Investigation of the Prevalence of Potential Drug-Drug Interactions in the Cardiology Department

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Data may be preliminary. 27 January 2025 V1 Latest version Share on Investigation of the Prevalence of Potential Drug-Drug Interactions in the Cardiology Department Authors : Jogaile Butauskaite 0009-0000-2613-2181 [email protected] , Austeja Zumbakyte , Lauryna Aukstikalne , Jolita Pancere , Skaiste Zukaitiene , and Egle Karinauske 0000-0002-4838-992X Authors Info & Affiliations https://doi.org/10.22541/au.173798834.43712426/v1 210 views 71 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract With increasing numbers of older adults worldwide, multimorbidity and polypharmacy are on the rise, highlighting the risks of potential drug-drug interactions (pDDIs). As cardiovascular agents are among the most prescribed medications, we performed an observational cross-sectional study to determine the prevalence of pDDIs in a cardiology department of a secondary hospital. Patient data was obtained from medical records and screened for pDDIs using the Micromedex drug interaction software. Descriptive statistics, Chi-square (χ2) test, Student’s t test and Pearson’s correlation test were used to analyse the results. Out of 50 participants, 45 (90%) had at least one pDDI. A total of 266 pDDIs were identified, with more than half classified as major. At least one major pDDI was found in 78% of patients. 42% of patients were at an increased risk of bleeding due to pDDIs. A statistically significant relationship was found between the detection of pDDIs and both patient age (p = 0.005) and the number of drugs used (p < 0.001). Our findings pose questions about the wider prevalence and risks of pDDIs among patients with cardiovascular disease. Investigation of the Prevalence of Potential Drug-Drug Interactions in the Cardiology Department Jogaile Butauskaite 1* , Austeja Zumbakyte 1 , Lauryna Aukstikalne 2 , Jolita Pancere 2 , Skaiste Zukaitiene 1,2 , Egle Karinauske 1,2* 1 Lithuanian University of Health Sciences, Medical Academy, Faculty of Medicine, Kaunas, Lithuania; [email protected] , [email protected] 2 Lithuanian University of Health Sciences, Medical Academy, Faculty of Medicine, Institute of Physiology and Pharmacology, Kaunas, Lithuania; [email protected] , [email protected] , [email protected] , [email protected] * Correspondence: [email protected] (Butauskaite, J) Abstract: With increasing numbers of older adults worldwide, multimorbidity and polypharmacy are on the rise, highlighting the risks of potential drug-drug interactions (pDDIs). As cardiovascular agents are among the most prescribed medications, we performed an observational cross-sectional study to determine the prevalence of pDDIs in a cardiology department of a secondary hospital. Patient data was obtained from medical records and screened for pDDIs using the Micromedex drug interaction software. Descriptive statistics, Chi-square (χ2) test, Student’s t test and Pearson’s correlation test were used to analyse the results. Out of 50 participants, 45 (90%) had at least one pDDI. A total of 266 pDDIs were identified, with more than half classified as major. At least one major pDDI was found in 78% of patients. 42% of patients were at an increased risk of bleeding due to pDDIs. A statistically significant relationship was found between the detection of pDDIs and both patient age (p = 0.005) and the number of drugs used (p < 0.001). Our findings pose questions about the wider prevalence and risks of pDDIs among patients with cardiovascular disease. Keywords: drug-drug interactions; drug safety; adverse drug reactions; polypharmacy; Key points: • At least one pDDI was detected in 90% of cases, with a total of 266 pDDIs identified. • More than half of detected pDDIs were considered major. At least one major pDDI was identified in 78% of cases. • The most common clinical risks associated with pDDIs included an increased likelihood of bleeding, which was observed in 42% of cases. • Factors significantly linked to a higher occurrence of pDDIs included patient age and the number of prescribed medications. Plain English Summary With the growing global population of older adults, multimorbidity and polypharmacy are increasing, elevating the risks of potential drug-drug interactions (pDDIs). Cardiovascular agents, being among the most commonly prescribed medications, prompted an observational cross-sectional study in a cardiology department to assess pDDI prevalence. A total of 266 pDDIs were identified, with more than half classified as major. The majority of participants had at least one pDDI, with many experiencing major interactions. At least one major pDDI was detected in 78% of cases. Our findings pose questions about the wider prevalence and risks of pDDIs among patients with cardiovascular disease. 1. Introduction Over the past century, increasing life expectancy has led to an unprecedented global rise in the number of older adults (individuals aged ≥ 65 years). By 2030, it is projected that older individuals will constitute up to 22.1% of the population in Europe and North America [1]. As a consequence of these demographic shifts, multimorbidity (the presence of two or more long-term health conditions) and polypharmacy (the regular use of five or more medications daily) are becoming increasingly common [2]. The simultaneous use of multiple medications heightens the risk of pDDIs, which occur when one drug affects the efficacy or toxicity of another co-administered drug. The prevalence of pDDIs among hospitalised patients ranges from 15% to 45% according to current research [3]. Certain pDDIs can be beneficial and purposefully used to enhance therapeutic outcomes, others may be clinically insignificant. However, many pDDIs are harmful and can lead to severe adverse drug reactions (ADRs) [4, 5]. In North America and Europe, pDDI-induced ADRs are estimated to account for 3-10% of all hospitalizations and are associated with longer hospital stays and higher treatment costs [6]. In Lithuania, limited studies have examined the prevalence of pDDIs in medical institutions, but their findings are consistent with global statistics. A 2017 study conducted in the therapeutic departments of a tertiary hospital identified 92 pDDIs among 121 patients [7]. Similarly, a 2019 observational study in the Psychiatry Department of a tertiary hospital found a pDDI frequency of 69.7% [8]. According to the European Society of Cardiology, 65-70% of individuals aged 60-79 and 79-86% of those aged 80 and older suffer from cardiovascular diseases. Additionally, about 65% of heart failure patients have five or more comorbid chronic diseases [2]. Consequently, the elderly population exhibits high drug consumption for cardiovascular conditions, frequent polypharmacy, and an elevated likelihood of pDDIs [2]. Given the scarcity of data on the prevalence of pDDIs among hospitalised cardiology patients, this study aims to determine the prevalence of pDDIs in the cardiology department of a secondary level hospital, evaluate their clinical significance, and assess the monitoring and interventions performed in response to pDDIs. The results of this study could inform the development of pDDI monitoring guidelines, ultimately reducing the incidence of harmful DDIs and ensuring safe, effective, and cost-effective treatment. 2. Materials and Methods 2.1 Design, Setting, and Population From 2021 to 2023 ”A Prospective Cross-Sectional Observational Study of the Prevalence of Problematic Pharmacotherapeutic Situations in Kaunas Hospital of the Lithuanian University of Health Sciences” was conducted. The study included 145 patients from the departments of Cardiology, Neurology, and Internal Medicine. It was approved by the Kaunas Regional Biomedical Research Ethics Committee (No. P1-BE 2-84/2021), which included permission for pDDI analysis. However, data related to pDDIs has not yet been published. This current study focuses on data from patients treated in the cardiology department (n = 50). The study included hospitalised cardiac patients aged 18 years or older who were taking at least two medications during their hospital stay and provided formal consent to participate. Patients who were receiving fewer than two medications or who refused to participate were excluded from the study. The study was conducted in accordance with the Basic & Clinical Pharmacology & Toxicology policy for experimental and clinical studies [9]. 2.2 Data Collection Patient data was collected using paper-based and electronic medical records throughout inpatient treatment. The following data were collected during the study: patient age, gender, weight, height, lean body mass (LBM), adjusted body mass, body mass index (BMI), plasma creatinine levels, main reason for hospital admission, other illnesses, prescribed medications with indications, dosage, start and end dates (if available) of ad-ministration, information on whether medication safety and efficacy were monitored; related laboratory and instrumental test data, outcomes. 2.3 Screening and Analysis of pDDIs Micromedex drug interaction database was used to objectively assess the presence and severity rating of potential pDDIs. This exact database was selected because in a 2015 systematic review by Roblek et al., Micromedex was identified as the most commonly used and, according to some authors, the most reliable due to the highest sensitivity [10]. Additionally, in 2016, Kheshti et al. conducted a comparative analysis of various pDDI screening tools (Epocrates, Lexi–Interact, Micromedex, iFacts, and Medscape). After thorough evaluation Micromedex and Lexi–Interact pDDI screening systems were identified as the most user-friendly and overall superior [11]. Only significant pDDIs were included, specifically those classified in the Micromedex database as contraindicated, major, or moderate. The most common pDDIs identified in the study were analysed, and the confidence level of the data on pDDIs was indicated based on information from the Micromedex database, described as follows: • Excellent: Controlled research data clearly demonstrate the existence of pDDIs. • Good: Strong evidence suggests the existence of pDDIs, but well-controlled studies are needed. • Satisfactory: Available data are sparse, but pharmacological features lead clinicians to suspect potential pDDIs, or well-described pDDIs common to pharmacologically similar drugs. • Unknown: The confidence level of the data is not known. The need for additional monitoring and treatment adjustments due to pDDIs was assessed based on the pDDI management guidelines in the Micromedex database. To reduce data dispersion, subjects were divided into two age groups: on the number of medications used, subjects were divided into three groups: prescribed <5 drugs, 5-9 drugs (polypharmacy), and ≥10 drugs (radical polypharmacy). When potential pDDIs were common to an entire drug group, the pDDIs were categorised according to the involved drug groups. For pDDIs specific to individual drugs within a group, the interaction was categorised by the drug name without mentioning the drug group. 2.4 Statistical Analysis Statistical analysis was performed using SPSS v29.0.1.0 software. Descriptive statistics were applied to characterise the study data. Nominal and categorical variables were presented using frequency tables. Interval variables (such as age, creatinine clearance, BMI, and the number of drugs prescribed) underwent normality testing. Normally distributed variables were summarised using the mean and standard deviation. To investigate the relationship between pDDIs (none vs. at least one) and categorical variables such as gender, age (< 65, ≥ 65 years old), number of drugs prescribed (< 5, 5-9, ≥ 10) glomerular filtration rate (GFR) categories, and BMI categories, the Chi-squared test was utilised. For interval variables such as age, BMI, creatinine clearance, and the number of medications used daily, the relationship with pDDIs (none vs. at least one) was assessed using Student’s t-test for independent samples. Pearson’s correlation was performed to determine the relationship between the number of pDDIs per patient and interval variables, including BMI, creatinine clearance, age, and the number of drugs prescribed. Student’s t-test was also used to compare the mean number of pDDIs per patient between males and females, as well as between those pre-scribed digoxin and those who were not. To analyse the relationship between the significance of detected pDDIs and categorical variables such as gender, age (<65, ≥65 years old), GFR categories, BMI categories, and pDDI significance the Chi-squared test was used. For interval variables such as age, BMI, creatinine clearance, and the number of medications used daily, the relationship with the significance of detected pDDIs (none vs. at least one) was assessed using Student’s t-test for independent samples. To determine the relationship between the necessity for additional monitoring and categorical variables such as gender, age categories, BMI categories, GFR categories, and pDDI significance, the Chi-squared test was applied. For interval variables such as age, BMI, creatinine clearance, and the number of prescribed medications, the relationship with the necessity for additional monitoring was assessed using Student’s t-test. The same approach was applied to determine the relationship between patient characteristics, pDDI significance, the necessity for additional monitoring, and the necessity to adjust treatment. A binomial logistic regression was initially planned to assess the influence of potential factors on the presence of at least one pDDI. However, due to the exceptionally high prevalence of at least one pDDI, the regression model did not fit the data and could not be validly used. A p-value of <0.05 was considered significant. 3. Results 3.1. Study population The study included 50 patients, with 29 women (58%) and 21 men (42%). The aver-age age of the patients was 75 years (range 49-92 years), with a standard deviation (SD) of 10.1 years, p > 0.05. Elderly patients (≥ 65 years) constituted the vast majority, with 43 subjects (86%) in this category. The mean number of drugs administered per patient was 8.7 (range 3-13), (SD = 2.4, p > 0.05). It was estimated that 92% (n = 46) of the patients in the study were exposed to polypharmacy or radical polypharmacy. Table 1 outlines the characteristics of the patients. Table 1. Patient characteristics BMI, kg/m2, mean (SD) 29.8 (5.0) 0.200 BMI category Normal (18.5 to < 25) 8 16.0 Overweight (25 to < 30) 18 36.0 Class 1 Obesity (30 to < 35) 16 32.0 Class 2 Obesity (35 to < 40) 6 12.0 Class 3 Obesity (≥ 40) 2 4.0 Creatinine clearance (Cockcroft-Gault), ml/min, mean (SD) 62.6 (29.4) 0.200 GFR category Normal or high (≥90) 7 14.0 Mildly decreased (60-89) 19 38.0 Mildly to moderately decreased (45-59) 9 18.0 Moderately to severely decreased (30-44) 8 16.0 Severely decreased (15-29) 7 14.0 Kidney failure (<15) 0 0 Hepatic function Normal 50 100 Child-Pugh A 0 0 Child-Pugh B 0 0 Child-Pugh C 0 0 3.2. Characteristics of pDDIs In total, 437 drugs were administered to the 50 patients included in the study, aver-aging 8.7 drugs per patient (SD = 2.4, p > 0.05). The minimum number of medications prescribed was 3 (n = 1), and the maximum was 13 (n = 2). A total of 266 pDDIs were identified during the study. At least one pDDI was found in 45 patients (90%). On average, 5.3 pDDIs were identified per patient (SD = 4.0, p > 0.05), with a maximum of 17 pDDIs in one patient (n = 1). More than half (55.3%) of all identified pDDIs were classified as major, the rest were of moderate severity. No contraindicated drug combinations were recorded during the study. At least one major pDDI was found in 78% of patients (n = 39). The most frequent pDDI was between perindopril and spironolactone (n = 7). The most common major pDDIs in the study were between loop diuretics and NSAIDs, ac-counting for 12.2% of major pDDIs (n = 18). According to Micromedex, this interaction may result in renal toxicity and a decreased diuretic effect. Moderate pDDIs were most commonly observed between beta blockers and NSAIDs, comprising 13.4% of moderate pDDIs (n = 16). Concomitant use of beta blockers and NSAIDs may result in an insufficient blood pressure-lowering effect of beta blockers. Other drug combinations most commonly leading to pDDIs and their potential clinical outcomes are described in Table 2 . It was estimated that 12.8% (n = 34) of the clinical manifestations of all identified pDDIs involved an increased risk of bleeding. The most common pDDIs increasing the risk of bleeding were found between NSAIDs and direct oral anticoagulants (DOACs) (n = 5), NSAIDs and platelet aggregation inhibitors (TAIs) (n = 4), low molecular weight heparins (LMWHs) and NSAIDs (n = 3), and glucocorticoids (GCs) and DOACs (n = 3). 42% (n = 21) of the study patients were at an increased risk of bleeding due to pDDIs. Table 2. The most common pDDIs and possible clinical outcomes Major pDDIs Loop diuretics and NSAIDs 18 12.2 Reduced diuretic effectiveness Nephrotoxicity Good Angiotensin converting enzyme inhibitors (ACEIs) and potassium-sparing diuretics (PSD) 15 10.2 Hyperkalaemia Good ACEi and furosemide 10 6.8 Severe hypotension Deterioration of renal function, including renal failure Fair PSD and NSAIDs 10 6.8 Reduced diuretic effectiveness Hyperkalaemia Nephrotoxicity Good Digoxin and PSD 6 4.1 Increased digoxin exposure Good ACEi and aspirin 5 3.4 Reduced hyponatremic and hypotensive effects of ACE inhibitors Fair NSAIDs and DOACs 5 3.4 Increased risk of bleeding Fair Moderate pDDIs Beta blockers and NSAIDs 16 13.4 Reduced antihypertensive effect Good ACEi and torsemide 15 12.6 Postural hypotension (first dose) Good Digoxin and loop diuretics 10 8.4 Increased risk of digoxin toxicity (nausea, vomiting, cardiac arrhythmias) Fair Beta blockers and digoxin 9 7.6 Increased risk of bradycardia Digoxin toxicity Good Beta blockers and metformin 8 6.7 Hypoglycaemia or hyperglycaemia Decreased symptoms of hypoglycaemia Good Angiotensin receptor blockers (ARBs) and PSD 5 4.2 Increased risk of hyperkalaemia Increased risk of serum creatinine elevation in heart failure patients Fair Of the 45 patients with at least one pDDI, 42 required additional monitoring, such as laboratory, clinical, and imaging tests, to track possible ADRs due to pDDIs, with one third (n = 14) monitored as needed. Treatment adjustment due to pDDIs was indicated for approximately half of the patients (n = 28), and performed in one patient during the study period. In the analysis of pDDIs determined during the study, it was estimated that 85% of all pDDIs (n = 226) required additional monitoring, which was provided in 61.1% of cases (n = 138). Treatment correction due to pDDIs was required in 33.8% of cases (n = 90), and was performed in 1.1% of cases (n = 1). To identify factors that could potentially increase the probability of pDDIs, the relationship between various patient characteristics (gender, age, BMI, creatinine clearance, number of drugs used) and pDDIs was evaluated. A statistically significant relationship was found between patient age and the detection of pDDIs (p = 0.005), indicating that pDDIs are recorded more frequently as patient age increases. Additionally, a statistically significant association was found between the number of drugs used and pDDIs (p < 0.001), meaning the more drugs a patient took, the more frequent the pDDIs. No significant associations were found between pDDIs and gender, BMI, or creatinine clearance. Given the wide range in the number of pDDIs per patient (min = 1, max = 17), we assessed associations between the number of pDDIs per patient and various characteristics (gender, age, BMI, creatinine clearance, number of medications). A strong positive correlation was found between the number of pDDIs and the number of drugs used (r = 0.691, p < 0.01). Additionally, a statistically significant relationship was found between the number of pDDIs per patient and the use of digoxin (p = 0.003). When analysing the relationship between patient characteristics and the significance of detected pDDIs, no statistically significant relationships were found. To determine fac-tors influencing the need for additional monitoring due to pDDIs, we evaluated the relationship between patient characteristics, the significance of pDDIs, and the need for additional monitoring. The need for additional monitoring was statistically significantly de-pendent on the severity of the interaction (χ2 = 7.819, df = 1, p = 0.005), with major pDDIs significantly more likely to require additional monitoring compared to moderate pDDIs. Additionally, pDDIs identified in women required additional monitoring more often than those identified in men (χ2 = 6.397, df = 1, p = 0.011). A statistically significant association was also found between the need for additional monitoring and the number of drugs used (p = 0.021), indicating that a higher number of drugs used increased the likelihood of requiring additional monitoring due to pDDIs. No statistically significant correlations were found between the need for additional monitoring and patients’ age, BMI, or creatinine clearance. Finally, the relationship between patient characteristics, recorded pDDIs, the need for additional monitoring, and the need for treatment correction was evaluated. Treatment adjustments were statistically significantly more often required for major pDDIs com-pared to moderate pDDIs (χ2 = 7.155, df = 1, p = 0.007). Additionally, pDDIs requiring treatment adjustments were significantly more frequent in patients with grade 3 obesity compared to other BMI categories (χ2 = 11.662, df = 4, p = 0.020). For pDDIs requiring additional monitoring, the need for treatment correction was statistically more frequent (χ2 = 5.611, df = 1, p = 0.018). Additionally, the need for treatment corrections was significantly dependent on the number of drugs used (p < 0.001). The greater the number of drugs used by the patient, the more often the need for treatment correction was identified. No statistically significant relationships were found between the need for treatment correction and the patient’s sex, age, or creatinine clearance. 4. Discussion The prevalence of pDDIs determined during the study reached 90%, confirming the hypothesis that the prevalence of pDDIs in the cardiology department would exceed 50%. Similar results were described by Allabi et al. and Khaled et al., with pDDI prevalences of 93% and 95% [12, 13], respectively. A slightly lower prevalence of pDDIs (68.1%) was found in the cardiology department at the University Hospital of Morocco [14]. The average number of drugs prescribed per patient was 8.7 (SD = 2.4, p > 0.05), which is lower compared to international studies where more than 10 drugs were pre-scribed per patient [13, 15, 16]. Despite the slightly lower average number of drugs used, the frequency of polypharmacy and radical polypharmacy was as high as 92%. In contrast, the Moroccan study reported an average of 5.2 drugs per patient [14]. The average number of pDDIs recorded per patient was 5.3 (SD = 4.0, p > 0.05), with a median of 5.0. Studies in hospitals in Pakistan and Oman reported higher median pDDIs of 8.5 and 9 [15, 16], respectively, while other studies reported lower medians (2-4.8) [17-19]. In this study, more than half (55.3%) of all recorded pDDIs were classified as major, according to the pDDI categories listed in the Micromedex database. No contraindicated drug combinations were identified. The results from Oman were similar, with major pDDIs accounting for 52.6% [16]. However, literature reviews found that moderate pDDIs predominated in other studies [13, 19-21]. The most frequently reported pDDIs were between perindopril and spironolactone (n = 7). The most common major pDDIs were found between loop diuretics and NSAIDs (12.2% of major pDDIs, n = 18). Moderate pDDIs were mostly observed between be-ta-blockers and NSAIDs (13.4% of moderate pDDIs, n = 16). Other studies of a similar model did not include pDDIs with non-cardiac drugs (e.g., NSAIDs), so an accurate comparison in this aspect is not possible. Additionally, not all relevant studies analysed the distribution of pDDIs by drug group, creating obstacles for comparative analysis. However, some authors also identified loop diuretics [13, 16] and beta-blockers [16] among the drug groups most often involved in pDDIs. A significant prevalence of pDDIs increasing the risk of bleeding was observed in studies, ranging from 16.4% to 20% [12, 15]. In this study, the prevalence of pDDIs associated with a higher risk of bleeding was slightly lower, at 12.8% of all identified pDDIs. However, 42% of study patients faced an increased risk of bleeding due to the detected pDDIs. Additional treatment monitoring was required in 93.3% of patients with at least one identified pDDI. All necessary additional monitoring was provided in one-third of patients (n = 14). The high prevalence of the need for additional monitoring is likely due to the fact that 78% of study patients experienced at least one major pDDI. Major pDDIs (χ2 = 7.819, df = 1, p = 0.005) and pDDIs identified in female patients (χ2 = 6.397, df = 1, p = 0.011) required additional monitoring more often. The more drugs the patient used, the more often pDDIs requiring additional monitoring were identified (p = 0.021). No further studies assessing the prevalence of the need for additional monitoring of pDDIs in the Cardiology Department could be found. However, given that the majority of studies found moderate pDDIs to be predominant [13, 19-21], it is likely that the identified need for additional treatment monitoring would have also been greater in this study. Treatment correction due to identified pDDIs was indicated in approximately half of the patients (n = 28) but was performed in only one patient during the study period. Since this was a prospective cross-sectional study, it cannot be ruled out that treatment corrections were made after data collection. Treatment adjustments were statistically significantly more often required for major pDDIs (χ2 = 7.155, df = 1, p = 0.007) and pDDIs requiring additional monitoring (χ2 = 5.611, df = 1, p = 0.018). Treatment adjustments were also more frequently needed in patients with grade 3 obesity (χ2 = 11.662, df = 4, p = 0.020). The higher the number of drugs used, the more often treatment adjustments were needed (p < 0.001). The need for treatment adjustments due to pDDIs was not evaluated in other analysed studies of similar design. However, since this study found that major pDDIs (requiring treatment adjustments more often) were more common than moderate pDDIs, unlike in many other studies [13, 19-21], it is likely that the identified need for treatment adjustments would have also been greater. Analysing the potential risk factors for pDDIs, a statistically significant relationship was found between the detection of pDDIs and both patient age (p = 0.005) and the number of drugs used (p < 0.001). Patients with polypharmacy or radical polypharmacy had statistically significantly more pDDIs (r = 0.691, p < 0.01) and more major pDDIs (χ2 = 19.783, df = 2, p < 0.001). This correlation between pDDIs, polypharmacy, and older age is supported by studies from various countries [13, 17, 18, 22-24]. A statistically significant relationship was also found between the number of pDDIs per patient and the use of digoxin (p = 0.003), which is frequently involved in significant pDDIs [19, 21]. Appropriate additional monitoring, such as digoxin blood concentration monitoring, and appropriate dose adjustments can help reduce the risk of ADRs [21]. No statistically significant relationship between gender and the occurrence of pDDIs was found. Although some studies report a higher prevalence in women [25] or men [19], most authors suggest that pDDIs are not associated with the patient’s gender [13, 15, 17, 21]. Our study aligns with international research and highlights the high prevalence of pDDIs in cardiology departments. To better understand the aetiology of pDDIs and develop optimal monitoring and management strategies, more studies of a similar model, especially in Western Europe, should be performed. For future studies to yield more substantial results, several limitations observed in this study should be addressed. Firstly, this study being unicentric and having a relatively modest sample size might have led to the omission of some significant factors influencing the pDDI rate. Further-more, clinical outcomes of patients related to pDDIs could not be followed in this study. Therefore, future studies should include a more representative sample and analyse the prevalence of ADRs due to pDDIs. 5. Conclusions A notably high prevalence of pDDIs was observed within the cardiology department. More than half of the recorded pDDIs were classified as major, with the remainder being moderate; no contraindicated drug combinations were identified. Additional monitoring for pDDIs was deemed necessary for the vast majority of study patients, with complete monitoring provided in one third of cases. Treatment adjustment due to pDDIs was deemed necessary for approximately half of the patients, and such adjustments were executed for one patient during the study period. Author Contributions: Conceptualization, Egle Karinauske and Jogaile Butauskaite; methodology, Egle Karinauske and Jogaile Butauskaite; formal analysis, Jogaile Butauskaite; investigation, Jogaile Butauskaite and Austeja Zumbakyte; resources, Egle Karinauske; data curation, Jogaile Butauskaite, Austeja Zumbakyte, Lauryna Aukstikalne, Jolita Pancere; writing—original draft preparation, Jogaile Butauskaite and Austeja Zumbakyte; writing—review and editing, Jogaile Butauskaite, Austeja Zumbakyte, Egle Karinauske, Skaiste Zukaitiene; visualization, Jogaile Butauskaite and Austeja Zumbakyte; supervision, Egle Karinauske, Skaiste Zukaitiene; project administration, Jogaile Butauskaite and Egle Karinauske. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Ethics Statement: The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of the Lithuanian University of Health Sciences (Protocol No. P1-BE 2-84/2021, 7 June 2021). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request. Conflicts of Interest: The authors declare no conflicts of interest. References 1. United Nations. Department of economic and social affairs. 2019. World populations prospects 2019. https://population.un.org/wpp/Publications/Files/WPP2019_ Highlights.pd (last accessed 11 March 2024). 2. Tamargo J, Kjeldsen KP, Delpón E, Semb AG, Cerbai E, Dobrev D, et al. Facing the challenge of polypharmacy when prescribing for older people with cardiovascular disease. 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A pharmacoepidemiologic study of drug interactions in a Brazilian teaching hospital. Clinics (Sao Paulo). 2006 Dec;61(6):515–20. Information & Authors Information Version history V1 Version 1 27 January 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords adverse drug reactions drug safety drug to drug interactions polypharmacy rational pharmacotherapy Authors Affiliations Jogaile Butauskaite 0009-0000-2613-2181 [email protected] Lietuvos sveikatos mokslu universitetas Medicinos fakultetas View all articles by this author Austeja Zumbakyte Lietuvos sveikatos mokslu universitetas Medicinos fakultetas View all articles by this author Lauryna Aukstikalne Lietuvos sveikatos mokslu universitetas Medicinos fakultetas View all articles by this author Jolita Pancere Lietuvos sveikatos mokslu universitetas Medicinos fakultetas View all articles by this author Skaiste Zukaitiene Lietuvos sveikatos mokslu universitetas Medicinos fakultetas View all articles by this author Egle Karinauske 0000-0002-4838-992X Lietuvos sveikatos mokslu universitetas Medicinos fakultetas View all articles by this author Metrics & Citations Metrics Article Usage 210 views 71 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Jogaile Butauskaite, Austeja Zumbakyte, Lauryna Aukstikalne, et al. 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