Biomedical subjects

C Jeleazcov

Publications and source records attributed to C Jeleazcov.

4 recordsLinked to original sources

PubMed2006

[Pharmacodynamics of two different propofol formulations].

BACKGROUND: Propofol is nowadays available in various lipid formulations. We compared two different propofol formulations with respect to pharmacodynamics, using the EEG and clinical signs. MATERIALS AND METHODS: Ten volunteers received Diprivan 1% and Propofol 1% MCT Fresenius as a computer controlled infusion with increasing propofol target concentrations. A sigmoid E(max) model with effect compartment was estimated for the median frequency of the EEG power spectrum, based on measured arterial propofol plasma concentrations. Clinical pharmacodynamics were assessed by reaction on acoustic stimuli, eyelid reflex and corneal reflex. RESULTS: The drugs did not differ in pharmacodynamics with respect to EEG (EC(50) 2.1+/-0.6 for Diprivan and 2.1+/-0.5 microg/ml for Propofol Fresenius) and clinical signs. The pharmacodynamic model was characterized by a steep concentration effect relationship and a distinct hysteresis between propofol plasma concentration and effect (k(e0) 0.12+/-0.04 and 0.12+/-0.5 min(-1)). CONCLUSIONS: The investigated lipid formulations have no influence on the pharmacodynamics of propofol.

Acoustic Stimulation↗

PubMed2004

[Accuracy of target-controlled infusion (TCI) with 2 different propofol formulations].

BACKGROUND: Target-controlled infusion (TCI) of propofol was initially realized as a device for prefilled syringes (Diprifusor). New TCI systems can be used with any propofol formulation. We compared two different propofol formulations with respect to accuracy of TCI and pharmacokinetics. MATERIALS AND METHODS: A total of 10 volunteers received Diprivan 1% and Propofol 1% MCT Fresenius as TCI using the pharmacokinetic model of the Diprifusor. The prediction error was determined from measured arterial concentrations. A three-compartment model was fitted to the concentration data. RESULTS: The median prediction error and the median absolute prediction error were -1.4% and 23.3% for Diprivan, and -5.9% and 17.8% for Propofol Fresenius. The drugs did not differ in pharmacokinetics but showed a smaller central volume of distribution than used for infusion control. CONCLUSIONS: The pharmacokinetic model of Diprifusor can also be used for TCI of Propofol Fresenius. The large volume of distribution in this model may cause an overshoot in concentration.

Adult↗

PubMed2004

Automated EEG preprocessing during anaesthesia: new aspects using artificial neural networks.

The computer-aided detection of artefacts became an essential task with increasing automation of quantitative electroencephalogram (EEG) analysis during anaesthesiological applications. The different algorithms published so far required individual manual adjustment or have been based on limited decision criteria. In this study, we developed an artificial neural networks-(ANN-)aided method for automated detection of artefacts and EEG suppression periods. 72 hr EEG recorded before, during and after anaesthesia with propofol have been evaluated. Selected parameterized patterns of 0.25 s length were used to train the ANN (22 input, 8 hidden and 4 output neurons) with error back propagation. The detection performance of the ANN-aided method was tested with processing epochs between 1 to10 s. Related to examiner EEG evaluation, the average detection performance of the method was 72% sensitivity and 80% specificity for artefacts and 90% sensitivity and 92% specificity for EEG suppression. The improvement in signal-to-noise ratio with automated artefact processing was 1.39 times for the spectral edge frequency 95 (SEF95) and 1.89 times for the approximate entropy (ApEn). We conclude that ANN-aided preprocessing provide an useful tool for automated EEG evaluation in anaesthesiological applications.

Algorithms↗

PubMed1999

Functional changes of ventricular late potentials by provocation with increase of heart rate. Evaluation during atrial pacing.

BACKGROUND: Standard methods fail to reveal late potentials in 20 to 30% of patients with ventricular arrhythmias after myocardial infarction. However, these patients may develop transient delayed ventricular activation during increases in heart rate. METHODS AND RESULTS: Atrial pacing was performed, at the rates of 100 min-1 and 120 min-1, in 50 patients after myocardial infarction. Twenty-six patients had a history of documented, sustained ventricular tachycardia, 12 had a history of ventricular fibrillation and 12 no history of ventricular arrhythmias. The low-noise surface electrocardiogram was analysed before and during atrial pacing in the time and frequency domains. Fifteen of 26 patients with ventricular tachycardia, four of 12 with ventricular fibrillation and three of 12 without ventricular arrhythmias experienced late potentials during sinus rhythm. Atrial pacing led to a shift of 26 +/- 15 ms of preexistent late potentials into the ST segment, this being greater in patients with anterior infarctions and to an increase in magnitude in patients with inferior infarctions. In patients without late potentials during sinus rhythm, atrial pacing provoked late potentials in eight of 11 patients with ventricular tachycardia, in four of eight patients with ventricular fibrillation and in one of nine patients without ventricular arrhythmias. Low amplitude signals (LAS) were increased in patients after inferior and filtered QRS in patients after anterior infarction. In 10 patients without cardiac disease no late potentials were detectable in the time and frequency domain either at rest or during increased heart rate. CONCLUSIONS: Increase in heart rate may unmask late potentials in patients prone to malignant ventricular arrhythmias. Therefore, functional late potential analysis with non-invasive clinical stress tests, i.e. exercise tests, should be performed only with an adequate rate response. This might identify patients at risk of malignant ventricular arrhythmias otherwise not identified with conventional late potential analysis.

Arrhythmias, Cardiac↗