Data mining experiments on the Angiotensin II-Antagonist in Paroxysmal Atrial Fibrillation (ANTIPAF-AFNET 2) trial: ‘exposing the invisible’
Author
Okutucu, S.
Katircioglu-Öztürk, D.
Oto, E.
Güvenir, H. A.
Karaagaoglu, E.
Oto, A.
Meinertz, T.
Goette, A.
Date
2016Source Title
EP Europace
Print ISSN
1099-5129
Electronic ISSN
1532-2092
Publisher
Oxford University Press
Volume
19
Issue
5
Pages
741 - 746
Language
English
Type
ArticleItem Usage Stats
273
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Metadata
Show full item recordAbstract
Aims: The aims of this study include (i) pursuing data-mining experiments on the Angiotensin II-Antagonist in Paroxysmal Atrial Fibrillation (ANTIPAF-AFNET 2) trial dataset containing atrial fibrillation (AF) burden scores of patients with many clinical parameters and (ii) revealing possible correlations between the estimated risk factors of AF and other clinical findings or measurements provided in the dataset. Methods: Ranking Instances by Maximizing the Area under a Receiver Operating Characteristics (ROC) Curve (RIMARC) is used to determine the predictive weights (Pw) of baseline variables on the primary endpoint. Chi-square automatic interaction detector algorithm is performed for comparing the results of RIMARC. The primary endpoint of the ANTIPAF-AFNET 2 trial was the percentage of days with documented episodes of paroxysmal AF or with suspected persistent AF. Results: By means of the RIMARC analysis algorithm, baseline SF-12 mental component score (Pw = 0.3597), age (Pw = 0.2865), blood urea nitrogen (BUN) (Pw = 0.2719), systolic blood pressure (Pw = 0.2240), and creatinine level (Pw = 0.1570) of the patients were found to be predictors of AF burden. Atrial fibrillation burden increases as baseline SF-12 mental component score gets lower; systolic blood pressure, BUN and creatinine levels become higher; and the patient gets older. The AF burden increased significantly at age >76. Conclusions: With the ANTIPAF-AFNET 2 dataset, the present data-mining analyses suggest that a baseline SF-12 mental component score, age, systolic blood pressure, BUN, and creatinine level of the patients are predictors of AF burden. Additional studies are necessary to understand the distinct kidney-specific pathophysiological pathways that contribute to AF burden. Published on behalf of the European Society of Cardiology.
Keywords
Atrial fibrillationBlood urea nitrogen
Creatinine
Data mining
Machine learning
RIMARC
SF-12
Angiotensin II antagonist
Creatinine
Nitrogen
Urea
Angiotensin receptor antagonist
Antiarrhythmic agent
Antihypertensive agent
Imidazole derivative
Olmesartan
Tetrazole derivative
Accuracy
Age
Algorithm
Article
Atrial fibrillation
Controlled study
Female
Human
Male
Measurement
Multicenter study
Paroxysmal atrial fibrillation
Priority journal
Prospective study
Randomized controlled trial
Risk factor
Short form 12
Systolic blood pressure
Urea nitrogen blood level
Weight
Age distribution
Aged
Atrial fibrillation
Comorbidity
Data mining
Double blind procedure
Hypertension
Incidence
Middle aged
Prevalence
Procedures
Sex ratio
Treatment outcome
Very elderly
Age Distribution
Aged
Aged, 80 and over
Angiotensin receptor antagonists
Anti-Arrhythmia agents
Antihypertensive agents
Atrial fibrillation
Comorbidity
Data mining
Double-Blind method
Female
Humans
Hypertension
Imidazoles
Incidence
Male
Middle aged
Prevalence
Risk factors
Sex distribution
Tetrazoles
Treatment outcome
Turkey
Permalink
http://hdl.handle.net/11693/37158Published Version (Please cite this version)
http://dx.doi.org/10.1093/europace/euw084Collections
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