Transcatheter aortic valve implantation with a self-expanding nitinol bioprosthesis: Prediction of the Need for Permanent Pacemaker Using Simple Baseline and Procedural Characteristics

Objective: To ascertain incidence and predictors of new permanent pacemaker (PPM) following transcatheter aortic valve implantation (TAVI) with the self-expanding aortic bioprosthesis. Background: TAVI with the Medtronic Corevalve (MCV) Revalving System (Medtronic, Minneapolis, MN) has been associated with important post-procedural conduction abnormalities and frequent need for PPM. Methods: Overall, 73 consecutive patients with severe symptomatic AS underwent TAVI with the MCV at two institutions; 10 patients with previous pacemaker and 3 patients with previous aortic valve replacement were excluded for this analysis. Clinical, echocardiographic, and procedural data were collected prospectively in a dedicated database. A standard 12-lead ECG was recorded in all patients at baseline, after the procedure and predischarge. Decision to implant PPM was taken according to current guidelines. Logistic multivariable modeling was applied to identify independent predictors of PPM at discharge. Results: Patients exhibited high-risk features as evidenced by advanced age (mean = 82.1 +/- 6.2 years) and high surgical scores (logistic EuroSCORE 23.0 +/- 12.8%, STS score 9.4 +/- 6.9%). The incidence of new PPM was 28.3%. Interventricular septum thickness and logistic Euroscore were the baseline independent predictors of PPM. When procedural variables were included, the independent predictors of PPM were interventricular septum thickness (OR 0.52; 95% CI 0.320.85) and the distance between noncoronary cusp and the distal edge of the prosthesis (OR 1.37; 95% CI 1.031.83). Conclusions: Conduction abnormalities are frequently observed after TAVI with self-expandable bioprosthesis and definitive pacing is required in about a third of the patients, with a clear association with depth of implant and small interventricular septum thickness. (c) 2011 Wiley Periodicals, Inc.

Multiple Radial Midpupil Lid Distances: A Simple Method for Lid Contour Analysis

Purpose: To describe a new computerized method for the analysis of lid contour based on the measurement of multiple radial midpupil lid distances. Design: Evaluation of diagnostic technology. Participants and Controls: Monocular palpebral fissure images of 35 patients with Graves' upper eyelid retraction and of 30 normal subjects. Methods: Custom software was used to measure the conventional midpupil upper lid distance (MPLD) and 12 oblique MPLDs on each 15 degrees across the temporal (105 degrees, 120 degrees, 135 degrees, 150 degrees, 165 degrees, and 180 degrees) and nasal (75 degrees, 60 degrees, 45 degrees, 30 degrees, 15 degrees, and 0 degrees) sectors of the lid fissure. Main Outcome Measures: Mean, standard deviation, 5th and 95th percentiles of the oblique MPLDs obtained for patients and controls. Temporal/nasal MPLD ratios of the same angles with respect to the midline. Results: The MPLDs increased from the vertical midline in both nasal and temporal sectors of the fissure. In the control group the differences between the mean central MPLD (90 degrees) and those up to 30 degrees in the nasal (75 degrees and 60 degrees) and temporal sectors (105 degrees and 120 degrees) were not significant. For greater eccentricities, all temporal and nasal mean MPLDs increased significantly. When the MPLDs of the same angles were compared between groups, the mean values of the Graves' patients differed from control at all angles (F = 4192; P<0.0001). The greatest temporal/nasal asymmetry occurred 60 degrees from the vertical midline. Conclusions: The measurement of radial MPLD is a simple and effective way to characterize lid contour abnormalities. In patients with Graves' upper eyelid retraction, the method demonstrated that the maximum amplitude of the lateral lid flare sign occurred at 60 degrees from the vertical midline. Financial Disclosure(s): The authors have no proprietary or commercial interest in any of the materials discussed in this article. Ophthalmology 2012; 119: 625-628 (C) 2012 by the American Academy of Ophthalmology.

Suitability of distance metrics as indexes of home-range size in tropical rodent species

Estimators of home-range size require a large number of observations for estimation and sparse data typical of tropical studies often prohibit the use of such estimators. An alternative may be use of distance metrics as indexes of home range. However, tests of correlation between distance metrics and home-range estimators only exist for North American rodents. We evaluated the suitability of 3 distance metrics (mean distance between successive captures [SD], observed range length [ORL], and mean distance between all capture points [AD]) as indexes for home range for 2 Brazilian Atlantic forest rodents, Akodon montensis (montane grass mouse) and Delomys sublineatus (pallid Atlantic forest rat). Further, we investigated the robustness of distance metrics to low numbers of individuals and captures per individual. We observed a strong correlation between distance metrics and the home-range estimator. None of the metrics was influenced by the number of individuals. ORL presented a strong dependence on the number of captures per individual. Accuracy of SD and AD was not dependent on number of captures per individual, but precision of both metrics was low with numbers of captures below 10. We recommend the use of SD and AD instead of ORL and use of caution in interpretation of results based on trapping data with low captures per individual.

CID: an efficient complexity-invariant distance for time series

The ubiquity of time series data across almost all human endeavors has produced a great interest in time series data mining in the last decade. While dozens of classification algorithms have been applied to time series, recent empirical evidence strongly suggests that simple nearest neighbor classification is exceptionally difficult to beat. The choice of distance measure used by the nearest neighbor algorithm is important, and depends on the invariances required by the domain. For example, motion capture data typically requires invariance to warping, and cardiology data requires invariance to the baseline (the mean value). Similarly, recent work suggests that for time series clustering, the choice of clustering algorithm is much less important than the choice of distance measure used.In this work we make a somewhat surprising claim. There is an invariance that the community seems to have missed, complexity invariance. Intuitively, the problem is that in many domains the different classes may have different complexities, and pairs of complex objects, even those which subjectively may seem very similar to the human eye, tend to be further apart under current distance measures than pairs of simple objects. This fact introduces errors in nearest neighbor classification, where some complex objects may be incorrectly assigned to a simpler class. Similarly, for clustering this effect can introduce errors by “suggesting” to the clustering algorithm that subjectively similar, but complex objects belong in a sparser and larger diameter cluster than is truly warranted.We introduce the first complexity-invariant distance measure for time series, and show that it generally produces significant improvements in classification and clustering accuracy. We further show that this improvement does not compromise efficiency, since we can lower bound the measure and use a modification of triangular inequality, thus making use of most existing indexing and data mining algorithms. We evaluate our ideas with the largest and most comprehensive set of time series mining experiments ever attempted in a single work, and show that complexity-invariant distance measures can produce improvements in classification and clustering in the vast majority of cases.