Abstract
Background
The 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.
Method
This 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.
Result
A 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.
Conclusion
This 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.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12877-026-07234-y.
Keywords: DRP, PCNE, Alzheimer's disease, Parkinson's disease
Introduction
Neurodegenerative diseases (NDDs) are a group of neurological disorders in which progressive loss of neurons in the central nervous system (CNS) or peripheral nervous system (PNS) leads to defects in specific brain functions [1]. Clinically, the diagnosis is primarily established by evaluating disease characteristics across three domains: cognitive, behavioral, and motor, using characteristic protein aggregation as a pathological marker [2]. Neurodegenerative disorders include Alzheimer’s disease (AD), Parkinson’s disease (PD), dementia with Lewy bodies (DLB), frontotemporal dementia (FTD), progressive supranuclear palsy (PSP), corticobasal ganglia syndrome, multiple system atrophy, amyotrophic lateral sclerosis, chronic traumatic encephalopathy, traumatic encephalopathy syndrome (TES), and Huntington’s disease (HD), as well as many other rare forms [3]. Among NDDs, AD and PD stand out as the most prevalent, carrying the heaviest social burden. AD is the most common neurodegenerative disease, clinically diagnosed with Aβ-amyloid, phosphorylated tau protein as a biomarker [4, 5]. It is estimated that more than 55 million people worldwide suffer from dementia, with AD being the most common form, accounting for 60–70% of the cases [6, 7]. More than 7 million people in China are reported to have AD, with a prevalence of AD of 3.21% among people aged 65 ≥ years [8]. As the second most prevalent neurodegenerative disease following AD, PD is diagnosed according to key clinical manifestations (such as bradykinesia, tremor, rigidity, and postural instability) using established clinical criteria [4, 5], with global estimates for 2019 that more than 8.5 million people have PD, with a prevalence rate of approximately 100–300 cases per 100,000 people per year [9–11]. China has the largest number of Parkinson’s disease patients in the world, accounting for more than half of the total number of PD patients worldwide [12]. With the deepening of population aging in China, NDDs seriously jeopardize the physical health of the elderly and cause a serious socioeconomic burden.
A drug-related problems (DRPs) is defined as any adverse event encountered by a patient during drug therapy that may affect the expected health outcome, including drug inefficacy, adverse drug effects, drug overdose, underdose, and drug interactions [7]. Patients with NDDs are predominantly elderly and are susceptible to DRPs due to age-related changes in pharmacokinetics and pharmacodynamics, cognitive decline, and dysfunction, which have been reported to occur in approximately 30%-70% of elderly patients according to previous studies [13–16]. A longitudinal study in France showed that more than 60% of patients with dementia tend to suffer from at least three other chronic diseases [17], among which hypertension, cardiovascular disease, diabetes mellitus, coronary artery disease, and other comorbidities are common [18]. Hospitalized patients with Parkinson’s disease frequently present with comorbidities, including hypertension, diabetes mellitus, and cerebrovascular diseases [19, 20]. Furthermore, those patients with comorbidities need to be treated with a variety of medications that significantly increase the risk of DRPs [21]. In conclusion, since DRPs are very common in patients with NDDs, there is a need for timely detection and management of DRPs. As mentioned, NDDs significantly burden healthcare, particularly affecting the elderly. Because these diseases are chronic and worsen over time, patients usually need long-term drugs. However, this, combined with aging effects and other health conditions, raises the risk of DRPs. Therefore, addressing DRPs in NDD patients is essential.
The Pharmaceutical Care Network Europe (PCNE) classification system is currently an internationally validated, simple, easy-to-use, and most commonly used classification system, including five aspects: type of problem, cause of DRP, intervention program, intervention recommendation, and DRP status category [22–24]. In the past 20 years, the PCNE classification system has been widely used in European countries for in-depth studies on DRPs. The results have confirmed that this classification system can help clinical pharmacists clarify DRPs as soon as possible so that they can implement interventions to promote the rational use of medication in the clinic [21, 25, 26]. For example, Hui Liu et al. studied the DRPs of PD patients in the neurology ward of a tertiary hospital in China based on the PCNE classification system and verified 274 DRPs in 209 PD inpatients, of which 83.3% of the population had at least 1 DRP. The prevalence of DRPs was high among PD patients [27]. Furthermore, certain conferences have proposed the PCNE Classification for Drug-Related Problems V9.00 as a tool designed to enhance the provision of pharmaceutical care services for diabetic patients [28]. This classification system has also been applied in various other areas, including the visceral surgical ward [29] and the identification of DRPs related to antibiotic use in cesarean Sect [30]. It is evident that the PCNE classification system, with its robust professionalism, wide-ranging applicability across diverse healthcare settings, and solid scientific foundation, plays an indispensable and central role in the comprehensive study and effective management of DRPs. This system not only provides a structured framework for identifying and categorizing DRPs but also enhances the precision and consistency of research, fostering evidence-based practices that improve patient care outcomes and optimize medication management strategies.
To date, the DRP characteristics of patients with NDDs have not been reported in the literature, and studies of DRPs in patients with NDDs are still lacking [8, 17, 18]. Therefore, the main objective of this study was to investigate the medication use of NDDs and record the DRPs. Specifically, we aimed to summarize the DRPs of NDD patients in the combined neurology and medicine outpatient clinic of a tertiary care hospital in Shanghai using the PCNE classification system (Version V9.1).
The results of this study can help determine the characteristics of DRPs in this population, including prevalence, types, causes, and influencing factors. This information can provide a theoretical basis for the future development of drug intervention strategies [31], and serve as a reference for future outpatient pharmacy monitoring to ensure rational clinical use of medication.
Materials and methods
Study design and setting
This retrospective study was conducted at Shanghai General Hospital, a tertiary teaching hospital, and was approved by the hospital’s Institutional Review Board. Between July 2023 and June 2024, patients attending the Neurology Physician-Pharmacist Collaborative Clinic of Shanghai General Hospital were enrolled following provision of informed consent. Given the differences in medication management strategies and patient monitoring frequency, this integrated care model—incorporating neurological expertise with pharmaceutical consultation—may not be fully representative of routine neurology outpatient practice. Nevertheless, the moderate sample size of patients at our institution bolsters the relevance of our study findings. The inclusion criteria stipulated that participants should be aged 18 years or older, have provided informed consent, carry a confirmed clinical diagnosis of a neurodegenerative disease, and have attended no fewer than two clinic visits(Baseline medication was administered at the first visit, and DRP after medication was evaluated at the second visit, which was conducted at 4 weeks after the first visit). Exclusion criteria included insufficient prescription or efficacy data, absence of identified DRPs, or being deemed otherwise unsuitable for the study by the investigators. A detailed breakdown of the inclusion and exclusion criteria is provided in Fig. 1.
Fig. 1.
Standard flow chart for inclusion and exclusion
Identification and classification of DRP
DRP refers to any adverse event experienced by a patient that is related to drug therapy and hinders the achievement of expected therapeutic goals [32]. Common types include inappropriate dosing, incorrect drug selection, adverse drug reactions, drug interactions, poor medication adherence, unreasonable treatment duration, and drug abuse, among others [7, 32, 33]. The DRPs involved in this study were identified prospectively during visits. DRPs were identified through comprehensive medication reviews, which involved analyzing patients’ medical histories, current medications, laboratory test results, and reported symptoms. Two pharmacists which have received professional training and corresponding qualifications independently assessed the DRPs. In cases of disagreement, the pharmacists first attempted to reach a consensus through discussion. If consensus could not be achieved, the case was discussed with a Senior Clinical Pharmacist to reach a comprehensive conclusion. Notably, the pharmacists achieved complete agreement regarding the presence or absence of DRPs for all patients. For the purpose of determining the overall prevalence, a patient was defined as having DRPs if any drug-related problem was identified during their medication course. Furthermore, given that patients may undergo multiple treatment regimens, DRPs were documented separately for each specific treatment, acknowledging that distinct DRPs may arise from different therapeutic interventions within the same individual. Although the presence of DRPs was unanimous, minor discrepancies existed in the detailed classification of individual cases—specifically regarding DRP types (potential or manifest), causes, and planned interventions. Consequently, we calculated inter-rater agreement using kappa for each dimension. The results revealed that the kappa values were all above 0.95, indicating a high level of consistency in their evaluations. These results have been shown in Supplemental Table 2. All DRPs are classified using PCNE classification version 9.1.
Data collection
Data were collected using a standardized data collection form based on the study objectives. This study documented the following characteristics of patients: age, gender, smoking history, alcohol consumption history, family history, medication and care, history of brain surgery, Follow-up period, type and quantity of comorbidities, imaging findings, type and quantity of medication. All data were double-checked for errors before analysis.
Statistical analysis
The sample size was calculated by using “Confidence Intervals for One Proportion” procedure of PASS 15.0. The sample size was found to be minimum of 200 patients with 60% DRP rate and 14% width of confidence interval (Clopper-Pearson method).
We calculated mean and SD (standard deviation) for continuous variables and frequencies and percentages for categorical variables. To investigate the association between basic characteristics and prevalence of DRP, the univariate logistic regression model was applied to calculate the crude OR (odds ratio) for each characteristic, and multivariable logistic regression analysis was conducted by including all variables in the model. Given the limited sample size of this study, we further performed multivariable logistic regression analysis with stepwise selection (entry α = 0.05, stay α = 0.10) to explore the significant factors and calculate the adjusted OR. Additionally, a full model logistic regression analysis incorporating all variables simultaneously was conducted as a sensitivity analysis. Given the heterogeneity in disease types, an exploratory subgroup analysis will be conducted separately in the patients with Alzheimer’s disease and Parkinson’s disease. The figure (excluded flow chat) was performed using the R software package (version 4.1.2). The remaining statistical analyses were performed using IBM SPSS Statistics 25. All reported p values were two-sided, and a p value < 0.05 was regarded as statistically significant.
Results
Demographic characteristics of participants
It shows that a total of 254 patients were included in this study, including 90 with AD and 171 with PD (Table 1). The overall average age of the patients is 71.21 ± 9.38 years old, and the average ages of the two diseases are also close to this. 51.57% of the total population are male, 26.38% have a history of smoking, 37.01% have a drinking habit, 11.02% of patients have a Follow-up period less than 4 weeks, and the average number of medications used by patients is 3.79 ± 1.10. All AD patients had family members taking medication to provide care, whereas only 40.94% of PD patients had this arrangement. The family history of both diseases is below 10%, and the history of brain surgery is approximately 16%. The average number of comorbidities in the total population is 3.10 ± 1.71, with atherosclerosis being the most common, accounting for over 50%. Among them, 61.11% of AD patients have atherosclerosis, and 49.12% of PD patients have atherosclerosis. The imaging results show that the proportion of brain atrophy, white matter lesions, and lacunar lesions in AD patients is greater than 50%, and higher than that in PD patients. During the outpatient follow-up, the majority of patients underwent DRP assessment at the second visit (approximately 4 weeks). However, 10 patients did not complete the assessment due to personal reluctance. We collected partial basic information from these 10 patients and compared it with the 254 patients who were enrolled in the study. As presented in Supplemental Table 1, there were no statistically significant differences between the two groups overall. Specifically, the 10 non-participating patients tended to be younger, have no family history of the disease, and had a slightly higher proportion of AD; however, these factors did not indicate significant selection bias.
Table 1.
Basic characteristics of the total population
| Index | Total Population (n = 254) |
Alzheimer’s disease (n = 90) |
Parkinson’s disease (n = 171) |
|---|---|---|---|
| Age, Mean (SD) | 71.21 ± 9.38 | 74.52(7.83) | 69.92(9.86) |
| Gender (Male), n (%) | 131(51.57) | 48(53.33) | 87(50.88) |
| Smoking, n (%) | 67(26.38) | 21(23.33) | 49(28.65) |
| Alcohol consumption, n (%) | 94(37.01) | 32(35.56) | 66(38.60) |
| Medication care, n (%) | 153(60.24) | 90(100.00) | 70(40.94) |
| Family history, n (%) | 20(7.87) | 8(8.89) | 12(7.02) |
| History of brain surgery, n (%) | 42(16.54) | 16(17.78) | 27(15.79) |
| Follow-up period < 4 weeks, n (%) | 28(11.02) | 14(15.56) | 14(8.19) |
| Number of comorbidities, Mean (SD) | 3.10(1.71) | 2.82(1.56) | 3.29(1.77) |
| Complications, n (%) | |||
| Sleep disorders | 111(43.70) | 39(43.33) | 77(45.03) |
| Dizziness | 28(11.02) | 6(6.67) | 22(12.87) |
| Headache | 3(1.18) | 1(1.11) | 2(1.17) |
| Cervical degenerative disease | 50(19.69) | 14(15.56) | 40(23.39) |
| Cognitive impairment | 60(23.62) | 0(0.00) | 60(35.09) |
| Arteriosclerosis | 135(53.15) | 55(61.11) | 84(49.12) |
| Hypertension | 111(43.70) | 41(45.56) | 74(43.27) |
| Thyroid nodule | 75(29.53) | 27(30.00) | 53(30.99) |
| Diabetes | 45(17.72) | 17(18.89) | 29(16.96) |
| Hyperlipidemia | 17(6.69) | 4(4.44) | 13(7.60) |
| Coronary heart disease | 16(6.30) | 6(6.67) | 12(7.02) |
| Fatty liver | 48(18.90) | 18(20.00) | 33(19.30) |
| Other | 88(34.65) | 26(28.89) | 64(37.43) |
| Imaging results, n (%) | |||
| Cerebral atrophy | 112(44.09) | 55(61.11) | 62(36.26) |
| White matter lesions of the brain | 97(38.19) | 51(56.67) | 51(29.82) |
| Lacunar lesion | 85(33.46) | 46(51.11) | 44(25.73) |
| Ischemic lesion | 79(31.10) | 37(41.11) | 46(26.90) |
| Number of medication, Mean (SD) | 3.79(1.10) | 3.81(0.99) | 3.78(1.15) |
| Number of patients with DRP, n (%) | 161(63.39) | 60(66.67) | 108(63.16) |
7 patients have both Alzheimer’s disease and Parkinson’s disease
SD standard deviation, DRP drug-related problem
Prevalence and classification of DRPs
A total of 161 out of 254 patients developed DRP [63.39% (95% CI 57.14% − 69.32%)], with 66.67% (95% CI 55.95% − 76.26%) of AD patients accompanied by DRP and 63.61% (95% CI 55.46% − 70.39%) of PD patients accompanied by DRP. The total number of DRP cases is 398 (142 cases in AD and 272 cases in PD). It shows the types and situations of DRP in this study (Table 2). According to version 9.1 of the PCNE classification tool, the proportion of treatment effectiveness issues is the highest (48.99%), followed by treatment safety (45.48%). Drug selection is the most common cause of DRPs (46.73%). Next is the dosage (39.20%), mostly due to low drug dosage (19.35%). In the cases of AD and PD, their main trends are similar to the overall population.
Table 2.
The proportion of problems and causes in DRP
| Index | Total Population (n = 398) |
Alzheimer’s disease (n = 142) |
Parkinson’s disease (n = 272) |
|---|---|---|---|
| P1, n (%) | 195(48.99) | 68(47.89) | 135(49.63) |
| P1.1 | 9(2.26) | 3(2.11) | 8(2.94) |
| P1.2 | 162(40.70) | 60(42.25) | 107(39.34) |
| P1.3 | 24(6.03) | 5(3.52) | 20(7.35) |
| P2, n (%) | 181(45.48) | 67(47.18) | 121(44.49) |
| P2.1 | 181(45.48) | 67(47.18) | 121(44.49) |
| P3, n (%) | 22(5.53) | 7(4.93) | 16(5.88) |
| P3.1 | 18(4.52) | 4(2.82) | 14(5.15) |
| P3.2 | 4(1.01) | 3(2.11) | 2(0.74) |
| C1, n (%) | 186(46.73) | 63(44.37) | 133(48.90) |
| C1.1 | 8(2.01) | 5(3.52) | 6(2.21) |
| C1.2 | 6(1.51) | 2(1.41) | 4(1.47) |
| C1.4 | 2(0.50) | 1(0.70) | 1(0.37) |
| C1.5 | 149(37.44) | 47(33.10) | 108(39.71) |
| C1.6 | 21(5.28) | 8(5.63) | 14(5.15) |
| C2, n (%) | 15(3.77) | 6(4.23) | 9(3.31) |
| C2.1 | 15(3.77) | 6(4.23) | 9(3.31) |
| C3, n (%) | 156(39.20) | 55(38.73) | 105(38.60) |
| C3.1 | 77(19.35) | 28(19.72) | 52(19.12) |
| C3.2 | 70(17.59) | 24(16.90) | 47(17.28) |
| C3.3 | 2(0.50) | 1(0.70) | 1(0.37) |
| C3.4 | 6(1.51) | 2(1.41) | 4(1.47) |
| C3.5 | 1(0.25) | 0(0.00) | 1(0.37) |
| C4, n (%) | 60(15.08) | 19(13.38) | 43(15.81) |
| C4.1 | 11(2.76) | 3(2.11) | 9(3.31) |
| C4.2 | 49(12.31) | 16(11.27) | 34(12.50) |
| C5, n (%) | 1(0.25) | 1(0.70) | 0(0.00) |
| C5.3 | 1(0.25) | 1(0.70) | 0(0.00) |
| C7, n (%) | 76(19.10) | 32(22.54) | 48(17.65) |
| C7.1 | 34(8.54) | 12(8.45) | 23(8.46) |
| C7.2 | 5(1.26) | 2(1.41) | 3(1.10) |
| C7.3 | 2(0.50) | 1(0.70) | 1(0.37) |
| C7.4 | 1(0.25) | 1(0.70) | 0(0.00) |
| C7.7 | 2(0.50) | 1(0.70) | 1(0.37) |
| C7.8 | 9(2.26) | 5(3.52) | 5(1.84) |
| C7.9 | 23(5.78) | 10(7.04) | 15(5.51) |
| C8, n (%) | 50(12.56) | 16(11.27) | 36(13.24) |
| C8.1 | 50(12.56) | 16(11.27) | 36(13.24) |
| C9, n (%) | 63(15.83) | 21(14.79) | 45(16.54) |
| C9.1 | 59(14.82) | 19(13.38) | 43(15.81) |
| C9.2 | 2(0.50) | 2(1.41) | 0(0.00) |
| C9.3 | 2(0.50) | 0(0.00) | 2(0.74) |
Clinical pharmacist interventions
Clinical pharmacist intervention is almost always provided at the drug level (98.99%), mainly including dose adjustment and usage method adjustment. At the same time, clinical pharmacist intervention at the prescription doctor level and patient level also exceeds 50%. The results showed that 90.70% of interventions were accepted, and 84.42% of interventions were fully implemented, while 37 interventions were rejected for various reasons, accounting for 9.30%. The intervention acceptance rate of AD patients (92.96%) is higher than that of PD patients (90.07%) (Table 3).
Table 3.
Planned interventions and intervention acceptance in DRP
| Index | Total Population (n = 398) |
Alzheimer’s disease (n = 142) |
Parkinson’s disease (n = 272) |
|---|---|---|---|
| I1, n (%) | 243(61.06) | 85(59.86) | 167(61.40) |
| I1.2 | 63(15.83) | 19(13.38) | 46(16.91) |
| I1.3 | 15(3.77) | 4(2.82) | 11(4.04) |
| I1.4 | 165(41.46) | 62(43.66) | 110(40.44) |
| I2, n (%) | 203(51.01) | 68(47.89) | 141(51.84) |
| I2.1 | 100(25.13) | 37(26.06) | 67(24.63) |
| I2.3 | 11(2.76) | 3(2.11) | 8(2.94) |
| I2.4 | 92(23.12) | 28(19.72) | 66(24.26) |
| I3, n (%) | 394(98.99) | 141(99.30) | 269(98.90) |
| I3.1 | 65(16.33) | 20(14.08) | 48(17.65) |
| I3.2 | 152(38.19) | 55(38.73) | 101(37.13) |
| I3.3 | 11(2.76) | 5(3.52) | 7(2.57) |
| I3.4 | 16(4.02) | 8(5.63) | 9(3.31) |
| I3.5 | 72(18.09) | 28(19.72) | 47(17.28) |
| I3.6 | 78(19.60) | 25(17.61) | 57(20.96) |
| A1, n (%) | 361(90.70) | 132(92.96) | 245(90.07) |
| A1.1 | 336(84.42) | 123(86.62) | 228(83.82) |
| A1.2 | 24(6.03) | 9(6.34) | 16(5.88) |
| A1.3 | 1(0.25) | 0(0.00) | 1(0.37) |
| A2, n (%) | 37(9.30) | 10(7.04) | 27(9.93) |
| A2.1 | 14(3.52) | 3(2.11) | 11(4.04) |
| A2.2 | 22(5.53) | 7(4.93) | 15(5.51) |
| A2.4 | 1(0.25) | 0(0.00) | 1(0.37) |
DRP outcomes of pharmacist interventions
After intervention by clinical pharmacists, more than 70% of DRP problems were completely solved, and the proportion of partially solved DRP problems in AD was higher than that in PD, while the proportion of unresolved DRP problems was lower than that in PD. Among the total population, 43 cases (10.80%) had DRP issues unresolved after pharmacist intervention, mainly due to a lack of collaboration between doctors (3.77%) and patients (3.02%) (Table 4).
Table 4.
The status of the DRP
| Index | Total Population (n = 398) |
Alzheimer’s disease (n = 142) |
Parkinson’s disease (n = 272) |
|---|---|---|---|
| O1, n (%) | 283(71.11) | 102(71.83) | 192(70.59) |
| O1.1 | 283(71.11) | 102(71.83) | 192(70.59) |
| O2, n (%) | 72(18.09) | 29(20.42) | 48(17.65) |
| O2.1 | 72(18.09) | 29(20.42) | 48(17.65) |
| O3, n (%) | 43(10.80) | 11(7.75) | 32(11.76) |
| O3.1 | 12(3.02) | 2(1.41) | 10(3.68) |
| O3.2 | 15(3.77) | 6(4.23) | 9(3.31) |
| O3.3 | 9(2.26) | 1(0.70) | 8(2.94) |
| O3.4 | 7(1.76) | 2(1.41) | 5(1.84) |
Factors that were associated with DRP
Among 254 patients with DRP, the results of full model logistic regression analysis showed that the factors associated with the presence of DRP included advanced age (OR = 1.055, 95% CI 1.001–1.113, P = 0.045), smoking (OR = 2.424, 95% CI 1.009–5.822, P = 0.048), Medication care (OR = 0.334, 95% CI 0.129–0.866, P = 0.024), follow-up period less than four weeks (OR = 4.297, 95% CI 1.072–17.225, P = 0.040), number of comorbidities (OR = 1.643, 95% CI 1.296–2.082, P < 0.001), dosage of medication (OR = 1.872, 95% CI 1.369–2.560, P < 0.001). Stepwise regression was used to identify related factors, and it was ultimately found that alcohol consumption, number of comorbidities, dosage of medication were associated with the occurrence of DRP (adjusted OR > 1, P < 0.05) (Table 5). The results of univariate logistic regression model were shown in supplemental Tables 3–5.
Table 5.
Analysis of the factors associated with DRP in the total population
| Index | No DRP (n = 93) |
DRP (n = 161) |
Full model logistic regression analysis | Logistic regression analysis with stepwise selection | ||
|---|---|---|---|---|---|---|
| OR(95% CI) | P | OR(95% CI) | P | |||
| Age | 68.66(9.94) | 72.68(8.73) | 1.055(1.001–1.113) | 0.045 | ||
| Smoking | ||||||
| No | 80(86.02) | 107(66.46) | Reference | |||
| Yes | 13(13.98) | 54(33.54) | 2.424(1.009–5.822) | 0.048 | ||
| Alcohol consumption | ||||||
| No | 73(78.49) | 87(54.04) | Reference | Reference | ||
| Yes | 20(21.51) | 74(45.96) | 1.956(0.865–4.424) | 0.107 | 2.925(1.496–5.720) | 0.002 |
| Medication care | ||||||
| No | 35(37.63) | 66(40.99) | Reference | |||
| Yes | 58(62.37) | 95(59.01) | 0.334(0.129–0.866) | 0.024 | ||
| Family history | ||||||
| No | 83(89.25) | 151(93.79) | Reference | |||
| Yes | 10(10.75) | 10(6.21) | 1.466(0.370–5.805) | 0.586 | ||
| History of brain surgery | ||||||
| No | 84(90.32) | 128(79.50) | Reference | |||
| Yes | 9(9.68) | 33(20.50) | 1.422(0.501–4.038) | 0.509 | ||
| Follow-up period < 4 weeks | ||||||
| No | 90(96.77) | 136(84.47) | Reference | Reference | ||
| Yes | 3(3.23) | 25(15.53) | 4.297(1.072–17.225) | 0.040 | 3.974(0.978–16.139) | 0.054 |
| Alzheimer’s or Parkinson’s disease | ||||||
| Alzheimer’s disease | 30(32.26) | 53(32.92) | Reference | |||
| Parkinson’s disease | 63(67.74) | 101(62.73) | 0.500(0.200-1.248) | 0.138 | ||
| Both | 0(0.00) | 7(4.35) | 255073.291(0.000-inf.) | 0.977 | ||
| Number of comorbidities | 2.26(1.57) | 3.58(1.60) | 1.643(1.296–2.082) | < 0.001 | 1.739(1.415–2.136) | < 0.001 |
| Dosage of medication | 3.32(1.36) | 4.06(0.82) | 1.872(1.369–2.560) | < 0.001 | 1.918(1.430–2.574) | < 0.001 |
| Cerebral atrophy | ||||||
| No | 63(67.74) | 79(49.07) | Reference | |||
| Yes | 30(32.26) | 82(50.93) | 1.894(0.694–5.175) | 0.213 | ||
| White matter lesions of the brain | ||||||
| No | 66(70.97) | 91(56.52) | Reference | |||
| Yes | 27(29.03) | 70(43.48) | 0.569(0.185–1.751) | 0.326 | ||
| Lacunar lesion | ||||||
| No | 68(73.12) | 101(62.73) | Reference | |||
| Yes | 25(26.88) | 60(37.27) | 0.977(0.381–2.510) | 0.962 | ||
| Ischemic lesion | ||||||
| No | 72(77.42) | 103(63.98) | Reference | |||
| Yes | 21(22.58) | 58(36.02) | 1.291(0.531–3.137) | 0.573 | ||
Full model logistic regression analysis: Given that only 1 female patient smoked and 5 reported alcohol consumption, all variables except for gender were included in the model
Logistic regression analysis with stepwise selection: Variables were screened with the criteria of entry α = 0.05 and stay α = 0.10
In the population of AD patients, stepwise regression was used to screen for related factors and a full model logistic regression analysis was established. The results showed that alcohol consumption, and patients with more comorbidities and medication were more likely to develop DRP (Table 6).
Table 6.
Analysis of the factors associated with DRP in the Alzheimer’s population
| Index | No DRP (n = 30) |
DRP (n = 60) |
Full model logistic regression analysis | Logistic regression analysis with stepwise selection | ||
|---|---|---|---|---|---|---|
| OR(95% CI) | P | OR(95% CI) | P | |||
| Age | 73.83(7.83) | 74.87(7.87) | 0.996(0.901–1.101) | 0.938 | ||
| Smoking | ||||||
| No | 28(93.33) | 41(68.33) | Reference | |||
| Yes | 2(6.67) | 19(31.67) | 4.898(0.675–35.520) | 0.116 | ||
| Alcohol consumption | ||||||
| No | 27(90.00) | 31(51.67) | Reference | Reference | ||
| Yes | 3(10.00) | 29(48.33) | 5.167(0.864–30.895) | 0.072 | 9.929(2.282–43.193) | 0.002 |
| Medication care | ||||||
| No | 0(0.00) | 0(0.00) | ||||
| Yes | 30(100.00) | 60(100.00) | - | - | ||
| Family history | ||||||
| No | 27(90.00) | 55(91.67) | Reference | |||
| Yes | 3(10.00) | 5(8.33) | 1.274(0.123–13.220) | 0.839 | ||
| History of brain surgery | ||||||
| No | 25(83.33) | 49(81.67) | Reference | |||
| Yes | 5(16.67) | 11(18.33) | 0.892(0.159–5.022) | 0.897 | ||
| Follow-up period < 4 weeks | ||||||
| No | 29(96.67) | 47(78.33) | Reference | |||
| Yes | 1(3.33) | 13(21.67) | 8.580(0.624-118.056) | 0.108 | ||
| Number of comorbidities | 2.10(1.45) | 3.18(1.50) | 1.719(1.061–2.783) | 0.028 | 1.677(1.120–2.510) | 0.012 |
| Dosage of medication | 3.33(1.15) | 4.05(0.81) | 2.541(1.291–5.001) | 0.007 | 2.384(1.319–4.309) | 0.004 |
| Cerebral atrophy | ||||||
| No | 13(43.33) | 22(36.67) | Reference | |||
| Yes | 17(56.67) | 38(63.33) | 0.505(0.069–3.729) | 0.503 | ||
| White matter lesions of the brain | ||||||
| No | 14(46.67) | 25(41.67) | Reference | |||
| Yes | 16(53.33) | 35(58.33) | 0.832(0.103–6.696) | 0.862 | ||
| Lacunar lesion | ||||||
| No | 16(53.33) | 28(46.67) | Reference | |||
| Yes | 14(46.67) | 32(53.33) | 1.749(0.355–8.609) | 0.492 | ||
| Ischemic lesion | ||||||
| No | 20(66.67) | 33(55.00) | Reference | |||
| Yes | 10(33.33) | 27(45.00) | 1.106(0.279–4.383) | 0.886 | ||
Full model logistic regression analysis: All variables were included in the model
Logistic regression analysis with stepwise selection: Variables were screened with the criteria of entry α = 0.05 and stay α = 0.10
In the population of Parkinson’s patients, a stepwise regression method was used to screen for relevant factors and a full model logistic regression analysis was established. The results showed that patients with advanced age, smoking, more comorbidities, and more medication had a higher probability of developing DRP, while those with medication care had a lower probability of developing DRP (adjusted OR = 0.214, 95% CI: 0.079–0.584. P = 0.003) (Table 7).
Table 7.
Analysis of the factors associated with DRP in the Parkinson ‘s population
| Index | No DRP (n = 30) |
DRP (n = 60) |
Full model logistic regression analysis | Logistic regression analysis with stepwise selection | ||
|---|---|---|---|---|---|---|
| OR(95% CI) | P | OR(95% CI) | P | |||
| Age | 73.83(7.83) | 74.87(7.87) | 1.107(1.035–1.184) | 0.003 | 1.096(1.040–1.156) | < 0.001 |
| Smoking | ||||||
| No | 28(93.33) | 41(68.33) | Reference | Reference | ||
| Yes | 2(6.67) | 19(31.67) | 2.245(0.771–6.538) | 0.138 | 2.918(1.156–7.364) | 0.023 |
| Alcohol consumption | ||||||
| No | 27(90.00) | 31(51.67) | Reference | |||
| Yes | 3(10.00) | 29(48.33) | 1.374(0.502–3.762) | 0.536 | ||
| Medication care | ||||||
| No | 0(0.00) | 0(0.00) | Reference | Reference | ||
| Yes | 30(100.00) | 60(100.00) | 0.195(0.066–0.575) | 0.003 | 0.214(0.079–0.584) | 0.003 |
| Family history | ||||||
| No | 27(90.00) | 55(91.67) | Reference | |||
| Yes | 3(10.00) | 5(8.33) | 1.974(0.305–12.769) | 0.475 | ||
| History of brain surgery | ||||||
| No | 25(83.33) | 49(81.67) | Reference | |||
| Yes | 5(16.67) | 11(18.33) | 2.402(0.526–10.972) | 0.258 | ||
| Follow-up period < 4 weeks | ||||||
| No | 29(96.67) | 47(78.33) | Reference | |||
| Yes | 1(3.33) | 13(21.67) | 3.313(0.473–23.184) | 0.228 | ||
| Number of comorbidities | 2.10(1.45) | 3.18(1.50) | 1.677(1.251–2.250) | < 0.001 | 1.789(1.360–2.355) | < 0.001 |
| Dosage of medication | 3.33(1.15) | 4.05(0.81) | 1.805(1.235–2.641) | 0.002 | 1.790(1.253–2.556) | 0.001 |
| Cerebral atrophy | ||||||
| No | 13(43.33) | 22(36.67) | Reference | |||
| Yes | 17(56.67) | 38(63.33) | 2.296(0.642–8.209) | 0.201 | ||
| White matter lesions of the brain | ||||||
| No | 14(46.67) | 25(41.67) | Reference | |||
| Yes | 16(53.33) | 35(58.33) | 0.554(0.130–2.354) | 0.423 | ||
| Lacunar lesion | ||||||
| No | 16(53.33) | 28(46.67) | Reference | |||
| Yes | 14(46.67) | 32(53.33) | 1.023(0.286–3.655) | 0.973 | ||
| Ischemic lesion | ||||||
| No | 20(66.67) | 33(55.00) | Reference | |||
| Yes | 10(33.33) | 27(45.00) | 1.222(0.345–4.332) | 0.756 | ||
Full model logistic regression analysis: All variables were included in the model
Logistic regression analysis with stepwise selection: Variables were screened with the criteria of entry α = 0.05 and stay α = 0.10
In addition, we also summarized the medication use of patients in the neurology outpatient department of the hospital (Fig. 2). Anti-Parkinson’s drugs, antianxiety or antidepressants, and sedatives and hypnotics are the three main drugs that cause total DRP in this study, accounting for more than half of the total DRP. The same applies to the medication use of PD patients. In contrast, AD is slightly different. The three main drug classes associated with DRP in AD are Anti-Parkinson’s drugs, neuro-stimulant agents, and sedatives and hypnotics.
Fig. 2.
Medication use among patients in the neurology outpatient department of the hospital
Discussion
Currently, few existing studies have specifically focused on the classification and evaluation of DRPs in the context of NDDs, a research gap addressed by the present work. By investigating factors associated with DRP occurrence in outpatients from a large Chinese hospital and performing statistical analyses, we aimed to compare the DRP landscape between PD and AD cohorts, thereby providing a foundation for more precise pharmaceutical care. Our findings revealed a DRP prevalence of 63.39%, with an average of 1.57 DRPs per patient. This prevalence and average number are comparable to those reported in other populations, such as an average of 1.31 DRPs per patient [27], suggesting that the challenges of polypharmacy and complex comorbidities inherent in NDDs align with broader DRP trends observed in similar patient groups. This investigation adhered rigorously to the PCNE classification framework, systematically documenting each DRP across its PCIAO dimensions. Statistical analysis was then performed at each level.
Turning to the demographic and clinical characteristics of our patient population, our study exhibited typical characteristics of NDD patients: a relatively advanced mean age, complex medication regimens, and multiple comorbidities. A notable distinction emerged regarding medication support: AD patients, potentially due to more pronounced cognitive impairment necessitating greater vigilance, received medication care from family/friends in 100% of cases. Conversely, PD patients, who often retain more functional independence, received medication care in a much smaller proportion of cases. Our findings suggest that reinforcing communication strategies with patient families and refining medication and care practices for PD patients hold significant potential for reducing the burden of DRPs in this population. This finding is also supported by numerous medical practices [34–37]. Furthermore, our results revealed that alcohol consumption was a significant factor for AD patients, whereas smoking was a significant factor for PD patients. Smoking can cause a rapid increase in nicotine concentration in the brain [38]. Scholars have shown that due to differences in affinity with the transport system, the transport of nicotine through the blood-brain barrier can affect the effectiveness of central nervous system drugs. For example, drugs used to treat AD and PD, such as memantine, donepezil, pramipexole, and amantadine, can significantly inhibit nicotine uptake by 77–85% [38]. Alcohol intake can stimulate the release of dopamine [39–41], which will affect the effectiveness of drugs targeting dopamine in NDDs. These literature findings facilitate our understanding that smoking and alcohol consumption are also important factors contributing to DRP. The co treatment of multiple diseases and multi drug therapy for the elderly is a global issue with potential medication risks and burden [42, 43]. In addition, the number of medications and comorbidities are risk factors for DRP, and are also related to the aforementioned reasons.
Furthermore, our analysis identified “treatment effectiveness” as the primary DRP issue and “drug selection” as the most common cause. This aligns with the complex nature of NDDs, where patient heterogeneity, progressive decline, and limited treatment options constrain optimal pharmacotherapy [44–46]. The symptomatic nature of current treatments for PD and AD, alongside the absence of disease-modifying therapies, inherently limits efficacy and contributes to DRPs [47, 48]. For PD and AD patients, the current treatment plans are mostly symptomatic, and there is currently no medication to improve the condition [49–51]. Meanwhile, the quantity of prescription drugs is also an important risk factor, and multi drug therapy is associated with a high risk of DRPs [52]. In addition, clinical pharmacists often provide interventions at the drug level, mainly adjusting dosage and usage methods. Although the intervention at the doctor and patient levels exceeded 50%, it still did not meet our expectations. Overall, the acceptance rate of intervention is relatively high, and the implementation of intervention is also good, but the lack of cooperation with doctors and patients is still the reason for the failure of intervention. Therefore, strengthening the training of clinical pharmacists and improving the trust of doctors and patients in clinical pharmacists is an urgent task to be completed.
Delving into the specifics of medication regimens, during the medication process of patients in this hospital, there are various types of drugs, and doctors will prescribe medication according to the individual situation of the patients. The main anti Parkinson’s drugs include Monoamine oxidase B inhibitor, dopamine receptor agonists, compound levodopa, Catechol-O-methyltransferase inhibitor, anticholinergic drug and amantadine etc. Notably, dose adjustment of anti-Parkinsonian drugs is particularly critical in the treatment of Parkinson’s disease. For instance, an insufficient dose of levodopa fails to effectively alleviate the core symptoms of Parkinson’s disease, while a high dose is prone to inducing motor complications—both of which are among the most common types of DRPs encountered in the clinical management of PD [53]. Anti-anxiety or antidepressant drugs mainly use selective serotonin reuptake inhibitors, serotonin noradrenaline reuptake inhibitors, norepinephrine dopamine reuptake inhibitor, etc. Delayed onset of efficacy of antidepressants (usually 2–4 weeks) may lead some patients to mistakenly believe that the drug is ineffective and stop taking it without authorization, which also constitutes a common DRP in clinical treatment [54]. Sedative and hypnotic drugs mainly use benzodiazepine drugs, non-benzodiazepine drugs, melatonin receptor agonist, etc. Long-term use or abrupt withdrawal of these drugs may lead to dependence, tolerance, and withdrawal reactions, which are among the most common DRPs associated with benzodiazepine use [55]. Common adverse reactions of these classes of drugs include dizziness, nausea, and xerostomia, etc. Some patients are unable to tolerate these adverse reactions, leading to poor medication compliance, which in turn contributes to the occurrence of DRPs. In addition, patients with PD and AD often require polypharmacy, resulting in a higher risk of drug-drug interactions. For example, the combination of monoamine oxidase B inhibitors and selective serotonin reuptake inhibitors carries the risk of serotonin syndrome, while the co-administration of benzodiazepines and antidepressants may enhance respiratory and cardiovascular depression [56–58]. All these factors are important contributors to the development of DRPs in clinical practice. A series of cases further demonstrate that in the multi disease co treatment and multi drug treatment of NDDs, the selection of drug types and doses is crucial, which is a risk factor for DRP.
Finally, regarding imaging findings, analysis showed that white matter lesions, lacunar lesions, and ischemic lesions did not significantly correlate with DRP occurrence. Despite this, brain lesions often complicate clinical decision-making and reduce patient compliance; notably, anticholinergic drugs can exacerbate cognitive impairment in susceptible individuals [59, 60]. Therefore, continued caution is warranted in patients with such brain pathologies.
Limitations
The limitations of the retrospective, single-center study design, as well as the specialized clinical setting, should be considered when interpreting our findings, particularly regarding internal and external validity and the generalizability of results. For internal validity, although uniform clinical protocols, standardized physician-pharmacist collaboration, and centralized electronic medical records at our tertiary teaching hospital reduced variability in data collection and exposure assessment, the retrospective data extraction may have introduced biases. Regarding external validity and generalizability, the study was conducted within a specialized physician-pharmacist joint clinic of a single tertiary teaching hospital in Shanghai, and the study population was limited to patients with NDDs attending this clinic—this integrated care model, which combines neurological expertise with pharmaceutical consultation, may not fully represent routine outpatient neurology practice. Our cohort mainly included patients with relatively complex conditions receiving specialized multidisciplinary care, differing from those in primary care or non-teaching hospitals in terms of demographics, disease severity, and medication management; additionally, the structured medication management approach and enhanced monitoring frequency in this specialized setting could potentially lead to different DRP detection rates compared to standard outpatient care. Regional differences in prescribing patterns further limit the generalizability of our findings. While this limitation should be acknowledged, the large patient volume at our tertiary hospital provides a robust foundation for our results. Future research is needed to address these limitations: multicenter, prospective studies recruiting patients from diverse healthcare settings (including routine clinical settings), with standardized DRP identification criteria and longitudinal follow-up, would help reduce selection and information biases, enhance internal and external validity, confirm our findings, and assess the impact of different care models on DRP occurrence in NDD patients.
In addition, although the comparative analysis indicated no significant selection bias, the exclusion of the 10 patients who declined to complete the DRP assessment remains a potential limitation of this study. Future studies with larger sample sizes should attempt to minimize such dropouts and specifically evaluate whether these demographic characteristics influence DRP occurrence to further validate the robustness of our conclusions.
Conclusion
This study focused on patients with Parkinson’s disease (PD) and Alzheimer’s disease (AD) at a large hospital in China. Through the scientific classification of drug-related problems (DRPs) based on the PCNE system, subsequent data analysis identified the demographic characteristics and influencing factors of DRPs in this cohort. The primary conclusion derived from our study data is that medication care reduces the risk of drug-related problems (DRPs) in the study sample. In contrast, the risk of DRPs is significantly elevated among patients receiving polypharmacy and those under the management of multiple comorbidities. For the PCNE classification, we strictly followed the “PCIAO” process to analyze the primary types and causes of DRPs in the study sample, as well as clinical pharmacists’ interventions. This holistic analytical approach not only offers a robust evidence base for the precision clinical management of PD and AD, but also delineates clear guidance for optimizing therapeutic regimens tailored to these two neurodegenerative diseases.
Supplementary Information
Acknowledgements
We would like to thank all patients who have participated in this study for agreeing to provide clinical information.
Abbreviations
- AD
Alzheimer's Disease
- APOE
Apolipoprotein E
- CNS
Central Nervous Syste\m
- DLB
Dementia with Lewy Bodies
- DRPs
Drug-Related Problems
- FTD
Frontotemporal Dementia
- NDDs
Neurodegenerative Diseases
- PCNE
The Pharmaceutical Care Network Europe
- PD
Parkinson's Disease
- PNS
Peripheral Nervous System
- PSP
Progressive Supranuclear Palsy
- RLS
Restless Legs Syndrome
Authors’ contributions
YT and GF designed the study.YT, RY, MZ was responsible for DRP identified and interpretation and drafting of the manuscript. CZ, MC and HP were involved in [data](javascript:;) [acquisition](javascript:;) . YQ and YG was responsible for the data curation and the revision of the manuscript. GF was responsible for funding acquisition and project administration.
Funding
This work is financially supported by grants from the Shanghai Hospital Development Center/ Technical standardization management and promotion project (SHDC22024202) and Shanghai Science and Technology Development Funds (22QA1411400).
Data availability
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
Declarations
Ethics approval and consent to participate
All intervention processes in this study were approved by Shanghai General Hospital Institutional Review Board (yuanlunkuai[2023]240). The patient’s participation is completely voluntary. Each participant or their legal guardian is required to sign a written informed consent form prior to data collection and pharmacist intervention. All methods are carried out in accordance with relevant guidelines and regulations. The right of participants not to participate is respected. Strictly protect the confidentiality and data information of patients.
Consent of publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Yuanjun Tang, Runjuan Yang and Meina Zhang contributed equally to this work and shared first authorship.
Contributor Information
Yaxing Gui, Email: YaxingGui@shsmu.edu.cn.
Guorong Fan, Email: fanguorong@sjtu.edu.cn.
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Data Availability Statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.