Clinical Results of Minimally Invasive Open-Heart Surgery in Patients with Mitral Valve Disease: Comparison of Parasternal and Low-Sternal Approach

Clinical results of minimally invasive mitral valve surgery were retrospectively reviewed, and two different surgical approaches were compared in this study. Between 1997 and 2004, a total of 86 patients with mitral valve disease underwent minimally invasive surgery at theYonsei University Cardiovascular Center. Age of patients averaged 41.6 ± 14.0 years and 69 patients were female. Surgical approach included lowsternal incisions with mini-sternotomy, and right parasternal or thoracotomy approach. Either direct aortic or femoral arterial and bicaval cannulations were used in all patients. Patients were divided into two groups according to the method of surgical approach (parasternal (P) vs low-sternal (L)), and the results were compared. Postoperative NYHA functional class improved to 1.1 ± 0.4 in all patients (no significant statistical difference between two groups). Mean wound length (P: 9.21 ± 1.10 vs L: 11.24 ± 0.82 cm, p<0.05), and mechanical ventilation time (P: 10.42 ± 4.36 vs L: 12.90 ± 5.00 min, p=0.04) was significantly shorter in parasternal group, and mean operation time(P:294.74 ± 59.41 vs. L:259.31 ± 54.36 min, p=0.03) was significantly shorter in low-sternal group. Mean cardiopulmonary bypass time, and aortic cross clamp time was also shorter in low-sternal group without statistical difference. There were 2 minor wound complications in all patients (p=NS), and no hospital death. Comparing the two different surgical approach of minimally invasive mitral valve surgery, parasternal approach is thought to be more beneficial in reducing postoperative scar, and intubation time

Prediction of second neurological attack in patients with clinically isolated syndrome using support vector machines

The aim of this study is to predict the conversion from clinically isolated syndrome to clinically definite multiple sclerosis using support vector machines. The two groups of converters and non-converters are classified using features that were calculated from baseline data of 73 patients. The data consists of standard magnetic resonance images, binary lesion masks, and clinical and demographic information. 15 features were calculated and all combinations of them were iteratively tested for their predictive capacity using polynomial kernels and radial basis functions with leave-one-out cross-validation. The accuracy of this prediction is up to 86.4% with a sensitivity and specificity in the same range indicating that this is a feasible approach for the prediction of a second clinical attack in patients with clinically isolated syndromes, and that the chosen features are appropriate. The two features gender and location of onset lesions have been used in all feature combinations leading to a high accuracy suggesting that they are highly predictive. However, it is necessary to add supporting features to maximise the accuracy. © 2013 IEEE