Dataset Information

Overbaked: assessing and predicting acute adverse reactions to Cannabis.

ABSTRACT:

Background

Trends toward legalizing cannabis may increase experimentation with the drug among less experienced users with limited knowledge of possible adverse reactions. This study explores the prevalence, frequency, and levels of distress produced by various acute adverse reactions to cannabis, as well as predictors of these reactions.

Methods

The Adverse Reactions Scale (ARS) was created and administered to a large sample of undergraduate college students (n?=?999) who were predominantly white (>?70%), female (>?70%), recreational (>?90%) cannabis users. The ARS was administered in an anonymous online survey measuring demographics, cannabis use patterns, cannabis use motives, personality, and negative affect.

Results

The most prevalent adverse reactions to cannabis were coughing fits, anxiety, and paranoia, which >?50% of the sample reported experiencing. The most frequently occurring reactions were coughing fits, chest/lung discomfort, and body humming, which occurred on approximately 30-40% of cannabis use sessions. Panic attacks, fainting, and vomiting were rated as the most distressing, with mean ratings falling between "moderately" and "quite" distressing. Multiple regression analyses revealed that lower frequency of cannabis use predicted increased frequency of adverse reactions. Symptoms of cannabis use disorder, conformity motives, and anxiety sensitivity were significant predictors of both the prevalence of, and distress caused by, adverse reactions.

Conclusions

Relative to past research, this study provides a more comprehensive account of possible adverse reactions to cannabis, and individual difference variables that predict these reactions. This study has implications for inexperienced cannabis users, as well as medical professionals and budtenders who provide information about cannabis use.

SUBMITTER: LaFrance EM

PROVIDER: S-EPMC7819287 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Publications

Overbaked: assessing and predicting acute adverse reactions to Cannabis.

LaFrance Emily M EM Stueber Amanda A Glodosky Nicholas C NC Mauzay Dakota D Cuttler Carrie C

Journal of cannabis research 20200102 1

<h4>Background</h4>Trends toward legalizing cannabis may increase experimentation with the drug among less experienced users with limited knowledge of possible adverse reactions. This study explores the prevalence, frequency, and levels of distress produced by various acute adverse reactions to cannabis, as well as predictors of these reactions.<h4>Methods</h4>The Adverse Reactions Scale (ARS) was created and administered to a large sample of undergraduate college students (n = 999) who were pre ...[more]

PMID: 33526120

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Project description:BackgroundEarly and accurate identification of potential adverse drug reactions (ADRs) for combined medication is vital for public health. Existing methods either rely on expensive wet-lab experiments or detecting existing associations from related records. Thus, they inevitably suffer under-reporting, delays in reporting, and inability to detect ADRs for new and rare drugs. The current application of machine learning methods is severely impeded by the lack of proper drug representation and credible negative samples. Therefore, a method to represent drugs properly and to select credible negative samples becomes vital in applying machine learning methods to this problem.ResultsIn this work, we propose a machine learning method to predict ADRs of combined medication from pharmacologic databases by building up highly-credible negative samples (HCNS-ADR). Specifically, we fuse heterogeneous information from different databases and represent each drug as a multi-dimensional vector according to its chemical substructures, target proteins, substituents, and related pathways first. Then, a drug-pair vector is obtained by appending the vector of one drug to the other. Next, we construct a drug-disease-gene network and devise a scoring method to measure the interaction probability of every drug pair via network analysis. Drug pairs with lower interaction probability are preferentially selected as negative samples. Following that, the validated positive samples and the selected credible negative samples are projected into a lower-dimensional space using the principal component analysis. Finally, a classifier is built for each ADR using its positive and negative samples with reduced dimensions. The performance of the proposed method is evaluated on simulative prediction for 1276 ADRs and 1048 drugs, comparing using four machine learning algorithms and with two baseline approaches. Extensive experiments show that the proposed way to represent drugs characterizes drugs accurately. With highly-credible negative samples selected by HCNS-ADR, the four machine learning algorithms achieve significant performance improvements. HCNS-ADR is also shown to be able to predict both known and novel drug-drug-ADR associations, outperforming two other baseline approaches significantly.ConclusionsThe results demonstrate that integration of different drug properties to represent drugs are valuable for ADR prediction of combined medication and the selection of highly-credible negative samples can significantly improve the prediction performance.

Dataset Information

Overbaked: assessing and predicting acute adverse reactions to Cannabis.

Background

Methods

Results

Conclusions

Publications

Overbaked: assessing and predicting acute adverse reactions to Cannabis.

Similar Datasets

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