European Journal of Cardiovascular Medicine

The global demographic transition toward an aging population has profound implications for healthcare systems. Elderly individuals frequently present with multiple chronic diseases such as hypertension, diabetes mellitus, ischemic heart disease, chronic kidney disease, and arthritis [1-3]. The management of these conditions often necessitates the concurrent use of multiple medications; a practice commonly referred to as polypharmacy [4-6].

While polypharmacy may be clinically justified in many cases, it is also associated with an increased risk of drug-drug interactions (DDIs), adverse drug reactions (ADRs), therapeutic failure, and higher healthcare costs. Elderly patients are particularly vulnerable due to age-related physiological changes, including altered pharmacokinetics and pharmacodynamics, reduced renal and hepatic clearance, and increased sensitivity to certain drug classes [7-9]. Consequently, the study of DDIs in this population is of paramount importance for ensuring safe and effective pharmacotherapy.

India, with its rapidly expanding elderly population, faces a unique challenge in managing polypharmacy. According to census projections, the proportion of individuals aged 60 years and above is expected to rise from 8.6% in 2011 to nearly 19% by 2050. This demographic shift is accompanied by a high prevalence of non-communicable diseases (NCDs), which account for more than 60% of total mortality in the country [10].

The pathophysiological basis for increased susceptibility to DDIs in the elderly lies in both disease-related and age-related changes. Chronic conditions such as diabetes and hypertension often lead to end-organ damage, including nephropathy, cardiomyopathy, and vascular dysfunction, which alter drug distribution and clearance [8, 9].

Age-related decline in renal glomerular filtration rate and hepatic metabolic capacity further compounds the risk. For example, reduced cytochrome P450 enzyme activity may prolong the half-life of drugs metabolized through hepatic pathways, while diminished renal clearance increases the risk of accumulation of renally excreted drugs [11].

Moreover, polypharmacy often involves drugs with overlapping mechanisms of action or adverse effect profiles. For instance, the concomitant use of antiplatelet agents and anticoagulants increases bleeding risk, while the combination of ACE inhibitors, diuretics, and NSAIDs may precipitate acute kidney injury. These interactions are not merely pharmacological curiosities but have direct clinical implications, often leading to prolonged hospitalization, increased morbidity, and even mortality [8-12].

International literature has extensively documented the prevalence and impact of DDIs in elderly populations. Studies from Europe and North America report that up to 30–40% of elderly patients on polypharmacy experience at least one clinically significant DDI [13]. In India, however, the evidence base remains limited. A few hospital-based studies have highlighted the high prevalence of polypharmacy and potential DDIs.

Despite the growing burden of polypharmacy in India, there is a paucity of comprehensive studies that systematically evaluate both the prevalence and clinical consequences of DDIs in elderly patients. Additionally, there is limited evidence on the patterns of drug classes most frequently involved in DDIs in Indian hospitals, which is essential for developing targeted interventions.

This study investigates the prevalence, nature, and clinical impact of drug-drug interactions (DDIs) among elderly patients (aged 65 and above) on polypharmacy—defined as the concurrent use of five or more medications—in a hospital setting. It hypothesizes that such patients experience a high rate of clinically significant DDIs, which are associated with adverse outcomes including increased adverse drug reactions, prolonged hospital stays, and elevated mortality risk. The study aims to quantify the prevalence of DDIs, identify the most common interaction types, analyze demographic and clinical factors contributing to DDIs, and assess their clinical consequences. Ultimately, it seeks to generate evidence-based recommendations to improve medication safety and therapeutic outcomes in this vulnerable population.

This observational and prospective study was conducted over a period of six months in the Department of General Medicine of tertiary care hospital of eastern India. The study was after approval from “Institutional Ethics Committee.” All research activities adhered strictly to the principles outlined in “Good Clinical Practice and the Declaration of Helsinki,” ensuring ethical integrity and the protection of patient rights throughout the study.

Study Population: The study included patients aged 65 years and above who were admitted to the hospital during the study period and were on polypharmacy, defined as the use of five or more medications. Only those who provided informed consent were enrolled. Patients were excluded if they had incomplete medical records, were discharged within 24 hours of admission, or were receiving palliative or end-of-life care.

Sample Size: With reported prevalence of DDI of 29% in a previous study [13], the minimum sample size required for 95% confidence and 0.05 precision and expected 1500 patients in 6 months was found to be 262.

Methodology

Data Collection: Data were collected using a standardized data collection form designed to capture key variables relevant to the study. This included demographic details such as age and gender; clinical characteristics including comorbidities and duration of hospital stay; comprehensive medication information encompassing the list of all prescribed drugs along with their respective doses and durations; and documentation of drug-drug interactions (DDIs), which were systematically identified and classified based on established interaction criteria.

Identification of Drug-Drug Interactions: DDIs were identified by systematically reviewing each patient's medication list using the Medscape® and Lexicomp® database. Potential interactions were categorized based on their severity into three groups: “major interactions, which posed a high risk of adverse clinical outcomes; moderate interactions, which had the potential for significant clinical effects; and minor interactions, which were considered to have limited clinical significance.”

Assessment of Clinical Outcomes: The clinical outcomes of patients who experienced DDIs were assessed through three key parameters: documented serious adverse events, the duration of hospital stay measured from admission to discharge, and the in-hospital mortality rate.

A serious adverse event (SAE) was defined as any untoward medical occurrence that results in death, is life‑threatening, requires initial or prolonged hospitalization, causes significant disability, leads to a congenital anomaly, or is otherwise deemed medically important because it may jeopardize the patient or require urgent intervention to prevent these outcomes.

Statistical Analysis: The statistical analysis in this study employed both descriptive and inferential methods to compare patients with and without major drug-drug interactions (DDIs). Continuous variables, such as age and duration of hospital stay, were analyzed using an unpaired t-test, presented as mean ± standard deviation. Categorical variables, including gender, comorbidities, and the presence of serious adverse events, were compared using Fisher's Exact Test, with results expressed as counts and percentages (n, %). The results for key continuous outcomes, such as the difference in hospital stay duration, were reported with their mean difference, standard error of the mean (SEM), and 95% confidence intervals (CI), with a p-value of less than 0.05 considered statistically significant.

Among 262 patients, minor DDIs were identified in 179 (68.32) prescriptions, moderate DDIs in 94 (35.88%) prescriptions, and major DDIs in 43 prescriptions 43 (16.41%) prescriptions with some prescriptions having greater than 1 type of potential DDI.

Several major DDIs were commonly observed during the study, each carrying a high risk of adverse clinical outcomes. These included additive central nervous system (CNS) depressant effects from the concurrent use of multiple sedative agents, and reduced drug absorption due to combinations such as sucralfate with digoxin, warfarin, or furosemide. Interactions involving levothyroxine with minerals or lanthanum, and quinolones or tetracyclines with mineral supplements, were noted to impair therapeutic efficacy. Bile acid sequestrants co-administered with hydrochlorothiazide or statins also posed significant risks. Additionally, the use of statins alongside drugs that elevate their plasma levels increased the potential for toxicity. Other high-risk combinations included non-steroidal anti-inflammatory drugs (NSAIDs) with selective serotonin reuptake inhibitors (SSRIs), and the simultaneous use of dual anticholinergic agents, both of which heightened the likelihood of serious adverse effects.

Table 1 compares baseline characteristics between patients with and without major drug-drug interactions (DDIs). The results indicate that patients with major DDIs were significantly older (mean age 74.23 vs. 70.87 years, p=0.0285) and had a significantly higher prevalence of dyslipidaemia (60.47% vs. 38.81%, p=0.0111) and neuropsychiatric disorders (37.21% vs. 13.24%, p=0.0006). Although not statistically significant, there were also notable trends toward higher rates of type 2 diabetes, cardiovascular disease, and infections in the major DDI group. Gender and other comorbidities such as hypertension, chronic kidney disease, and cerebrovascular disorders did not differ significantly between the groups.

Table 1: Comparison of Baseline Characteristics between Patients with Major DDIs and without Major DDIs

*Fisher’s Exact Test, **Unpaired t test     

Table 2 compares the duration of hospital stay between patients with and without major DDIs. Patients with major DDIs had a significantly longer mean hospital stay (9.57 hours) compared to those without major DDIs (7.03 hours), with a mean difference of -2.54 hours (p<0.0001). The 95% confidence interval for this difference (-3.169 to -1.911) does not include zero, reinforcing the statistical significance of the finding. This suggests that the presence of major DDIs is associated with increased hospitalization time.

Table 2: Comparison of Duration of Hospital Stay between Patients with Major DDIs and without Major DDIs

Table 3 compares the occurrence of serious adverse events between the two groups. A significantly higher proportion of patients with major DDIs experienced serious adverse events (16.28%) compared to those without major DDIs (5.02%), with a p-value of 0.0154.

Table 3: Comparison of Serious Adverse Events between Patients with Major DDIs and without Major DDIs

Figure 1: Comparison of Number of Medications between Patients with Major DDIs and without Major DDIs

Patients with major DDIs had greater number of medications per prescriptions as compared to patients without major DDIs [Figure 1].

The high prevalence of Drug-Drug Interactions (DDIs) observed in our study (Major DDIs: 16.41%) is firmly rooted in the principles of pharmacokinetics and pharmacodynamics, a finding consistently echoed in the scientific literature. The core mechanism driving this phenomenon is polypharmacy, where the use of multiple medications increases the probability of drugs interfering with each other. Our study's key finding—that a higher number of medications is strongly associated with major DDIs—is the central pillar of this scientific background.

This mechanistic link is powerfully demonstrated in the previous studies. Doan et al. (2013) and Guthrie et al. (2015) quantitatively established that the risk of DDIs escalates dramatically with the number of drugs prescribed, with odds ratios (ORs) as high as 26.8 for patients on ≥15 medications [14, 15]. The work of Georgiev et al. (2022) delves deeper into the pharmacokinetic specifics, highlighting that a significant portion of these interactions occur at the biotransformation level, predominantly involving the cytochrome P450 (CYP) enzyme system, particularly CYP3A4 [16].

The types of major DDIs we identified, such as additive CNS depression and reduced drug absorption, are classic examples of pharmacodynamic and pharmacokinetic interactions, respectively. These are mirrored in other studies: Sheikh-Taha & Asmar (2021) and Burato et al. (2021) reported high risks from CNS depressants and cardiovascular drug combinations [17, 18], while Nusair et al. (2020) and Oliveira et al. (2021) also found cardiovascular and diuretic agents to be frequently implicated [19, 20].

The clinical significance of our results is profound, demonstrating that major DDIs are not just a theoretical concern but are directly associated with detrimental patient outcomes. Our study found that patients with major DDIs had a significantly longer duration of hospital stay (9.57 vs. 7.03 hours, p<0.0001) and a higher incidence of serious adverse events (16.28% vs. 5.02%, p=0.0154). This translates to increased healthcare costs, greater patient burden, and tangible harm.

When placed in the context of previous research, our findings both confirm and refine our understanding of this global issue.  The prevalence of major DDIs in our cohort (16.41%) falls within the wide range (8.34% to 100%) reported in the systematic review by Oliveira et al. (2021) and is consistent with the 28.74% major interaction rate found in the meta-analysis by Alemayehu et al. (2024) [21]. This confirms that DDIs are a pervasive problem across different healthcare settings and geographies, from North America and Europe to the Middle East and Africa.

Our finding that patients with major DDIs are concentrated in the higher medication count categories (>8 drugs) is the most consistent result across nearly all comparative studies. The odds ratios reported by Georgiev et al. (2022) (OR 25.535 for pDDIs with >7 drugs) and Guthrie et al. (2015) (OR 26.8 for ≥15 drugs) provide robust, large-scale validation for our observation. This underscores that medication regimen complexity is the primary driver of interaction risk [15, 16].

While many studies, such as Nusair et al. (2020), primarily document the prevalence of DDIs, our study importantly establishes a direct link to clinical outcomes (longer stay, more adverse events) [19]. This strengthens the argument made by studies like Bennett et al. (2014), which linked medication burden to falls, and moves beyond mere identification to demonstrating concrete consequences [22]. The need for this is highlighted by Sheikh-Taha & Asmar (2021), who found a high rate of severe DDIs but did not report on associated outcomes [17].

Our study identifies specific patient characteristics associated with major DDIs, notably older age and specific comorbidities like neuropsychiatric disorders and dyslipidaemia. This aligns with Guthrie et al. (2015), who identified older age as a risk factor, and Burato et al. (2021), who showed that clinical setting (nursing homes with frailer patients) influences DDI exposure [15, 18]. The link to neuropsychiatric disorders is particularly salient, as these patients often require multiple CNS-active drugs, a known high-risk combination as noted in our study and others.

In summary, our study's results are scientifically grounded in the principle of polypharmacy-driven interactions and are clinically significant for demonstrating a clear association with worse patient outcomes. When compared with previous research, our findings reinforce that polypharmacy is the dominant risk factor while providing critical, patient-centered evidence of the real-world harm caused by major DDIs. This underscores the urgent need for systematic medication review and the use of advanced screening tools in high-risk, polymedicated elderly populations to mitigate these risks and improve patient safety.

A key limitation of this study is its single-center design and relatively modest sample size, which highlight the need for a multi-centric design to generate more robust statistical and clinical significance in this context.

In conclusion, this study confirms a high prevalence of clinically significant drug-drug interactions (DDIs) within a cohort of hospitalized patients, with major DDIs identified in 16.41% of prescriptions. It robustly demonstrates that these major DDIs are strongly associated with a higher medication burden and lead to detrimental clinical outcomes, including a significantly prolonged hospital stay and an increased rate of serious adverse events. These findings underscore that major DDIs are a critical patient safety issue, highlighting the urgent need for systematic interventions such as comprehensive medication reconciliation at all care transitions to accurately assess medication regimens, and formal review by clinical pharmacologists who can provide expert feedback on complex polypharmacy. Integrating these proactive strategies into standard clinical practice is essential to mitigate risks, optimize therapeutic outcomes, and improve the overall quality of care for older, polymedicated patients.

1. Sun X, Li X. Editorial: Aging and chronic disease: public health challenge and education reform. Front Public Health. 2023 May
   9;11:1175898. doi: 10.3389/fpubh.2023.1175898.
2. Patel P, Muhammad T, Sahoo H. The burden of disease-specific multimorbidity among older adults in India and its states: evidence from LASI. BMC Geriatr. 2023 Jan 30;23(1):53. Available from: https://bmcgeriatr.biomedcentral.com/articles/10.1186/s12877-023-03728-1
3. Khan MR, Malik MA, Akhtar SN, Yadav S, Patel R. Multimorbidity and its associated risk factors among older adults in India. BMC Public Health. 2022;22(1):746. Available from: https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-022-13181-1
4. Varghese D, Ishida C, Patel P, et al. Polypharmacy. [Updated 2024 Feb 12]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK532953
5. Hung A, Kim YH, Pavon JM. Deprescribing in older adults with polypharmacy. BMJ. 2024;385:e074892. Available from: https://www.bmj.com/content/385/bmj-2023-074892
6. Davies LE, Spiers G, Kingston A, Todd A, Adamson J, Hanratty B. Adverse outcomes of polypharmacy in older people: Systematic review of reviews. J Am Med Dir Assoc. 2020 Feb;21(2):181-187. Available from: https://www.sciencedirect.com/science/article/pii/S1525861019307741
7. Alhumaidi RM, Bamagous GA, Alsanosi SM, Alqashqari HS, Qadhi RS, Alhindi YZ, Ayoub N, Falemban AH. Risk of Polypharmacy and Its Outcome in Terms of Drug Interaction in an Elderly Population: A Retrospective Cross-Sectional Study. J Clin Med. 2023 Jun 10;12(12):3960. doi: 10.3390/jcm12123960.
8. Mohamed MR, Mohile SG, Juba KM, Awad H, Wells M, Loh KP, et al. Association of polypharmacy and potential drug-drug interactions with adverse treatment outcomes in older adults with advanced cancer. Cancer. 2023;129(7):1096-1104. Available from: https://doi.org/10.1002/cncr.34642
9. Sheikh-Taha M, Asmar M. Polypharmacy and severe potential drug-drug interactions among older adults with cardiovascular disease in the United States. BMC Geriatr. 2021;21:233. Available from: https://doi.org/10.1186/s12877-021-02183-0
10. Bhagavathula AS, Vidyasagar K, Chhabra M, Rashid M, Sharma R, Bandari DK, Fialova D. Prevalence of polypharmacy, hyperpolypharmacy and potentially inappropriate medication use in older adults in India: a systematic review and meta-analysis. Front Pharmacol. 2021;12:685518. doi:10.3389/fphar.2021.685518
11. Drenth-van Maanen AC, Wilting I, Jansen PAF. Prescribing medicines to older people—How to consider the impact of ageing on human organ and body functions. Br J Clin Pharmacol. 2020;86(10):1921–1930. doi:10.1111/bcp.14094
12. Pichini S, Di Trana A, García-Algar O, Busardò FP. Editorial: Drug-drug interactions in pharmacology. Front Pharmacol. 2023 Feb 13;14:1155738. doi: 10.3389/fphar.2023.1155738.
13. Hughes JE, Waldron C, Cahir C. Prevalence of drug-drug interactions in older community-dwelling individuals: a systematic review and meta-analysis. Drugs Aging. 2023;40:117-134. Available from: https://doi.org/10.1007/s40266-022-01001-5
14. Doan J, Zakrzewski-Jakubiak H, Roy J, Turgeon J, Tannenbaum C. Prevalence and risk of potential cytochrome P450–mediated drug-drug interactions in older hospitalized patients with polypharmacy. Ann Pharmacother. 2013;47(3):324–32.
15. Guthrie B, Makubate B, Hernandez-Santiago V, Dreischulte T. The rising tide of polypharmacy and drug-drug interactions: population database analysis 1995–2010. BMC Med. 2015;13:74.
16. Georgiev KD, Hvarchanova N, Stoychev E, Kanazirev B. Prevalence of polypharmacy and risk of potential drug-drug interactions among hospitalized patients with emphasis on the pharmacokinetics. J Int Med Res. 2022;50(2):03000605211070183.
17. Sheikh-Taha M, Asmar R. Polypharmacy and severe potential drug-drug interactions among older adults with cardiovascular disease in the United States. BMC Geriatr. 2021;21:634.
18. Burato F, Tramarin A, Piva S, Cattani G, Cattaneo D, Pea F. Comparing the prevalence of polypharmacy and potential drug-drug interactions in nursing homes and in the community dwelling elderly of Emilia Romagna region. Front Pharmacol. 2021;11:624888.
19. Nusair MB, Arabyat R, Al-Azzam S, Al-Azzam HB, Alzoubi KH. The prevalence and severity of potential drug-drug interactions among adult polypharmacy patients at outpatient clinics in Jordan. Saudi Pharm J. 2020;28(2):155–60.
20. Oliveira LM, Diel JAC, Nunes A, Dal Pizzol TS. Prevalence of drug interactions in hospitalised elderly patients: a systematic review. Eur J Hosp Pharm. 2021;28(1):4–10.
21. Alemayehu T, Mengistie B, Mekonnen D, Tadesse T. Prevalence of potential drug‒drug interactions and associated factors among elderly patients in Ethiopia: a systematic review and meta-analysis. Glob Health Res Policy. 2024;9:11.
22. Bennett A, Gnjidic D, Gillett M, Carroll PR, Avery AJ, Beilby J, et al. Prevalence and impact of fall-risk-increasing drugs, polypharmacy, and drug–drug interactions in robust versus frail hospitalised falls patients: a prospective cohort study. Drugs Aging. 2014;31(2):225–32.