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      Data mining experiments on the Angiotensin II-Antagonist in Paroxysmal Atrial Fibrillation (ANTIPAF-AFNET 2) trial: ‘exposing the invisible’

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      Author
      Okutucu, S.
      Katircioglu-Öztürk, D.
      Oto, E.
      Güvenir, H. A.
      Karaagaoglu, E.
      Oto, A.
      Meinertz, T.
      Goette, A.
      Date
      2016
      Source 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
      Article
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      Abstract
      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 fibrillation
      Blood 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/11693/37158
      Published Version (Please cite this version)
      http://dx.doi.org/10.1093/europace/euw084
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      • Department of Computer Engineering 1308

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