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

Okutucu, S.
Katircioglu-Öztürk, D.
Oto, E.
Güvenir, H. A.
Karaagaoglu, E.
Oto, A.
Meinertz, T.
Goette, A.
Source Title
EP Europace
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Oxford University Press
741 - 746
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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.

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Book Title
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
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