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Although clinicians have traditionally used the Finnegan Neonatal Abstinence Scoring Tool to assess the severity of neonatal opioid withdrawal, a newer function-based approach - the Eat, Sleep, Console care approach - is increasing in use. Whether the new approach can safely reduce the time until infants are medically ready for discharge when it is applied broadly across diverse sites is unknown. In this cluster-randomized, controlled trial at 26 U.S. hospitals, we enrolled infants with neonatal opioid withdrawal syndrome who had been born at 36 weeks' gestation or more. At a randomly assigned time, hospitals transitioned from usual care that used the Finnegan tool to the Eat, Sleep, Console approach. During a 3-month transition period, staff members at each hospital were trained to use the new approach. The primary outcome was the time from birth until medical readiness for discharge as defined by the trial. Composite safety outcomes that were assessed during the first 3 months of postnatal age included in-hospital safety, unscheduled health care visits, and nonaccidental trauma or death. A total of 1305 infants were enrolled. In an intention-to-treat analysis that included 837 infants who met the trial definition for medical readiness for discharge, the number of days from birth until readiness for hospital discharge was 8.2 in the Eat, Sleep, Console group and 14.9 in the usual-care group (adjusted mean difference, 6.7 days; 95% confidence interval [CI], 4.7 to 8.8), for a rate ratio of 0.55 (95% CI, 0.46 to 0.65; P<0.001). The incidence of adverse outcomes was similar in the two groups. As compared with usual care, use of the Eat, Sleep, Console care approach significantly decreased the number of days until infants with neonatal opioid withdrawal syndrome were medically ready for discharge, without increasing specified adverse outcomes. (Funded by the Helping End Addiction Long-term (HEAL) Initiative of the National Institutes of Health; ESC-NOW ClinicalTrials.gov number, NCT04057820.).

WITHDRAWN: M. Sawada MD, N. Otsuka MD, PhD, K. Nagashima MD, PhD, R. Watanabe MD, PhD, Y. Wakamatsu MD, PhD, S. Hayashida MD, PhD, S. Hirata MD, M. Hirata MD, S. Kurokawa MD, PhD, Y. Okumura MD, PhD, Clinical implication of the patient's disease awareness and adherence to medications in patients undergoing atrial fibrillation ablation, Journal of Arrhythmia 40, no. 1 (2023): 57‐66, https://doi.org/10.1002/joa3.12965 The above article, first published online on 6 December 2023, on Wiley Online Library (onlinelibrary.wiley.com), has been withdrawn by agreement between the authors, the Editors in Chief Kazuo Matsumoto and Young‐Hoon Kim, the Japanese Heart Rhythm Society, and John Wiley and Sons Australia Ltd. The withdrawal has been made because the authors had not obtained permission to use the MMAS‐8 scale reported in the article. The authors apologize for this error.

There are no FDA-approved pharmacotherapies for cannabis dependence. Cannabis is the most widely used illicit drug in the world, and patients seeking treatment for primary cannabis dependence represent 25% of all substance use admissions. We conducted a phase IIa proof-of-concept pilot study to examine the safety and efficacy of a calcium channel/GABA modulating drug, gabapentin, for the treatment of cannabis dependence. A 12-week, randomized, double-blind, placebo-controlled clinical trial was conducted in 50 unpaid treatment-seeking male and female outpatients, aged 18-65 years, diagnosed with current cannabis dependence. Subjects received either gabapentin (1200 mg/day) or matched placebo. Manual-guided, abstinence-oriented individual counseling was provided weekly to all participants. Cannabis use was measured by weekly urine toxicology and by self-report using the Timeline Followback Interview. Cannabis withdrawal symptoms were assessed using the Marijuana Withdrawal Checklist. Executive function was measured using subtests from the Delis-Kaplan Executive Function System. Relative to placebo, gabapentin significantly reduced cannabis use as measured both by urine toxicology (p=0.001) and by the Timeline Followback Interview (p=0.004), and significantly decreased withdrawal symptoms as measured by the Marijuana Withdrawal Checklist (p<0.001). Gabapentin was also associated with significantly greater improvement in overall performance on tests of executive function (p=0.029). This POC pilot study provides preliminary support for the safety and efficacy of gabapentin for treatment of cannabis dependence that merits further study, and provides an alternative conceptual framework for treatment of addiction aimed at restoring homeostasis in brain stress systems that are dysregulated in drug dependence and withdrawal.

To date, no screening tools for alcohol withdrawal syndromes (AWS) have been validated in the medically ill. Although several tools quantify the severity of AWS (e.g., Clinical Institute Withdrawal Assessment for Alcohol [CIWA]), none identify subjects at risk of AWS, thus missing the opportunity for timely prophylaxis. Moreover, there are no validated tools for the prediction of complicated (i.e., moderate to severe) AWS in the medically ill. Our goals were (1) to conduct a systematic review of the published literature on AWS to identify clinical factors associated with the development of AWS, (2) to use the identified factors to develop a tool for the prediction of alcohol withdrawal among patients at risk, and (3) to conduct a pilot study to assess the validity of the tool. For the creation of the Prediction of Alcohol Withdrawal Severity Scale (PAWSS), we conducted a systematic literature search using PRISMA (preferred reporting items for systematic reviews and meta-analyses) guidelines for clinical factors associated with the development of AWS, using PubMed, PsychInfo, MEDLINE, and Cochrane Databases. Eligibility criteria included: (i) manuscripts dealing with human subjects, age 18 years or older, (ii) manuscripts directly addressing descriptions of AWS or its predisposing factors, including case reports, naturalistic case descriptions, and all types of clinical trials (e.g., randomized, single-blind, or open label studies), (iii) manuscripts describing characteristics of alcohol use disorder (AUD), and (iv) manuscripts dealing with animal data (which were considered only if they directly dealt with variables described in humans). Obtained data were used to develop the Prediction of Alcohol Withdrawal Severity Scale, in order to assist in the identification of patients at risk for complicated AWS. A pilot study was conducted to assess the new tool's psychometric qualities on patients admitted to a general inpatient medicine unit over a 2-week period, who agreed to participate in the study. Blind to PAWSS results, a separate group of researchers retrospectively examined the medical records for evidence of AWS. The search produced 2802 articles describing factors potentially associated with increased risk for AWS, increased severity of withdrawal symptoms, and potential characteristics differentiating subjects with various forms of AWS. Of these, 446 articles met inclusion criteria and underwent further scrutiny, yielding a total of 233 unique articles describing factors predictive of AWS. A total of 10 items were identified as correlated with complicated AWS (i.e., withdrawal hallucinosis, withdrawal-related seizures, and delirium tremens) and used to construct the PAWSS. During the pilot study, a total of 68 subjects underwent evaluation with PAWSS. In this pilot sample the sensitivity, specificity, and positive and negative predictive values of PAWSS were 100%, using the threshold score of 4. The results of the literature search identified 10 items which may be correlated with risk for complicated AWS. These items were assembled into a tool to assist in the identification of patients at risk: PAWSS. The results of this pilot study suggest that PAWSS may be useful in identifying risk of complicated AWS in medically ill, hospitalized individuals. PAWSS is the first validated tool for the prediction of severe AWS in the medically ill and its use may aid in the early identification of patients at risk for complicated AWS, allowing for prophylaxis against AWS before severe alcohol withdrawal syndromes develop.

Background and purpose Alcohol withdrawal seizures (AWS) are a well‐known complication of chronic alcohol abuse, but there is currently little knowledge of their long‐term relapse rate and prognosis. The aims of this study were to identify risk factors for AWS recurrence and to study the overall outcome of patients after AWS. Methods In this retrospective single‐center study, we included patients who were admitted to the Emergency Department after an AWS between January 1, 2013 and August 10, 2021 and for whom an electroencephalogram (EEG) was requested. AWS relapses up until April 29, 2022 were researched. We compared history, treatment with benzodiazepines or antiseizure medications (ASMs), laboratory, EEG and computed tomography findings between patients with AWS relapse (r‐AWS) and patients with no AWS relapse (nr‐AWS). Results A total of 199 patients were enrolled (mean age 53 ± 12 years; 78.9% men). AWS relapses occurred in 11% of patients, after a median time of 470.5 days. Brain computed tomography (n = 182) showed pathological findings in 35.7%. Risk factors for relapses were history of previous AWS (p = 0.013), skull fractures (p = 0.004) at the index AWS, and possibly epileptiform EEG abnormalities (p = 0.07). Benzodiazepines or other ASMs, taken before or after the index event, did not differ between the r‐AWS and the nr‐AWS group. The mortality rate was 2.9%/year of follow‐up, which was 13 times higher compared to the general population. Risk factors for death were history of AWS (p < 0.001) and encephalopathic EEG (p = 0.043). Conclusions Delayed AWS relapses occur in 11% of patients and are associated with risk factors (previous AWS >24 h apart, skull fractures, and pathological EEG findings) that also increase the epilepsy risk, that is, predisposition for seizures, if not treated. Future prospective studies are mandatory to determine appropriate long‐term diagnostic and therapeutic strategies, in order to reduce the risk of relapse and mortality associated with AWS.

Symptoms of alcohol withdrawal syndrome (AWS) may develop within 6–24 h after the abrupt discontinuation or decrease of alcohol consumption. Symptoms can vary from autonomic hyperactivity and agitation to delirium tremens. The gold-standard treatment for AWS is with benzodiazepines (BZDs). Among the BZDs, different agents (i.e., long-acting or short-acting) and different regimens (front-loading, fixed-dose or symptom-triggered) may be chosen on the basis of patient characteristics. Severe withdrawal could require ICU admission and the use of barbiturates or propofol. Other drugs, such as α 2 -agonists (clonidine and dexmetedomidine) and β-blockers can be used as adjunctive treatments to control neuroautonomic hyperactivity. Furthermore, neuroleptic agents can help control hallucinations. Finally, other medications for the treatment for AWS have been investigated with promising results. These include carbamazepine, valproate, sodium oxybate, baclofen, gabapentin and topiramate. The usefulness of these agents are discussed.