The use of transtelephonic loop recorders for the assessment of symptoms and arrhythmia recurrence after radiofrequency catheter ablation.

Radiofrequency catheter ablation (RFA) is an effective treatment of arrhythmias. However, patients often remain symptomatic after the procedure. We aimed to assess the arrhythmia recurrence after successful RFA in relation to patients' symptoms using transtelephonic loop recorders. Thirty-six consecutive patients (age 50 +/- 14 years, 17 males/19 females) were enrolled after successful RFA for atrioventricular (AV) nodal reentrant tachycardia (n = 21), AV reentrant tachycardia (n = 8), atrial tachycardia (n = 2), atrial fibrillation/flutter (n = 4), and ventricular tachycardia (n = 1). During 23 +/- 6 days of follow-up, 679 events were recorded, 246 of which were true arrhythmic events, mostly (56%) asymptomatic. The vast majority of these true arrhythmic events were due to trivial arrhythmias (extrasystoles or sinus tachycardia), equally distributed among symptomatic and asymptomatic episodes. Arrhythmia relapse was shown in four patients, who had a total of nine episodes, eight of which were symptomatic. No high degree AV block was detected. Overall, symptom recurrence had low sensitivity (44%) and high specificity (95%) for the detection of any arrhythmia, and high sensitivity (89%) but low specificity (58%) for the detection of relapse. In conclusion, transtelephonic monitoring was a useful tool for the assessment of symptoms after RFA and its use may be reserved for the most symptomatic patients to detect a relapse or to reassure them for the benign nature of their symptoms.

A novel approach for incremental uncertainty rule generation from databases with missing values handling: application to dynamic medical databases.

Current approaches for mining association rules usually assume that the mining is performed in a static database, where the problem of missing attribute values does not practically exist. However, these assumptions are not preserved in some medical databases, like in a home care system. In this paper, a novel uncertainty rule algorithm is illustrated, namely URG-2 (Uncertainty Rule Generator), which addresses the problem of mining dynamic databases containing missing values. This algorithm requires only one pass from the initial dataset in order to generate the item set, while new metrics corresponding to the notion of Support and Confidence are used. URG-2 was evaluated over two medical databases, introducing randomly multiple missing values for each record's attribute (rate: 5-20% by 5% increments) in the initial dataset. Compared with the classical approach (records with missing values are ignored), the proposed algorithm was more robust in mining rules from datasets containing missing values. In all cases, the difference in preserving the initial rules ranged between 30% and 60% in favour of URG-2. Moreover, due to its incremental nature, URG-2 saved over 90% of the time required for thorough re-mining. Thus, the proposed algorithm can offer a preferable solution for mining in dynamic relational databases.