ZIB

Hits per page

hits 1 - 2 | 2 hits

Sorting

Electronic Resource

β-Carbolines in chronic alcoholics following trauma (1996)

SPIES, CLAUDIA ; ROMMELSPACHER, HANS ; WINKLER, THOMAS ; [et al.]

Oxford, UK : Blackwell Publishing Ltd

Addiction biology 1 (1996), S. 0

add to mindlist on the mindlist

Details

ISSN: 1369-1600

Source: Blackwell Publishing Journal Backfiles 1879-2005

Topics: Medicine

Notes: In our society every second polytraumatized patient is a chronic alcoholic. A patient's alcohol-related history is often unavailable and laboratory markers are not sensitive or specific enough to detect alcohol-dependent patients who are at risk of developing alcohol withdrawal syndrome (AWS) during their post-traumatic intensive care unit (ICU) stay. Previously, it has been found that plasma levels of norharman are elevated in chronic alcoholics. We investigated whether β-carbolines, i.e. harman and norharman levels, could identify chronic alcoholics following trauma and whether possible changes during ICU stay could serve as a predictor of deterioration of clinical status. Sixty polytraumatized patients were transferred to the ICU following admission to the emergency room and subsequent surgery. Chronic alcoholics were included only if they met the DSM-III-R and ICD-10 criteria for alcohol dependence or chronic alcohol abuse/harmful use and their daily ethanol intake was ≥ 60 g. Harman and norharman levels were assayed on admission and on days 2, 4, 7 and 14 in the ICU. Harman and norharman levels were determined by high pressure liquid chromatography. Elevated norharman levels were found in chronic alcoholics (n = 35) on admission to the hospital and remained significantly elevated during their ICU stay. The area under the curves (AUC) showed that norharman was comparable to carbohydrate-deficient transferrin (CDT) and superior to conventional laboratory markers in detecting chronic alcoholics. Seventeen chronic alcoholics developed AWS; 16 of these patients experienced hallucinations or delirium. Norharman levels were significantly increased on days 2 and 4 in the ICU in patients who developed AWS compared with those who did not. An increase in norharman levels preceded hallucinations or delirium with a median period of approximately 3 days. The findings that elevated norharman levels are found in chronic alcoholics, that the AUC was in the range of CDT on admission and that norharman levels remained elevated during the ICU stay, support the view that norharman is a specific marker for alcoholism in traumatized patients. Since norharman levels increased prior to the onset of hallucinations and delirium it seems reasonable to investigate further the potential role of norharman as a possible substance which triggers AWS.

Type of Medium: Electronic Resource

URL: http://dx.doi.org/10.1080/1355621961000124726

Permalink

Library	Location	Call Number	Volume/Issue/Year	Availability

Others were also interested in ...

Paper (German National Licenses)

Fulltext

Electronic Resource

New Approaches for the Detection and Analysis of Electroencephalographic Burst-Suppression Patterns in Patients under Sedation (1999)

Leistritz, Lutz ; Jäger, Heinrich ; Schelenz, Christoph ; [et al.]

Springer

Journal of clinical monitoring and computing 15 (1999), S. 357-367

add to mindlist on the mindlist

Details

ISSN: 1573-2614

Keywords: Equipment: neural networks ; analysis techniques: Hilbert transformation ; monitoring: burst-suppression EEG ; pattern recognition

Source: Springer Online Journal Archives 1860-2000

Topics: Computer Science , Medicine

Notes: Abstract An automatic EEG pattern detection unit was developed and tested for the recognition of burst-suppression periods and for the separation of burst from suppression patterns. The median, standard deviation and the 95% edge frequency were computed from single channels of the EEG within a moving window and completed by the continuous computation of frequency band power via an adapted Hilbert resonance filter. These parameters were given to the inputs of two hierarchically arranged artificial neural networks (NNs). The output signals of NNs indicate the suppression and burst phases. The burst recognition was focused on the precise recognition of the burst onset. In subsequent processing steps the time course of percentages of burst patterns within their corresponding burst-suppression-phases was calculated and the time locations of burst onsets can be used to trigger an averaging for a burst-related analysis. The data for our investigations were derived from the routine EEG derivations of 12 patients with various neurosurgical diseases. A group-related training of the NNs was realized. For the group-related trained NNs EEG data for 6 patients were used for training and the data of 6 other patients for testing the classification performance of the pattern recognition units. Additionally, the reliability of the detection algorithm was tested with data of two patients with convulsive state, resistant to treatment, and burst-suppression like pattern EEG.