Management of potentially inappropriate medication use among older adult’s patients in primary care settings: description of an interventional prospective non-randomized study
Notice bibliographique
Résumé
BACKGROUND: The management of inappropriate medication use in older patients suffering from multimorbidity and polymedication is a major healthcare challenge. In a primary care setting, a medication review is an effective tool through which a pharmacist can collaborate with a practitioner to detect inappropriate drug use. AIM: This project described the implementation of a systematic process for the management of potentially inappropriate medication use among Lebanese older adults. Its aim was to involve pharmacists in geriatric care and to suggest treatment optimization through the analysis of prescriptions using explicit and implicit criteria. METHOD: This study evaluated the medications of patients over 65 years taking a minimum of five chronic medications a day in different regions of Lebanon. Descriptive statistics for all the included variables using mean and standard deviation (Mean (SD)) for continuous variables and frequency and percentage (n, (%)) for multinomial variables were then performed. RESULTS: A total of 850 patients (50.7% women, 28.6% frail, 75.7 (8.01) mean age (SD)) were included in this study. The mean number of drugs per prescription was 7.10 (2.45). Roughly 88% of patients (n = 748) had at least one potentially inappropriate drug prescription: 66.4% and 64.4% of the patients had at least 1 drug with an unfavorable benefit-to-risk ratio according to Beers and EU(7)-PIM respectively. Nearly 50.4% of patients took at least one medication with no indication. The pharmacists recommended discontinuing medication for 76.5% of the cases of drug related problems. 26.6% of the overall proposed interventions were implemented. DISCUSSION: The rate of potentially inappropriate drug prescribing (PIDP) (88%) was higher than the rates previously reported in Europe, US, and Canada. It was also higher than studies conducted in Lebanon where it varied from 22.4 to 80% depending on the explicit criteria used, the settings, and the medical conditions of the patients. We used both implicit and explicit criteria with five different lists to improve the detection of all types of inappropriate medication use since Lebanon obtains drugs from many different sources. Another potential source for variation is the lack of a standardized process for the assessment of outpatient medication use in the elderly. CONCLUSION: The prevalence PIDP detected in the sample was higher than the percentages reported in previous literature. Systematic review of prescriptions has the capacity to identify and resolve pharmaceutical care issues thus improving geriatric care.
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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 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».