Effects of a comprehensive rehabilitation program on quality of life in patients with chronic heart failure

This evaluation was performed to assess the effects of a new, comprehensive outpatient rehabilitation program on generic and disease-specific quality of life related to exercise tolerance in stable chronic heart failure patients. Fifty-one patients (aged 59+/-11 years; 84% men) were treated for 12 weeks. Patients underwent optimized drug treatment, exercise training, and counseling and education. At baseline and at the end of the program, functional status, exercise capacity, and quality of life were assessed using the Medical Outcomes Study 36-item Short-Form Health Survey and the Minnesota Living with Heart Failure Questionnaire. Left ventricular ejection fraction and New York Heart Association functional class, as well as measures of physical fitness and walking distance covered in 6 minutes, improved significantly (by 11%-20% and by 58% on average, respectively). Physical functioning (effect size, 0.38; p<0.0001), role functioning (effect size, 0.17; p<0.05), and mental component score (effect size, 0.47; p<0.0001) on the questionnaire improved significantly. Disease-specific quality of life improved in sum score (effect size, 0.24; p<0.0001) and physical component score (effect size, 0.35; p<0.0001). The latter was inversely correlated to improvement in peak power output (r= -0.31; p<0.05). In patients with stable chronic heart failure, significant improvements in both generic and disease-specific quality of life related to improved exercise tolerance can be achieved within 12 weeks of comprehensive rehabilitation.

Validation of statistical shape model based reconstruction of the proximal femur - A morphology study

Seventeen bones (sixteen cadaveric bones and one plastic bone) were used to validate a method for reconstructing a surface model of the proximal femur from 2D X-ray radiographs and a statistical shape model that was constructed from thirty training surface models. Unlike previously introduced validation studies, where surface-based distance errors were used to evaluate the reconstruction accuracy, here we propose to use errors measured based on clinically relevant morphometric parameters. For this purpose, a program was developed to robustly extract those morphometric parameters from the thirty training surface models (training population), from the seventeen surface models reconstructed from X-ray radiographs, and from the seventeen ground truth surface models obtained either by a CT-scan reconstruction method or by a laser-scan reconstruction method. A statistical analysis was then performed to classify the seventeen test bones into two categories: normal cases and outliers. This classification step depends on the measured parameters of the particular test bone. In case all parameters of a test bone were covered by the training population's parameter ranges, this bone is classified as normal bone, otherwise as outlier bone. Our experimental results showed that statistically there was no significant difference between the morphometric parameters extracted from the reconstructed surface models of the normal cases and those extracted from the reconstructed surface models of the outliers. Therefore, our statistical shape model based reconstruction technique can be used to reconstruct not only the surface model of a normal bone but also that of an outlier bone.

Comparative assessment of conventional and self-ligating appliances on the effect of mandibular intermolar distance in adolescent nonextraction patients: a single-center randomized controlled trial

Our aim in this study was to compare intermolar widths after alignment of crowded mandibular dental arches in nonextraction adolescent patients between conventional and self-ligating brackets.

Emergency and elective implantation of covered stent systems in iatrogenic arterial injuries

To evaluate the effectiveness and safety of covered stents for the management of iatrogenic arterial injury.

Distance measures for image segmentation evaluation

The task considered in this paper is performance evaluation of region segmentation algorithms in the ground-truth-based paradigm. Given a machine segmentation and a ground-truth segmentation, performance measures are needed. We propose to consider the image segmentation problem as one of data clustering and, as a consequence, to use measures for comparing clusterings developed in statistics and machine learning. By doing so, we obtain a variety of performance measures which have not been used before in image processing. In particular, some of these measures have the highly desired property of being a metric. Experimental results are reported on both synthetic and real data to validate the measures and compare them with others.