Demographics, treatment and outcomes of atrial fibrillation in a developing country: the population-based TuRkish Atrial Fibrillation (TRAF) cohort.

AIMS: Although atrial fibrillation (AF) is increasingly common in developed countries, there is limited information regarding its demographics, co-morbidities, treatments and outcomes in the developing countries. We present the profile of the TuRkish Atrial Fibrillation (TRAF) cohort which provides real-life data about prevalence, incidence, co-morbidities, treatment, healthcare utilization and outcomes associated with AF. METHODS AND RESULTS: The TRAF cohort was extracted from MEDULA, a health insurance database linking hospitals, general practitioners, pharmacies and outpatient clinics for almost 100% of the inhabitants of the country. The cohort includes 507 136 individuals with AF between 2008 and 2012 aged >18 years who survived the first 30 days following diagnosis. Of 507 136 subjects, there were 423 109 (83.4%) with non-valvular AF and 84 027 (16.6%) with valvular AF. The prevalence was 0.80% in non-valvular AF and 0.28% in valvular AF; in 2012 the incidence of non-valvular AF (0.17%) was higher than valvular AF (0.04%). All-cause mortality was 19.19% (97 368) and 11.47% (58 161) at 1-year after diagnosis of AF. There were 35 707 (7.04%) ischaemic stroke/TIA/thromboembolism at baseline and 34 871 (6.87%) during follow-up; 11 472 (2.26%) major haemorrhages at baseline and 10 183 (2.01%) during follow-up, and 44 116 (8.69%) hospitalizations during the follow-up. CONCLUSION: The TRAF cohort is the first population-based, whole-country cohort of AF epidemiology, quality of care and outcomes. It provides a unique opportunity to study the patterns, causes and impact of treatments on the incidence and outcomes of AF in a developing country.

Predictors of sinus rhythm after electrical cardioversion of atrial fibrillation: results from a data mining project on the Flec-SL trial data set.

AIMS: Data mining is the computational process to obtain information from a data set and transform it for further use. Herein, through data mining with supportive statistical analyses, we identified and consolidated variables of the Flecainide Short-Long (Flec-SL-AFNET 3) trial dataset that are associated with the primary outcome of the trial, recurrence of persistent atrial fibrillation (AF) or death. METHODS AND RESULTS: The 'Ranking Instances by Maximizing the Area under the ROC Curve' (RIMARC) algorithm was applied to build a classifier that can predict the primary outcome by using variables in the Flec-SL dataset. The primary outcome was time to persistent AF or death. The RIMARC algorithm calculated the predictive weights of each variable in the Flec-SL dataset for the primary outcome. Among the initial 21 parameters, 6 variables were identified by the RIMARC algorithm. In univariate Cox regression analysis of these variables, increased heart rate during AF and successful pharmacological conversion (PC) to sinus rhythm (SR) were found to be significant predictors. Multivariate Cox regression analysis revealed successful PC as the single relevant predictor of SR maintenance. The primary outcome risk was 3.14 times (95% CI:1.7-5.81) lower in those who had successful PC to SR than those who needed electrical cardioversion. CONCLUSIONS: Pharmacological conversion of persistent AF with flecainide without the need for electrical cardioversion is a powerful and independent predictor of maintenance of SR. A strategy of flecainide pretreatment for 48 h prior to planned electrical cardioversion may be a useful planning of a strategy of long-term rhythm control.

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

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.

Application of the RIMARC algorithm to a large data set of action potentials and clinical parameters for risk prediction of atrial fibrillation.

Ex vivo recorded action potentials (APs) in human right atrial tissue from patients in sinus rhythm (SR) or atrial fibrillation (AF) display a characteristic spike-and-dome or triangular shape, respectively, but variability is huge within each rhythm group. The aim of our study was to apply the machine-learning algorithm ranking instances by maximizing the area under the ROC curve (RIMARC) to a large data set of 480 APs combined with retrospectively collected general clinical parameters and to test whether the rules learned by the RIMARC algorithm can be used for accurately classifying the preoperative rhythm status. APs were included from 221 SR and 158 AF patients. During a learning phase, the RIMARC algorithm established a ranking order of 62 features by predictive value for SR or AF. The model was then challenged with an additional test set of features from 28 patients in whom rhythm status was blinded. The accuracy of the risk prediction for AF by the model was very good (0.93) when all features were used. Without the seven AP features, accuracy still reached 0.71. In conclusion, we have shown that training the machine-learning algorithm RIMARC with an experimental and clinical data set allows predicting a classification in a test data set with high accuracy. In a clinical setting, this approach may prove useful for finding hypothesis-generating associations between different parameters.