Anxiety level and EEG indicators in alcohol dependence: a model for predicting the duration of therapeutic remission

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Galkin SA, Bokhan NA. [Anxiety level and EEG indicators in alcohol dependence: a model for predicting the duration of therapeutic remission]. Rossiiskii psikhiatricheskii zhurnal [Russian Journal of Psychiatry]. 2022;(1):34-39. Russian

Abstract

In an empirical study, 86 patients with alcohol dependence were examined in order to build a prognostic model of the duration of alcohol remission. The Hamilton Anxiety Scale (HARS) was used as clinical data. The values of spectral analysis of power and coherence for θ-, α- and β-rhythms were used as electroencephalographic data. With the help of discriminant analysis, a model was obtained that allows predicting the duration of therapeutic remission with high accuracy in patients with alcohol dependence.

Keywords alcohol dependence; remission; prognosis; anxiety; electroencephalography

References

1. Sudhinaraset M, Wigglesworth C, Takeuchi DT. Social and Cultural Contexts of Alcohol Use: Influences in a Social-Ecological Framework. Alcohol Res. 2016;38(1):35–45. PMID: 27159810 2. WHO Geneva. Global Health Risks. Mortality and burden of disease attributable to selected major risk factor. 2009. p. 63. 3. Loskutov DV, Nevmatulin ASh. [Dependence of mortality from external causes from alcohol consumption]. Social'nye aspekty zdorov'ya naseleniya. 2020;(3):8. (In Russ.) 4. Mandel AI, Gutkevich EV, Peshkovskaya AG, et al. [Family psychotherapy of co-dependent relatives of alcoholism patients taking into account the data of family genetic analysis and individual psychological diagnostics: results and evaluation of effectiveness]. Sibirskij vestnik psihiatrii i narkologii. 2018;(1):81–8. (In Russ.) 5. Kosolapov VP, Letnikova LI, Manakin II, et al. [Analysis of addictive behavior and alcohol dependence taking into account medical and social risk factors]. Sistemnyj analiz i upravlenie v biomedicinskih sistemah. 2018;(1):137–46. (In Russ.) 6. Koshkina EA, Pavlovskaya NI, Yagudina RI, et al. [Medico-social and economic consequences of alcohol abuse in Russia]. Social'nye aspekty zdorov'ya naseleniya. 2010;(2):3. (In Russ.) 7. Shulkin LM, Kosenko NA, Kosenko VG, et al. [Clinical review of remission of alcohol dependence]. Kubanskij nauchnyj medicinskij vestnik. 2015;(2):153–9. (In Russ.) 8. Collins SE. Associations Between Socioeconomic Factors and Alcohol Outcomes. Alcohol Res. 2016;38(1):83–94. PMID: 27159815 9. Galkin SA, Peshkovskaya AG, Bokhan NA. [Possible clinical and electroencephalographic factors for predicting the duration of remission in patients with alcohol dependence]. Rossiiskii psikhiatricheskii zhurnal [Russian Journal of Psychiatry]. 2021;(1):47–52. (In Russ.) 10. Bokhan NA, Semke VYa. Komorbidnost' v narkologii. Tomsk; 2009. 498 p. (In Russ.) 11. Gimeno C, Dorado ML, Roncero C, et al. Treatment of Comorbid Alcohol Dependence and Anxiety Disorder: Review of the Scientific Evidence and Recommendations for Treatment. Front Psychiatry. 2017;8:173. DOI: https://doi.org/10.3389/fpsyt.2017.00173 12. Fama R, Le Berre AP, Sullivan EV. Alcohol's Unique Effects on Cognition in Women: A 2020 Review to Envision Future Research and Treatment. Alcohol Res. 2020;40(2):3. DOI: https://doi.org/10.35946/arcr.v40.2.03 13. Sullivan EV, Harris RA, Pfefferbaum A. Alcohol's effects on brain and behavior. Alcohol Res Health. 2010;33(1–2):127–43. PMID: 23579943 14. Galkin SA, Roschina OV, Kisel NI, et al. [Clinical and neurophysiological features of alcohol dependence and its comorbidity with affective disorders]. Zh Nevrol Psihiatr Im SS Korsakova. 2020;(10):56–9. (In Russ.) 15. Maksimova IV. [Cognitive and electroencephalographic changes in patients with alcohol dependence who suffered a convulsive seizure]. Sibirskij vestnik psihiatrii i narkologii. 2018;(2):89–92. (In Russ.) 16. Lapin IA, Mitrofanov AA. [The use of mathematical EEG analysis for differential diagnosis of bipolar and unipolar depressions (on the example of discriminant analysis of spectral power, coherence and hemispheric asymmetry)]. Social'naya i klinicheskaya psihiatriya. 2017;(2):69–74. (In Russ.) 17. Iznak AF, Iznak EV, Abramova LI, et al. [Models of quantitative prognosis of the therapeutic response of patients with depression according to the parameters of the initial EEG]. Fiziologiya cheloveka. 2019;(6):36–43. (In Russ.) 18. Galkin SA, Vasilyeva SN, Ivanova SA, et al. [Electroencephalographic markers of resistance of depressive disorders to pharmacotherapy and determination of a possible approach to individual prognosis of therapy effectiveness]. Psihiatriya. 2021;(2):39–45. (In Russ.) 19. Mednova IA, Kornetova EG, Ivanova SA. [Prediction model of metabolic syndrome in patients with paranoid schizophrenia]. Sibirskij vestnik psihiatrii i narkologii. 2020;(3):45–50. (In Russ.) 20. Gimeno C, Dorado ML, Roncero C, et al. Treatment of Comorbid Alcohol Dependence and Anxiety Disorder: Review of the Scientific Evidence and Recommendations for Treatment. Front Psychiatry. 2017;8:173. DOI: https://doi.org/10.3389/fpsyt.2017.00173 21. Fedorova SS. [Pharmacotherapy and psychotherapy of combined anxiety disorders and alcoholism]. Zh Nevrol Psihiatr Im SS Korsakova. 2013;(6–2):58–62. (In Russ.) 22. Davydova TV, Nevidimova TI, Vetrile LA, et al. [The ratio of antibodies to neurotransmitters in the blood sera of women with alcohol dependence and depressive disorders]. Byulleten' eksperimental'noj biologii i mediciny. 2021;(6):686–9. (In Russ.) 23. Banerjee N. Neurotransmitters in alcoholism: A review of neurobiological and genetic studies. Indian J Hum Genet. 2014;20(1):20–31. DOI: https://doi.org/10.4103/0971-6866.132750 24. Kibitov AO, Brodyansky VM, Rybakova KV, et al. [Modulation of GABA and glutamate systems as a promising pharmacological target for pathogenetic therapy of alcohol dependence: possibilities of pharmacogenetic analysis based on a double-blind placebo-controlled study]. Voprosy narkologii. 2018;(1):48–86. (In Russ.) 25. Kinreich S, McCutcheon VV, Aliev F, et al. Predicting alcohol use disorder remission: a longitudinal multimodal multi-featured machine learning approach. Transl Psychiatry. 2021;11(1):166. DOI: https://doi.org/10.1038/s41398-021-01281-2



DOI: http://dx.doi.org/10.47877/1560-957Х-2022-10104

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