Quetiapine versus trazodone in reducing rehospitalization for alcohol dependence: a large data-base study

Sleep abnormalities associated with alcohol, cannabis, cocaine, and opiate use: a comprehensive review

Sleep abnormalities are associated with acute and chronic use of addictive substances. Although sleep complaints associated with use and abstinence from addictive substances are widely recognized, familiarity with the underlying sleep abnormalities is often lacking, despite evidence that these sleep abnormalities may be recalcitrant and impede good outcomes. Substantial research has now characterized the abnormalities associated with acute and chronic use of alcohol, cannabis, cocaine, and opiates. This review summarizes this research and discusses the clinical implications of sleep abnormalities in the treatment of substance use disorders.

Assessment and treatment of insomnia in adult patients with alcohol use disorders

Insomnia in patients with alcohol dependence has increasingly become a target of treatment due to its prevalence, persistence, and associations with relapse and suicidal thoughts, as well as randomized controlled studies demonstrating efficacy with behavior therapies and non-addictive medications. This article focuses on assessing and treating insomnia that persists despite 4 or more weeks of sobriety in alcohol-dependent adults. Selecting among the various options for treatment follows a comprehensive assessment of insomnia and its multifactorial causes. In addition to chronic, heavy alcohol consumption and its effects on sleep regulatory systems, contributing factors include premorbid insomnia; co-occurring medical, psychiatric, and other sleep disorders; use of other substances and medications; stress; environmental factors; and inadequate sleep hygiene. The assessment makes use of history, rating scales, and sleep diaries as well as physical, mental status, and laboratory examinations to rule out these factors. Polysomnography is indicated when another sleep disorder is suspected, such as sleep apnea or periodic limb movement disorder, or when insomnia is resistant to treatment. Sobriety remains a necessary, first-line treatment for insomnia, and most patients will have some improvement. If insomnia-specific treatment is needed, then brief behavioral therapies are the treatment of choice, because they have shown long-lasting benefit without worsening of drinking outcomes. Medications work faster, but they generally work only as long as they are taken. Melatonin agonists; sedating antidepressants, anticonvulsants, and antipsychotics; and benzodiazepine receptor agonists each have their benefits and risks, which must be weighed and monitored to optimize outcomes. Some relapse prevention medications may also have sleep-promoting activity. Although it is assumed that treatment for insomnia will help prevent relapse, this has not been firmly established. Therefore, insomnia and alcohol dependence might be best thought of as co-occurring disorders, each of which requires its own treatment.

The effects of quetiapine on sleep in recovering alcohol-dependent subjects: a pilot study

OBJECTIVE

The aim of this hypothesis-generating pilot study was to assess prospectively the objective and subjective effects of treatment with quetiapine XR on sleep during early recovery from alcohol dependence (AD).

METHODS

Recovering subjects with AD and sleep disturbance complaints were treated with quetiapine XR (n = 10) or matching placebo pills (n = 10) for 8 weeks. Polysomnography was used to assess sleep objectively, and the Insomnia Severity Index and Pittsburgh Sleep Quality Index were used to measure subjective insomnia. Other assessment measures included the 10-minute psychomotor vigilance task (for neurobehavioral functioning), the time-line follow-back measure (for alcohol consumption), the Penn Alcohol Craving Scale (for alcohol craving), the Patient Health Questionnaire-9 item scale (for depressive symptoms), and the Beck Anxiety Inventory (for anxiety symptoms).

RESULTS

Although there was no effect of quetiapine XR on sleep efficiency (time spent asleep/total recording time), there was a pre-to-post reduction in wake after sleep onset time (P = 0.03) and nonsignificant trends for increases in sleep onset latency (SOL) and stage 2 sleep time. A time × drug interaction was seen for the subjective insomnia, such that quetiapine XR-treated subjects reported greater initial improvement in their subjective insomnia, but the difference was not sustained. There were no differences between treatment groups on other measures or medication compliance.

CONCLUSION

Quetiapine XR improves objective sleep continuity and transiently improves subjective insomnia early in recovery from AD.

Big data and clinicians: a review on the state of the science

BACKGROUND

In the past few decades, medically related data collection saw a huge increase, referred to as big data. These huge datasets bring challenges in storage, processing, and analysis. In clinical medicine, big data is expected to play an important role in identifying causality of patient symptoms, in predicting hazards of disease incidence or reoccurrence, and in improving primary-care quality.

OBJECTIVE

The objective of this review was to provide an overview of the features of clinical big data, describe a few commonly employed computational algorithms, statistical methods, and software toolkits for data manipulation and analysis, and discuss the challenges and limitations in this realm.

METHODS

We conducted a literature review to identify studies on big data in medicine, especially clinical medicine. We used different combinations of keywords to search PubMed, Science Direct, Web of Knowledge, and Google Scholar for literature of interest from the past 10 years.

RESULTS

This paper reviewed studies that analyzed clinical big data and discussed issues related to storage and analysis of this type of data.

CONCLUSIONS

Big data is becoming a common feature of biological and clinical studies. Researchers who use clinical big data face multiple challenges, and the data itself has limitations. It is imperative that methodologies for data analysis keep pace with our ability to collect and store data.