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Record W1579530153 · doi:10.4212/cjhp.v68i3.1455

Application of the Beers Criteria to Alternate Level of Care Patients in Hospital Inpatient Units

2015· article· en· W1579530153 on OpenAlexaffvenueabout
Heather Slaney, Stacey MacAulay, J. Irvine‐Meek, Joshua Murray

Bibliographic record

VenueThe Canadian Journal of Hospital Pharmacy · 2015
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsHorizon Health NetworkMoncton HospitalJaneway Children's Health and Rehabilitation Centre
Fundersnot available
KeywordsMedicineAdverse effectBeers CriteriaEmergency medicineLogistic regressionRetrospective cohort studyOdds ratioInternal medicinePediatricsIntensive care medicinePolypharmacy

Abstract

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Background: The Beers criteria were developed to help in identifying potentially inappropriate medications (PIMs) for elderly patients. These medications are often associated with adverse events and limited effectiveness in older adults. Patients awaiting an alternate level of care (ALC patients) are those who no longer require acute care hospital services and are waiting for placement elsewhere. They are often elderly, have complex medication regimens, and are at high risk of adverse events. At the time of this study no studies had applied the Beers criteria to ALC patients in Canadian hospitals.Objectives: To determine the proportion of ALC patients receiving PIMs and the proportion experiencing selected PIM-related adverse events. Methods: A retrospective chart review of ALC patients 65 years of age or older was performed to identify PIMs and the occurrence of selected adverse events (specifically central nervous system [CNS] events, falls, bradycardia, hypoglycemia, seizures, insomnia, gastrointestinal bleeding, and urinary tract infections). A logistic regression model with a random intercept for each patient was constructed to estimate odds ratios and probabilities of adverse events.Results: Fifty-two ALC patients were included in the study. Of these, 48 (92%) were taking a PIM. Of the 922 adverse events evaluated, 407 (44.1%) were associated with a regularly scheduled PIM. Among patients who were taking regularly scheduled PIMs, there was a significantly increased probability of an adverse CNS event and of a fall (p < 0.001 for both). The most common PIM medication classes were first-generation antihistamines (24 [46%] of the 52 patients), antipsychotics (21 patients [40%]), short-acting benzodiazepines (15 patients [29%]), and nonbenzodiazepine hypnotics (14 patients [27%]).Conclusions: A high proportion of ALC patients were taking PIMs and experienced an adverse event that may have been related to these drugs. These findings suggest that the ALC population might benefit from regular medication review and monitoring to prevent or detect adverse events.RÉSUMÉContexte : Les critères de Beers ont été élaborés afin d’aider à détecter l’utilisation de médicaments potentiellement inappropriés (MPI) auprès des patients âgés. L’on associe souvent les MPI à des événements indésirables, et leur efficacité chez les personnes âgées est limitée. Les patients en attente d’un autre niveau de soins (patients ANS) sont ceux qui ne nécessitent plus de soins de courte durée de l’hôpital et qui attendent d’être déplacés vers un autre établissement. Il s’agit souvent de personnes âgées ayant une panoplie complexe de traitements médicamenteux et présentant un risque élevé de subir des événements indésirables. Au moment de la présente recherche, aucune étude n’avait appliqué les critères de Beers aux patients ANS des hôpitaux canadiens.Objectifs : Déterminer quelles sont les proportions de patients ANS qui reçoivent des MPI et qui subissent certains événements indésirables choisis liés à ces médicaments.Méthodes : Une analyse rétrospective des dossiers médicaux de patients ANS âgés de 65 ans et plus a été réalisée dans le but de relever les MPI ainsi que les cas de certains événements indésirables choisis (particulièrement les événements liés au système nerveux central, les chutes, la bradycardie, l’hypoglycémie, les convulsions, l’insomnie, les hémorragies gastro-intestinales et les infections urinaires). On a mis au point un modèle de régression logistique avec ordonnée à l’origine aléatoire pour chaque patient afin d’estimer les risques relatifs approchés ainsi que les probabilités d’événements indésirables.Résultats : Au total, 52 patients ANS ont été admis à l’étude. De ceuxci, 48 (92 %) prenaient un MPI. Des 922 événements indésirables analysés, 407 (44,1 %) ont été associés à un MPI administré régulièrement. Parmi les patients prenant des MPI à une fréquence régulière, la probabilité de subir une chute ou un événement indésirable lié au système nerveux central était grandement accrue (p < 0,001 pour chacun). Les MPI les plus fréquents étaient : les antihistaminiques de première génération (24 [46 %] des 52 patients), les antipsychotiques (21 patients [40 %]), les benzodiazépines à action brève (15 patients [29 %]) et les hypnotiques non-benzodiazépines (14 patients [27 %]).Conclusions : Un grand nombre de patients ANS prenaient des MPI et avaient subi un événement indésirable qui ouvait avoir été en lien avec ces médicaments. Ces résultats laissent croire que les patients ANS pourraient tirer avantage d’évaluations fréquentes de la pharmacothérapie et de surveillance afin de prévenir les événements indésirables ou de les détecter.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.153
GPT teacher head0.393
Teacher spread0.241 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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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Citations21
Published2015
Admission routes3
Has abstractyes

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