Literature DB >> 22622368

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

Thomas Hillemacher1, Helge Frieling, Julia Wilhelm, Annemarie Heberlein, Deniz Karagülle, Stefan Bleich, Bernd Lenz, Johannes Kornhuber.   

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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Year:  2012        PMID： 22622368     DOI： 10.1007/s00702-012-0825-8

Source DB:  PubMed          Journal:  J Neural Transm (Vienna)        ISSN： 0300-9564            Impact factor:   3.575


  31 in total

1.  Biological markers for alcohol withdrawal seizures: a retrospective analysis.

Authors:  Deniz Karagülle; Annemarie Heberlein; Julia Wilhelm; Helge Frieling; Johannes Kornhuber; Stefan Bleich; Thomas Hillemacher
Journal:  Eur Addict Res       Date:  2012-01-24       Impact factor: 3.015

2.  Alteration of prolactin serum levels during alcohol withdrawal correlates with craving in female patients.

Authors:  Thomas Hillemacher; Kristina Bayerlein; Julia Wilhelm; Udo Reulbach; Helge Frieling; Dominikus Bönsch; Johannes Kornhuber; Stefan Bleich
Journal:  Addict Biol       Date:  2005-12       Impact factor: 4.280

3.  Vascular determinants of epilepsy: the Rotterdam Study.

Authors:  X Li; M M Breteler; M C de Bruyne; H Meinardi; W A Hauser; A Hofman
Journal:  Epilepsia       Date:  1997-11       Impact factor: 5.864

4.  Alcoholism-associated hyperhomocysteinemia and previous withdrawal seizures.

Authors:  Kristina Bayerlein; Thomas Hillemacher; Udo Reulbach; Brigitte Mugele; Wolfgang Sperling; Johannes Kornhuber; Stefan Bleich
Journal:  Biol Psychiatry       Date:  2005-06-15       Impact factor: 13.382

5.  The kindling hypothesis: further evidence from a U.S. national study of alcoholic men.

Authors:  B M Booth; F C Blow
Journal:  Alcohol Alcohol       Date:  1993-09       Impact factor: 2.826

6.  Probabilities of alcohol high-risk drinking, abuse or dependence estimated on grounds of tobacco smoking and nicotine dependence.

Authors:  Ulrich John; Christian Meyer; Hans-Jürgen Rumpf; Ulfert Hapke
Journal:  Addiction       Date:  2003-06       Impact factor: 6.526

Review 7.  Ethylglucuronide as a biomarker for ethanol detection.

Authors:  Julie C Kissack; Jaime Bishop; Amber Leatherwood Roper
Journal:  Pharmacotherapy       Date:  2008-06       Impact factor: 4.705

Review 8.  Homocysteine, alcoholism and its molecular networks.

Authors:  S Bleich; T Hillemacher
Journal:  Pharmacopsychiatry       Date:  2009-05-11       Impact factor: 5.788

9.  Alcoholism and smoking.

Authors:  J R DiFranza; M P Guerrera
Journal:  J Stud Alcohol       Date:  1990-03

Review 10.  Epilepsy in the elderly.

Authors:  Gina Mapes Jetter; Jose E Cavazos
Journal:  Semin Neurol       Date:  2008-07-24       Impact factor: 3.420
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  6 in total

1.  Health behaviors among people with epilepsy--results from the 2010 National Health Interview Survey.

Authors:  Wanjun Cui; Matthew M Zack; Rosemarie Kobau; Sandra L Helmers
Journal:  Epilepsy Behav       Date:  2015-02-10       Impact factor: 2.937

Review 2.  Using Agent-Based Models to Develop Public Policy about Food Behaviours: Future Directions and Recommendations.

Authors:  Philippe J Giabbanelli; Rik Crutzen
Journal:  Comput Math Methods Med       Date:  2017-03-21       Impact factor: 2.238

Review 3.  Alcohol withdrawal syndrome: mechanisms, manifestations, and management.

Authors:  S Jesse; G Bråthen; M Ferrara; M Keindl; E Ben-Menachem; R Tanasescu; E Brodtkorb; M Hillbom; M A Leone; A C Ludolph
Journal:  Acta Neurol Scand       Date:  2016-09-01       Impact factor: 3.209

4.  Prediction for cardiovascular diseases based on laboratory data: An analysis of random forest model.

Authors:  Xi Su; Yongyong Xu; Zhijun Tan; Xia Wang; Peng Yang; Yani Su; Yangyang Jiang; Sijia Qin; Lei Shang
Journal:  J Clin Lab Anal       Date:  2020-07-29       Impact factor: 2.352

5.  Identifying binge drinkers based on parenting dimensions and alcohol-specific parenting practices: building classifiers on adolescent-parent paired data.

Authors:  Rik Crutzen; Philippe J Giabbanelli; Astrid Jander; Liesbeth Mercken; Hein de Vries
Journal:  BMC Public Health       Date:  2015-08-05       Impact factor: 3.295

6.  Identifying small groups of foods that can predict achievement of key dietary recommendations: data mining of the UK National Diet and Nutrition Survey, 2008-12.

Authors:  Philippe J Giabbanelli; Jean Adams
Journal:  Public Health Nutr       Date:  2016-02-16       Impact factor: 4.022
  6 in total

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