B-290 A perspective on commonly used drugs in patients receiving treatment for opioid use disorder in Ontario, Canada
Notice bibliographique
Résumé
Abstract Background Urine drug screens (UDS) are limited to a few drug classes of interest and are typically done by immunoassay-based (IA) methods. Screening results can then be confirmed by liquid chromatography coupled with tandem mass spectrometry methods (LC-MS/MS) if required. Medication-assisted treatment (MAT) has been used for treatment and monitoring of Opioid Use Disorders (OUD). Medications used in MAT are usually methadone and/or a combination of buprenorphine and naloxone. The aim of this is study was to see what other drugs the participants from the Pharmacogenetics of Opioid Substitution Treatment Response (POST) study in Ontario, Canada, were taking in addition to the prescribed medications. Methods Two hundred POST study participants provided their urine samples to be tested on LC-MS/MS after their urines had been screened by IA. There were 99 drugs tested in this method. The kits were provided by Chromsystems (Grafelfing, Germany) and we followed their procedure for the analysis of these drugs. We investigated the participants’ demographics by age and gender/sex, the most commonly used drugs, their most common combinations, and the number of participants who were taking = 2 drugs, confirmed by LC-MS/MS. We also checked discordances between IA and LC-MS/MS for MATs, and amphetamine and methamphetamine. All the analyses and pertaining graphs were done in Microsoft Excel (Microsoft Corporation). Results The average age for all participants was 39.5 years. The participants were divided into 5 age groups (20-29.9, 30-30.9, 40-49.9, 50-50.9, = 60) and two sexes (males and cis/trans-females). Majority (n=161, 80.5%) of the participants were of the European descent, and 43.0 % were females. The three most commonly abused drugs were selected for a more detailed demographics analysis: THC-COOH (n=96, 48%), amphetamine and methamphetamine (n=47, 23.5%, for both). THC-COOH was present in 11.5% participants aged 20-29.9, 16.5% aged 30-39.9, 10% aged 40-49.9, 8% aged 50-59.9 and 2% = 60 years of age. THC-COOH was present in 31.5% males and 16% females (0.5% trans-females). Both amphetamine and methamphetamine were present in 23.5% of the participants (n=47), and both were present in 15% males and 8.5% females, as expected. There was only a slight difference between age groups. The percentage of the most common drug combination was as follows: amphetamine and methamphetamine (42%); amphetamine, methamphetamine combined with THC-COOH (19%); and amphetamine, methamphetamine combined with norfentanyl (18%). The greatest number of participants who took = 2 drugs confirmed by LC-MS/MS (n=40, 20%) had 3 drugs in urine. Surprisingly, 26.5% of participants who were compliant with their MATs, confirmed by LC-MS/MS, were negative on IA screens. Thirty-one participants (15.5%) were falsely positive for either amphetamine or methamphetamine on IA, after being confirmed negative on LC-MS/MS. Conclusion This work has shed important light into what populations across Ontario, who are receiving MAT for OUD, concurrently use in addition to their prescribed treatment. It confirmed the importance of LC-MS/MS for confirmation of the compliance with MAT, as well as confirmation of commonly taken illicit drugs.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,005 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».