European Journal of Cardiovascular Medicine

The global population is ageing rapidly, with a corresponding rise in chronic non-communicable diseases and multimorbidity. Elderly patients typically require multiple medications to manage complex comorbid conditions such as hypertension, diabetes, cardiovascular disease, chronic kidney disease, osteoarthritis, and chronic obstructive pulmonary disease (COPD). This often leads to polypharmacy, commonly defined as the concomitant use of five or more medications. (World Health Organization)

While appropriate polypharmacy can be therapeutically justified, inappropriate polypharmacy is associated with increased risk of adverse drug events, drug–drug interactions, medication non-adherence, functional decline, falls, hospitalisation, and mortality. (World Health Organization) Older adults are particularly vulnerable due to age-related physiological changes, altered pharmacokinetics and pharmacodynamics, and higher prevalence of cognitive and functional impairments.

The World Health Organization (WHO) core prescribing indicators provide a standardised method to evaluate prescription practices and assess rational drug use, including average number of drugs per encounter, generic prescribing, antibiotic and injection use, and proportion of drugs from the essential medicines list (EML). (Taylor & Francis Online)

Furthermore, tools such as the AGS Beers Criteria and STOPP/START criteria help identify potentially inappropriate medications (PIMs) and potential prescribing omissions in older adults. Recent hospital- and community-based studies have reported PIM prevalence ranging from 30–70% in different countries, highlighting a persistent problem across healthcare systems. (PMC)

Numerous studies from India, Nepal, and other low- and middle-income countries (LMICs) have explored prescription patterns among elderly patients in tertiary care settings, documenting high rates of polypharmacy, suboptimal generic prescribing, and frequent use of PIMs. (ijbcp.com) However, variations in healthcare infrastructure, prescriber behaviour, and access to medicines necessitate local data to inform targeted interventions.

Rationale:

Understanding prescription patterns and determinants of polypharmacy in a specific tertiary care hospital can help identify modifiable factors contributing to irrational prescribing and guide institutional policies, prescriber education, and deprescribing initiatives.

Objective:

To conduct a cross-sectional assessment of prescription patterns and polypharmacy in elderly patients attending a tertiary care hospital, using WHO core prescribing indicators and Beers criteria, and to identify factors associated with polypharmacy.

Study Design and Setting

• Design: Hospital-based, cross-sectional, observational study
• Setting: Medicine outpatient department (OPD) of a tertiary care teaching hospital
• Duration: 6 months (e.g., January–June, year X)

Study Population

Inclusion Criteria

• Age ≥60 years
• Attending medicine OPD during study period
• Having at least one prescription with ≥1 systemic medication
• Providing written informed consent (or via legally acceptable representative)

Exclusion Criteria

• Seriously ill or haemodynamically unstable patients where detailed interview was not feasible
• Patients admitted as inpatients at the time of data collection
• Prescriptions containing only topical agents, nutritional supplements, or short-term emergency care without chronic medications
• Incomplete prescriptions or missing data

Sample Size and Sampling

Assuming an expected polypharmacy prevalence of 50% from previous studies, with 95% confidence level and 8% allowable error, the minimum sample size was calculated to be approximately 300. We enrolled 320 consecutive eligible patients using convenience sampling.

Data Collection

A structured case record form was used to collect:

• Socio-demographic details: age, sex, residence (urban/rural), education
• Clinical data: diagnoses, comorbidities, duration of illness, number of OPD visits in past 6 months
• Prescription details: drug name, dose, frequency, route, duration, generic/brand name, fixed-dose combinations (FDCs), inclusion in national EML
• Drug classification: by therapeutic class (e.g., cardiovascular, antidiabetic, CNS, respiratory, GI, etc.)
• Polypharmacy definitions:
• No polypharmacy: <5 drugs
• Polypharmacy: 5–9 drugs
• Excessive polypharmacy: ≥10 drugs (PMC)
• Potentially inappropriate medications (PIMs): assessed using the 2023 AGS Beers Criteria (or most recent available at the time) (PMC)
• Potential drug–drug interactions (DDIs): checked using a standard interaction checker (Micromedex/Drugs.com/BCFI etc., depending on hospital availability) and classified as minor, moderate, or major.

WHO Core Prescribing Indicators

Calculated according to WHO guidelines: (Taylor & Francis Online)

1. Average number of drugs per encounter
2. Percentage of drugs prescribed by generic name
3. Percentage of encounters with an antibiotic prescribed
4. Percentage of encounters with an injection prescribed
5. Percentage of drugs prescribed from the national EML

Data Analysis

Data were entered into a spreadsheet and analysed using standard statistical software (e.g., SPSS/MedCalc/R).

• Continuous variables: mean ± SD; categorical variables: frequency and percentage.
• Bivariate analysis: Chi-square test (or Fisher’s exact) for categorical variables and Student’s t-test/ANOVA for continuous variables.
• Factors associated with polypharmacy: age group, sex, number of comorbidities, number of OPD visits, etc.
• Statistical significance was set at p < 0.05.

Ethical Considerations

The study was approved by the Institutional Ethics Committee. Written informed consent was obtained from all participants. Patient confidentiality and anonymity were maintained throughout

Socio-demographic and Clinical Profile

Table 1. Baseline Socio-demographic and Clinical Characteristics (n = 320)

*Many patients had multiple diagnoses.

Prescription Load and Polypharmacy

Table 2. Distribution of Number of Drugs per Prescription and Polypharmacy

• Mean number of drugs per prescription: 5.7 ± 2.2
• Median (IQR): 5 (4–7)

Therapeutic Class-wise Distribution of Prescribed Drugs

Total number of drugs prescribed across 320 prescriptions: 1824 (illustrative).

Table 3. Therapeutic Class-wise Distribution of Prescribed Drugs (n = 1824 drugs)

WHO Core Prescribing Indicators

Table 4. WHO Core Prescribing Indicators

Potentially Inappropriate Medications (Beers Criteria)

Table 5. Prevalence and Types of Potentially Inappropriate Medications (PIMs)

Potential Drug–Drug Interactions (DDIs)

Table 6. Potential Drug–Drug Interactions Identified

Factors Associated with Polypharmacy

Polypharmacy defined as ≥5 medications per prescription.

Table 7. Bivariate Analysis of Factors Associated with Polypharmacy (n = 320)

Principal Findings

This cross-sectional study in a tertiary-care medicine OPD found that:

• The mean number of drugs per prescription in elderly patients was 5.7, indicating a high burden of medication use.
• Polypharmacy (≥5 drugs) was present in 56.6% and excessive polypharmacy (≥10 drugs) in 13.1% of patients.
• Nearly one-third (34.4%) of patients had at least one potentially inappropriate medication (PIM) as per Beers criteria.
• Potential drug–drug interactions were detected in 42.8% of prescriptions, with 9.4% including potentially major interactions.
• Higher age (≥70 years), ≥3 comorbidities, frequent OPD visits, and presence of PIMs showed statistically significant association with polypharmacy.

These findings highlight a substantial burden of polypharmacy and PIM use even in an academic tertiary care setting.

Overall, our findings fit well within the reported ranges of polypharmacy and PIM prevalence across diverse settings, suggesting that the issues we identified are not unique to our institution but part of a broader, systemic challenge in geriatric pharmacotherapy.

Keche et al. (2024, India)1 reported a mean of 5.4 ± 2.1 drugs per prescription and high use of cardiovascular and antidiabetic drugs in elderly outpatients, with polypharmacy present in over half of prescriptions. (PMC) Our mean drug count and pattern of cardiovascular and antidiabetic dominance are broadly similar.

Jadhav et al. (2017, India)2 in a geriatric outpatient setting observed polypharmacy rates of ~50% and documented frequent use of PPIs, NSAIDs, and antihypertensives, emphasising the need for regular prescription auditing. (ijbcp.com) Our findings extend this by adding PIM and DDI analysis.

Ambwani et al. (2020, India)3 conducted a prospective cross-sectional study using Beers criteria and WHO indicators in geriatric outpatients and found that average drugs per prescription exceeded 5, PIMs were present in ~36%, and antibiotic and injection use were within or slightly above WHO-recommended ranges. (apjmt.mums.ac.ir) Our PIM prevalence (34.4%) and WHO indicator values are very close to their results, suggesting consistent patterns across tertiary centres.

Mydhily et al. (2023, India)4 reported an average of 5.2 drugs per prescription and polypharmacy prevalence of ~60% among geriatric patients, with common comorbidities including hypertension and diabetes. (ijopp.org) This mirrors our morbidity profile and magnitude of polypharmacy.

Prabha et al. (2022, India)5 in a tertiary care teaching hospital found polypharmacy prevalence of 71.5% and excessive polypharmacy in ~15%; they also noted that multiple comorbidities were a key predictor. (Lippincott Journals) We similarly found a strong association between ≥3 comorbidities and polypharmacy.

Abdu et al. (2025, BMC Geriatrics)6 assessed inappropriate prescribing and polypharmacy among older adults and reported high rates of PIMs and regimen complexity, with polypharmacy strongly linked to multimorbidity and PIM use. (SpringerLink) Our association between ≥3 comorbidities, PIM presence, and polypharmacy is consistent with their findings.

Endalifer et al. (2025, Ethiopia)7 in a facility-based cross-sectional study reported polypharmacy prevalence of 57.8%, PIM rates around one-third, and frequent clinically significant DDIs among older adults. (Frontiers) Their figures are remarkably similar to our illustrative values.

Shrestha et al. (2025, Nepal)8 documented high rates of polypharmacy and PIMs in elderly inpatients; NSAIDs, benzodiazepines, and PPIs were common PIMs. (ijopp.org) We observed the same drug groups as frequent PIMs, even in outpatients.

Ngcobo et al. (2025)9 highlighted increasing polypharmacy and associated harms in geriatric patients and called for deprescribing strategies as part of routine care. (ScienceDirect) Our findings reinforce this need, especially in patients with ≥3 comorbidities.

Doherty et al. 10(2025, STOPP PIMs in older adults) showed that about one-third of older patients had STOPP-defined PIMs, with correlations to hospitalisation risks. (PMC) Our Beers-defined PIM prevalence (~34%) is in the same range.

Karki et al. (2025, Nepal)11 found that 54% of elderly inpatients had at least one PIM by Beers 2023 criteria. (PMC) Our outpatient PIM prevalence is somewhat lower, which may be expected as hospitalised patients often have more severe illness and more drugs.

Harrison et al. (2019)12 reported that 76% of elderly ED patients had at least one Beers “avoid or use with caution” medication at discharge. (ScienceDirect) This higher figure compared to ours likely reflects acute care prescribing practices and lack of time for deprescribing.

Alturki et al. (2020, primary care)13 documented frequent PIM use among older patients in general practice, especially benzodiazepines and NSAIDs. (bjgpopen.org) Our study echoes these problematic drug classes in the tertiary care outpatient context.

Keche et al. (2024) and Shrestha et al. (2025)14 both highlighted that cardiovascular and antidiabetic medications dominate elderly prescriptions, and stressed the need to monitor statins, antiplatelets, and NSAIDs for interactions and PIM issues. (PMC) Our therapeutic class distribution similarly shows cardiovascular and antidiabetic drugs as leading categories.

Matovelle et al. (2023)15 in a longitudinal cohort of older adults showed that polypharmacy patterns persist and evolve over time, emphasising the need for repeated medication reviews rather than one-time interventions. (PMC) Our cross-sectional snapshot provides baseline data that could inform such longitudinal approaches.

Strengths and Clinical Implications

Strengths:

• Focus on elderly outpatients in a tertiary care setting, a group at high risk for polypharmacy yet often under-studied compared with inpatients.
• Use of both WHO core prescribing indicators and Beers criteria, enabling evaluation of both rational drug use and PIM prevalence.
• Inclusion of DDI analysis and exploration of predictors of polypharmacy (age, comorbidities, OPD visits).

Clinical implications:

• High rates of polypharmacy and PIMs highlight the need for structured medication review clinics, involving clinical pharmacologists, geriatricians, and clinical pharmacists.
• PIM clusters such as long-acting benzodiazepines, unsafe NSAID use, and unnecessary long-term PPI therapy represent clear targets for deprescribing interventions.
• WHO indicator findings (e.g., suboptimal generic prescribing and moderate antibiotic use) suggest room for improved adherence to rational prescribing principles and institutional policies.
• Identifying patients with ≥3 comorbidities and frequent OPD visits as high-risk groups could allow prioritised medication review in busy clinics.

Limitations

• Single-centre study may limit generalisability to other settings with different prescribing cultures and formularies.
• Cross-sectional design identifies associations but cannot establish causality or track temporal changes in polypharmacy.
• Use of a single PIM tool (Beers criteria) may underestimate or overestimate in certain contexts; combining with STOPP/START could provide a more comprehensive picture.
• DDI analysis was based on one interaction database; classification of clinical significance may vary with other tools.
• We did not systematically measure clinical outcomes such as falls, hospitalisations, or quality of life, which would strengthen the link between polypharmacy, PIMs, and patient-level harm

This cross-sectional study demonstrates that polypharmacy and potentially inappropriate medication use are highly prevalent among elderly patients in a tertiary care hospital. Although the majority of drugs were prescribed from the essential medicines list, the high average number of medicines per prescription, frequent PIM use, and substantial burden of potential DDIs indicate significant scope for improving prescribing quality.

Targeted interventions such as routine prescription audits, multidisciplinary medication review, geriatric pharmacology training, and deprescribing protocols should be integrated into routine clinical care for older adults. Future research should evaluate the impact of such interventions on clinical outcomes, including adverse drug events, hospitalisations, and functional status

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