Journal of Addictive Diseases最新文献

Introduction: Patients who inject drugs disproportionately experience frequent patient-directed discharge and readmissions. Assessment of quality of life may provide insight into individual barriers limiting health and addiction treatment. The relationship between QoL and discharge outcomes is unclear, particularly in the context of PWID in the inpatient setting.

Objectives: This study aimed to characterize the relationship between QoL, discharge outcomes, and treatment decisions of PWID in the inpatient setting.

Methods: This was a single-centered study conducted at an urban tertiary care center during a six-month period. Patients self-administered a modified Drug User QoL scale. Demographic information, disposition, and medication therapies were obtained retrospectively. Comparisons were made based on disposition and treatment.

Results: Of 58 eligible participants, 36 completed the DUQOL. The median age was 38 years; 48% were male, and 54% reported homelessness. DUQOL scores were lower for PDD patients than non-PDD patients. Patients who initiated medications of opioid use disorder inpatient had higher DUQOL scores compared to those who declined. Initiation of MOUD was associated with significantly lower odds of PDD; however, there was no significant difference in 30-day readmission. DUQOL domains that had a discordance between importance and satisfaction on the DUQOL included "Housing," "Health," "Sense of Future," "Feeling Good," and "Being Useful."

Conclusion: Higher QoL was observed to have better clinical outcomes and MOUD acceptance. DUQOL scores, particularly domains where there is a high discordance between satisfaction and importance, may serve as an important tool in predicting clinical outcomes and guiding treatment decisions.

Background: Tobacco use disorder (TUD) remains the leading preventable cause of death globally, yet fewer than one-third of users receive guideline-concordant care due to workforce shortages and training gaps. Emerging artificial intelligence (AI) systems, particularly large language models (LLMs), may help expand access to evidence-based cessation support, but their clinical competence remains insufficiently characterized.

Objective: To systematically evaluate the clinical accuracy, safety, guideline adherence, and communication quality of five leading LLMs across standardized tobacco-cessation scenarios.

Methods: We developed 84 clinical vignettes, covering five core competency domains (screening, diagnosis, pharmacotherapy, behavioral counseling, and harm reduction) based on DSM-5-TR, U.S. Public Health Service Guidelines, NICE NG209, ASAM guidelines, SAMHSA protocols, and the WHO MPOWER framework. Five LLMs, GPT-4.5, Claude 3.5 Sonnet, Gemini 2.5 Pro, Llama 3.1-70B, and DeepSeek-V3, were independently evaluated by four addiction-medicine experts across clinical accuracy, guideline adherence, safety, and clinical utility.

Results: GPT-4.5 and Claude 3.5 Sonnet achieved the highest composite scores (M = 4.25, SD = 0.68; M = 4.18, SD = 0.71), with 74-78% of responses rated ≥4.0 and superior safety performance (88% ≥4.0). Gemini 2.5 Pro showed moderate performance (M = 3.72; 52% ≥4.0). Open-weight models (Llama 3.1-70B, M = 3.48; DeepSeek-V3, M = 3.35) lagged behind overall, with only 34-38% achieving the ≥4.0 benchmark.

Conclusion: All evaluated AI systems demonstrated competence in tobacco-cessation counseling, but GPT-4.5 and Claude 3.5 Sonnet reached performance levels consistent with supervised clinical use and safety-critical scenarios. Clinician oversight remains essential for all medication-based interventions, and open-weight models warrant further validation before consideration for clinical implementation.

Background: Comorbid depression and anxiety are prevalent among individuals with substance use disorders (SUDs), complicating treatment outcomes and increasing relapse risk. Poor efficacy and safety concerns often limit pharmacological options for these psychiatric symptoms. Non-pharmacological interventions (NPIs) have gained traction, yet their relative effectiveness remains uncertain.

Objective: This study aimed to evaluate the comparative efficacy of NPIs-including cognitive-behavioral therapy (CBT), mindfulness-based approaches, exercise therapy, and neuromodulation-in reducing depression, anxiety, and substance craving among individuals with SUDs.

Methods: A systematic search of PubMed, Embase, and Cochrane Library (up to February 2025) identified 24 RCTs (1,437 participants, 901 males) with DSM/ICD-diagnosed SUDs, following PRISMA-NMA guidelines (PROSPERO: CRD420250654383). Outcomes encompassed depressive/anxiety symptoms (primary) and craving (secondary). Data were analyzed via Bayesian network meta-analysis (NMA), reported as standardized mean differences (SMDs) with 95% credible intervals (CrIs), with treatment efficacy ranked by surface under the cumulative ranking curve (SUCRA) values.

Results: Exercise therapy ranked highest for alleviating depression (SUCRA: 90.77%; SMD -0.79, 95% CrI:-1.04 to -0.54 vs. placebo), while physical-stimulus (PS) therapy and mindfulness interventions showed robust efficacy across outcomes. Subgroup analyses identified disease-specific efficacy profiles: cognitive behavioral therapy (CBT) demonstrated superiority for opioid use disorder (OUD)-related anxiety (SUCRA: 76.83%; SMD 0.74, 95% CrI:0.26-1.25 vs. placebo).

Conclusion: This NMA highlights varied efficacy among PS therapy, mindfulness-based, and exercise interventions in alleviating depression, anxiety, and craving in SUD patients. Results advocate integrating neuromodulatory and behavioral therapies into comprehensive addiction care. Future research should prioritize longitudinal mechanistic studies and personalized protocols to enhance treatment durability and recovery outcomes.

Brief interventions are widely implemented in substance use treatment because of their feasibility, scalability, and cost-effectiveness. Despite robust evidence from controlled trials, clinicians frequently report inconsistent and short-lived outcomes when these interventions are delivered in routine practice. This perspective article argues that brief interventions often fail not due to theoretical limitations, but because of contextual, relational, and implementation-level shortcomings. Drawing on existing literature and practice-based insights, the article identifies common reasons for failure, including inadequate assessment of readiness to change, superficial application of motivational techniques, neglect of craving-related processes, limited attention to identity and meaning, and absence of follow-up or reinforcement. Practical strategies are proposed to enhance real-world effectiveness, including reframing brief interventions as sequenced micro-interventions, integrating craving-focused coping skills, personalizing intervention targets, and incorporating low-intensity booster contacts. The implications of these adaptations for outpatient and community addiction services are discussed.

Background: The rising prevalence of fentanyl has complicated buprenorphine (BUP) initiation due to increased risks for precipitated withdrawal. Low-dose initiation (LDI), or "micro-dosing", has been proposed as an alternative to standard-dose initiation (SDI) to improve tolerability and engagement with care. This study aimed to compare inpatient and post-discharge outcomes of LDI versus SDI among patients with opioid use disorder (OUD) undergoing medically managed withdrawal.

Methods: This retrospective cohort study included adults admitted for medically managed OUD withdrawal during two time periods: SDI (February 2020-April 2021) and LDI (May 2021-August 2022). Sociodemographic, clinical, and treatment data were extracted from electronic medical records. Outcomes included inpatient length of stay (LOS), BUP acceptance and discharge dose, transition to rehabilitation, readmissions within one year, BUP refill rates, and outpatient treatment engagement. Analyses included logistic regression and negative binomial regression with adjustment for baseline urine drug screen results.

Results: The sample included 184 patients (127 SDI, 57 LDI) with a mean age of 37 years, predominately White N=140 (76.1%) and female. Groups were demographically similar. Inpatient outcomes did not differ in LOS, BUP acceptance, or discharge dose. The LDI group was less likely to transition to rehabilitation (26% vs 48%). LDI was associated with significantly lower odds of readmission within 1 year, while 30-day readmission rates showed a similar directional trend that did not reach statistical significance. Post-discharge BUP refill rates and outpatient attendance rates were similar between groups.

Conclusions: LDI and SDI produced comparable outcomes across most measures, with LDI associated with fewer readmissions over one year. Both approaches appear to be viable strategies in the fentanyl era.

Introduction: According to the Centers for Disease Control and Prevention (CDC), in 2023 alone, there were approximately 35,000 deaths that involved methamphetamine. Because methamphetamine is commonly used with alcohol, our objective was to investigate risk factors for concurrent use, which are underexplored.

Methods: This was a secondary analysis of deidentified publicly available data (2021-2023) collected by the National Survey of Drug Use and Health (NSDUH). Our primary aim was to identify risk factors for concurrent use of methamphetamine and alcohol. We used the random forest method and conducted variable importance analyses to identify the most important predictors for concurrent use, and subsequently binomial logistic regression for identifying risk factors.

Results: Between 2021 and 2023, there were a total of 219 (0.13% of all respondents) respondents (449,590 respondents weighted) that reported concurrent use of methamphetamine and alcohol. The top three predictors per the random forest model were (reflective of past year behaviors): driving under the influence of a selected illicit drug (Gini: 0.08), emergency department visit for substance use (Gini: 0.05), and fentanyl misuse (Gini: 0.046). Of the top 10 most important predictors, only 3 were statistically significant: income (p < 0.001), driving under the influence of any drug (aOR: 18.6, p < 0.0001); and perceived health status (p = 0.02).

Conclusion/discussion: Our study identified several important predictors and risk factors for concurrent use of methamphetamine and alcohol. Due to our study's low rate for the outcome of interest, future studies should further investigate risk factors and targeted interventions should be put into place to mitigate risky behaviors.

Background: Cannabis use disorder (CUD) commonly co-occurs with depression, post-traumatic stress disorder (PTSD), anxiety, and attention-deficit/hyperactivity disorder (ADHD), resulting in poorer outcomes and underscoring the need for tailored interventions. Contingency management (CM) is one of the most effective behavioral treatments for substance use disorders, and emerging applications of artificial intelligence (AI) may enhance CM by predicting relapse risk and personalizing incentives. This review evaluates evidence on integrated interventions for CUD with co-occurring disorders and the developing role of AI-enhanced CM.

Methods: A systematic search of PubMed, PsycINFO, Embase, and Web of Science (through October 2025) identified clinical studies and systematic reviews on tailored interventions for CUD with depression, PTSD, anxiety, or ADHD, as well as research on AI-driven CM. Data were extracted on study design, interventions, and outcomes.

Results: Thirty-eight studies met inclusion criteria. Integrated cognitive-behavioral therapies improved psychiatric symptoms and reduced cannabis use, particularly in depression and PTSD. Pharmacotherapies showed inconsistent benefits, while ADHD-focused behavioral and stimulant-based approaches demonstrated promising reductions in cannabis use. AI applications such as machine-learning prediction of relapse using smartphone/sensor data, remote CM delivery, and reinforcement-learning-based incentive optimization-improved attendance, abstinence verification, and reward efficiency.

Conclusion: Integrated psychosocial approaches and AI-augmented CM offer complementary pathways for improving outcomes in individuals with CUD and co-occurring disorders. Combining personalized psychotherapy with adaptive, technology-assisted reinforcement may strengthen treatment efficacy.

Background: Alcohol withdrawal delirium (AWD) is the most severe and potentially fatal manifestation of alcohol withdrawal syndrome (AWS). Early identification of patients at risk is critical for preventing medical complications and optimizing withdrawal management. Although the Clinical Institute Withdrawal Assessment for Alcohol Scale, Revised (CIWA-Ar), is widely used to assess AWS severity, its predictive value for AWD remains unclear, particularly in Asian inpatient settings.

Objective: To examine the incidence of AWS and AWD among patients with alcohol use disorder (AUD) in a Japanese rehabilitation center and to identify admission factors associated with AWD onset.

Methods: Among 295 consecutively admitted patients, 273 were analyzed; 22 were excluded for ongoing AWD at admission. AWS was evaluated using CIWA-Ar throughout hospitalization. Associations between admission variables and AWD onset were analyzed using logistic regression and receiver operating characteristic (ROC) analysis.

Results: Of 273 patients, 152 (55.7%) exhibited AWS and 24 (8.8%) developed AWD during hospitalization. Stepwise logistic regression identified only the CIWA-Ar score at admission as a significant predictor (odds ratio = 1.50; 95% confidence interval, 1.28-1.74; p < 0.001). ROC analysis yielded an area under the curve (AUC) of 0.906 with a CIWA-Ar cutoff of 4 (sensitivity 91.7%, specificity 84.3%).

Conclusions: The admission CIWA-Ar score is a strong and practical predictor of AWD in AUD inpatients. Even mild withdrawal symptoms may indicate early autonomic hyperactivity and high AWD risk, underscoring the need for vigilant monitoring and timely intervention.

Background: Substance use disorder (SUD) is associated with systemic inflammation and cognitive vulnerability. Peripheral biomarkers may help characterize biological risk across substances and comorbidities. Turkey has experienced a recent rise in methamphetamine and pregabalin misuse, underscoring the need for biomarker-based clinical profiling.

Methods: A total of 321 inpatients were evaluated in a single inpatient addiction treatment unit in Turkey (2019-2025). Exclusion criteria included severe somatic disease, preexisting psychiatric diagnoses, and incomplete records. Admission data included demographics, primary substance, DSM-5 comorbidities, biomarkers (C-reactive protein [CRP], neutrophil-to-lymphocyte ratio, monocyte-to-lymphocyte ratio, platelet-to-lymphocyte ratio, Systemic Immune-Inflammation Index, Hemoglobin × Albumin/Leukocyte × Platelet Index, Inflammatory Burden Index [IBI]). Analyses used appropriate parametric/non-parametric tests, analysis of covariance, and logistic regression adjusted for age, sex, and comorbidities.

Results: IBI and CRP differed significantly across substance groups, with higher inflammatory burden among methamphetamine, alcohol, and MDMA users. After multiple-comparison correction, IBI remained statistically significant, whereas CRP required cautious interpretation. Longer duration of use correlated with higher inflammatory burden. Depressive (14.6%) and anxiety disorders (16.2%) were the most common comorbidities. Bipolar disorder, although infrequent, clustered with higher inflammatory markers. Gender patterns reflected international trends, with greater alcohol/cannabis use in male and earlier, faster dependence progression in female. Multivariate models showed that methamphetamine and alcohol independently predicted elevated IBI after adjustment.

Conclusions: IBI and CRP appear to be the most sensitive indicators of systemic inflammation in SUD, particularly in stimulant and alcohol users. Elevation of IBI supports its potential role as a cognition-related biomarker. Findings highlight biological heterogeneity across substances, genders, and comorbidities, and emphasize the need for future prospective studies with neurocognitive testing and structured tobacco documentation.

Background: Marijuana usage is on the rise. It is becoming more dangerous because of the significantly greater amounts of tetrahydrocannabinol (THC) and more accessible because of legalization. This paper aims to examine the usage of cannabis in adolescents aged 12-17 and determine the multifaceted effects and dangers that come with the usage.

Methods: This study queried data from the National Survey on Drug Use and Health. The database was queried and tested until significant numbers were found. Age was added as a control variable to ensure the data was of just adolescents aged 12-17 and not a wide array of ages. We compared adolescents who use marijuana to adolescents who did not use marijuana in four topics: gender, race, number of school days missed,and grades.

Results: The overall pooled prevalence of cannabis use in adolescents ages 12-17 is 11.4% (95% CI: 10.70-12.20%). Cannabis use was slightly more prominent among females than males and in white adolescents, Hispanics, and blacks. The higher the marijuana use, the more likely adolescents are to skip class and the less likely they are to perform well grade-wise.

Conclusion: Approximately 1 in 10 individuals who try marijuana get develop a use disorder.When narrowing the pool down to just teenagers, that number increases to one in six adolescentswho try marijuana being with marijuana addiction.Therefore, there is a need to educate teenagers and their families about the effects and consequences of marijuana use. These consequences are greater and more severe than most think.