PSYCHOTROPIC MEDICATIONS AND MOTOR VEHICLE COLLISIONS IN PATIENTS WITH DEMENTIA
Notice bibliographique
Résumé
To the Editor: To test the association between psychotropic medications and motor vehicle crashes (MVCs) in drivers with dementia, a population-based, case-crossover study linking transportation and healthcare databases was conducted from April 1, 1997, to March 31, 2005, in Ontario, Canada. Patients with dementia were identified according to prescriptions for cholinesterase inhibitors or a dementia diagnosis. A cohort of adults aged 65 and older who had dementia, were licensed drivers, and were involved in an MVC at age 67 or older during the study period was constructed. Subjects who lived in a long-term care facility or received palliative care or had a diagnosis of an alcohol- or drug-related disorder, amnestic disorder, delirium, “other mental disorders due to brain damage and dysfunction and to physical disease,” personality disorder, or epilepsy were excluded. The date of collision in the collisions database served as the index date for all subsequent analyses. Exposure to psychotropic medication was defined as any prescription for benzodiazepines, antidepressants, or antipsychotics in the 120 days before the index date. Topical corticosteroid and antifungal agents were used as neutral controls. A case-crossover design, a technique for assessing the risks associated with a transient exposure, was used.1 A pair-matched analytical approach was used to contrast drug exposure during the 4 months before the collision with the same exposures 1 year before the collision. A cohort of 210,550 patients with dementia during the study period was identified, of whom 40,508 (19.2%) had active driver's licenses. Of those with active licenses, 9,763 (24.1%) were involved in a collision, most of which antedated the diagnosis of dementia (77.9%). “At fault” collisions were common (57.2%) occurrences, and 24.5% of collisions led to personal injury. Of the 8,690 individuals who experienced an MVC and were included in the case-crossover study, psychotropic medications were prescribed to 2,242 (33.0%). Overall, 1,996 (23.0%) received benzodiazepines, 1,544 (17.8%) antidepressants, and 135 (1.6%) antipsychotics. Topical antifungals were prescribed for 608 subjects (7.0%) and topical corticosteroids for 1,441 (16.6%). In total, 610 patients with dementia received a prescription for a psychotropic medication only in the 4 months before the collision, 397 received one only in the same 4 months the year before, and 1,845 received one in both intervals. Psychotropic prescriptions were associated with a significantly greater risk of MVC (odds ratio (OR)=1.54, 95% confidence interval (CI)=1.35–1.74). Antipsychotics were associated with the highest risk of a collision, benzodiazepines were associated with a modestly greater risk, and the risk with antidepressants was intermediate. As expected, topical antifungals and corticosteroid medications were not associated with a significant collision risk (Figure 1). Stratification according to age, sex, and season yielded consistent results. Risk of medication exposure with motor vehicle collisions in patients with dementia: psychotropic prescriptions, odds ratio (OR)=1.54, 95% confidence interval (CI)=1.35–1.74; antipsychotics, OR=3.35, 95% CI=1.95–5.76; antidepressants, OR=1.82, 95% CI=1.56–2.13; and benzodiazepines, OR=1.12, 95% CI=1.01–1.34). Control medications: topical antifungals, OR=0.95, 95% CI=0.79–1.13 and topical corticosteroids, OR=0.97, 95% CI=0.86–1.09. Later-generation antidepressants (selective serotonin reuptake inhibitors and other newer agents) were associated with a higher risk of an MVC (OR=2.15, 95% CI 1.78–2.60) than first-generation antidepressants (cyclic agents and irreversible monoamine oxidase A inhibitors; OR=1.31, 95% CI=1.07–1.61). To control for temporal trends, the MVC risk associated with psychotropic prescriptions was similar when determined using the control intervals of 1 year before MVC (OR=1.56, 95% CI=1.37–1.77) and 2 years before MVC (OR= 1.90, 95% CI=1.68–2.14) in a cohort aged 68 and older at the index date (n=8,323). It was found that older drivers with dementia have frequent psychiatric comorbidity, as demonstrated by their frequent receipt of psychotropic medications, and that these prescriptions were associated with an approximately 50% greater risk of an MVC. The overall results bolster past research in patients not selected for dementia.2,3 The major exception is a case-crossover study that found no risk of an MVC associated with antidepressants,4 although that study included only 43 collisions in subjects aged 65 and older exposed to antidepressants. Our theory is that the greater risk of an MVC reflects the underlying indication for the prescriptions rather than the pharmacological properties of the drugs themselves, given the paradoxically higher risk found with the newer than the older antidepressants. Although behavioral disturbances sometimes predict driving cessation in dementia,5 this restriction is incomplete, and patients who do not cease driving may be at distinctly higher risk. Limitations of this study include the inability to quantify the risks with continuous drug exposure or the effects of adherence, dose response, or severity of cognitive impairment. The emergence of psychiatric symptoms or the prescription of psychotropic medications for patients with dementia should prompt physicians to evaluate the effect on road safety. The authors would like to acknowledge the support of Muhammad Mamdani, PharmD, MA, MPH, who was involved in conceptual contributions and received no compensation; Don DeBoer, MMath, who was involved in data acquisition and received no compensation; Nelson Chong, BSc, who was involved in data acquisition and received no compensation; and the Ministry of Transportation of Ontario, which provided data and received no compensation. Preliminary data were presented at the American Neuropsychiatric Association Annual Meeting February 18, 2007, and the Harvey Stancer Research Day, University of Toronto, June 21, 2007. Conflict of Interest: Mark Rapoport received fees for speaking from the Alzheimer's Society of St. Thomas (January 2007), Janssen-Ortho (January and September 2007), and Novartis (September 2007); received reimbursement for attending a symposium by Janssen-Ortho (April 2007); and received funds for research from the Canadian Institute of Health Research, Physician's Services Inc. Foundation and the Ontario Neurotrauma Foundation. Nathan Herrmann received research support, consulting fees and speaker's honoraria from Lundbeck, Novartis, Janssen, Pfizer, and Neurochem. David Juurlink provided expert opinion in a medicolegal matter regarding the potential adverse effects of an antipsychotic drug. Author Contributions: Mark Rapoport: concept and design, acquisition of subjects and/or data, analysis and interpretation of data, and preparation of manuscript. Nathan Herrmann, Frank Molnar, Paula Rochon, and John Morris: concept and design, analysis and interpretation of data, and preparation of manuscript. Brandon Zagorski: acquisition of subjects and/or data and analysis and interpretation of data. Dallas Seitz: analysis and interpretation of data and preparation of manuscript. Donald Redelmeier: concept and design, acquisition of subjects and/or data, analysis and interpretation of data and preparation of manuscript. Sponsor's Role: The study was funded by the Physician's Services Inc. Foundation (PSI) (PSI 04-37). The PSI provided the funds necessary to conduct the study, but had no role in study design, collection, analysis, interpretation of data, writing, or decision to submit the manuscript.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».