Screening for Adverse Drug Reactions in Dementia Patients on Cholinesterase Inhibitor Therapy
Notice bibliographique
Résumé
Dementia is a clinical syndrome of a progressive functional decline and cognitive impairment.1 Globally an estimated 46.6 million people live with dementia, and this number is projected to rise to 152 million people by 2050.2, 3 Despite the high prevalence of dementia, treatment options are limited. Donepezil, galantamine, and rivastigmine are the three cholinesterase inhibitors (ChEIs) approved for the treatment of mild, moderate, and severe (donepezil and rivastigmine) dementia due to Alzheimer's disease (AD). These medications aim to provide a symptomatic benefit; however, they do not stop, slow, or reverse the progression of AD dementia, and they can cause adverse drug reactions (ADRs) that may threaten patient quality of life and well-being.4 The Food and Drug Administration Adverse Event Reporting System, the Canada Vigilance Adverse Reaction Database, and the French National Multicenter Pharmacovigilance Study have highlighted the underreporting of ChEI-related ADRs because providers do not systematically screen their dementia patients who are taking ChEIs for ADRs. Most providers rely on spontaneous reporting,5 and this approach may lead to iatrogenic complications that are overlooked in patients with dementia and could be prevented if systematic screening for ADRs occurred.5, 6 The spontaneous reporting method of ADRs used by providers may also contribute to selective underreporting of the ADRs, especially for nonspecific ones that could easily be misattributed to the progression of dementia itself.7 The lack of a systematic approach to identifying ADRs in the dementia patient population undergoing ChEI therapy for cognitive impairment increases the risk of mortality, morbidity, and poor quality of life in the geriatric population. To improve ADR detection, we designed and implemented a systematic approach to screen ADRs in dementia patients receiving ChEIs to evaluate if the detection of ADRs could be enhanced using a screening questionnaire based on the most frequent and consequential side effects reported in postmarketing studies.5, 6, 8 The format of the screening tool was developed from the literature on enhancing health literacy9, 10 (Supplementary Figure S1). The aims of this quality improvement project were to (1) implement a systematic ADR screening process for all dementia patients on ChEI therapy; (2) compare the prevalence of ChEI-related ADRs reported before and after implementation of the screening process; and (3) assess the impact on perceived increase in workload due to data collection, distribution of the screening tool (medical assistants), and use of the screening tool (advanced practice registered nurses). A pre- and posttest design was used to evaluate the implementation of a new workflow and screening questionnaire used by medical assistants and advanced practice registered nurses teams to identify systematically the ADRs associated with ChEI therapy among dementia patients seen at a neurology clinic specializing in cognitive impairment. Records of 239 patients seen in the 6-week preimplementation period (n = 135) and in the 6-week postimplementation period (n = 134) were reviewed. The mean age of the patients was 78 ± 8.0 years in the preimplementation group and 79 ± 7.6 years in the postimplementation group. The percentage of women was 56% and 47%, respectively. No statistically significant difference was noted based on sex and the average age between the two groups (P > .05). A statistically significant increase in reported ADRs was observed after the implementation of a screening questionnaire. Before implementation, 15 of 135 patients (11.1%) reported ADRs compared with 82 of 134 (61.2%) following implementation of the questionnaire: [x2 (1, n = 269) = 73.2; P < .001]. The most frequently reported ADRs were runny nose (25%), followed by anxiety (21%) and aggression (19%) (Figure 1). Of the nine staff members involved in the new process, only one indicated an increase in workload. In summary, the rate of observed ADRs increased from 11.1% to 61.2% with no significant increase in perceived staff workload when a screening tool was used in this specialty clinic. These results suggest the process is both feasible and effective. The next step will be to spread the use of the screening questionnaire across the department. Before engaging in this step, it would be worthwhile to conduct a study examining the impact of this new process on therapeutic decisions and on patient quality of life. The major limitation of this project was differentiating reported ADRs from the expected symptoms due to the progression of the disease, especially neuropsychiatric symptoms. The duration for the implementation of the project was brief (6 weeks), and staff participation was voluntary. Implementation of screening for ADRs on a larger scale may uncover new barriers. We anticipate that this practice change will improve the identification of ADRs in dementia patients on ChEI therapy and thereby improve patient safety. There may be additional value in implementing this tool on a larger scale and for a longer period, to support research that will establish ADR profiles of specific agents and their impact on therapeutic decision making and quality of life. The authors appreciate the contribution of Dr. Julie Thompson in computing and interpreting the data for this project. The authors have declared no conflicts of interest for this article. Conception: Saraon, Bernick, and McConnell. Interpretation of data: Saraon, Bernick, McConnell, and Wint. Drafting of the manuscript: Saraon and McConnell. Revising the manuscript for important intellectual content: Bernick, McConnell, Wint, and Saraon. Accuracy of the manuscript and critical revision of the manuscript for important intellectual content: Saraon and McConnell. No sponsor. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».