Background: The purpose of this study was to analyze hemodynamic changes in patients treated with percutaneous coronary intervention (PCI) at an early stage of acute myocardial infarction (AMI) and at 1-month follow-up. Methods: Patients with AMI (n = 27) who underwent PCI were analyzed using impedance cardiography (ICG). ICG data were collected continuously (beat by beat) during the whole PCI procedure and thereafter at every 60 s for the next 24 h. Blood pressure was taken every 10 min and stored for analysis. Additionally the following parameters were measured: cardiac index (CI), stroke volume index (SVi), left cardiac work index (LCWi), contractility index (CTi), ventricular ejection time (VET), systemic vascular resistance index (SVRi), thoracic fluid content index (TFCi) and heart rate (HR). Results: In the first 24 h after PCI all the contractility parameters including CI, SVi, LCWi, CTi and VET significantly decreased, whereas HR, SVRi and TFCi increased compared to baseline. All of the parameters examined got normalized at 1 month. The CI, SVi, LCWi, CTi, SVRi did not significantly differ from baseline, however the HR and VET were significantly lower compared to first day after PCI. Conclusions: Cardiac performance deteriorates early after PCI and normalizes after 1 month in patients with an AMI. ICG is useful for hemodynamic monitoring of AMI patients during and after invasive therapy.
This paper presents a new approach to computer supported esophageal pH-metry measurement analysis performed in order to diagnose gastroesophageal reflux disease. In this approach wavelet analysis was used to analyse the esophageal pH-metry course. The research was performed on three groups of pH-metry courses: whole 24-hour pH-metry course, sleep only pH-metry course and 20 minutes after the end of a meal pHmetry course. After performing a 128 level decomposition of the pH-metry course, the Wx was defined as a parameter of extreme differential. This parameter was used to distinguish patients esophageal pH-metry results and on that basis classify patients as healthy or sick. Using this method the sensitivity of 77% was achieved.
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