Repeating noninvasive risk stratification improves prediction of outcome in ICD patients.

BACKGROUND: Noninvasive risk stratification aims to detect abnormalities in the pathophysiological mechanisms underlying ventricular arrhythmias. We studied the predictive value of repeating risk stratification in patients with an implantable cardioverter-defibrillator (ICD). METHODS: The EUTrigTreat clinical study was a prospective multicenter trial including ischemic and nonischemic cardiomyopathies and arrhythmogenic heart disease. Left ventricular ejection fraction ≤40% (LVEF), premature ventricular complexes >400/24 hr (PVC), non-negative microvolt T-wave alternans (MTWA), and abnormal heart rate turbulence (HRT) were considered high risk. Tests were repeated within 12 months after inclusion. Adjusted Cox regression analysis was performed for mortality and appropriate ICD shocks. RESULTS: In total, 635 patients had analyzable baseline data with a median follow-up of 4.4 years. Worsening of LVEF was associated with increased mortality (HR 3.59, 95% CI 1.17-11.04), as was consistent abnormal HRT (HR 8.34, 95%CI 1.06-65.54). HRT improvement was associated with improved survival when compared to consistent abnormal HRT (HR 0.10, 95%CI 0.01-0.82). For appropriate ICD shocks, a non-negative MTWA test or high PVC count at any moment was associated with increased arrhythmic risk independent of the evolution of test results (worsening: HR 3.76 (95%CI 1.43-9.88) and HR 2.50 (95%CI 1.15-5.46); improvement: HR 2.80 (95%CI 1.03-7.61) and HR 2.45 (95%CI 1.07-5.62); consistent: HR 2.47 (95%CI 0.95-6.45) and HR 2.40 (95%CI 1.33-4.33), respectively). LVEF improvement was associated with a lower arrhythmic risk (HR 0.34, 95%CI 0.12-0.94). CONCLUSIONS: Repeating LVEF and HRT improved the prediction of mortality, whereas stratification of ventricular arrhythmias may be improved by repeating LVEF measurements, MTWA and ECG Holter monitoring.

Common source identification of images in large databases.

Photo-response non-uniformity noise patterns are a robust way to identify the source of an image. However, identifying a common source of images in a large database may be impractical due to long computation times. In this paper a solution for large volume digital camera identification is proposed, which combines, and sometimes slightly modifies, existing methods for a 500 times improvement in the speed of common source identification. Single image comparisons are often plagued by considerable noise contamination from scene content and random noise, which makes it harder to accomplish reliable common source identification. Therefore a new method is introduced that can increase true positive rates by more than 45% at very low computation costs. Analysis of real data from a fraud case shows the effectiveness of the proposed method. As a whole the proposed solution makes it possible to analyze a large database in forensically relevant time, without resorting to large and expensive computer clusters.