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Indicators for elevated risk factors for alcohol-withdrawal seizures: an analysis using a random forest algorithm

descriptionPublicationkeyboard_double_arrow_right Article , Journal 24 May 2012Publisher:Springer Science and Business Media LLCJournal:Journal of Neural Transmission, volume 119, pages 1,449-1,453 (issn: 0300-9564, eissn: 1435-1463,

Authors: Stefan Bleich; Helge Frieling; Julia Wilhelm; Johannes Kornhuber; Thomas Hillemacher; Deniz Karagülle; Annemarie Heberlein; +1 Authors

doi: 10.1007/s00702-012-0825-8

pmid: 22622368

Indicators for elevated risk factors for alcohol-withdrawal seizures: an analysis using a random forest algorithm

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Abstract

Alcohol-withdrawal seizures (AWS) are an important and relevant complication during detoxification in alcohol-dependent patients. Therefore, it is important to evaluate the individual risk for AWS. We apply a random forest algorithm to assess possible predictive markers in a large sample of 200 alcohol-dependent patients undergoing alcohol withdrawal. This analysis showed that the combination of homocysteine, prolactin, blood alcohol concentration on admission, number of preceding withdrawals, age and the number of cigarettes smoked may successfully predict AWS. In conclusion, the results of this analysis allow for origination of further research, which should include additional biological and psychosocial parameters as well as consumption behaviour.

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Keywords

Adult, Male, Ethanol, Central Nervous System Depressants, Middle Aged, Alcohol Withdrawal Seizures, Prolactin, Young Adult, Predictive Value of Tests, Risk Factors, Area Under Curve, Humans, Female, Homocysteine, Algorithms, Aged

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