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PSYCHOTROPIC MEDICATIONS AND MOTOR VEHICLE COLLISIONS IN PATIENTS WITH DEMENTIA

2008· letter· en· W2170145535 on OpenAlexafffundabout
Mark Rapoport, Nathan Herrmann, Frank Molnar, Paula A. Rochon, David N. Juurlink, Brandon Zagorski, Dallas Seitz, John C. Morris, Donald A. Redelmeier

Bibliographic record

VenueJournal of the American Geriatrics Society · 2008
Typeletter
Languageen
FieldHealth Professions
TopicOlder Adults Driving Studies
Canadian institutionsQueen's UniversityInstitute for Clinical Evaluative SciencesBaycrest HospitalUniversity of OttawaHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersMinistère des TransportsUniversity of Toronto
KeywordsMedicineDementiaCohortPolypharmacyPopulationDeliriumPsychiatryCohort studyMedical prescriptionPoison controlPediatricsDiseaseEmergency medicineInternal medicine

Abstract

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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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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.436
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.312
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations17
Published2008
Admission routes3
Has abstractyes

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