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

  • Biological Psychiatry - Original Article
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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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Authors and Affiliations

  1. Center for Addiction Research (CARe), Department for Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany

    Thomas Hillemacher, Helge Frieling, Julia Wilhelm, Annemarie Heberlein, Deniz Karagülle & Stefan Bleich

  2. Department for Psychiatry and Psychotherapy, University Hospital Erlangen, Erlangen, Germany

    Bernd Lenz & Johannes Kornhuber

Authors
  1. Thomas Hillemacher
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  2. Helge Frieling
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  3. Julia Wilhelm
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  4. Annemarie Heberlein
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  5. Deniz Karagülle
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  6. Stefan Bleich
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  7. Bernd Lenz
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  8. Johannes Kornhuber
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Correspondence to Thomas Hillemacher.

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Hillemacher, T., Frieling, H., Wilhelm, J. et al. Indicators for elevated risk factors for alcohol-withdrawal seizures: an analysis using a random forest algorithm. J Neural Transm 119, 1449–1453 (2012). https://doi.org/10.1007/s00702-012-0825-8

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  • DOI: https://doi.org/10.1007/s00702-012-0825-8

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