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Zhong Y, Zhou L, Xu J, Huang H. Predicting prognosis outcomes of primary central nervous system lymphoma with high-dose methotrexate-based chemotherapeutic treatment using lipidomics. Neurooncol Adv 2024; 6:vdae119. [PMID: 39119277 PMCID: PMC11306931 DOI: 10.1093/noajnl/vdae119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/10/2024] Open
Abstract
Background Primary central nervous system lymphoma (PCNSL) is a rare extranodal lymphomatous malignancy which is commonly treated with high-dose methotrexate (HD-MTX)-based chemotherapy. However, the prognosis outcome of HD-MTX-based treatment cannot be accurately predicted using the current prognostic scoring systems, such as the Memorial Sloan-Kettering Cancer Center (MSKCC) score. Methods We studied 2 cohorts of patients with PCNSL and applied lipidomic analysis to their cerebrospinal fluid (CSF) samples. After removing the batch effects and features engineering, we applied and compared several classic machine-learning models based on lipidomic data of CSF to predict the relapse of PCNSL in patients who were treated with HD-MTX-based chemotherapy. Results We managed to remove the batch effects and get the optimum features of each model. Finally, we found that Cox regression had the best prediction performance (AUC = 0.711) on prognosis outcomes. Conclusions We developed a Cox regression model based on lipidomic data, which could effectively predict PCNSL patient prognosis before the HD-MTX-based chemotherapy treatments.
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Affiliation(s)
- Yi Zhong
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, 200438, China
| | - Liying Zhou
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, 200438, China
| | - Jingshen Xu
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, 200438, China
| | - He Huang
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, 200438, China
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Donoghue K, Boniface S, Brobbin E, Byford S, Coleman R, Coulton S, Day E, Dhital R, Farid A, Hermann L, Jordan A, Kimergård A, Koutsou ML, Lingford-Hughes A, Marsden J, Neale J, O'Neill A, Phillips T, Shearer J, Sinclair J, Smith J, Strang J, Weinman J, Whittlesea C, Widyaratna K, Drummond C. Adjunctive Medication Management and Contingency Management to enhance adherence to acamprosate for alcohol dependence: the ADAM trial RCT. Health Technol Assess 2023; 27:1-88. [PMID: 37924307 PMCID: PMC10641712 DOI: 10.3310/dqkl6124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2023] Open
Abstract
Background Acamprosate is an effective and cost-effective medication for alcohol relapse prevention but poor adherence can limit its full benefit. Effective interventions to support adherence to acamprosate are therefore needed. Objectives To determine the effectiveness of Medication Management, with and without Contingency Management, compared to Standard Support alone in enhancing adherence to acamprosate and the impact of adherence to acamprosate on abstinence and reduced alcohol consumption. Design Multicentre, three-arm, parallel-group, randomised controlled clinical trial. Setting Specialist alcohol treatment services in five regions of England (South East London, Central and North West London, Wessex, Yorkshire and Humber and West Midlands). Participants Adults (aged 18 years or more), an International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, diagnosis of alcohol dependence, abstinent from alcohol at baseline assessment, in receipt of a prescription for acamprosate. Interventions (1) Standard Support, (2) Standard Support with adjunctive Medication Management provided by pharmacists via a clinical contact centre (12 sessions over 6 months), (3) Standard Support with adjunctive Medication Management plus Contingency Management that consisted of vouchers (up to £120) to reinforce participation in Medication Management. Consenting participants were randomised in a 2 : 1 : 1 ratio to one of the three groups using a stratified random permuted block method using a remote system. Participants and researchers were not blind to treatment allocation. Main outcome measures Primary outcome: self-reported percentage of medication taken in the previous 28 days at 6 months post randomisation. Economic outcome: EuroQol-5 Dimensions, a five-level version, used to calculate quality-adjusted life-years, with costs estimated using the Adult Service Use Schedule. Results Of the 1459 potential participants approached, 1019 (70%) were assessed and 739 (73 consented to participate in the study, 372 (50%) were allocated to Standard Support, 182 (25%) to Standard Support with Medication Management and 185 (25%) to Standard Support and Medication Management with Contingency Management. Data were available for 518 (70%) of participants at 6-month follow-up, 255 (68.5%) allocated to Standard Support, 122 (67.0%) to Standard Support and Medication Management and 141 (76.2%) to Standard Support and Medication Management with Contingency Management. The mean difference of per cent adherence to acamprosate was higher for those who received Standard Support and Medication Management with Contingency Management (10.6%, 95% confidence interval 19.6% to 1.6%) compared to Standard Support alone, at the primary end point (6-month follow-up). There was no significant difference in per cent days adherent when comparing Standard Support and Medication Management with Standard Support alone 3.1% (95% confidence interval 12.8% to -6.5%) or comparing Standard Support and Medication Management with Standard Support and Medication Management with Contingency Management 7.9% (95% confidence interval 18.7% to -2.8%). The primary economic analysis at 6 months found that Standard Support and Medication Management with Contingency Management was cost-effective compared to Standard Support alone, achieving small gains in quality-adjusted life-years at a lower cost per participant. Cost-effectiveness was not observed for adjunctive Medication Management compared to Standard Support alone. There were no serious adverse events related to the trial interventions reported. Limitations The trial's primary outcome measure changed substantially due to data collection difficulties and therefore relied on a measure of self-reported adherence. A lower than anticipated follow-up rate at 12 months may have lowered the statistical power to detect differences in the secondary analyses, although the primary analysis was not impacted. Conclusions Medication Management enhanced with Contingency Management is beneficial to patients for supporting them to take acamprosate. Future work Given our findings in relation to Contingency Management enhancing Medication Management adherence, future trials should be developed to explore its effectiveness and cost-effectiveness with other alcohol interventions where there is evidence of poor adherence. Trial registration This trial is registered as ISRCTN17083622 https://doi.org/10.1186/ISRCTN17083622. Funding This project was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 27, No. 22. See the NIHR Journals Library website for further project information.
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Affiliation(s)
- Kim Donoghue
- Research Department of Clinical, Educational and Health Psychology, University College London, London, UK
- National Addictions Centre, Addictions Department, Institute of Psychiatry, Psychology and Neuroscience King's College London, London, UK
| | - Sadie Boniface
- National Addictions Centre, Addictions Department, Institute of Psychiatry, Psychology and Neuroscience King's College London, London, UK
- Institute of Alcohol Studies, London, UK
| | - Eileen Brobbin
- National Addictions Centre, Addictions Department, Institute of Psychiatry, Psychology and Neuroscience King's College London, London, UK
| | - Sarah Byford
- Institute of Psychiatry, Psychology and Neuroscience, King's Health Economics, King's College London, London UK
| | - Rachel Coleman
- Faculty of Health Sciences, Institute for Clinical and Applied Health Research (ICAHR), University of Hull, Hull, UK
| | - Simon Coulton
- Centre for Health Services Studies, University of Kent, Canterbury, Kent, UK
| | - Edward Day
- Institute for Mental Health, University of Birmingham, Birmingham, UK
| | - Ranjita Dhital
- National Addictions Centre, Addictions Department, Institute of Psychiatry, Psychology and Neuroscience King's College London, London, UK
- Arts and Sciences Department, University College London, London, UK
| | - Anum Farid
- National Addictions Centre, Addictions Department, Institute of Psychiatry, Psychology and Neuroscience King's College London, London, UK
- What Works for Children's Social Care, London, UK
| | - Laura Hermann
- National Addictions Centre, Addictions Department, Institute of Psychiatry, Psychology and Neuroscience King's College London, London, UK
- Faculty of Health Sciences, Institute for Clinical and Applied Health Research (ICAHR), University of Hull, Hull, UK
| | - Amy Jordan
- National Addictions Centre, Addictions Department, Institute of Psychiatry, Psychology and Neuroscience King's College London, London, UK
- Black Country Healthcare NHS Foundation Trust, West Bromwich, UK
| | - Andreas Kimergård
- National Addictions Centre, Addictions Department, Institute of Psychiatry, Psychology and Neuroscience King's College London, London, UK
| | | | - Anne Lingford-Hughes
- Division of Psychiatry, Department of Brain Sciences, Imperial College London, London, UK
| | - John Marsden
- National Addictions Centre, Addictions Department, Institute of Psychiatry, Psychology and Neuroscience King's College London, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
| | - Joanne Neale
- National Addictions Centre, Addictions Department, Institute of Psychiatry, Psychology and Neuroscience King's College London, London, UK
| | - Aimee O'Neill
- Faculty of Medicine, University of Southampton, Southampton, UK
| | - Thomas Phillips
- Faculty of Health Sciences, Institute for Clinical and Applied Health Research (ICAHR), University of Hull, Hull, UK
| | - James Shearer
- Institute of Psychiatry, Psychology and Neuroscience, King's Health Economics, King's College London, London UK
| | - Julia Sinclair
- Faculty of Medicine, University of Southampton, Southampton, UK
| | - Joanna Smith
- Faculty of Medicine, University of Southampton, Southampton, UK
| | - John Strang
- National Addictions Centre, Addictions Department, Institute of Psychiatry, Psychology and Neuroscience King's College London, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
| | - John Weinman
- School of Cancer & Pharmaceutical Sciences, King's College London, London, UK
| | - Cate Whittlesea
- Research Department of Practice and Policy, UCL School of Pharmacy, University College London, London, UK
| | - Kideshini Widyaratna
- Institute of Psychiatry Psychology and Neuroscience, Department of Psychology, King's College London, London, UK
| | - Colin Drummond
- National Addictions Centre, Addictions Department, Institute of Psychiatry, Psychology and Neuroscience King's College London, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
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Wallach JD, Gueorguieva R, Phan H, Witkiewitz K, Wu R, O’Malley SS. Predictors of abstinence, no heavy drinking days, and a 2-level reduction in World Health Organization drinking levels during treatment for alcohol use disorder in the COMBINE study. Alcohol Clin Exp Res 2022; 46:1331-1339. [PMID: 35616436 PMCID: PMC9887652 DOI: 10.1111/acer.14877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 05/10/2022] [Accepted: 05/17/2022] [Indexed: 02/02/2023]
Abstract
BACKGROUND Data from trials of medications for alcohol use disorder (AUD) can be used to identify predictors of drinking outcomes regardless of treatment, which can inform the design of future trials with heterogeneous populations. Here, we identified predictors of abstinence, no heavy drinking days, and a 2-level reduction in World Health Organization (WHO) drinking levels during treatment for AUD in the Combined Pharmacotherapies and Behavioral Interventions (COMBINE) Study. METHODS We utilized data from the COMBINE Study, a randomized placebo-controlled trial evaluating the efficacy of naltrexone and acamprosate, both alone and in combination, for AUD (n = 1168). A tree-based machine learning algorithm was used to construct classification trees predicting abstinence, no heavy drinking days, and a 2-level reduction in WHO drinking levels in the last 4 weeks of treatment, based on 89 baseline variables. RESULTS The final tree for predicting abstinence had one split based on consecutive days abstinent prior to randomization, with a higher proportion of subjects achieving abstinence among those classified as abstinent for >2 versus ≤2 consecutive weeks prior to randomization (66% vs. 29%). The final tree for predicting no heavy drinking days in the last 4 weeks of treatment had three splits based on consecutive days abstinent, age, and total Alcohol Dependence Scale score at baseline. Seventy-three percent of the subjects classified as abstinent for >2 consecutive weeks prior to randomization had no heavy drinking days in the last 4 weeks of treatment. Among those classified as abstinent ≤2 consecutive weeks prior, three additional splits showed that younger subjects (age ≤44 years; 37%), and older subjects (age >44) with a total Alcohol Dependence Scale score >13 and complete abstinence (56%) or other drinking goals (35%), were less likely to have no heavy drinking days than older subjects with a total Alcohol Dependence Scale score ≤13 (67%). The final tree for predicting a 2-level reduction in WHO levels had no splits. CONCLUSIONS Consecutive days abstinent prior to randomization may predict abstinence and no heavy drinking days and total Alcohol Dependence Scale score and age may predict no heavy drinking days. The 2-level reduction in WHO levels outcome may be less likely to discriminate based on multiple patient characteristics.
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Affiliation(s)
- Joshua D. Wallach
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut, USA
| | - Ralitza Gueorguieva
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA
- Department of Psychiatry, Yale Medical School, New Haven, Connecticut, USA
| | - Huong Phan
- Department of Statistics, University of Washington, Washington, USA
| | - Katie Witkiewitz
- Department of Psychology, Center on Alcohol, Substance Use, and Addictions, Albuquerque New Mexico, USA
| | - Ran Wu
- Department of Psychiatry, Yale Medical School, New Haven, Connecticut, USA
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Senn S, Volken T, Rösner S, Wieber F. What is the relapse risk during treatment? Survivor analysis of single and multiple relapse events in inpatients with alcohol use disorder as part of an observational study. J Subst Abuse Treat 2022; 138:108754. [DOI: 10.1016/j.jsat.2022.108754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Revised: 01/21/2022] [Accepted: 02/17/2022] [Indexed: 11/28/2022]
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5
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Schückher F, Berglund K, Engström I, Sellin T. Predictors for Abstinence in Socially Stable Women Receiving Treatment for Alcohol Use Disorder. ALCOHOLISM TREATMENT QUARTERLY 2022. [DOI: 10.1080/07347324.2021.2018957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Fides Schückher
- Faculty of Medicine and Health, Örebro University, Orebro, Sweden
| | | | - Ingemar Engström
- Faculty of Medicine and Health, Örebro University, Orebro, Sweden
| | - Tabita Sellin
- Faculty of Medicine and Health, University Healthcare Research Center, Örebro University, Sweden
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Haass-Koffler CL, Piacentino D, Li X, Long VM, Lee MR, Swift RM, Kenna GA, Leggio L. Differences in Sociodemographic and Alcohol-Related Clinical Characteristics Between Treatment Seekers and Nontreatment Seekers and Their Role in Predicting Outcomes in the COMBINE Study for Alcohol Use Disorder. Alcohol Clin Exp Res 2020; 44:2097-2108. [PMID: 32997422 PMCID: PMC7722230 DOI: 10.1111/acer.14428] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 07/29/2020] [Indexed: 01/04/2023]
Abstract
BACKGROUND One of the challenges in early-stage clinical research aimed at developing novel treatments for alcohol use disorder (AUD) is that the enrolled participants are heavy drinkers, but do not seek treatment for AUD. AIMS To compare nontreatment seekers with alcohol dependence (AD) from 4 human laboratory studies conducted at Brown University (N = 240; 65.4% male) to treatment seekers with AD from the multisite COMBINE study (N = 1,383; 69.1% male) across sociodemographic and alcohol-related clinical variables and to evaluate whether the variables that significantly differentiate the 2 samples predict the 3 main COMBINE clinical outcomes: time to relapse, percent days abstinent (PDA), and good clinical outcome. METHODS Sample characteristics were assessed by parametric and nonparametric testing. Three regression models measured the association between the differing variables and the 3 main COMBINE clinical outcomes. RESULTS The nontreatment seekers, compared to the treatment seekers, were more ethnically diverse, less educated, single, and working part-time or unemployed (p's < 0.05); they met fewer DSM-IV AD criteria and had significantly lower scores on alcohol-related scales (p's < 0.05); they were less likely to have a father with alcohol problems (p < 0.0001) and had a significantly earlier age of onset and longer duration of AD (p's < 0.05); they also had significantly more total drinks, drinks per drinking day, heavy drinking days (HDD), and lower PDA in the 30 days prior to baseline (p's < 0.0001 to <0.05). Having more HDD in the 30 days prior to baseline predicted all of the 3 COMBINE clinical outcomes. All the other characteristics mentioned above that differed significantly between the 2 groups predicted at least 1 of the 3 COMBINE clinical outcomes, except for level of education, age of onset, and duration of AD. CONCLUSIONS The observed differences between groups should be considered in efforts across participant recruitment at different stages of the development of new treatments for AUD.
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Affiliation(s)
- Carolina L. Haass-Koffler
- Center for Alcohol and Addiction Studies, Department of Psychiatry and Human Behavior, Brown University, Providence, RI
- Center for Alcohol and Addiction Studies, Department of Behavioral and Social Sciences, School of Public Health, Brown University, Providence, RI
- Clinical Psychoneuroendocrinology and Neuropsychopharmacology Section, National Institute on Drug Abuse Intramural Research Program and National Institute on Alcohol Abuse and Alcoholism Division of Intramural Clinical and Biological Research, National Institutes of Health, Baltimore and Bethesda, MD
| | - Daria Piacentino
- Clinical Psychoneuroendocrinology and Neuropsychopharmacology Section, National Institute on Drug Abuse Intramural Research Program and National Institute on Alcohol Abuse and Alcoholism Division of Intramural Clinical and Biological Research, National Institutes of Health, Baltimore and Bethesda, MD
- Center on Compulsive Behaviors, National Institutes of Health, Bethesda, MD
| | - Xiaobai Li
- Biostatistics and Clinical Epidemiology Services, National Institutes of Health, Bethesda, MD
| | - Victoria M. Long
- Center for Alcohol and Addiction Studies, Department of Behavioral and Social Sciences, School of Public Health, Brown University, Providence, RI
| | - Mary R. Lee
- Clinical Psychoneuroendocrinology and Neuropsychopharmacology Section, National Institute on Drug Abuse Intramural Research Program and National Institute on Alcohol Abuse and Alcoholism Division of Intramural Clinical and Biological Research, National Institutes of Health, Baltimore and Bethesda, MD
| | - Robert M. Swift
- Center for Alcohol and Addiction Studies, Department of Psychiatry and Human Behavior, Brown University, Providence, RI
- Veterans Affairs Medical Center, Providence, RI
| | - George A. Kenna
- Center for Alcohol and Addiction Studies, Department of Psychiatry and Human Behavior, Brown University, Providence, RI
| | - Lorenzo Leggio
- Center for Alcohol and Addiction Studies, Department of Behavioral and Social Sciences, School of Public Health, Brown University, Providence, RI
- Clinical Psychoneuroendocrinology and Neuropsychopharmacology Section, National Institute on Drug Abuse Intramural Research Program and National Institute on Alcohol Abuse and Alcoholism Division of Intramural Clinical and Biological Research, National Institutes of Health, Baltimore and Bethesda, MD
- Center on Compulsive Behaviors, National Institutes of Health, Bethesda, MD
- Medication Development Program, National Institute on Drug Abuse Intramural Research Program, National Institutes of Health, Baltimore, MD
- Division of Addiction Medicine, Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD
- Department of Neuroscience, Georgetown University Medical Center, Washington, DC
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Salazar de Pablo G, Studerus E, Vaquerizo-Serrano J, Irving J, Catalan A, Oliver D, Baldwin H, Danese A, Fazel S, Steyerberg EW, Stahl D, Fusar-Poli P. Implementing Precision Psychiatry: A Systematic Review of Individualized Prediction Models for Clinical Practice. Schizophr Bull 2020; 47:284-297. [PMID: 32914178 PMCID: PMC7965077 DOI: 10.1093/schbul/sbaa120] [Citation(s) in RCA: 86] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
BACKGROUND The impact of precision psychiatry for clinical practice has not been systematically appraised. This study aims to provide a comprehensive review of validated prediction models to estimate the individual risk of being affected with a condition (diagnostic), developing outcomes (prognostic), or responding to treatments (predictive) in mental disorders. METHODS PRISMA/RIGHT/CHARMS-compliant systematic review of the Web of Science, Cochrane Central Register of Reviews, and Ovid/PsycINFO databases from inception until July 21, 2019 (PROSPERO CRD42019155713) to identify diagnostic/prognostic/predictive prediction studies that reported individualized estimates in psychiatry and that were internally or externally validated or implemented. Random effect meta-regression analyses addressed the impact of several factors on the accuracy of prediction models. FINDINGS Literature search identified 584 prediction modeling studies, of which 89 were included. 10.4% of the total studies included prediction models internally validated (n = 61), 4.6% models externally validated (n = 27), and 0.2% (n = 1) models considered for implementation. Across validated prediction modeling studies (n = 88), 18.2% were diagnostic, 68.2% prognostic, and 13.6% predictive. The most frequently investigated condition was psychosis (36.4%), and the most frequently employed predictors clinical (69.5%). Unimodal compared to multimodal models (β = .29, P = .03) and diagnostic compared to prognostic (β = .84, p < .0001) and predictive (β = .87, P = .002) models were associated with increased accuracy. INTERPRETATION To date, several validated prediction models are available to support the diagnosis and prognosis of psychiatric conditions, in particular, psychosis, or to predict treatment response. Advancements of knowledge are limited by the lack of implementation research in real-world clinical practice. A new generation of implementation research is required to address this translational gap.
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Affiliation(s)
- Gonzalo Salazar de Pablo
- Early Psychosis: Interventions and Clinical-detection Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, 16 De Crespigny Park, London, UK,Institute of Psychiatry and Mental Health, Department of Child and Adolescent Psychiatry, Hospital General Universitario Gregorio Marañón School of Medicine, Universidad Complutense, Instituto de Investigación Sanitaria Gregorio Marañón, CIBERSAM, Madrid, Spain,Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Erich Studerus
- Division of Personality and Developmental Psychology, Department of Psychology, University of Basel, Basel, Switzerland
| | - Julio Vaquerizo-Serrano
- Early Psychosis: Interventions and Clinical-detection Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, 16 De Crespigny Park, London, UK,Institute of Psychiatry and Mental Health, Department of Child and Adolescent Psychiatry, Hospital General Universitario Gregorio Marañón School of Medicine, Universidad Complutense, Instituto de Investigación Sanitaria Gregorio Marañón, CIBERSAM, Madrid, Spain,Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Jessica Irving
- Early Psychosis: Interventions and Clinical-detection Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, 16 De Crespigny Park, London, UK
| | - Ana Catalan
- Early Psychosis: Interventions and Clinical-detection Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, 16 De Crespigny Park, London, UK,Department of Psychiatry, Basurto University Hospital, Bilbao, Spain,Mental Health Group, BioCruces Health Research Institute, Bizkaia, Spain,Neuroscience Department, University of the Basque Country UPV/EHU, Leioa, Spain
| | - Dominic Oliver
- Early Psychosis: Interventions and Clinical-detection Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, 16 De Crespigny Park, London, UK
| | - Helen Baldwin
- Early Psychosis: Interventions and Clinical-detection Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, 16 De Crespigny Park, London, UK
| | - Andrea Danese
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK,Social, Genetic and Developmental Psychiatry Centre, King’s College London, London, UK,National and Specialist CAMHS Clinic for Trauma, Anxiety, and Depression, South London and Maudsley NHS Foundation Trust, London, UK
| | - Seena Fazel
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, the Netherlands,Department of Public Health, Erasmus MC, Rotterdam, the Netherlands
| | - Daniel Stahl
- Biostatistics Department, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Paolo Fusar-Poli
- Early Psychosis: Interventions and Clinical-detection Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, 16 De Crespigny Park, London, UK,OASIS Service, South London and Maudsley NHS Foundation Trust, London, UK,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy,National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK,To whom correspondence should be addressed; tel: +44-0-20-7848-0900, fax:+44-0-20-7848-0976, e-mail:
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8
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Berglund K. Commentary on Witkiewitz et al. (2020): WHO risk drinking classification system is promising in clinical trials, but needs to be tested in other patient groups. Addiction 2020; 115:1681-1682. [PMID: 32458518 DOI: 10.1111/add.15102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Accepted: 04/27/2020] [Indexed: 11/30/2022]
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9
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Alessi SM, Barnett NP, Petry NM. Objective continuous monitoring of alcohol consumption for three months among alcohol use disorder treatment outpatients. Alcohol 2019; 81:131-138. [PMID: 30710610 DOI: 10.1016/j.alcohol.2019.01.008] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Revised: 01/14/2019] [Accepted: 01/17/2019] [Indexed: 11/15/2022]
Abstract
BACKGROUND Transdermal alcohol sensing technology allows for objective continuous monitoring of alcohol use. The purpose of this study was to characterize alcohol consumption measured with this technology among alcohol use disorder treatment outpatients in two clinical trials. METHODS Participants were community-based alcohol treatment outpatients in usual care (N = 63) during the first three years of studies that monitored drinking with the secure continuous remote alcohol monitor (SCRAMx®) ankle bracelet. Research visits for uploading SCRAMx data occurred every other week in Study 1 (n = 43) and once weekly in Study 2 (n = 20), for 3 months. Staff used timeline follow-back procedures to collect self-reports of drinking frequency and magnitude at each research visit. RESULTS In the 90 days before intake, 85.7% (n = 54) of participants reported consuming alcohol, and consumption occurred on a median (interquartile range) 35.7% (29.0%) of days. During the treatment period, per SCRAMx versus self-report, the percentage who drank was 92.1% (n = 58) versus 46.6% (n = 30), p = .03, and consumption occurred on 16.5% (36.5%) versus 0.0% (21.4%) of days, respectively, p < .001. Median longest duration of abstinence was 26.0 (25.0) versus 39.0 (58.0) days, respectively, p < .001. Breath alcohol concentration, estimated from SCRAMx data, ranged from 0.02 to 0.91 g/dL. CONCLUSIONS This is the first study to quantify alcohol consumption among alcohol treatment outpatients using transdermal sensor technology. Results indicate that most patients drank while in outpatient care. Issues to consider for future applications of this technology and implications for alcohol treatment are discussed.
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Affiliation(s)
- Sheila M Alessi
- University of Connecticut School of Medicine, 263 Farmington Avenue, Farmington, CT 06030-3944, United States.
| | - Nancy P Barnett
- Center for Alcohol and Addiction Studies, School of Public Health, Brown University, Box G-S121-5, Providence, RI 02912, United States.
| | - Nancy M Petry
- University of Connecticut School of Medicine, 263 Farmington Avenue, Farmington, CT 06030-3944, United States
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10
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Mindfulness-based relapse prevention for alcohol dependence: Findings from a randomized controlled trial. J Subst Abuse Treat 2019; 100:8-17. [PMID: 30898331 DOI: 10.1016/j.jsat.2019.01.013] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Revised: 01/15/2019] [Accepted: 01/16/2019] [Indexed: 12/20/2022]
Abstract
OBJECTIVES To assess the effects of mindfulness-based relapse prevention for alcohol dependence (MBRP-A) intervention on drinking and related consequences. METHODS 123 alcohol-dependent adults in early recovery, recruited from outpatient treatment programs, were randomly assigned to MBRP-A (intervention plus usual-care; N = 64) or Control (usual-care-alone; N = 59) group. MBRP-A consisted of eight-weekly sessions and home practice. Outcomes were assessed at baseline, 8 weeks and 26 weeks (18 weeks post-intervention), and compared between groups using repeated measures analysis. RESULTS Outcome analysis included 112 participants (57 MBRP-A; 55 Control) who provided follow-up data. Participants were 41.0 ± 12.2 years old, 56.2% male, and 91% white. Prior to "quit date," they reported drinking on 59.4 ± 34.8% (averaging 6.1 ± 5.0 drinks/day) and heavy drinking (HD) on 50.4 ± 35.5% of days. Their drinking reduced after the "quit date" (before enrollment) to 0.4 ± 1.7% (HD: 0.1 ± 0.7%) of days. At 26 weeks, the MBRP-A and control groups reported any drinking on 11.5 ± 22.5% and 5.9 ± 11.6% of days and HD on 4.5 ± 9.3% and 3.2 ± 8.7% of days, respectively, without between-group differences (ps ≥ 0.05) in drinking or related consequences during the follow-up period. Three MBRP-A participants reported "relapse," defined as three-consecutive HD days, during the study. Subgroup analysis indicated that greater adherence to session attendance and weekly home practice minutes were associated with improved outcomes. CONCLUSIONS MBRP-A as an adjunct to usual-care did not show to improve outcomes in alcohol-dependent adults in early recovery compared to usual-care-alone; a return to drinking and relapse to HD were rare in both groups. However, greater adherence to MBRP-A intervention may improve long-term drinking-related outcomes.
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Witkiewitz K, Pearson MR, Hallgren KA, Maisto SA, Roos CR, Kirouac M, Wilson AD, Montes KA, Heather N. Who achieves low risk drinking during alcohol treatment? An analysis of patients in three alcohol clinical trials. Addiction 2017; 112:2112-2121. [PMID: 28511286 PMCID: PMC5673549 DOI: 10.1111/add.13870] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2016] [Revised: 02/08/2017] [Accepted: 05/05/2017] [Indexed: 01/31/2023]
Abstract
BACKGROUND AND AIMS There is evidence that low-risk drinking is possible during the course of alcohol treatment and can be maintained following treatment. Our aim was to identify characteristics associated with low-risk drinking during treatment in a large sample of individuals as they received treatment for alcohol dependence. DESIGN Integrated analysis of data from the Combined Pharmacotherapies and Behavioral Intervention (COMBINE) study, Project MATCH (Matching Alcoholism Treatments to Client Heterogeneity) and the United Kingdom Alcohol Treatment Trial (UKATT) using repeated-measures latent class analysis to identify patterns of drinking and predictors of low-risk drinking patterns during treatment. SETTING United States and United Kingdom. PARTICIPANTS Patients (n = 3589) with alcohol dependence receiving treatment in an alcohol clinical trial were primarily male (73.0%), white (82.0%) and non-married (41.7%), with an average age of 42.0 (standard deviation = 10.7). MEASUREMENTS Self-reported weekly alcohol consumption during treatment was assessed using the Form-90 and validated with biological verification or collateral informants. FINDINGS Seven patterns of drinking during treatment were identified: persistent heavy drinking (18.7% of the sample), increasing heavy drinking (9.6%), heavy and low-risk drinking (6.7%), heavy drinking alternating with abstinence (7.9%), low-risk drinking (6.8%), increasing low-risk drinking (10.5%) and abstinence (39.8%). Lower alcohol dependence severity and fewer drinks per day at baseline significantly predicted low-risk drinking patterns [e.g. each additional drink prior to baseline predicted a 27% increase in the odds of expected classification in heavy drinking versus low-risk drinking patterns; odds ratio = 1.27 (95% confidence interval (CI) = 1.10, 1.47, P = 0.002]. Greater negative mood and more heavy drinkers in the social network were significant predictors of expected membership in heavier drinking patterns. CONCLUSIONS Low-risk drinking is achievable for some individuals as they undergo treatment for alcohol dependence. Individuals with lower dependence severity, less baseline drinking, fewer negative mood symptoms and fewer heavy drinkers in their social networks have a higher probability of achieving low-risk drinking during treatment.
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Dale V, Heather N, Adamson S, Coulton S, Copello A, Godfrey C, Hodgson R, Orford J, Raistrick D, Tober G. Predicting drinking outcomes: Evidence from the United Kingdom Alcohol Treatment Trial (UKATT). Addict Behav 2017; 71:61-67. [PMID: 28273487 DOI: 10.1016/j.addbeh.2017.02.023] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2016] [Revised: 02/13/2017] [Accepted: 02/14/2017] [Indexed: 11/26/2022]
Abstract
AIMS To explore client characteristics that predict drinking outcomes using data from the UK Alcohol Treatment Trial (UKATT). METHODS Multiple linear regression was used to determine if there were any characteristics, measured before the start of treatment, that could predict drinking outcomes at three and 12months, as measured by percent day abstinent (PDA) and drinks per drinking day (DDD) over the preceding 90days. RESULTS Lower baseline DDD score and greater confidence to resist drinking predicted lower DDD at both three and twelve months following entry to treatment. In addition to baseline PDA and having greater confidence to resist heavy drinking, female gender, aiming for abstinence, more satisfaction with family life and a social network that included less support for drinking were predictors of percent days abstinent. CONCLUSIONS Overall the strongest and most consistent predictors of outcome were confidence to avoid heavy drinking and social support for drinking. More predictors were identified for percent of days abstinent than for drinks per drinking day. For percent of days abstinent, a number of client characteristics at baseline consistently predicted outcome at both month three and month twelve.
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Dunn KE, Harrison JA, Leoutsakos JM, Han D, Strain EC. Continuous Abstinence During Early Alcohol Treatment is Significantly Associated with Positive Treatment Outcomes, Independent of Duration of Abstinence. Alcohol Alcohol 2016; 52:72-79. [PMID: 27567268 DOI: 10.1093/alcalc/agw059] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2016] [Revised: 07/13/2016] [Accepted: 08/01/2016] [Indexed: 01/30/2023] Open
Abstract
AIMS Neither the predictive value of early continuous abstinence in alcohol use disorder (AUD) or the point at which this effect may emerge has been evaluated. This analysis of the Combined Pharmacotherapies and Behavioral Interventions (COMBINE) clinical trial evaluated whether abstinence early in treatment was a predictor of longer term abstinence. METHODS Participants who stated a goal of total abstinence (N = 954) were dichotomized into Early Abstainer vs. Nonabstainers and were compared on a variety of drinking outcome measures that are frequently used in clinical trial evaluations of alcohol treatment strategies, as a function of duration of early continuous abstinence. RESULTS Significant differences existed for every outcome. Early Abstinence was significantly associated with fewer drinks per drinking day, number of drinking and number of heavy drinking days, and longer time to first drinking and first heavy drinking day. Effects were evident within the first week. The magnitude of all effects increased as the duration of early abstinence (1-4 weeks) increased, though the size of increase varied across the outcomes. CONCLUSIONS These data provide evidence that drinking at the beginning of alcohol treatment is significantly and robustly associated with drinking throughout and at the end of a clinical trial treatment for AUD. Early drinking may be a useful early index to identify whether patients are responding positively to a treatment strategy, and provides a useful method for tailoring treatment to patients that is consistent with a personalized medicine approach.
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Affiliation(s)
- Kelly E Dunn
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins, University School of Medicine, Baltimore, MD, USA
| | - Joseph A Harrison
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins, University School of Medicine, Baltimore, MD, USA
| | - Jeannie-Marie Leoutsakos
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins, University School of Medicine, Baltimore, MD, USA
| | - Dingfen Han
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins, University School of Medicine, Baltimore, MD, USA
| | - Eric C Strain
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins, University School of Medicine, Baltimore, MD, USA
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