ArticleBMC geriatrics2026
Classifying drug-related problems of neurodegenerative diseases in the physician-pharmacist joint clinic of neurology: an application of the PCNE method.
Article in BMC geriatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
backgroundThe prevalence of neurodegenerative diseases (NDDs) is escalating, and complex medication regimens lead to a high incidence of drug-related problems (DRPs). This study analyzes DRPs in this population to identify their incidence and causes. The findings aim to provide a theoretical basis for clinical intervention strategies and outpatient pharmacy monitoring, ultimately ensuring rational drug use and enhancing patient safety.
methodThis study was conducted among patients with neurodegenerative diseases at a major hospital in Shanghai, China, between July 2023 and June 2024. The Pharmaceutical Care Network Europe (PCNE) classification system version 9.1 was used to identify DRPs. Data was entered and analyzed using SPSS software. Full model and stepwise logistic regression analyses were used to identify predictors of DRP occurrence in the total sample and Alzheimer’s disease (AD)/Parkinson’s disease (PD) subgroups. Outpatient medications were summarized. A p-value of less than 0.05 was considered statistically significant.
resultA total of 254 patients (90 AD, 171 PD) were involved, resulting in 398 DRPs. The most commonly encountered type of DRP was treatment effectiveness (48.99%), and drug selection (46.73%) was the most common cause. The majority of clinical pharmacist interventions were provided at the drug level (98.99%), primarily involving dose adjustment and usage method adjustment. The acceptance level of interventions by prescribers was high (90.70%), with the acceptance rate in AD patients (92.96%) being higher than that in PD patients (90.07%). Through pharmacist intervention, over 70% of DRPs were completely resolved. Anti-Parkinson’s disease drugs, antianxiety or antidepressant drugs, and sedatives and hypnotics were the three main drug classes contributing to DRPs. Lifestyle habits (smoking, drinking), the number of comorbidities, and the dose of medication were factors associated with the development of DRPs.
conclusionThis study finding revealed that DRPs were prevalent in patients with PD and AD. Medication care was a protective factor, whereas polypharmacy and the presence of multiple comorbidities were significantly associated with an elevated risk of DRPs. Based on the PCNE classification and the “PCIAO” process, clinical pharmacists’ involvement in precision management offers a robust evidence base and provides clear guidance for optimizing therapeutic regimens.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.