A machine learning approach to risk assessment for alcohol withdrawal syndrome.

At present, risk assessment for alcohol withdrawal syndrome relies on clinical judgment. Our aim was to develop accurate machine learning tools to predict alcohol withdrawal outcomes at the individual subject level using information easily attainable at patients' admission. An observational machine learning analysis using nested cross-validation and out-of-sample validation was applied to alcohol-dependent patients at two major detoxification wards (LMU, n = 389; TU, n = 805). 121 retrospectively derived clinical, blood-derived, and sociodemographic measures were used to predict 1) moderate to severe withdrawal defined by the alcohol withdrawal scale, 2) delirium tremens, and 3) withdrawal seizures. Mild and more severe withdrawal cases could be separated with significant, although highly variable accuracy in both samples (LMU, balanced accuracy [BAC] = 69.4%; TU, BAC = 55.9%). Poor outcome predictions were associated with higher cumulative clomethiazole doses during the withdrawal course. Delirium tremens was predicted in the TU cohort with BAC of 75%. No significant model predicting withdrawal seizures could be found. Our models were unique to each treatment site and thus did not generalize. For both treatment sites and withdrawal outcome different variable sets informed our models' decisions. Besides previously described variables (most notably, thrombocytopenia), we identified new predictors (history of blood pressure abnormalities, urine screening for benzodiazepines and educational attainment). In conclusion, machine learning approaches may facilitate generalizable, individualized predictions for alcohol withdrawal severity. Since predictive patterns highly vary for different outcomes of withdrawal severity and across treatment sites, prediction tools should not be recommended for clinical practice unless adequately validated in specific cohorts.

Alcohol withdrawal syndrome: diagnostic and therapeutic methods.

Alcohol withdrawal syndrome (AWS) is a medical emergency, rare in the general population, but very common among alcoholic individuals, which can lead to severe complications when unrecognized or late treated. It represents a clinical condition which can evolve in few hours or days following an abrupt cessation or reduction of alcohol intake and is characterized by hyperactivity of the autonomic nervous system resulting in the development of typical symptoms. According to DSM-5 criteria, the alcohol withdrawal syndrome is defined as such: if patients present at least two of typical signs and symptoms. The Clinical Institute Withdrawal Assessment of Alcohol Scale, revised version (CIWA-Ar), is the tool for assessing the severity of AWS. The support to patient with AWS includes pharmacological intervention as well as general support, restoration of biochemical imbalances and specific therapy. Regarding the pharmacological treatment, benzodiazepines represent the gold standard, in particular long-acting benzodiazepines, administered with a gradual reduction up to cessation.

Alcohol withdrawal upregulates mRNA encoding for CaV2.1-α1 subunit in the rat inferior colliculus.

We previously reported increased current density through P-type voltage-gated Ca2+ channels in inferior colliculus (IC) neurons during alcohol withdrawal. However, the molecular correlate of this increased P-type channel current is currently unknown. Here, we probe changes in mRNA and protein expression of the pore-forming CaV2.1-α1 (P/Q-type) subunits in IC neurons during the course of alcohol withdrawal-induced seizures (AWSs). Rats received three daily doses of ethanol or the vehicle every 8 h for 4 consecutive days. The IC was dissected at various time intervals following alcohol withdrawal, and the mRNA and protein levels of the CaV2.1-α1 subunits were measured. In separate experiments, rats were tested for acoustically evoked seizure susceptibility 3, 24, and 48 h after alcohol withdrawal. AWSs were observed 24 h after withdrawal; no seizures were observed at 3 or 48 h or in the control-treated rats. Compared to control-treated rats, the mRNA levels of the CaV2.1-α1 subunit were increased 1.9-fold and 2.1-fold at 3 and 24 h, respectively; change in mRNA expression was nonsignificant at 48 h following alcohol withdrawal. Western blot analyses revealed that protein levels of the CaV2.1-α1 subunits were not altered in IC neurons following alcohol withdrawal. We conclude that expression of the Cacna1a mRNA increased before the onset of AWS susceptibility, suggesting that altered CaV2.1 channel expression may play a role in AWS pathogenesis.

Convulsões relacionadas ao alcoolismo: atualização / Seizures related to alcoholism: update

OBJETIVO: Revisão sobre crises convulsivas relacionadas ao alcoolismo,discutindo sua classificação, fisiopatologia, investigação diagnóstica e seu tratamento. MÉTODO: Revisão não sistemática de artigos utilizando-se os unitermos: "alcoholism", "alcohol", "seizures" e "withdrawal". Priorizou-se a utilização de artigos que apresentassem associação desses unitermos no título. Foram utilizadas as bases de dados do PubMed, Lilacs e Google Scholar. RESULTADOS: Foram encontrados 2.362 artigos associando os unitermos no título, tendo sido escolhidos 26 artigos em inglês, 3 em português, 1 manual e 1 tese em inglês para a elaboração desta revisão. CONCLUSÃO: As crises convulsivas relacionadas ao álcool representam uma das mais graves complicações do alcoolismo. O diagnóstico e o tratamento corretos melhoram o prognóstico desses indivíduos, diminuindo o risco de complicações, a recorrência de crises, a ocorrência de status epilepticus ou a evolução para um quadro de delirium tremens.

OBJECTIVE: Review alcoholism related seizures, discussing classification,pathophysiology, diagnosis and treatment. METHOD: A non-systematic review was performed of articles using the keywords: "alcoholism", "alcohol", "seizures", and "withdrawal". Articles with the combination of these keywords in the title were favored. The search was performed on PubMed, Lilacs database and Google Scholar. RESULTS: Using these search terms 2,362 articles were found, being selected 26 articles in English, 3 articles in Portuguese, 1 English manual, and 1 thesis in English to elaborate this review. CONCLUSION: Seizures related to alcohol are one of the most serious complications of alcoholism. The correct diagnosis and treatment improves the prognosis of these individuals, decreasing the risk of complications,seizure recurrence, status epilepticus and the progression to delirium tremens.

Altered voltage-gated calcium channels in rat inferior colliculus neurons contribute to alcohol withdrawal seizures.

We have previously reported that enhanced susceptibility to alcohol withdrawal seizures (AWS) parallels the enhancement of the current density of high-threshold voltage-gated Ca(2+) (CaV) channels in rat inferior colliculus (IC) neurons. However, whether this increased current density is a cause or consequence of AWS is unclear. Here, I report changes in the current density of CaV channels in IC neurons during the course of alcohol withdrawal and the potential anticonvulsant effect of intra-IC infusions of L- and P-type CaV channel antagonists. Whole-cell currents were activated by depolarizing pulses using barium as the charge carrier. Currents and seizure susceptibility were evaluated in control animals 3h after alcohol intoxication, as well as 3h (before AWS), 24h (when AWS susceptibility is maximal), and 48h (when AWS susceptibility is no longer present) after alcohol withdrawal. Nifedipine, nimodipine (L-type antagonists) or ω-agatoxin TK (P-type antagonist) were infused intra-IC to probe the role of CaV channels in the pathogenesis of AWS. CaV current density and conductance in IC neurons were significantly increased 3 and 24h after alcohol withdrawal compared with the control group or the group tested 3h following ethanol intoxication. Blockade of L-type CaV channels within the IC completely suppressed AWS, and inhibition of P-type channels reduced AWS severity. These findings suggest that the enhancement of CaV currents in IC neurons occurs prior to AWS onset and that alterations in L- and P-type CaV channels in these neurons may underlie the pathogenesis of AWS.

Predictive value of the bispectral index for burst suppression on diagnostic electroencephalogram during drug-induced coma.

STUDY PURPOSE: To determine correlation and predictive value between data obtained with the bispectral index (BIS) and diagnostic electroencephalogram (EEG) in determining degree of burst suppression during drug-induced coma. This study seeks to answer the question: "To what degree can EEG suppression and burst count as measured by diagnostic EEG during drug-induced coma be predicted from data obtained from the BIS such as BIS value, suppression ratio (SR), and burst count?" BACKGROUND/SIGNIFICANCE: During drug-induced coma, cortical EEG is the gold standard for real-time monitoring and drug titration. Diagnostic EEG is, from setup through data analysis, labor intensive, costly, and difficult to maintain uniform clinician competency. BIS monitoring is less expensive, less labor-intensive, and easier to interpret data and establish/maintain competency. Validating BIS data versus diagnostic EEG facilitates effective brain monitoring during drug-induced coma at lower cost with similar outcomes. METHOD: This is a prospective, observational cohort study. Four consecutive patients receiving drug-induced coma/EEG monitoring were enrolled. BIS was initiated after informed consent. Variables recorded per minute included presence or absence of EEG burst suppression, burst count, BIS value over time, burst count, and SR. Pearson's product-moment and Spearman rank coefficient for BIS value and SR versus burst count were performed. Regression analysis was utilized to plot BIS values versus bursts/minute on EEG as well as SR versus burst count on EEG. EEG/BIS data were collected from digital data files and transcribed onto data sheets for corresponding time indices. RESULTS: Four patients yielded 1,972 data sets over 33 hours of EEG/BIS monitoring. Regression coefficient of 0.6673 shows robust predictive value between EEG burst count and BIS SR. Spearman rank coefficient of -0.8727 indicates strong inverse correlation between EEG burst count and BIS SR. Pearson's correlation coefficient between EEG versus BIS burst count was .8256 indicating strong positive correlation. Spearman's rank coefficient of 0.8810 and Pearson's correlation coefficient of .6819 showed strong correlation between BIS value versus EEG burst count. Number of patients (4) limits available statistics and ability to generalize results. Graphs and statistics show strong correlation/predictive value for BIS parameters to EEG suppression. CONCLUSIONS: This study is the first to measure correlation and predictive value between BIS monitoring and diagnostic EEG for degree of EEG suppression and burst count in the adult population. Available statistic tests and graphing of variables from BIS and diagnostic EEG show strong correlation and predictive value between both monitoring technologies during drug-induced coma. These support using BIS value, SR, and burst count to predict degree of EEG suppression in real time for titrating metabolic suppression therapy.

Understanding the addiction cycle: a complex biology with distinct contributions of genotype vs. sex at each stage.

Ethanol abuse can lead to addiction, brain damage and premature death. The cycle of alcohol addiction has been described as a composite consisting of three stages: intoxication, withdrawal and craving/abstinence. There is evidence for contributions of both genotype and sex to alcoholism, but an understanding of the biological underpinnings is limited. Utilizing both sexes of genetic animal models with highly divergent alcohol withdrawal severity, Withdrawal Seizure-Resistant (WSR) and Withdrawal Seizure-Prone (WSP) mice, the distinct contributions of genotype/phenotype and of sex during addiction stages on neuroadaptation were characterized. Transcriptional profiling was performed to identify expression changes as a consequence of chronic intoxication in the medial prefrontal cortex. Significant expression differences were identified on a single platform and tracked over a behaviorally relevant time course that covered each stage of alcohol addiction; i.e., after chronic intoxication, during peak withdrawal, and after a defined period of abstinence. Females were more sensitive to ethanol with higher fold expression differences. Bioinformatics showed a strong effect of sex on the data structure of expression profiles during chronic intoxication and at peak withdrawal irrespective of genetic background. However, during abstinence, differences were observed instead between the lines/phenotypes irrespective of sex. Confirmation of identified pathways showed distinct inflammatory signaling following intoxication at peak withdrawal, with a pro-inflammatory phenotype in females but overall suppression of immune signaling in males. Combined, these results suggest that each stage of the addiction cycle is influenced differentially by sex vs. genetic background and support the development of stage- and sex-specific therapies for alcohol withdrawal and the maintenance of sobriety.

Alcohol and drugs in epilepsy: pathophysiology, presentation, possibilities, and prevention.

The potentially serious outcomes from ingestion of and dependence on toxins make this an important topic for epileptologists. We must be aware of the potential for harm from compounds that may be freely available, yet patients may try to conceal their use. Problematic compounds may cause seizures either acutely or on withdrawal: Their use may reduce effectiveness of antiepileptic drugs, or may simply promote and enhance chaotic lifestyles. Any or all of these factors may worsen seizure control or even directly cause seizures. This article highlights the pathophysiology behind provoked seizures, provides clues to diagnosis, and then outlines the steps that clinicians should take to reduce the deleterious effects of toxic compounds.

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

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.

Running wheel activity protects against increased seizure susceptibility in ethanol withdrawn male rats.

Ethanol withdrawal is a dysphoric condition that arises from termination of ethanol intake by dependent individuals. Common withdrawal symptoms include anxiety, increased reactivity to stimuli and increased seizure susceptibility as well as the risk of increased seizure severity. We use an animal model of dependence and withdrawal to study withdrawal behaviors and potential underlying neurobiological mechanisms. For a number of years, we have quantified pentylenetetrazol seizure thresholds as an assessment of ethanol withdrawal at both one day and three days of withdrawal. Typically, we see a significant decrease in seizure threshold (increased sensitivity to seizure induction) that persists through three days of withdrawal for male rats. Increasing evidence indicates that voluntary exercise affords protection against various challenges to physical and psychological health, including ethanol-related challenges. Therefore, the current study investigated the effect of voluntary wheel running on seizure susceptibility following chronic ethanol administration and withdrawal. We found that voluntary wheel running attenuated the increased sensitivity to pentylenetetrazol-induced seizures observed with ethanol withdrawal, at both the one-day and three-day time points. This result was especially interesting as animals with access to the running wheels consumed more of the ethanol-containing diet. These findings showed that chronic voluntary wheel running reduces the severity of ethanol withdrawal in our animal model and suggest that exercise-based interventions may have some utility in the clinical management of heavy drinking and alcohol withdrawal.