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.

A prescription fraud detection model.

Prescription fraud is a main problem that causes substantial monetary loss in health care systems. We aimed to develop a model for detecting cases of prescription fraud and test it on real world data from a large multi-center medical prescription database. Conventionally, prescription fraud detection is conducted on random samples by human experts. However, the samples might be misleading and manual detection is costly. We propose a novel distance based on data-mining approach for assessing the fraudulent risk of prescriptions regarding cross-features. Final tests have been conducted on adult cardiac surgery database. The results obtained from experiments reveal that the proposed model works considerably well with a true positive rate of 77.4% and a false positive rate of 6% for the fraudulent medical prescriptions. The proposed model has the potential advantages including on-line risk prediction for prescription fraud, off-line analysis of high-risk prescriptions by human experts, and self-learning ability by regular updates of the integrative data sets. We conclude that incorporating such a system in health authorities, social security agencies and insurance companies would improve efficiency of internal review to ensure compliance with the law, and radically decrease human-expert auditing costs.

Validation of the EuroSCORE risk models in Turkish adult cardiac surgical population.

OBJECTIVE: The aim of this study was to validate additive and logistic European System for Cardiac Operative Risk Evaluation (EuroSCORE) models on Turkish adult cardiac surgical population. METHODS: TurkoSCORE project involves a reliable web-based database to build up Turkish risk stratification models. Current patient population consisted of 9443 adult patients who underwent cardiac surgery between 2005 and 2010. However, the additive and logistic EuroSCORE models were applied to only 8018 patients whose EuroSCORE determinants were complete. Observed and predicted mortalities were compared for low-, medium-, and high-risk groups. RESULTS: The mean patient age was 59.5 years (± 12.1 years) at the time of surgery, and 28.6% were female. There were significant differences (all p<0.001) in the prevalence of recent myocardial infarction (23.5% vs 9.7%), moderate left ventricular function (29.9% vs 25.6%), unstable angina (9.8% vs 8.0%), chronic pulmonary disease (13.4% vs 3.9%), active endocarditis (3.2% vs 1.1%), critical preoperative state (9.0% vs 4.1%), surgery on thoracic aorta (3.7% vs 2.4%), extracardiac arteriopathy (8.6% vs 11.3%), previous cardiac surgery (4.1% vs 7.3%), and other than isolated coronary artery bypass graft (CABG; 23.0% vs 36.4%) between Turkish and European cardiac surgical populations, respectively. For the entire cohort, actual hospital mortality was 1.96% (n=157; 95% confidence interval (CI), 1.70-2.32). However, additive predicted mortality was 2.98% (p<0.001 vs observed; 95%CI, 2.90-3.00), and logistic predicted mortality was 3.17% (p<0.001 vs observed; 95%CI, 3.03-3.21). The predictive performance of EuroSCORE models for the entire cohort was fair with 0.757 (95%CI, 0.717-0.797) AUC value (area under the receiver operating characteristic, AUC) for additive EuroSCORE, and 0.760 (95%CI, 0.721-0.800) AUC value for logistic EuroSCORE. Observed hospital mortality for isolated CABG was 1.23% (n=75; 95%CI, 0.95-1.51) while additive and logistic predicted mortalities were 2.87% (95%CI, 2.82-2.93) and 2.89% (95%CI, 2.80-2.98), respectively. AUC values for the isolated CABG subset were 0.768 (95%CI, 0.707-0.830) and 0.766 (95%CI, 0.705-0.828) for additive and logistic EuroSCORE models. CONCLUSION: The original EuroSCORE risk models overestimated mortality at all risk subgroups in Turkish population. Remodeling strategies for EuroSCORE or creation of a new model is warranted for future studies in Turkey.