniversity of California, San Francisco · USUniversity of Delaware · USNational Yang Ming Chiao Tung University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHypertrophic cardiomyopathy (HCM) patients have a high incidence of atrial fibrillation (AF) and increased stroke risk, even with low CHA

methodsHCM patients with documented paroxysmal/persistent/permanent AF (n = 191) were considered AF cases, and the remaining patients in sinus rhythm (n = 640) were tagged as No-AF. We evaluated 93 clinical variables; the most informative variables useful for distinguishing AF from No-AF cases were selected based on the 2-sample

resultsWe identified 18 highly informative variables that are positively (n = 11) and negatively (n = 7) correlated with AF in HCM. Next, patient records were represented via these 18 variables. Data imbalance resulting from the relatively low number of AF cases was addressed via a combination of oversampling and undersampling strategies. We trained and tested multiple classifiers under this sampling approach, showing effective classification. Specifically, an ensemble of logistic regression and naïve Bayes classifiers, trained based on the 18 variables and corrected for data imbalance, proved most effective for separating AF from No-AF cases (sensitivity = 0.74, specificity = 0.70, C-index = 0.80).

conclusionsOur model (HCM-AF-Risk Model) is the first machine learning-based method for identification of AF cases in HCM. This model demonstrates good performance, addresses data imbalance, and suggests that AF is associated with a more severe cardiac HCM phenotype.

Identifiers

PMID34169259
PMCPMC8209373
OpenAlexW3127150774

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.