Background There is paucity of data focusing on females' outcomes after the use of impeller pumps percutaneous ventricular assist devices (IPVADs). Methods Patients who received IPVADs during the period of October 1st, 2015‐December 31, 2017, were identified from the United States National Readmission Database. A 1:1 propensity score matching was used to compare the outcomes between females and males. Results A total of 19,278 (Female = 5,456; Male = 13,822) patients were included in the current analysis. After propensity score matching and among all‐comers who were treated with IPVADs, females had higher in‐hospital major adverse events (MAEs) (38 vs. 32.6%, p < .01), mortality (31 vs. 28%, p < .01), vascular complications (3.3 vs. 2.1%, p < .01), major bleeding (7.8 vs. 4.8%, p < .01), nonhome discharges (21.6 vs. 16.3%; p < .01), and longer length of stay (7 days [IQR 2–12] vs. 6 days [IQR 2–12], p = .02) with higher 30‐day readmission rate compared to males (20.5 vs.16.4%, p < .01). Furthermore, among patients who received the IPVADs for high‐risk percutaneous coronary intervention (HRPCI), females continued to have worse MAEs, which was driven by high rates of major bleeding. However, among patients who received IPVADs for cardiogenic shock (CS) the outcomes of females and males were comparable. Conclusions Among all‐comers who received IPVADs, females suffered higher morbidity and mortality compared to males. Higher morbidity driven mainly by higher rates of major bleeding was seen among females who received IPVADs for the hemodynamic support during HRPCI and comparable outcomes were observed when the IPVADs were used for CS.

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We reviewed CTA and transesophageal echocardiographic images obtained in 861 consecutive patients with a history of AF undergoing same-day CTA and transesophageal echocardiogram (TEE) before AF ablation at a single hospital (2006-2013). CTA findings of LAA filling defects from acquisitions without electrocardiogram gating were compared to TEE features of LAA stasis (grade 0-4) and thrombus. Stasis grade 0 or 1 by TEE in the absence of thrombus was defined as a negative result. In addition, LAA peak flow velocity was assessed by TEE. Average age was 61 ± 10 years and 75% were male. On CTA, 161 patients (19%) had LAA filling defects on CTA and 21 had ≥grade 2 stasis on TEE, including two with thrombus, resulting in a positive predictive value of only 13%. However, among 670 CTA-negative patients, 669 (99%) were negative for thrombus or stasis by TEE with one false-negative CTA in a patient with grade 2 stasis by TEE but no thrombus, yielding a negative predictive value of 99.9%. Slow LAA Doppler flow velocity was the most important determinant of false-positive CTA results in multivariate analysis (P < 0.0001) CONCLUSION: LAA filling defects on CT are associated with slow LAA flow velocity. AF patients without LAA filing defects on CT are free of significant stasis and thrombus on TEE. It may be possible to eliminate TEE in up to 80% of AF ablation patients based on negative CTA findings.

We sought to determine normal gender-specific strain values in healthy subjects using Feature Tracking (FT-MRI). Background Feature tracking (FT-MRI) is a novel MRI based method to analyze myocardial strain that is fast, simple and has potential for clinical use. Similar to echocardiographic "speckle tracking" myocardial "features" can be tracked on routine steady state free precession or gradient echo images without the need for additional tagged imaging. Methods Global and regional strains including circumferential (endocardial and epicardial Ecc), radial (Err) and longitudinal (Ell) strains were obtained with FT-MRI (Diogenes v Tomtec Systems) in 60 carefully screened normal subjects. Strains were derived from three long

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This paper describes an algorithm for classifying textures using wavelet transforms, A set of subband features improves the classification performance and is used to lower the computational complexity. The performance of our algorithm classifies better than tree-structured wavelet transform methods with lower complexity. We show that the choice of wavelet basis is critical.I. INTRODUCTION Texture analysis has found many important applications in such areas as medical imaging, computer vision, and remote sensing. There have been many studies in texture classification based on statistical, structural and spectral properties of textures. Wavelet based methods are shown to be efficient in detection, classification and segmentation. A large class of natural textures can be modeled as quasiperiodic signals whose dominant frequencies are located in the middle frequency channels. To fully utilize the power of the wavelet transform, frequency selective features with great discrimination ability must be extracted. This paper is organized as follows. In section TI, we briefly review the theory of wavelets. Our proljosed feature selection is described in section 111. Experimental results are reported in section IV and we conclude in section V. WAVELETS AND FILTERBANKSThis paper is restricted to the 2-band orthonormal wavelet basis. A 2-band compactly supported orthonormal wavelet basis is characterized by a scaling filter, b(n), and the wavelet filter, h,(n), of finite lengths N and satisfymg the linear condition [ 11(1) n=O and for all kEZ the quadratic condition N -I JI=O The scaling function, e(t), and the wavelet function, q,(t), are defined by the dyadic difference equation i E { O,I} (3) n As can be seen from (3), both the scaling function and wavelet functions depend on the selection of ho(n) and hl(n).In the orthogonal case, the two filters are related as where N is the length of the filters. Therefore, the mother wavelet depends on the selection of the low pass filter coefficients h&). /?,(ti)= (-1)n/?,3(2?d-1-/2) (4) 0-7803-5491-.5/99/$10.00 0 1999 IEEE 1053 111. METHODOLOGY We develop our algorithm by addressing the three main issues of multichannel texture classification: 1) Feature extraction within each channel 2) Channel selection and 3)Feature combination among channels.For signal analysis and classification problems, energy concentration in a subband has been used as a criterion for its fiuther decomposition [2], i.e. at each level, subbands of maximum energy are identified and decomposed further. In these schemes, the energy map of the surviving subbands is used as the feature set.However, for classification purposes, a criterion based on the difference between signals of different classes is preferable. The desired signals may be quite similar in the high-energy bands and may contain significant information in the low energy bands. Instead of decomposing the subbands hrther based on some criterion. e.g. energy concentration, only one level of the wavelet transform decomposition is used and features are obt...

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