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Record W4365450030 · doi:10.1093/ijpp/riad021.002

438 Patterns and appropriateness of prescribing for people with dementia in ambulatory care in Saudi Arabia: A cross-sectional study

2023· article· en· W4365450030 on OpenAlexaff
Nourah A. Alageel, Carmel Hughes, M.H. Al-Whaibi, Walid Alkeridy, Heather E. Barry

Bibliographic record

VenueInternational Journal of Pharmacy Practice · 2023
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicinePolypharmacyDementiaBeers CriteriaLogistic regressionOdds ratioCross-sectional studyPopulationMedical prescriptionAmbulatoryFamily medicineDescriptive statisticsPediatricsEnvironmental healthDiseaseInternal medicineNursing

Abstract

fetched live from OpenAlex

Abstract Introduction People with dementia (PwD) often have several comorbidities for which medications are prescribed. Consequently, polypharmacy and potentially inappropriate prescribing (PIP) are highly prevalent amongst PwD and have been linked with negative health outcomes (1). To date, no studies have been conducted in Saudi Arabia which describe current prescribing practices, polypharmacy or appropriateness of medications prescribed for PwD. Aim To explore prescribing trends, estimate the prevalence of polypharmacy and PIP, and investigate associations between PIP and polypharmacy, age and gender among PwD in ambulatory care in Saudi Arabia. Methods A cross-sectional, retrospective study was conducted using data from electronic health records in a large tertiary hospital in Saudi Arabia. Patients were eligible to be included if they were aged ≥65 years, had a dementia diagnosis, and had visited the ambulatory care clinic during the study period 01/01/2019 – 31/12/2021. Patients who died during the study period were excluded. Polypharmacy was indicated by use of ≥5 chronic medications. Prescribing appropriateness was assessed by applying a subset of the Screening Tool of Older Persons Potentially Inappropriate Prescriptions (STOPP) criteria, comprising 78 indicators (2). Descriptive statistics were used to describe the study population. Overall prevalence of PIP and the prevalence per each STOPP criterion were calculated as a proportion of all eligible persons in the dataset. Logistic regression analysis was used to investigate associations between PIP, polypharmacy, age and gender; adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. Data extraction and analysis were conducted using SPSS v27. Results The study population included 287 PwD. The majority were female (n=161, 56.0%) and the mean age was 78.8 [standard deviation (SD) ±8.0) years. The mean number of medications prescribed was 9.0 (±4.2) and polypharmacy was observed in 82.6% (n=237) of patients. The overall PIP prevalence during the study period was 61.0% (n=175). The most common instances of PIP were drugs prescribed beyond the recommended duration (n=90, 31.4%), drugs prescribed without an evidence-based clinical indication (n=78, 27.2%), proton pump inhibitors (PPIs) at full therapeutic dosage for >8 weeks (n=75, 26.0%), acetylcholinesterase inhibitors with concurrent treatment with drugs that reduce heart rate (n=60, 21.0%), and anticholinergic/antimuscarinic drug use (n=28, 9.8%). Whilst polypharmacy was found to be strongly associated with PIP (OR 21.9, 95% CI 8.3 – 57.5), no association was observed between PIP and age or gender after adjustments for polypharmacy. Conclusion To our knowledge, this is the first study to evaluate polypharmacy, prescribing appropriateness, and factors associated with PIP for PwD in Saudi Arabia. Findings have revealed a high prevalence of PIP among PwD that is strongly associated with polypharmacy. However, our focus was on PwD in one tertiary care hospital and thus findings may not be generalisable beyond this setting. Future research should involve PwD and other key stakeholders to develop practical interventions to decrease the burden of polypharmacy and PIP and optimise medication use for this vulnerable patient population. References 1. Delgado J, Jones L, Bradley MC, Allan LM, Ballard C, Clare L, Fortinsky RH, Hughes CM, Melzer D. Potentially inappropriate prescribing in dementia, multi-morbidity and incidence of adverse health outcomes. Age and Ageing. 2021;50(2): 457-464. 2. O’Mahony D, O’Sullivan D, Byrne S, O’Connor MN, Ryan C, Gallagher P. STOPP/START criteria for potentially inappropriate prescribing in older people: Version 2. Age and Ageing. 2015; 44(2): 213–218.

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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)0.001
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 score0.011
Threshold uncertainty score0.381

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.111
GPT teacher head0.457
Teacher spread0.346 · 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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Citations1
Published2023
Admission routes1
Has abstractyes

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