Polytherapy and Potential Drug–Drug Interactions in Geriatric Patients of a Tertiary-care Hospital

Abstract

Introduction:

Polytherapy, the concomitant use of five or more drugs, is a prevalent public health problem, especially in the elderly. The elderly are susceptible to multiple chronic illnesses, which makes the use of polytherapy inevitable; however, it increases the risk of potential drug–drug interactions (PDDIs). Moreover, the use of multiple medications not only increases the risk of PDDIs but also the use of potentially inappropriate medication (PIM), medication non-adherence leading to skipped doses/overdoses. The aim of this study was to investigate the polytherapy prevalence and its implications, such as PDDIs, prevalence of PIM using the Beers Criteria-2023, and analyse the fixed-dose combinations (FDC) prescribed to the elderly.

Methods:

A retrospective cross-sectional study was conducted on 100 patients visiting the geriatric OPD in a tertiary-care hospital in Gujarat, India. Prescription data were analysed for PDDIs using Medscape.com. PIM analysis was done using the AGS-Beers Criteria-2023. Lastly, all FDCs prescribed to patients were checked for their inclusion in the WHO Essential Medicines List-2025/National List of Essential Medicines-2022.

Results:

It was found that 80% (95% CI: 72%–88%) of the patients were on polytherapy. Drugs affecting the cardiovascular system were the most prescribed. A total of 537 PDDIs were flagged, with 99.07% of those flagged in patients with polytherapy. It was also found that 78.38% (95% CI: 69.0%–87.8%) of the patients aged 65+ were on at least one PIM, with the most common PIM being proton-pump inhibitors and benzodiazepines/z-drugs. Additionally, only 23.29% of the different FDCs prescribed to patients were included in either the WHO EML-2025 or NLEM-2022.

Conclusion:

This research underscores high polytherapy prevalence, calling for vigilant policies on rational prescribing, practice of evidence-based medicine, medication reviews, and strict policies on FDC formulation.

Keywords

Polytherapy polypharmacy drug–drug interactions (DDI)potential drug–drug interactions (PDDI)potentially inappropriate medication (PIM)fixed-dose combinations (FDC)pharmacovigilance Beers Criteria

Introduction

Polytherapy, defined as the regular use of five or more medications simultaneously, is widespread in the elderly.^[1] This can be attributed to the elderly often suffering from multiple chronic conditions, requiring complex pharmacotherapy.^[2] Consequently, increasing the risk of drug–drug interactions (DDIs), where one drug modifies the effects/safety of another, which adversely affects clinical outcomes.

In India, excessive and irrational prescribing becomes a major economic burden on families, as illustrated by the estimate that 50% of the family spending on healthcare in Indian families is on unnecessary medications/investigations.^[3]

Despite polytherapy being a widespread practice among the geriatric population, its implications, such as potential DDIs and use of potentially inappropriate medications (PIM), are still under research, especially in the Indian scenario.

According to the UNFPA, while India has the highest number of young people, ageing is rapidly progressing. Currently, over 153 million people are aged 60 and above in our country.^[4]

The elderly, apart from being a vulnerable population, are also the highest consumers of drugs.^[5] Due to age-related physiological changes, they have multiple comorbidities and are more prone to hospitalisations and are often on multiple drugs simultaneously. However, they also experience altered drug pharmacokinetics.

Medical institutes and professionals use the American Geriatrics Society Beers Criteria globally to assess PIM use in the elderly. Such medicines may harm the patient more than benefit them. Hence, it is important to avoid the use of PIM, especially in this vulnerable population. However, due to a lack of prescription audits and medication reviews, PIM use in the elderly is still prevalent, especially in the Indian scenario.

With the use of multiple medications, not only does the risk of ADRs and DDIs increase, but also there is an increased medication non-adherence leading to skipped doses/overdoses, increased risk of falls/fractures and cognitive impairment. It may also lead to decreased quality of life, increased or prolonged hospitalisations, nutritional deficiencies and become a financial burden for the patient.

The need for pharmacovigilant and rational prescribing practice in the elderly is imperative, calling for meticulous research in this field.

Objectives

Primary Objectives

To determine the proportion of polytherapy among geriatric patients in a tertiary-care hospital in Ahmedabad, Gujarat.

To identify and classify potential drug–drug interactions (PDDIs) based on severity (minor, moderate, and severe).

To identify PIM prescribed among geriatric patients, based on the Beers Criteria (2023).

To analyse drugs prescribed as fixed-dose combinations (FDC) and evaluate their status in the WHO Essential Medicine List and National List of Essential Medicines.

Secondary Objectives

To compare the proportion of PDDIs in patients receiving more than five medicines to those on fewer medications (patients with polytherapy vs. those without polytherapy).

To identify the most prescribed drug combinations contributing to PDDIs in geriatric patients.

To evaluate the correlation of demographic factors (age, gender) and comorbid conditions (e.g., diabetes, hypertension, chronic kidney disease) on the prevalence of polytherapy and PDDIs.

Methodology

Study Design

This study employed a retrospective comparative cross-sectional design by examining patient prescriptions and records from the geriatric outpatient department of a tertiary-care hospital in Western India, after taking permission from the Department of Medicine. Data from patients with polytherapy and those without polytherapy were compared simultaneously.

Study Population

Patient records of both male and female patients above the age of 60 years, who visited the geriatric OPD, were included in the study. Only patients with detailed and complete medical records, including prescription history and comorbid conditions, were included.

Study Variables

The independent variables were the presence of polytherapy in patients, prescribed drug combinations, patient demographics (age, gender), and comorbid conditions.

The dependent variables were PDDIs, their severity, and PIM.

Sampling Method and Procedure

Convenience sampling was used to select patients due to the retrospective nature of the study, feasibility and availability of complete medical records and resources. A similar study conducted by Vittalrao et al. (2023) in Southern India calculated a sample size of 96 patients for their study.^[6] As this is an initial exploratory study on awareness regarding geriatric prescription practices that focuses on detailed identification of potential DDIs and their classification, along with PIM use using the latest Beers Criteria and trends in FDCs prescribed, in West India, a sample size of 100 patients was considered sufficient to assess prescribing trends, PIM use and FDC trends. Patient confidentiality was maintained using codes and ID numbers.

Data Collection and Analysis

Ethics approval was obtained from the Institutional Review Board (IRB) before commencing with the study. Data from patients who visited the geriatric OPD from November 2024 to February 2025 were collected with prior permission from the Department of Medicine.

Prescriptions were collected, and then an Excel spreadsheet was compiled with prescription data: patients’ age, gender, date of OPD visit, presenting complaints/diagnoses, comorbid conditions, drugs prescribed, FDCs and their types. Prescriptions were analysed from a single visit per patient. Comorbidities in patients were confirmed using the medical records of the patients from the hospital.

Next, Medscape software was used for analysing the prescriptions and identifying any PDDIs. All the PDDIs identified were compiled in a separate spreadsheet, followed by classification into pharmacokinetic or pharmacodynamic interaction. Simultaneously, Excel formulas were used to count the frequency of every identified PDDI. For drugs not approved by the US-FDA (e.g., Domperidone), software such as Drugbank.com was used to identify PDDIs. Medscape and DrugBank.com were used for the assessment of the severity of the PDDIs identified. The preferred software for assessment of PDDIs was Medscape; Drugbank.com was only used when a patient was on a drug not approved by the US-FDA, as Medscape does not provide details about such drugs. In case any discrepancy arose in the severity classification of PDDIs between Medscape and DrugBank.com, the classification given by the software that has wider clinical adoption, Medscape, was used.

The American Geriatrics Society 2023 updated Beers Criteria was used to flag all the PIM prescribed to patients aged 65+. Frequency analysis of each PIM flagged, mean number of PIM per patient (65 years+) and the overall prevalence of PIM use were calculated.^[7]

Further, the type of FDC most prescribed was analysed, and an evaluation of the status of each FDC was done using the WHO model list of essential medicines (24th list released in 2025) and the National List of Essential Medicines (NLEM-2022).^[8,9]

Statistical Analysis

Basic data analysis was conducted on Microsoft Excel. Correlations between variables were calculated using Python libraries integrated within Microsoft Excel. Normality testing of all continuous variables was done using the Shapiro-Wilk test. Age was the only continuous variable in our study; hence was subjected to normality tests, which revealed a non-normal distribution. Other numeric variables were discrete counts, hence not subjected to normality tests. Accordingly, non-parametric tests such as Spearman’s correlation test, chi-square test, and Mann-Whitney U-test were used where appropriate.

Continuous variables are presented as mean ± SD with 95% confidence intervals. Similarly, proportions are reported with 95% confidence intervals.

The primary objective of this retrospective cross-sectional study was descriptive and comparative analysis of prescribing patterns in geriatric healthcare, determining the prevalence of polytherapy, identifying and classifying PDDIs, identifying PIM use, and trends of FDCs prescribed, exploring associations between polytherapy, PDDIs, PIMs and demographic factors. This study was not designed to determine independent predictors of PDDIs or PIM use; multivariate analysis was not conducted. Additionally, our statistical approach is consistent with published literature for a similar study design.

Results

Patient Demographics

This study included 100 patients, evenly split by gender, all aged over 60 years. The majority were aged 65-69 years (n = 37), followed by 60-64 (n = 26). The mean age of the patient was 68.35 years (SD = 6.16), and the median age was 67 years [Table 1].

Table 1.

Age distribution of the patients, average number of drugs prescribed and average number of PDDIs* in every age group

Age	Frequency N = 100		Average Number of Drugs Prescribed	Average Number of PDDIs*
Age	Female	Male	Average Number of Drugs Prescribed	Average Number of PDDIs*
60–64	16	10	8.92	5.81
65–69	22	15	8.46	3.83
70–74	4	15	9.10	6.58
75–79	4	9	9.85	5
80–84	3	1	15	11.75
85–89	1	0	13	7

Note: *PDDIs, potential drug–drug interactions.

Age was the only continuous variable in our study, which, on subjection to normality tests (Shapiro-Wilk test), did not approximate a normal distribution (P < .05).

Common presenting complaints/diagnoses were musculoskeletal pain (knee/back/cervical; n = 25), constipation (n = 17), anxiety/depression (n = 14), insomnia (n = 13), and diabetes mellitus (n = 11).

Thirty percent of the male patients (n = 15) were newly diagnosed with prostatomegaly and/or benign prostatic hyperplasia.

The average number of diagnoses per patient was 2.44, slightly higher in females (2.64) than in males (2.24).

Among comorbidities, 63% of the patients had hypertension, 35% had Type 2 diabetes mellitus. Other common comorbidities were ischaemic heart disease (IHD) (n = 12), dyslipidaemia (n = 9), hypothyroidism (n = 9), and cerebrovascular accident (stroke) (n = 6).

Prevalence of Polytherapy

A total of 919 drugs were prescribed to the 100 patients. Polytherapy was prevalent in 80% of the patients (95% CI: 72%–88%). In males, the prevalence (84%) was higher than in females (76%). The average number of drugs prescribed per patient was: 9.19 (SD = 5.19) (male patients: 9.50, female patients: 8.88, 95% CI: 8.17%–10.21%). A chi-square test of independence (χ² = 0.56, P = .453) showed no significant association between gender and polytherapy (range of number of drugs prescribed: 1-26).

Table 1 shows the average number of drugs prescribed in each age group. The association between age and number of drugs prescribed had a weak correlation and was not statistically significant (Spearman’s rank correlation coefficient ρ = 0.14, P = .176).

Distribution of polytherapy was as follows: 40% of the patients were on 5-9 drugs, 22% were on 10-14 drugs; 14% were on 15-19 drugs, and 4% were on 20+ drugs.

Prescribing Patterns

Drugs affecting the cardiovascular system were most prescribed (26%, n = 239), followed by multivitamins and supplements (24.26%, n = 223) and drugs affecting the gastrointestinal system (13.82%, n = 127) [Table 2].

Table 2.

Frequency of various drug classes prescribed

Drug Class		Frequency
Drugs Affecting the Cardiovascular System Statins Angiotensin-receptor blockers Calcium channel blockers Beta-blockers Loop diuretics Thiazide & thiazide-like diuretics Aldosterone antagonists Nitrates Antiplatelet drugs Others	74 42 38 35 9 13 6 5 7 10	239
Multivitamins & Supplements Pyridoxine Folic acid Cyanocobalamin Cholecalciferol Calcium Others	32 36 48 35 30 42	223
Drugs Affecting Gastrointestinal System Proton-pump inhibitors Prokinetic agents Laxatives Others	40 25 56 6	127
Drugs Affecting Endocrine System Metformin SGLT-2 Inhibitors* Sulfonylureas DPP-4 Inhibitors* Levothyroxine Others	36 15 17 17 12 18	115
Non-steroidal Anti-inflammatory Drugs Aspirin Paracetamol Aceclofenac Diclofenac Others	39 16 9 7 7	78
Drugs Affecting Central Nervous System Benzodiazepines Non-benzodiazepines (Z-drugs) GABA* analogues SSRIs & SNRIs* Skeletal muscle relaxants Others	12 9 10 14 5 10	60
Drugs Affecting Autonomic Nervous System α1-blockers Anticholinergics Others	18 4 7	29
Autocoids Antihistaminic Antigout agent	14 5	19
Miscellaneous		29 grand total = 919

Note: *DPP-4, dipeptidyl peptidase-4; GABA, gamma-aminobutyric acid; SGLT, sodium-glucose co-transporter; SNRI, serotonin norepinephrine reuptake inhibitors; SSRI, selective serotonin reuptake inhibitors.

Statins (n = 74), Vitamin B12 (n = 45), angiotensin-receptor blockers (n = 42), Aspirin (n = 39), calcium channel blockers (n = 38), and metformin (n = 36) were among the most prescribed drugs.

Potential Drug–Drug Interactions

A total of 537 PDDIs were identified. 99.07% (532) (95% CI: 98.26%–99.88%) of those were among patients on polytherapy. The P value for the association between the total number of drugs prescribed and the number of PDDIs was highly significant (P < .001). The Spearman’s rank correlation coefficient was 0.79, suggestive of a strong positive relationship. Clinically, this suggests that as the number of drugs prescribed increases, the risk of DDIs rises substantially.

A Mann-Whitney U-test for the association between the presence of polytherapy (yes) and PDDI yielded a statistically significant value of 1497.5 (P < .001).

A statistically significant moderate association was found between the number of comorbidities in a patient and the number of PDDIs (Spearman correlation coefficient: ρ = 0.45, P < .005). On average, there were 5.37 PDDIs (SD = 7.0) (95% CI: 4.00%–6.74%) per patient. The average is higher in male patients (5.86) than in female patients (4.88). Nonetheless, this difference was not statistically significant (Mann-Whitney U-test, P = .06).

The drugs that contributed the most to the number of PDDIs identified were Aspirin (26.44%, n = 142), calcium supplements (16.39%, n = 88), Telmisartan (13.41%, n = 72), Metoprolol (11.17%, n = 60), Metformin (9.87%, n = 53) and Torsemide (8.19%, n = 44). Together, these drugs contributed to 85.16% of the total identified PDDIs [Table 3].

Table 3.

Drugs most involved in PDDIs and most frequently identified potential drug–drug interactions

Drugs Most Commonly Involved in PDDIs*
Sr. No.	Drug Name	Frequency
1.	Aspirin	142
2.	Calcium supplements	88
3.	Telmisartan	72
4.	Metoprolol	60
5.	Metformin	53
6.	Torsemide	44
7.	Aceclofenac	30
8.	Glimepiride	27
9.	Chlorthalidone	26
10.	Spironolactone	26
Most Frequent PDDIs*
	Drug Pair	Frequency	Type of Interaction
1.	Cholecalciferol + calcium carbonate	26	Pharmacokinetic
2.	Telmisartan + Atorvastatin	18	Pharmacokinetic
3.	Aspirin + telmisartan	17	Pharmacodynamic
4.	Aspirin + metoprolol	15	Pharmacodynamic
5.	Metformin + folic acid	14	Pharmacokinetic
6.	Aspirin + folic acid	13	Pharmacokinetic
7.	Aspirin + calcium carbonate	12	Pharmacokinetic
8.	Aspirin + glimepiride	11	Pharmacokinetic
9.	Metoprolol + telmisartan	11	Pharmacodynamic
10.	Calcium carbonate + rosuvastatin	10	Pharmacokinetic

Note: *PDDIs, potential drug–drug interactions.

Table 3 shows the most common drug interactions flagged in the sample. In terms of severity of PDDIs, 76.72% were moderate, 21.42% minor, and 1.86% severe. 90.91% of the severe PDDIs identified were pharmacodynamic interactions [Figure 1]. Table 4 shows the various interactions flagged as severe by the drug-interaction checker software (Medscape).

Figure 1.

Pie chart showing classification of severity of potential drug–drug interactions (PDDIs) identified (total number of potential drug–drug interactions ‘N’ = 537)

Table 4.

Potential drug–drug interactions classified as severe interactions by Medscape

Severe Potential Drug–Drug Interactions	Type of Interaction	Mechanism
Disodium hydrogen citrate + levofloxacin	Pharmacokinetic	Disodium hydrogen citrate decreases levels of levofloxacin by inhibition GI* absorption
Ciprofloxacin + domperidone	Pharmacodynamic	The risk or severity of QTc prolongation can increase
Aspirin + ramipril	Pharmacodynamic	May result in a significant decrease in renal function. NSAIDs* may diminish the antihypertensive effect of ACE* inhibitors due to reduced production of vasodilating prostaglandins
Aspirin + lisinopril	Pharmacodynamic	May result in a significant decrease in renal function. NSAIDs* may diminish the antihypertensive effect of ACE* inhibitors due to reduced production of vasodilating prostaglandins
Lisinopril + pregabalin	Pharmacodynamic	Additive risk of angioedema of the face, mouth, and neck. Angioedema may lead to respiratory compromise
Glyceryl trinitrate + sildenafil	Pharmacodynamic	Additive vasodilation, potentially fatal hypotension
Sildenafil + prazosin	Pharmacodynamic	Sildenafil increases the effects of Prazosin by pharmacodynamic synergism, which can lead to fatal hypotension
Amitriptyline + duloxetine	Pharmacodynamic	Both drugs increase serotonin levels
Diclofenac + ramipril	Pharmacodynamic	May result in a significant decrease in renal function. NSAIDs* may diminish the antihypertensive effect of ACE* inhibitors due to reduced production of vasodilating prostaglandins

Note: *ACE, angiotensin converting enzyme; GI, gastrointestinal; NSAIDs, non-steroidal anti-inflammatory drugs.

Pharmacodynamic interactions accounted for 54.93% (295) (95% CI: 50.73%–59.14%) of all PDDIs [Figure 2].

Figure 2.

Donut pie chart showing the type of potential drug–drug interactions identified (total number of potential drug–drug interactions, ‘N’ = 537)

PIM (Beers Criteria-2023)

This study included 74 patients aged 65 and above, and for these patients, PIM analysis was done based on the AGS Beer’s Criteria (2023) for PIM [Table 2].^[7] Out of these, 78.38% (95% CI: 69.0%–87.8%) of the patients (n = 58) were prescribed one or more PIM. The average number of PIM per patient was 1.02 (SD = 1.17). It was higher in female patients (n = 1.14) than in male patients (n = 0.9). A Mann-Whitney U-test P value of .61 rendered this difference as statistically insignificant.

A total of 102 (11.1%) drugs out of the 919 drugs prescribed were identified as PIM. The most commonly prescribed PIMs were proton-pump inhibitors (29.41%, n = 30), benzodiazepines/Z-drugs (16.67%, n = 17), Sulfonylurea compounds (12.76%, n = 13), oral form of Mineral oil (Liquid paraffin, 10.78%, n = 11), and NSAIDs, including Aspirin for prevention of IHDs (10.78%, n = 11) [Table 5]. Tamsulosin (alpha one antagonist) was irrationally prescribed to 8% (n = 4) of the female patients. Therapeutic duplication was seen in 21% of the patients. It was common with cholecalciferol (n = 6), with cyanocobalamin (n = 4), metformin (n = 4) and with paracetamol, domperidone, and folic acid (n = 3 each).

Table 5.

Top 5 most common PIM* in patients aged 65+ (Beers Criteria-2023)

Sr. No.	PIM* Identified	Frequency
1.	Proton-pump inhibitors	30
2.	Benzodiazepines & Z-drugs	17
3.	Sulfonylurea drugs	13
4.	Mineral oil (liquid paraffin)	11
5.	NSAIDs*	11

Note: *NSAIDs, non-steroidal anti-inflammatory drugs; PIM, potentially inappropriate medication.

Fixed-dose Combinations

Over 200 FDCs were identified. The average number of FDC prescribed per patient was 2.36 (SD = 1.87, 95% CI: 1.99%–2.73%). Among male patients, the average was 2.44, while among female patients 2.28. The difference in the average number of FDC prescribed to male and female patients was not statistically significant. (Mann-

Whitney U-test, P = .49). Table 6 shows the most common type of FDCs prescribed. A total of 73 different FDCs were identified, followed by assessment of their status in the WHO EML 2025 and/or NLEM 2022. It was found that only 17 (23.29%) (95% CI: 13.59%–32.98%) were included in either of the lists.

Table 6.

Classification of fixed-dose combinations prescribed, their frequency and their status of inclusion in the WHO Essential Medicines List 2025/national essential medicines list 2022

Classification of FDC*	Frequency	Number of Formulations	Inclusion in WHO EML-2025/NLEM-2022*
Multivitamin and supplements	48	11	3/11 (27.27%)
Antihypertensive agents	34	14	11/14 (78.57%)
Hypolipidemic and antiplatelet agents	33	5	2/5 (40%)
Oral hypoglycaemic agents	27	9	0
Ulcer-protective agents	25	5	0
Analgesic agents	17	9	0
Laxatives	15	1	0
Central nervous system	14	8	1/8 (12.5%)
Others	23	11	0
Total	236	73	17/73 (23.29%)

Note: *FDC, fixed-dose combinations; NLEM 2022, national essential medicines list 2022; WHO EML-2025, WHO Essential Medicines List 2025.

Discussion

Our study, conducted in West India, revealed that the prevalence rate of polytherapy was 80% (95% CI: 72%–88%). Cardio-metabolic drugs and multivitamins were the most prescribed. 99.07% (95% CI: 98.26%–99.88%) of the 537 PDDIs were flagged in patients on polytherapy (P < .001), among which Aspirin contributed to 26.44% of all PDDIs. It was found that 78.38% (95% CI: 69.0%–87.8%) of all patients aged 65+ received at least one PIM; the most common PIMs were proton-pump inhibitors, followed by benzodiazepines/Z-compounds.

Study Limitations

Before we interpret these findings, it is imperative to address the limitations of our study. This cross-sectional study only establishes an association between polytherapy and PDDIs, not causation, and convenience sampling introduces selection bias. A relatively small sample size of 100 prescriptions may limit statistical power in detecting weaker associations. However, this is an initial exploratory study towards awareness regarding geriatric prescribing practices, and large-scale studies can be planned in the future. The single-centre design limits generalisability as patients who visited the geriatric OPD may not adequately represent the wider population. A DDI flagged by the database may not reflect its true clinical severity. Our study design limited follow-up to determine if any DDIs were reported. As our primary aim was to quantify and characterise the risk associated with polytherapy, rather than to assess clinical outcomes, we did not evaluate the actual impact of the DDIs; there is an absence of clinical outcome data. Moreover, confounding variables such as comorbidities, genetics, and genetic variation in drug pharmacokinetics affect the polytherapy-PDDIs relationship. Lastly, multivariate regression modelling was not conducted due to sample size constraints, which could result in overfitting of the model and unreliable results. Further, interrelated prospective predictors could introduce multicollinearity and strain the interpretation of the model. Hence, descriptive statistics and correlation analysis were deemed appropriate for the study design. Future cohort studies with larger sample sizes may investigate the impact of polytherapy and associated PDDIs and identify predictors of PDDIs and PIM use by employing multivariate analysis.

Notwithstanding, this study uniquely flags PDDIs, investigates PIM use via the Beers Criteria-2023, and evaluates FDCs in prescriptions. Additionally, it highlights the trends and implications of polytherapy in Gujarat, where research in this field has been limited in the past decade.

Existing literature—a meta-analysis by Bhagavathula et al. (2021) investigating the regional variations in the polypharmacy prevalence found that it was more prevalent in West India and North-east India.^[2] Rising life expectancy, new drug development, increasing incidence of chronic illnesses requiring complex treatment, along with age-related physiological changes, together drive the rise of polytherapy in geriatric patients.

In our study, common comorbidities were hypertension, diabetes mellitus, and IHD. Such chronic disorders often require complex pharmacotherapy, thus necessitating polytherapy. Consequently, cardiovascular and metabolic drugs were the most prescribed. This reflects the global transition towards non-communicable diseases, which demand elaborate treatment strategies with multiple drugs for their synergistic management. Our finding corroborates a US-based study that reported higher rates of polypharmacy in patients with cardio-metabolic conditions.^[10]

Overall, 99.07% (ρ = 0.79) of the identified PDDIs were flagged among patients on polytherapy, highlighting the need for every prescriber to appropriately assess the risk-benefit ratio before prescribing multiple medications.

Most PDDIs identified were moderate, only requiring close monitoring. The most common PDDI flagged was between cholecalciferol-calcium supplements (cholecalciferol increases the GI absorption of calcium supplements), which is a desired, beneficial interaction. However, therapeutic duplication with these supplements was prevalent, which increases the risk of hypervitaminosis. With the rising awareness of the benefits of Vitamin D on bone health and immunity, its supplementation is on the rise among not only the elderly, but also in other age groups. Hypervitaminosis D can lead to hypercalcaemia, hypercalciuria, nephrocalcinosis and even chronic renal failure.^[11]

The pharmacokinetic interaction between Atorvastatin-Telmisartan was the second most common interaction flagged. Telmisartan increases the toxicity of Atorvastatin, heightening the risk of myopathy. The moderate, pharmacodynamic interaction between Aspirin-Telmisartan was also commonly flagged (Aspirin, an NSAID, decreases the antihypertensive effect of ARBs by decreasing prostaglandin synthesis). Aspirin was responsible for 26.44% of the flagged PDDIs. It is a non-selective COX inhibitor that interacts with various other drug classes, decreasing their efficacy and simultaneously increasing their toxicity. It decreases the antihypertensive effects of beta-blockers, ACE inhibitors, and ARBs, and blunts the diuretic action of loop diuretics and thiazides. It displaces sulfonylureas from their plasma-protein binding site, inhibits their metabolism and increases the risk of hypoglycaemia in a patient receiving both these drugs (pp. 229–232).^[12]

In the elderly, diabetes mellitus associated with dyslipidaemia is a widespread problem. Despite Sulfonylureas being a PIM (Beers Criteria-2023), they are often prescribed to diabetic patients along with an FDC of a Statin (Atorvastatin/Rosuvastatin) with Aspirin for dyslipidaemia. This practice may increase the risk of falls and fractures due to hypoglycaemia; it could also lead to fatal neuroglycopenic coma. Moreover, sulfonylureas confer a higher risk of cardiovascular events, all-cause mortality, and hypoglycaemia than other alternatives.^[7]

NSAIDs (Aspirin and Diclofenac) and ACE inhibitors (Ramipril and Lisinopril) were most involved in severe PDDIs flagged. The severe PDDI between Aspirin and Ramipril was flagged in two prescriptions. ACE inhibitors promote the production of vasodilatory prostaglandins, which enhance their antihypertensive effect. Aspirin attenuates this effect. Existing literature states that certain antihypertensives: renin-angiotensin-aldosterone inhibitors, diuretics, and beta-blockers are more prone to this interaction. Additionally, NSAIDs and ACE inhibitors administered together could increase the risk of acute kidney injury, considering both drug classes affect renal function. It is recommended to avoid chronic use of NSAIDs in hypertensives on treatment with ACE inhibitors. Physicians may consider prescribing calcium channel blockers that are unaffected by this interaction.^[13]

Severe PDDI between Glyceryl trinitrate and sildenafil can lead to potentially fatal hypotension, while Ciprofloxacin and Domperidone taken together increase the risk of QTc prolongation, in turn increasing the risk of potentially fatal, polymorphic ventricular tachycardia (Torsades de Pointes). The detection of such severe PDDIs in prescriptions highlights the need for consideration of deprescribing strategies and active involvement of clinical pharmacologists and pharmacists in geriatric healthcare.

The elderly experience altered drug pharmacokinetics due to a reduction in renal function, hepatic perfusion and declined drug metabolising hepatic microsomal enzymes. Drug absorption is also relatively slow due to reduced gut motility and perfusion (pp. 79-80).^[12] On one hand, due to altered hepatic/renal function and on the other, the requirement for complex pharmacotherapy for chronic conditions, makes this population susceptible to cumulative drug toxicity. In our study, therapeutic duplication was noted in 21% of the prescriptions, mainly with vitamins: cholecalciferol and cyanocobalamin, metformin, and paracetamol. Duplication with fat-soluble multivitamins can lead to hypervitaminosis, as previously discussed. Metformin overdosage can lead to hypoglycaemia, and rarely lactic acidosis, which can even lead to mortality (p. 322).^[12]

A study conducted by Pandya et al. (2013) in West India, on the prevalence of PIM in the hospitalised elderly, reported that 40% of the patients received at least one PIM (Beers Criteria-2012).^[5] Using the latest Beer’s Criteria-2023, we found that 78.38% of the patients aged 65+ received at least one PIM. PPIs were the most common PIM identified, followed by benzodiazepines/Z-drugs. The rationale behind PPIs being a PIM is the increased risk of Clostridium difficile infection, pneumonia, GI malignancies, bone loss, and fractures. According to the Beers Criteria, the elderly are more sensitive to benzodiazepines/Z-drugs, have slower metabolism of longer-acting agents; moreover, these drugs increase the risk of cognitive impairment, delirium, falls, and fractures in this vulnerable population, hence are classified as PIM. Mineral oil is a common medication prescribed to patients for the treatment of constipation; however, it can cause aspiration pneumonia; hence, it should be avoided.^[7] Additionally, four female patients were prescribed Tamsulosin for possible UTIs. While Tamsulosin is used off-label for lower urinary tract symptoms (LUTS) in females, it is not US-FDA approved for this use. Hence, Tamsulosin prescribed without evidence-based clinical indications was also considered to be an irrational prescription. The highly prevalent PIM use and therapeutic duplication illustrate the need for every physician to practice pharmacovigilant prescribing and medication reviews.

Further, we analysed FDCs prescribed in this sample based only on their inclusion status in the WHO EML-2025 and/or NLEM-2022, finding only 23.29% of the different FDCs on either list. The most common type of FDCs prescribed were multivitamins and supplements. While promising convenience, they deliver hardly. Most such FDCs contain too few doses to meet the daily requirement of a patient. Furthermore, FDCs available in the Indian market contain drugs that can potentially cause harm, for example, Thiocolchicoside, which has been withdrawn by the FDA due to the risk of aneuploidy. FDCs containing Serratopeptidase, an enzyme claimed to promote rapid resolution of inflammation, are also available in the Indian market. However, no scientific evidence has been found to support this claim, thereby unnecessarily increasing the number of API increasing the cost of the FDC.^[14] The market is flooded with various FDCs, and while a few might benefit by increasing medication compliance, treatment efficacy and reducing side-effects, most are a financial burden on the patient. Individual drug pharmacokinetics are a mismatch, overall rendering them less efficacious and increasing the risk of PDDIs.

Conclusion

To conclude, polytherapy and its attendant risks- PDDIs, PIM, and irrational FDCs constitute a major challenge in geriatric healthcare in India. This study delineates a detailed relationship between polytherapy, comorbidities and irrational prescribing practices, exhorting the need for rigorous FDC formulation policies and mandatory medication reviews at every healthcare centre. Every prescriber should be made aware of the use of pharmacotherapy that may be inappropriate for the elderly, they should be educated on following guidelines tailored for the elderly and should be encouraged to practice evidence-based medicine. This study opens doors for future multi-centred cohort studies that assess the outcomes, such as hospital stays, ADRs, DDIs, and morbidity in relation to polytherapy. Research will enhance rational prescribing, generate awareness, and lead to the overall betterment of the health of the people of India.

Supplemental material

Supplemental material for this article is available online.

Footnotes

Acknowledgements

We would like to thank the Department of Medicine at SVP Hospital for their cooperation, permission and support and the Department of Pharmacology at Smt. NHLMMC for their guidance and support.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Institutional ethical committee approval number

IRB letter attached below, as no IRB number is available.

Informed consent

Permission was taken from the Department of Medicine prior to accessing patient prescriptions and records.

Credit author statement

Shivangi Shukla: Concept, design, definition of intellectual content, review of literature, data collection, data analysis, statistical analysis, preparation of manuscript, manuscript editing, manuscript review.

Jiya Thosar: Review of literature, data analysis, manuscript review.

Supriya Malhotra: Concept, design, definition of intellectual content, statistical analysis, preparation of manuscript, manuscript editing, manuscript review.

Ami Parikh: Design, definition of intellectual content, manuscript review.

Data availability statement

Data are available on request.

Use of artificial intelligence

No AI was used in writing the manuscript.

Presentation at a meeting/conference

Yes.

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