How Accurate Are Blood (or Breath) Tests for Identifying Self-Reported Heavy Drinking Among People with Alcohol Dependence?

AIMS: Managing patients with alcohol dependence includes assessment for heavy drinking, typically by asking patients. Some recommend biomarkers to detect heavy drinking but evidence of accuracy is limited. METHODS: Among people with dependence, we assessed the performance of disialo-carbohydrate-deficient transferrin (%dCDT, ≥1.7%), gamma-glutamyltransferase (GGT, ≥66 U/l), either %dCDT or GGT positive, and breath alcohol (> 0) for identifying 3 self-reported heavy drinking levels: any heavy drinking (≥4 drinks/day or >7 drinks/week for women, ≥5 drinks/day or >14 drinks/week for men), recurrent (≥5 drinks/day on ≥5 days) and persistent heavy drinking (≥5 drinks/day on ≥7 consecutive days). Subjects (n = 402) with dependence and current heavy drinking were referred to primary care and assessed 6 months later with biomarkers and validated self-reported calendar method assessment of past 30-day alcohol use. RESULTS: The self-reported prevalence of any, recurrent and persistent heavy drinking was 54, 34 and 17%. Sensitivity of %dCDT for detecting any, recurrent and persistent self-reported heavy drinking was 41, 53 and 66%. Specificity was 96, 90 and 84%, respectively. %dCDT had higher sensitivity than GGT and breath test for each alcohol use level but was not adequately sensitive to detect heavy drinking (missing 34-59% of the cases). Either %dCDT or GGT positive improved sensitivity but not to satisfactory levels, and specificity decreased. Neither a breath test nor GGT was sufficiently sensitive (both tests missed 70-80% of cases). CONCLUSIONS: Although biomarkers may provide some useful information, their sensitivity is low the incremental value over self-report in clinical settings is questionable

Impact of alcohol use disorder severity on human immunodeficiency virus (HIV) viral suppression and CD4 count in three international cohorts of people with HIV.

Alcohol use has been linked to worse human immunodeficiency virus (HIV) immunologic/virologic outcomes, yet few studies have explored the effects of alcohol use disorder (AUD). This study assessed whether AUD severity is associated with HIV viral suppression and CD4 count in the three cohorts of the Uganda Russia Boston Alcohol Network for Alcohol Research Collaboration on HIV/AIDS (URBAN ARCH) Consortium. People with HIV (PWH) in Uganda (n = 301), Russia (n = 400), and Boston (n = 251), selected in-part based on their alcohol use, were included in analyses. Logistic and linear regressions were used to assess the cross-sectional associations between AUD severity (number of DSM-5 diagnostic criteria) and (1) HIV viral suppression, and (2) CD4 count (cells/mm <sup>3</sup> ) adjusting for covariates. Analyses were conducted separately by site. The proportion of females was 51% (Uganda), 34% (Russia), and 33% (Boston); mean age (SD) was 40.7 (9.6), 38.6 (6.3), and 52.1 (10.5), respectively. All participants in Uganda and all but 27% in Russia and 5% in Boston were on antiretroviral therapy. In Uganda, 32% met criteria for AUD, 92% in Russia, and 43% in Boston. The mean (SD) number of AUD criteria was 1.6 (2.4) in Uganda, 5.6 (3.3) in Russia, and 2.4 (3.1) in Boston. Most participants had HIV viral suppression (Uganda 92%, Russia 57%, Boston 87%); median (IQR) CD4 count was 673 (506, 866), 351 (201, 542), and 591 (387, 881), respectively. In adjusted models, there were no associations between AUD severity and HIV viral suppression: adjusted odds ratios (AOR) (95%CI) per 1 additional AUD criterion in Uganda was 1.08 (0.87, 1.33); Russia 0.98 (0.92, 1.04); and Boston 0.95 (0.84, 1.08) or CD4 count: mean difference (95%CI) per 1 additional criterion: 5.78 (-7.47, 19.03), -3.23 (-10.91, 4.44), and -8.18 (-24.72, 8.35), respectively. In three cohorts of PWH, AUD severity was not associated with HIV viral suppression or CD4 count. PWH with AUD in the current era of antiretroviral therapy can achieve virologic control

Alcohol consumption patterns in HIV-infected adults with alcohol problems.

OBJECTIVE: To understand patterns of alcohol consumption and baseline factors associated with favorable drinking patterns among HIV-infected patients. METHODS: We studied drinking patterns among HIV-infected patients with current or past alcohol problems. We assessed drinking status in 6 month intervals. Based on National Institute on Alcohol Abuse and Alcoholism guidelines a favorable drinking pattern was defined as not drinking risky amounts at each assessment or decreased drinking over time. All other patterns were defined as unfavorable. Logistic regression models were used to identify baseline factors associated with a favorable pattern. RESULTS: Among 358 subjects, 54% had a favorable drinking pattern with 44% not drinking risky amounts at every assessment, and 11% decreasing consumption over time. Of the 46% with an unfavorable pattern, 4% drank risky amounts each time, 5% increased, and 37% both decreased and increased consumption over time. Current alcohol dependence and recent marijuana use were negatively associated with a favorable pattern, while older age and female gender, and having a primary HIV risk factor of injection drug use were positively associated with a favorable pattern. CONCLUSION: Many HIV-infected adults with alcohol problems have favorable drinking patterns over time, and alcohol consumption patterns are not necessarily constant. Identifying HIV-infected adults with a pattern of risky drinking may require repeated assessments of alcohol consumption

Screening and brief intervention for lower-risk drug use in primary care: A pilot randomized trial.

The efficacy of screening and brief intervention for lower-risk drug use is unknown. This pilot study tested the efficacy of two brief interventions (BIs) for drug use compared to no BI in primary care patients with lower-risk drug use identified by screening. We randomly assigned participants identified by screening with Alcohol Smoking and Substance Involvement Screening Test (ASSIST) drug specific scores of 2 or 3 to: no BI, a brief negotiated interview (BNI), or an adaptation of motivational interviewing (MOTIV). Primary outcome was number of days use of main drug in the past 30 as determined by validated calendar method at 6 months. Analyses were performed using negative binomial regression adjusted for baseline use and main drug. Of 142 eligible adults, 61(43 %) consented and were randomized. Participant characteristics were: mean age 41; 54 % male; 77 % black. Main drug was cannabis 70 %, cocaine 15 %, prescription opioid 10 %; 7% reported injection drug use and mean days use of main drug (of 30) was 3.4. At 6 months, 93 % completed follow-up and adjusted mean days use of main drug were 6.4 (no BI) vs 2.1 (BNI) (incidence rate ratio, IRR 0.33[0.15-0.74]) and 2.3 (MOTIV) (IRR 0.36[0.15-0.85]). BI appears to have efficacy for preventing an increase in drug use in primary care patients with lower-risk use identified by screening. These findings raise the potential that less severe patterns of drug use in primary care may be uniquely amenable to brief intervention and warrant replication

Anxiety, Depression, and Pain Symptoms: Associations With the Course of Marijuana Use and Drug Use Consequences Among Urban Primary Care Patients.

This exploratory study aims to investigate whether anxiety, depression, and pain are associated with changes in marijuana use and drug use consequences among primary care patients. In all, 331 adult primary care patients with marijuana as the only drug used were followed prospectively to investigate associations between anxiety/depression symptoms (no/minimal symptoms; anxiety or depression symptoms; symptoms of both) and pain (1-10 scale: none [0]; low [1-3]; medium [4-6]; high [7-10]) (independent variables) and substance use outcomes in regression models. These outcomes were changes (over 6 months) in primary outcomes: marijuana use days (past 30); and drug use consequences (Short Inventory of Problems-Drugs [SIP-D]); secondary outcomes-drug use risk (Alcohol, Smoking, and Substance Involvement Screening Test [ASSIST] score for drugs). At baseline, 67% reported no/minimal anxiety/depression symptoms, 16% anxiety or depression symptoms, 17% both; 14% reported no pain, 16% low, 23% medium, 47% high pain level. Mean (SD) number of marijuana use days was 16.4 (11.6), mean SIP-D 5.9 (9.0), mean ASSIST 12.5 (7.8); no significant association was found between anxiety/depression and marijuana use changes. Given the same baseline status for SIP-D and ASSIST, respectively, those with anxiety or depression had greater increases in SIP-D (adjusted mean difference [95% confidence interval] +3.26 [1.20; 5.32], P = 0.004) and borderline significant increases in ASSIST (+3.27 [-0.12; 6.65], P = 0.06) compared with those without anxiety or depression; those with both anxiety and depression had greater increases in ASSIST (+5.42 [2.05; 8.79], P = 0.003), but not SIP-D (+1.80 [-0.46; 4.06], P = 0.12). There was no significant association between pain and marijuana use and SIP-D changes. Given the same baseline ASSIST level, those with high pain level had greater increases in ASSIST (+4.89 [1.05; 8.72], P = 0.04) compared with those with no pain. In these exploratory analyses, anxiety, depression, and high pain level appear to be associated with increases in drug-related harm among primary care patients using marijuana

Anxiety, Depression, and Pain Symptoms: Associations With the Course of Marijuana Use and Drug Use Consequences Among Urban Primary Care Patients.

This exploratory study aims to investigate whether anxiety, depression, and pain are associated with changes in marijuana use and drug use consequences among primary care patients. In all, 331 adult primary care patients with marijuana as the only drug used were followed prospectively to investigate associations between anxiety/depression symptoms (no/minimal symptoms; anxiety or depression symptoms; symptoms of both) and pain (1-10 scale: none [0]; low [1-3]; medium [4-6]; high [7-10]) (independent variables) and substance use outcomes in regression models. These outcomes were changes (over 6 months) in primary outcomes: marijuana use days (past 30); and drug use consequences (Short Inventory of Problems-Drugs [SIP-D]); secondary outcomes-drug use risk (Alcohol, Smoking, and Substance Involvement Screening Test [ASSIST] score for drugs). At baseline, 67% reported no/minimal anxiety/depression symptoms, 16% anxiety or depression symptoms, 17% both; 14% reported no pain, 16% low, 23% medium, 47% high pain level. Mean (SD) number of marijuana use days was 16.4 (11.6), mean SIP-D 5.9 (9.0), mean ASSIST 12.5 (7.8); no significant association was found between anxiety/depression and marijuana use changes. Given the same baseline status for SIP-D and ASSIST, respectively, those with anxiety or depression had greater increases in SIP-D (adjusted mean difference [95% confidence interval] +3.26 [1.20; 5.32], P = 0.004) and borderline significant increases in ASSIST (+3.27 [-0.12; 6.65], P = 0.06) compared with those without anxiety or depression; those with both anxiety and depression had greater increases in ASSIST (+5.42 [2.05; 8.79], P = 0.003), but not SIP-D (+1.80 [-0.46; 4.06], P = 0.12). There was no significant association between pain and marijuana use and SIP-D changes. Given the same baseline ASSIST level, those with high pain level had greater increases in ASSIST (+4.89 [1.05; 8.72], P = 0.04) compared with those with no pain. In these exploratory analyses, anxiety, depression, and high pain level appear to be associated with increases in drug-related harm among primary care patients using marijuana

Which fast nearest neighbour search algorithm to use?

Choosing which fast Nearest Neighbour search algorithm to use depends on the task we face. Usually kd-tree search algorithm is selected when the similarity function is the Euclidean or the Manhattan distances. Generic fast search algorithms (algorithms that works with any distance function) are only used when there is not specific fast search algorithms for the involved distance function. In this work we show that in real data problems generic search algorithms (i.e. MDF-tree) can be faster that specific ones (i.e. kd-tree).The authors thank the Spanish CICyT for partial support of this work through project TIN2009-14205-C04-C1 and la Consellería de Educación de la Comunidad Valenciana through project PROMETEO/2012/01