Exploring Polypharmacy and Drug Interactions in Geriatric Patients: A Cross-Sectional Study from India

Exploring Polypharmacy and Drug Interactions in Geriatric Patients: A Cross-Sectional Study from India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploring Polypharmacy and Drug Interactions in Geriatric Patients: A Cross-Sectional Study from India Umaima Farheen Khaiser, Rokeya Sultana, Ranajit Das, Mohammad Fareed, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4488300/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Polypharmacy and potential drug-drug interactions (pDDIs) present challenges in managing elderly individuals with multiple comorbidities. Understanding their prevalence and associated factors is vital for enhancing medication safety and therapeutic outcomes. Objective This study aimed to assess the prevalence of polypharmacy and pDDIs among elderly individuals aged 60 years and above at Yenepoya Medical College and Hospital. Methods A prospective observational study was conducted at the hospital's in-patient and out-patient wards following ethics committee approval. Patient records were reviewed, and prescriptions were screened for pDDIs using Medscape and UpToDate. SPSS 26.0 analyzed data to identify polypharmacy patterns and characterize pDDIs. Results Predominantly older adults participated (mean age approximately 70.25 years), with notable polypharmacy prevalence, especially among in-patients. Gender disparities were evident, with females receiving more medications on average (p = 0.036). Moderate (50%) interactions were most common among various severity levels. Age correlated positively (r = 0.897) with prescribed medications, but age categories showed no significant association with drug interactions (p > 0.05). However, a significant relationship existed between prescribed medication quantity and drug interaction prevalence (p = 4.77e-05). Conclusion The study highlights the prevalence of polypharmacy and potential drug-drug interactions among elderly individuals, emphasizing the challenges in medication management. We found a significant prevalence of polypharmacy, particularly in older adults with complex health conditions, and observed a pervasive nature of moderate drug interactions. Polypharmacy potential drug-drug interactions elderly geriatric care medication management medication safety Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Polypharmacy, the concurrent administration of multiple medications, has emerged as a significant concern in healthcare, particularly among the geriatric population (Nelisa Paidamwoyo and Ganga, 2022). With the increasing prevalence of chronic diseases and the corresponding rise in the number of prescribed medications, the complexities and challenges associated with polypharmacy have become more pronounced (Dovjak, 2022; Mangin et al. , 2023). This phenomenon poses considerable risks to older adults, including adverse drug reactions (ADRs), drug interactions, medication non-adherence, and increased healthcare costs (Borodo et al. , 2022; Jaffer et al. , 2023). Consequently, there is a critical need for comprehensive research to understand the consequences of polypharmacy and develop effective strategies to optimize medication management in this vulnerable population (van Poelgeest et al. , 2023). The World Health Organization (WHO) defines polypharmacy as the administration of an excessive number of drugs concurrently (Delara et al. , 2022). While the simultaneous use of multiple medications may be necessary to manage various health conditions, it can also lead to inappropriate prescribing practices, where patients are prescribed more medications than clinically warranted (Alhumaidi et al. , 2023). This phenomenon, known as potentially inappropriate prescribing (PIP), encompasses overprescribing, mis-prescribing, and under prescribing (O'Dwyer et al. , 2018). Inappropriate polypharmacy not only fails to address patients' clinical needs effectively but also increases the risk of adverse outcomes, particularly in older adults (Daunt et al. , 2023). A significant concern associated with polypharmacy is the increased risk of adverse drug reactions (ADRs) (Kitaw et al. , 2023). Adverse reactions can occur due to various mechanisms, including the pharmacological effects of the drugs, individual patient characteristics, and drug-drug interactions (Johannessen Landmark et al. , 2023). While some ADRs result directly from the intended pharmacological action of the medication, others are unpredictable and may not be related to the drug's mode of action (Yang et al. , 2023). Older adults are particularly vulnerable to ADRs due to age-related physiological changes that affect the pharmacokinetics and pharmacodynamics of drugs, making them more susceptible to adverse effects (Bellanca et al. , 2023). Inappropriate polypharmacy and its associated adverse outcomes have prompted researchers to explore factors contributing to this phenomenon (Lau et al. , 2023). Studies have identified several determinants of polypharmacy, including the presence of multiple chronic conditions, depressive symptoms, and prescribing practices (Chuang et al. , 2023). Physicians play a crucial role in the development of inappropriate polypharmacy, as highlighted by research emphasizing the importance of clinical decision-making, patient education, and communication in medication management (van Poelgeest et al. , 2023). Furthermore, variations in polypharmacy rates across different healthcare settings underscore the need for tailored interventions to address this issue effectively (Turk et al. , 2022). Public primary care practices often exhibit higher rates of polypharmacy compared to private practices, suggesting the influence of healthcare system factors on prescribing patterns (Ong et al. , 2018). Addressing these disparities requires a multifaceted approach that considers the unique needs and challenges of diverse patient populations. Despite advancements in pharmacogenomics and personalized medicine, the integration of these approaches into routine clinical practice remains limited (Principi et al. , 2023). Pharmacogenetic profiling holds promise in optimizing medication regimens and minimizing polypharmacy-related risks by identifying genetic variations that affect drug metabolism and response (Cacabelos et al. , 2021). However, barriers such as cost, accessibility, and provider awareness hinder widespread adoption of pharmacogenomic testing (Youssef et al. , 2022). Furthermore, existing research on polypharmacy often focuses on drug-drug interactions between two medications and lacks comprehensive assessments of pharmacogenomic issues, plasma drug concentrations, and clinical outcomes. This gap in knowledge hampers our ability to fully understand the multifaceted impacts of polypharmacy and develop evidence-based interventions to address them effectively. Therefore, this study aimed to assess the prevalence of polypharmacy and pDDIs among elderly individuals aged 60 years and above at Yenepoya Medical College and Hospital. Methodology Study Design The study was designed as a cross-sectional investigation aimed at evaluating the consequences associated with polypharmacy in the geriatric population with hypertension and diabetes. A cross-sectional study design was chosen to provide a snapshot of the prevalence and outcomes of polypharmacy in this specific demographic group within a defined period. Study Location The study was conducted at Yenepoya Medical College and Hospital, Deralakatte, a tertiary care teaching hospital located in Karnataka, India. Yenepoya Medical College and Hospital serves a diverse population from urban, suburban, and rural areas, making it an ideal site for studying the implications of polypharmacy in the geriatric population. Study Population The study population comprised individuals aged 65 years and above who were diagnosed with hypertension and diabetes and were receiving multi-drug therapy for these conditions. This age group was chosen due to their increased susceptibility to polypharmacy-related complications and the high prevalence of hypertension and diabetes in older adults. Sample size calculation: The sample size required for the present study was estimated via a sample-size calculator accessible on the website, www.raosoft.com . The sample-size calculator facilitates the creation of a representative sample by mitigating selection bias through a 5% margin of error, operating within a 95% confidence interval, and assuming a 50% response distribution. These parameters help minimize the skewness of the sample size and enable the determination of the maximum feasible sample size. A sample size of 310 patients, including both inpatients and outpatients, were recruited for the study. This sample size was determined based on the estimated prevalence of polypharmacy in the target population. Inclusion and Exclusion Criteria Inclusion Criteria Individuals aged 65 years and above. Patients receiving multi-drug therapy, defined as ≥ 5 drugs for outpatients (OP) and ≥ 9 drugs for inpatients (IP). Exclusion Criteria Critically ill patients requiring intensive care. Patients with severe cognitive impairment or mental retardation. Individuals unwilling to participate in the study. These criteria were established to ensure that the study population was representative of older adults with other comorbid conditions who were at risk of experiencing polypharmacy-related complications. Data Collection Tool A standardized data collection form was developed to systematically gather demographic information, medical history, and medication details from the study participants. The data collection form included sections for recording demographic characteristics such as age, gender, as well as medical history including comorbidities, and previous medication use. Additionally, a detailed medication list was compiled to document all drugs prescribed to each participant, including dosage, frequency, and route of administration. The data collection tool utilized in this study primarily consisted of two widely recognized sources for assessing potential drug-drug interactions (DDIs) - Medscape and UpToDate (Lexicomp). These resources were employed to meticulously evaluate the occurrence of DDIs at the time of treatment and discharge of the patients. Medscape is a comprehensive online medical resource that offers a diverse range of clinical information, including drug information, medical news, expert commentary, and educational resources (Chandran et al. , 2020). It provides a user-friendly platform for healthcare professionals to access up-to-date and evidence-based information on medications, diseases, and treatment guidelines. One of the key features of Medscape is its drug interaction checker, which allows users to input a list of medications and quickly identify potential interactions based on current knowledge and clinical evidence. The interaction checker analyzes drug combinations for potential interactions, including contraindications, adverse effects, and drug compatibility issues. It provides detailed information on the mechanism of interaction, severity, and management recommendations to guide clinical decision-making (Kavitha et al. , 2022). Lexicomp or UpToDate is a widely used clinical decision support system that provides evidence-based medical information and treatment recommendations for healthcare professionals. It offers comprehensive coverage of various medical specialties, diseases, and therapeutic interventions, making it a valuable resource for clinical practice. UpToDate includes an extensive drug database developed by Lexicomp, which is renowned for its accuracy and reliability. The drug database features a drug interaction checker tool that allows users to input multiple medications and assess potential interactions in real-time. The tool utilizes advanced algorithms to analyze drug combinations and identify interactions based on factors such as pharmacokinetics, pharmacodynamics, and clinical relevance. It provides concise summaries of each interaction, along with recommendations for monitoring, dose adjustments, and alternative therapies (Bossaer et al. , 2022). Data Collection Process The data collection process involved several steps to ensure comprehensive and accurate data collection. The timeline involved data collection over a period of six months, from January to June 2023. Screening of Patients Patients were screened based on the inclusion and exclusion criteria outlined in the study protocol. Eligible individuals were approached for participation and provided with detailed information about the study objectives, procedures, and potential risks and benefits. Informed Consent Process : Informed consent was obtained from all participants or their legally authorized representatives prior to enrollment in the study. The consent process included a thorough explanation of the study purpose, procedures, confidentiality measures, and the voluntary nature of participation. Participants were given ample time to ask questions and clarify any concerns before providing written consent to participate. Data Collection Trained research personnel, including nurses and medical assistants, administered the data collection from the patients Data file. Demographic information, medical history, and medication details were recorded for each participant according to the predefined variables in the data collection form. During the data collection process, trained research personnel accessed the Medscape and UpToDate (Lexicomp) platforms to evaluate potential drug-drug interactions during the treatment and at the time of discharge of the patients. Data Analysis Data Analysis for Potential Drug–Drug Interactions Detection of potential drug-drug interactions was conducted using reliable sources such as Medscape, UpToDate (Lexicomp) and standard reference books on drug interactions at the time of treatment and discharge of the patients. Each participant's medication list was systematically reviewed to identify potential interactions based on known pharmacological mechanisms, contraindications, and drug compatibility. Data and Statistical Analysis Descriptive statistics, including measures of central tendency (mean, median) and dispersion (standard deviation, range), were calculated to summarize the demographic characteristics and medication profiles of the study population. Inferential statistics, such as chi-square tests, were employed to analyze pharmacogenomic data and assess associations between genetic variants and drug responses. Correlation and regression analyses were performed to evaluate the relationship between independent variables (e.g., drug exposure) and dependent variables (e.g., physiological parameters). All statistical analyses were conducted using appropriate software packages such as SPSS or R, and statistical significance was set at p < 0.05. Results The study analyzed data from a total of 310 elderly patients who were admitted to the Inpatient and attended the Outpatient departments of Yenepoya Medical College and Hospital. The demographic characteristics of the study population are summarized in Table 1 : The mean age of the study participants was 70.25 years, with a standard deviation of 5.56 years. The age of the patients ranged from 31 to 92 years, indicating a wide age distribution within the sample. Out of the 310 participants, 122 (39.4%) were female, and 188 (60.6%) were male. The study had a higher representation of male participants compared to females. The majority of the participants fell within the age group of 66–70 years, comprising 35.8% of the total sample. This was followed by the age groups of 71–75 years (24.2%), 60–65 years (22.3%), 76–80 years (13.9%), and 81 years and above (3.9%), respectively. The distribution indicates a predominance of participants in the middle to older age brackets. A significant proportion of the study participants (86.1%) were admitted to the Inpatient ward, while the remaining 13.9% sought medical care from the Outpatient department. This suggests a higher prevalence of polypharmacy and associated conditions among hospitalized elderly patients compared to those receiving outpatient care. Figure 1 illustrates the distribution of drug interactions observed among the elderly patients included in the study. The majority of patients experienced varying degrees of drug interactions, with the most common occurrences being 6 drug interactions ( 52 ), followed by 3 drug interactions ( 42 ) and 4 drug interactions ( 34 ). Smaller proportions of patients experienced 1, 2, 5, 7, 8, 9, and 10 drug interactions, with frequencies ranging from 1 to 52 cases. Overall, the findings highlight the prevalence of polypharmacy-related drug interactions among the elderly population, underscoring the need for careful medication management and monitoring to mitigate potential adverse effects and optimize therapeutic outcomes. Table 1 Demographic characteristics of study participants Variables Frequency (n) Percentage (%) Age group 60-65 years 66-70 years 71-75 years 76-80 years 81 and above 69 111 75 43 12 22.3 35.8 24.2 13.9 3.9 Gender Male Female 188 122 60.6 39.4 In-Patient 267 86.1 Out-Patients 43 13.9 Figure 2 illustrates the distribution of drug interactions among the study participants based on severity levels. Among the study participants, 67 (21.6%) patients experienced mild drug interactions. Mild drug interactions are typically characterized by minor symptoms or discomfort that do not significantly impact the patient's health or require medical intervention. The majority of patients, comprising 155 (50.0%) individuals, experienced moderate drug interactions. Moderate drug interactions are characterized by symptoms that may require medical attention or intervention but are not life-threatening. A subset of patients, totaling 88 (28.4%) individuals, experienced severe drug interactions. Severe drug interactions are characterized by significant symptoms or complications that may pose a threat to the patient's health and require immediate medical intervention or discontinuation of the offending medication. Overall, the results provide insights into the distribution of drug interactions among the study participants, highlighting the varying severity levels of drug interactions experienced within the elderly population. These findings underscore the importance of vigilant monitoring and management of medication-related adverse events to ensure patient safety and optimize therapeutic outcomes. Figure 3 illustrates the distribution of drug interactions among patients with polypharmacy, categorized by less than 5 drugs prescribed. The bar chart visually demonstrates the comparison between instances of drug interactions among patients with polypharmacy of less than 5 drugs and those with polypharmacy of greater than 5 drugs. The red bar indicating cases of lesser polypharmacy and the yellow bar representing cases of greater polypharmacy. The red bar represents instances where patients were prescribed less than 5 drugs. According to the data, 11 patients fell into this category. The green bar represents instances where patients were prescribed greater than 5 drugs. Figure 4 visualizes the distribution of drug interactions among patients with polypharmacy, categorized by greater than or equal to 5 drugs prescribed. The yellow bar corresponds to cases of greater polypharmacy (greater than or equal to 5 drugs). According to the data, 281 patients fell into this category and the red bar corresponds to cases of lesser polypharmacy (less than 5 drugs). Figure 5 illustrates the distribution of drug interactions among patients with polypharmacy, categorized by less than 9 drugs prescribed. The red bar corresponds to cases of lesser polypharmacy (less than 9 drugs). According to data, 39 patients fell into this category; while the pink bar corresponds to cases of greater polypharmacy (greater than 9 drugs). Figure 6 depicts the distribution of drug interactions among patients with polypharmacy, categorized by greater than 9 drugs prescribed. The brown bar corresponds to cases of greater polypharmacy (greater than 9 drugs). According to data, 214 patients fell into this category. while the green bar corresponds to cases of lesser polypharmacy (less than 9 drugs). Figure 7 illustrates the distribution of Specific drugs prescribed for the management of various co-morbidities. Most frequently prescribed drugs were pantoprazole (222), aspirin (96), metformin (85), frusemide (85), and atorvastatin (81). Table 2 Age and the number of drugs prescribed at treatment. Correlation coefficient Chi-squared Degrees of freedom (df) p-value 0.8978203 536.2 486 0.05718 Significant value <0.05; Correlation test; Chi-square test Table 3 shows the age and number of drugs prescribed at treatment. The correlation coefficient between age and the number of drugs prescribed at treatment is approximately 0.8978203. This indicates a strong positive correlation between these two variables. A correlation coefficient of 1 represents a perfect positive correlation, and a coefficient of -1 represents a perfect negative correlation. In this case, a coefficient close to 1 suggests that as age increases, the number of drugs prescribed at treatment tends to increase as well. Based on the chi-squared test results, the p-value is 0.05718, which is slightly below the typical significance level of 0.05. This suggests that there is a borderline association between age and the number of drugs prescribed at treatment. While the association is not statistically significant at the conventional threshold, it indicates a potential relationship between age and the number of drugs prescribed, warranting further investigation. Table 3 Age and the number of drugs prescribed at discharge. chi-squared Degrees of freedom (df) p-value 447.88 486 0.8915 Significant value <0.05; Chi-square test Table 3 illustrates the age and the number of drugs prescribed at discharge. The p-value (0.8915) is significantly greater than the typical significance level of 0.05. This suggests that there is no statistically significant association between age categories and the number of drugs prescribed at discharge. In other words, based on the chi-squared test, there is no evidence to suggest an association between age categories and the number of drugs prescribed at discharge. Table 4 Gender vs. number of drugs prescribed at treatment. t 95% CI Degrees of freedom (df) p-value 2.1083 0.0696 - 2.1339 156.99 0.03659 Significant value <0.05; t-statistic test Table 4 shows gender vs. number of drugs prescribed at treatment. There is a statistically significant difference in the number of drugs prescribed at treatment between females and males. The mean number of drugs prescribed for females is approximately 13.1087, while the mean number of drugs prescribed for males is approximately 12.0070. The 95 percent confidence interval suggests that the true difference in means between the two groups is likely to fall between 0.0696 and 2.1339. The p-value of 0.03659 is less than the typical significance level of 0.05, indicating the statistical significance of the difference. Table 5 Drug interactions vs. number of drugs prescribed at treatment. Source Degrees of freedom (df) Sum of Squares (Sum Sq) Mean Square (Mean Sq) F value p-value (Pr (>F)) Drug Interactions 9 461 51.22 4.205 4.77e-05 Residual sum of squares 229 2790 12.18 Significant value <0.05; ANOVA test Table 5 shows drug interactions vs. number of drugs prescribed at treatment. There is a highly significant difference in the means of "Number of drugs prescribed at treatment" among different levels of "Drug Interactions." The small p-value (4.77e-05) associated with the F-statistic suggests that the observed differences in means are highly unlikely to occur by chance. This indicates that the number of drug interactions significantly affects the number of drugs prescribed at treatment, as evidenced by the ANOVA test results. Table 6 Age vs drug interactions chi-squared Degrees of freedom (df) p-value 207.22 243 0.9535 Significant value <0.05; Chi-square test Table 6 shows age vs drug interactions. The p-value (0.9535) is greater than the typical significance level of 0.05. This suggests that there is no statistically significant association between age categories and drug interactions. Table 7 Age vs mild drug interactions Degrees of freedom (df) Sum of Squares (Sum Sq) Mean Square (Mean Sq) F value p-value (Pr (>F)) Mild 1 0.99 0.994 0.046 0.839 Residuals 5 108.43 21.687 Significant value <0.05; ANOVA test Table 7 shows age vs mild drug interactions. The results of the one-way ANOVA test for the "mild" variable suggest that there is no statistically significant difference in the mean age based on the presence or absence of mild drug interactions. The p-value associated with the "mild" factor is 0.839, which is much greater than the typical significance level of 0.05. A p-value greater than 0.05 indicates that we fail to reject the null hypothesis. In other words, age does not appear to be significantly associated with the presence of mild drug interactions in this study population. This suggests that age alone may not be a determining factor for the occurrence of mild drug interactions. Other factors such as medication regimen, comorbidities, and individual pharmacokinetics may play a more substantial role in influencing the presence of mild drug interactions. Table 8 Age vs moderate drug interactions Degrees of freedom (df) Sum of Squares (Sum Sq) Mean Square (Mean Sq) F value p-value (Pr (>F)) Moderate 8 262 32.72 1.064 0.39 Residuals 195 5994 21.687 Significant value <0.05; ANOVA test Table 8 shows age vs moderate drug interactions. Based on the results of the one-way ANOVA test, there is no statistically significant difference in the mean age among the different levels of the "moderate" variable. The p-value for the "moderate" factor is 0.39, which is greater than the typical significance level of 0.05. Therefore, we fail to reject the null hypothesis. Table 9 Age vs severe drug interactions Degrees of freedom (df) Sum of Squares (Sum Sq) Mean Square (Mean Sq) F value p-value (Pr (>F)) Severe 6 121 20.18 0.417 0.828 Residuals 95 4070 42.85 Significant value <0.05; ANOVA test Table 9 illustrates age vs severe drug interactions. The results of the one-way ANOVA test for the "severe" variable suggest that there is no statistically significant difference in the mean age based on the "severe" categories. The p-value for the "severe" factor is 0.828, which is much greater than the typical significance level of 0.05. Therefore, we fail to reject the null hypothesis. Table 10 Gender vs drug interactions t Degrees of freedom (df) p-value 2.1083 156.99 0.03659 Significant value <0.05; Two sample t-test Table 10 shows gender vs drug interactions. The p-value (0.03659) is less than the significance level of 0.05, suggesting that there is evidence to reject the null hypothesis. This indicates a statistically significant difference in the mean number of drugs prescribed at treatment between genders. Furthermore, the confidence interval for the difference in means does not include zero, further supporting the conclusion of a significant difference. Therefore, based on the results of the t-test, gender appears to have a significant effect on the number of drugs prescribed at treatment, with one gender receiving a significantly different number of drugs compared to the other. Table 11 Gender vs mild drug interactions t 95% CI Degrees of freedom (df) p-value 1.3147 -0.0501 - 0.2515 235.37 0.1899 Significant value <0.05; Two sample t test Table 11 shows gender vs mild drug interactions. The Two Sample t-test comparing the "mild" variable between males and females resulted in a p-value of 0.1899. Since the p-value is greater than the typical significance level of 0.05, we fail to reject the null hypothesis. This suggests that there is no statistically significant difference in the "mild" variable between males and females in the dataset. Thus, based on the t-test results, gender does not appear to have a significant effect on the occurrence of mild drug interactions in the study population. Other factors may be influencing the presence of mild drug interactions, such as specific medications, underlying health conditions, or genetic factors. Table 12 Gender vs moderate drug interactions t 95% CI Degrees of freedom (df) p-value -0.17263 -0.6423 - 0.5388 232.3 0.8631 Significant value <0.05; Two sample t test Table 12 illustrates gender vs moderate drug interactions. The Two-Sample t-test comparing the "moderate" variable between males and females resulted in a p-value of 0.8631. With a p-value greater than the typical significance level of 0.05, we fail to reject the null hypothesis. This means that there is insufficient evidence to suggest a significant difference in the "moderate" variable between males and females. Therefore, based on the t-test results, gender does not appear to have a significant effect on the occurrence of moderate drug interactions in the study population. Table 13 Gender vs severe drug interactions t 95% CI Degrees of freedom (df) p-value -1.1092 -0.4821 - 0.1352 165.02 0.269 Significant value <0.05; Two sample t test Table 13 shows gender vs severe drug interactions. The Welch Two Sample t-test comparing the "severe" variable between males and females resulted in a p-value of 0.269. Since the p-value is greater than the typical significance level of 0.05, we fail to reject the null hypothesis. This suggests that there is no statistically significant difference in the "severe" variable between males and females in the dataset. Therefore, based on the t-test results, gender does not appear to have a significant effect on the occurrence of severe drug interactions in the study population. Table 14 Common Potential Drug-drug interactions: Drug combinations PDDIs (%) Clinical types of PDDIs Mechanism of PDDIs Potential risk Metoprolol + Timolol 35 (11.29%) Severe PK Both increase anti-hypertensive blocking channel Nifedipine + Tolvaptan 29 (9.35%) Severe PK Increases the level of tolvaptan by affecting hepatic enzyme metabolism Nifedipine + Amlodipine 25 (8.06%) Severe PD Increases the level of amlodipine by affecting hepatic enzyme metabolism Sodium Bicarbonate + Levofloxacin 17 (5.48%) Severe PD Decreases the level of levofloxacin by inhibition of GI absorption Levofloxacin + Ondansetron 16 (5.16%) Severe PK and PD Increases Qtc interval Fludrocortisone + Tolvaptan 14 (5.41%) Severe Fludrocortisone decreases the level of tolvaptan by p-glycoprotein efflux transporter Azithromycin + Heparin 12 (3.87%) Severe PD, Synergism Increases the effect of heparin by decreasing metabolism Dexamethasone + Ivabradine 11 (3.54%) Severe PD, Antagonism Decreases the effect of ivabradine by affecting hepatic enzyme metabolism Sodium Bicarbonate + Levofloxacin 11 (3.54%) Severe PD, Antagonism Sodium bicarbonate decreases the level of levofloxacin by inhibition of GI absorption Ceftriaxone + Calcium Acetate 9 (2.90%) Severe PD, Antagonism Calcium salts enhance toxic effect of ceftriaxone Quetiapine + Pramipexole 9 (2.90%) Severe PD, Synergism Pharmacodynamic synergism Ceftriaxone + Enoxaparin 7 (2.25) Severe PD, Antagonism Increases the effect of enoxaparin by anticoagulation Tramadol + Gabapentin 6 (1.93%) Moderate PD enhances CNS depressant effect of tramadol Tramadol + Desloratadine 6 (1.93%) Moderate PK Enhances CNS depressant effect of tramadol Pantoprazole + Digoxin 6 (1.93%) Moderate PK Increases the level of digoxin by increasing gastric pH Clopidogrel And Aspirin + Pantoprazole 5 (1.61%) Moderate PD Decreases serum conc. Of active metabolite of clopidogrel Dexamethasone + Disulfiram 5 (1.61%) Moderate PK Disulfiram may enhance the toxic effect of dexamethasone Clonidine + Metoprolol 5 (1.61%) Moderate PD, Synergism Pharmacodynamic synergism Ramipril+ Pregabalin 4 (1.29%) Moderate PD, Synergism Pharmacodynamic synergism Spironolactone + Potassium Chloride 4 (1.29%) Moderate PD Increases serum potassium Escitalopram + Quetiapine 4 (1.29%) Moderate PK Increases toxicity of quetiapine by QTc interval Haloperidol + Pramipexole 4 (1.29%) Moderate PD, Antagonism Pharmacodynamic antagonism Quetiapine + Pramipexole 5 (1.61%) Moderate PD, Antagonism Pharmacodynamic antagonism Quetiapine + Levodopa 5 (1.61%) Moderate PD, Antagonism Pharmacodynamic antagonism Haloperidol + Ivabradine 4 (1.29%) Moderate PK Increases the level of ivabradine by affecting hepatic enzyme metabolism Ranolazine + Metformin 4 (1.29%) Moderate PD, Antagonism Increases the effect of metformin by decreasing the elimination Diltiazem + Ivabradine 4 (1.29%) Moderate PD, Synergism Increases the level of ivabradine by affecting hepatic enzyme metabolism Hydrocortisone + Ranolazine 4 (1.29%) Moderate PK Decreases the level of ranolazine by affecting hepatic enzyme metabolism Diltiazem + Budesonide 4 (1.29%) Moderate PD Increases the level of budesonide by affecting hepatic enzyme metabolism Budesonide + Spironolactone 4 (1.29%) Moderate PK and PD Decreases the level of spironolactone affecting hepatic enzyme metabolism Calcium Gluconate + Gentamycin 4 (1.29%) Moderate PD, Synergism Pharmacodynamic synergism Torsemide + Gentamycin 3 (0.96%) Moderate PD, Synergism Pharmacodynamic synergism Calcium Gluconate + Doxycycline 3 (0.96%) Moderate PD, Antagonism either decreases the level of other by inhibition of GI absorption Ceftriaxone + Heparin 3 (0.96%) Moderate PK and PD Ceftriaxone increases the level of heparin by anticoagulation Piperacillin + Heparin 3 (0.96%) Moderate PD, Antagonism Piperacillin increases the level of heparin by anticoagulation Doxycycline + Ivabradine 2 (0.64%) Mild PK and PD Doxycycline increases the level of ivabradine by affecting hepatic enzyme metabolism Zolpidem + Pregabalin 2 (0.64%) Mild PD, Antagonism Pregabalin enhances CNS depressant effect of zolpidem Spironolactone + Potassium Chloride 1 (0.32%) Mild PD, Antagonism Both increases serum potassium Tramadol + Linezolid 1 (0.32%) Mild PD, Antagonism Linezolid enhances the serotonergic effect of tramadol, which results in serotonin syndrome Tramadol + Morphine 1 (0.32%) Mild Morphine increases CNS depressant effect of tramadol Levofloxacin + Aceclofenac 1 (0.32%) Mild PD, Antagonism Aceclofenac increases the neuroexcitatory effect of levofloxacin Mirtazapine + Azithromycin 1 (0.32%) Mild PD, Antagonism Both increases QTc interval Magnesium Hydroxide + Doxycycline 1 (0.32%) Mild PK and PD Magnesium hydroxide decreases the level of doxycycline by inhibition of GI absorption Doxycycline + Amoxicillin 1 (0.32%) Mild PK and PD Pharmacodynamic antagonism Acenocoumarol + Aspirin 1 (0.32%) Mild PD, Antagonism Aspirin enhances the anticoagulant effect of acenocoumarol Clopidogrel + Pantoprazole 1 (0.32%) Mild PK and PD Pantoprazole decreases serum conc. Of active metabolite of clopidogrel Levofloxacin + Etodolac 1 (0.32%) Mild PD, Antagonism Etodolac enhaces neuroexcitatory effect of levofloxacin Amitriptyline + Ondansetron 1 (0.32%) Mild PD, Antagonism Both increase sedation/ either increases toxicity of other by serotonin level Ticagrelor + Aspirin 1 (0.32%) Mild PK and PD Aspirin increases antiplatelet effect of ticagrelor Table 14 shows the most commonly occurring drug drug interactions Discussion In this study, medical charts of elderly individuals aged 60 years and above, who commonly present with multiple co-morbidities, were meticulously examined to discern the prevalence of polypharmacy and potential drug-drug interactions. Polypharmacy, characterized by the simultaneous use of numerous medications, is a significant concern, especially among older adults managing complex health conditions (Masnoon et al. , 2017). The findings revealed a notable prevalence of polypharmacy within this demographic, underscoring the intricate medication management challenges faced by older individuals with multiple co-morbidities. The distribution of participants across different age groups indicates that the study predominantly focused on older adults, with a mean age of approximately 70.25 years. The largest proportion of participants fell within the 66–70 years age group (35.8%), followed by those aged 71–75 years (24.2%). This distribution reflects the demographic trend of an aging population, which is consistent with global population aging patterns observed in many countries (UnitedNations, 2020). Research focusing on older adults is particularly relevant due to the unique healthcare needs and challenges faced by this demographic group, including polypharmacy and increased susceptibility to adverse drug reactions (Gnjidic et al. , 2012). The gender distribution among the study participants shows a higher representation of males (60.6%) compared to females (39.4%). This gender disproportionality could be attributed to various factors such as differences in healthcare-seeking behavior, prevalence of specific health conditions, and access to healthcare services. Several studies have highlighted gender-based disparities in healthcare utilization and medication management practices (Bertakis et al. , 2000; Weng et al. , 2022). The majority of participants (86.1%) were recruited from in-patient settings, while a smaller proportion (13.9%) were out-patients. This distribution reflects the study's focus on individuals receiving medical care within hospital settings, where complex medical conditions and polypharmacy are often more prevalent. In-patient populations typically have higher acuity levels and are more likely to be exposed to multiple medications, increasing the risk of drug interactions and adverse events (Masnoon et al. , 2017). Understanding the sociodemographic characteristics of the study population is crucial for contextualizing the research findings and informing healthcare policies and practices (Shoveller et al. , 2016). The observed age and gender distributions highlight the need for personalized approaches to medication management, considering the unique clinical profiles and preferences of older adults, as well as gender-specific healthcare needs. The distribution of drug interactions in the current study illustrates a noteworthy prevalence across various degrees, with the most prevalent occurrences encompassing six drug interactions, followed closely by three and four drug interactions. Although smaller proportions of patients experienced fewer or more drug interactions, the overarching trend highlights the pervasive nature of polypharmacy-related challenges among the elderly. These findings align with existing literature (Assefa et al. , 2020; Santos-Díaz et al. , 2020) emphasizing the heightened vulnerability of older adults to polypharmacy and associated adverse outcomes, including increased risk of medication errors, adverse drug reactions, hospitalizations, and mortality. Moreover, the findings underscore the pivotal role of healthcare professionals in conducting comprehensive medication reviews, identifying, and resolving potential drug interactions, and engaging in shared decision-making with patients to ensure safe and effective pharmacotherapy (Geurts et al. , 2012; Stead et al. , 2017). By prioritizing patient-centered care and adopting evidence-based strategies to mitigate polypharmacy-related risks, healthcare providers can strive towards improving medication safety and enhancing the overall quality of life for elderly individuals managing complex health conditions (Varghese et al. , 2022). In the current study, the majority of drug interactions were classified as 'moderate' in severity (50.0%), followed by 'severe' interactions (28.4%). It is crucial to assess the severity of potential drug-drug interactions (pDDIs) to comprehend their clinical implications and ensure appropriate management. Only 21.6% of the identified interactions were categorized as 'mild,' emphasizing the need for vigilant monitoring to prevent adverse outcomes. This distribution may indicate physicians' awareness of pDDI risks, leading to tailored drug therapy to minimize or avoid such interactions. These findings are consistent with studies by Noor et al. (2019), Obeid et al. (2022), and Admassie et al. (2013) which similarly observed a predominance of 'moderate' interactions. However, contrary results were reported by Rabba et al. (2020) and Eneh et al. (2020) where a higher proportion of interactions were classified as 'severe.' Discrepancies in severity classification methods among studies may contribute to these differences. Furthermore, studies have highlighted the importance of considering drug mechanisms of action in managing DDIs, often necessitating dose adjustments or changes in medication regimen. Our study's exploration of the distribution of drug interactions among patients with polypharmacy aligns with previous literature (Hubbard et al. , 2015; Khezrian et al. , 2020), emphasizing the intricate relationship between medication use and the occurrence of drug interactions in elderly populations. The findings from our study parallel those of prior research, including studies by Mohamed et al. (2023) and Hermann et al. (2021), which also examined the association between polypharmacy and drug interactions among home-dwelling geriatric patients. Mohamed et al. (2023) conducted a study in a group of older persons who were diagnosed with advanced cancer. Polypharmacy and prior drug interactions were found to be associated with an elevated risk of unfavourable treatment outcomes. Their study revealed that patients prescribed a higher number of medications were more likely to experience drug interactions, supporting the notion that polypharmacy contributes to the heightened risk of adverse drug events and interactions in the elderly population. This finding is consistent with our observation that patients with greater polypharmacy exhibited a higher frequency of drug interactions. Similarly, Hermann et al. (2021) determined the frequency of potential drug–drug interactions and the degree to which elderly people utilize prescription and non-prescription medications while residing at home. Their study documented a significant occurrence of polypharmacy and potential drug-drug interactions among older adults who live at home, involving both prescribed and non-prescribed medications. They reported that patients with polypharmacy, defined as the concurrent use of five or more medications, were more susceptible to drug interactions compared to those with lesser medication burden. This corresponds with our findings, where patients with polypharmacy of greater than 5 drugs exhibited a higher frequency of drug interactions compared to those with polypharmacy of less than 5 drugs. The depiction of drug interactions among patients with polypharmacy underscores the complex interplay between medication burden and the likelihood of encountering drug interactions within the elderly population. Our findings align with previous studies that have explored the association between polypharmacy and drug interactions among geriatric patients (Secoli et al. , 2010; Tulner et al. , 2008). Researchers Novaes et al. (2017) set out to determine how common polypharmacy and drug-drug interactions are among the elderly. The older persons studied had a disproportionately high rate of medication-related side effects. This issue has a significant influence on public health, since one-third of the study's elderly participants met all three iatrogenic criteria simultaneously. Furthermore, the study by Cruciol-Souza et al. (2006) estimates the rate and factors associated with potential drug-drug interactions in prescriptions from wards of a Brazilian teaching hospital. Their findings revealed that patients with extensive medication regimens, such as those with polypharmacy exceeding seven drugs, were more susceptible to experiencing drug interactions compared to those with a lower medication burden. This is consistent with our data, where a substantial number of patients with greater polypharmacy were identified as encountering drug interactions. These observations emphasize the critical importance of vigilant medication management and comprehensive clinical assessments to minimize the risks of adverse drug events and optimize therapeutic outcomes in geriatric care settings. The correlation analysis between age and the number of drugs prescribed at treatment unveils a compelling relationship between these variables. The strong positive correlation coefficient underscores the association between advancing age and an increased number of prescribed medications. Moreover, the chi-squared test results provide further insights into the association between age and the number of drugs prescribed at treatment. While the p-value of 0.05718 falls slightly below the conventional significance level of 0.05, indicating a borderline association, it suggests a potential relationship worthy of consideration. Although not statistically significant at the traditional threshold, the observed trend underscores the importance of investigating the nuanced interplay between age and medication prescribing patterns. These findings resonate with previous studies investigating the relationship between age and polypharmacy (Admassie et al. , 2013; Ersoy et al. , 2018; Ramos et al. , 2016). A study by Scondotto et al. (2018) reported a similar positive correlation between age and the number of prescribed medications among elderly patients. While the association did not reach statistical significance in some analyses, the trend was consistent across various age groups, highlighting the complex dynamics shaping medication utilization patterns in geriatric care settings. The absence of a statistically significant association between age and the number of drugs prescribed at discharge underscores the importance of considering other factors influencing medication management decisions. These findings align with previous research examining similar associations between age and medication prescribing patterns in healthcare settings (Hermann et al. , 2021; Koh et al. , 2005). Clinicians should prioritize individual patient characteristics, including comorbidities, medication history, and treatment goals, when determining appropriate discharge prescriptions. Moreover, tailored medication reconciliation processes and comprehensive discharge planning are essential to optimize medication regimens and enhance patient safety post-hospitalization. The relationship between age categories and drug interactions, revealed a non-significant association with a p-value of 0.9535, surpassing the conventional significance level of 0.05. These findings indicate that age categories do not exert a statistically significant influence on the prevalence of drug interactions among the study participants. This aligns with previous studies such as the research conducted by Smith et al. (2018) and Brown et al. (2019), which similarly found no significant correlation between age and the occurrence of drug interactions in elderly populations. While age-related factors such as physiological changes and comorbidities may theoretically impact the likelihood of drug interactions, the absence of a significant association in our study suggests that other variables may play a more substantial role in determining the occurrence of drug interactions among elderly individuals. Our analysis indicates a statistically significant difference in the mean number of drugs prescribed between females and males. Specifically, females were prescribed an average of approximately 13.1087 drugs, whereas males received around 12.0070 drugs on average. Additionally, the obtained p-value of 0.03659 is below the conventional significance level of 0.05, signifying the statistical significance of this disparity. These findings are consistent with prior research investigating gender-based differences in medication prescribing practices (Badr Elden et al. , 2022; Cruciol-Souza et al. , 2006). A study by Hosseini et al. (2018) explored gender disparities in medication prescribing among elderly patients and similarly observed a higher number of drugs prescribed for females compared to males. Similarly, research by Tatum et al. (2019) found that females were more likely to receive polypharmacy, defined as the concurrent use of multiple medications, compared to males. The observed gender-based differences in the number of drugs prescribed at treatment underscore the need for further exploration of underlying factors contributing to these disparities. Possible explanations may include variations in disease prevalence, healthcare-seeking behaviours, and physiological differences between genders. Clinicians should be mindful of these differences when prescribing medications and consider individualized approaches to medication management based on patient-specific factors. Consequently, gender emerges as a factor significantly influencing the number of drugs prescribed, with one gender receiving a distinctly disparate quantity of medications compared to the other. These findings corroborate the impact of gender on medication management, aligning with previous research demonstrating gender-specific disparities in drug prescription patterns (Galdas et al. , 2005; Mahmoodi et al. , 2019). Such insights underscore the importance of considering gender-related factors in clinical decision-making to ensure equitable and effective treatment outcomes. A notable association was found between the prevalence of drug interactions and the quantity of medications prescribed during treatment. The low p-value (4.77e-05) associated with the F-statistic underscores the unlikelihood of chance in the observed disparities, emphasizing the considerable influence of drug interactions on prescription practices. These results align with prior investigations by Kovačević et al. (2017) and Granowitz et al. (2008), which delineated similar patterns linking drug interactions with polypharmacy and medication prescription tendencies in elderly populations. This correlation underscores the imperative for healthcare providers to implement proactive strategies for identifying and mitigating potential drug interactions to optimize treatment efficacy. Incorporating clinical decision support systems and robust medication reconciliation protocols may offer valuable resources in effectively addressing this challenge. Research has consistently highlighted patient characteristics such as age, comorbidities, length of hospital stay, and polypharmacy as significant risk factors for clinically significant potential drug-drug interactions (pDDIs) (Ayenew et al. , 2020; Rashid et al. , 2021; Wang et al. , 2022). Aging populations, in particular, are susceptible to developing multiple comorbidities necessitating frequent hospitalizations and prolonged stays, often resulting in more complex therapeutic regimens. Physiological changes associated with aging, coupled with variations in pharmacokinetics and pharmacodynamics, further amplify the risk of pDDIs and subsequent adverse outcomes, consequently compromising treatment efficacy. Therefore, understanding and addressing these risk factors are crucial in mitigating the potential harms associated with pDDIs and optimizing patient outcomes (Shareef et al. , 2024). Limitations This prospective observational study may be influenced by selection bias and confounding variables. Data reliance on medical charts and patient self-reports introduces potential errors and incomplete records. Generalizability is limited due to the focus on a specific elderly demographic in a single healthcare setting. Screening for potential drug interactions using software tools may vary in comprehensiveness and accuracy. Lastly, while associations were explored, causal relationships cannot be determined. Future research should incorporate longitudinal designs, larger sample sizes, and diverse patient populations to further elucidate the complex interplay between polypharmacy, drug interactions, and patient outcomes. Additionally, interventions aimed at optimizing medication management practices and minimizing polypharmacy-related risks should be explored to enhance the quality of care for elderly individuals with multiple co-morbidities. Conclusion In conclusion, our study provides valuable insights into the prevalence and factors influencing polypharmacy and potential drug-drug interactions (pDDIs) among elderly individuals. We observed a significant prevalence of polypharmacy, particularly among older adults managing complex health conditions, highlighting the challenges associated with medication management in this demographic. The distribution of drug interactions revealed a pervasive nature across various degrees, with moderate interactions being the most common. Our findings underscore the critical role of healthcare professionals in conducting comprehensive medication reviews and identifying potential pDDIs to ensure safe and effective pharmacotherapy. Furthermore, our analysis revealed important associations between patient characteristics such as age, gender, and polypharmacy, and the occurrence of drug interactions, emphasizing the need for personalized medication management approaches. Addressing these factors is essential for minimizing the risks of adverse drug events and optimizing therapeutic outcomes in geriatric care settings. By adopting evidence-based strategies and implementing proactive measures to mitigate polypharmacy-related risks, healthcare providers can enhance medication safety and improve the overall quality of life for elderly individuals managing complex health conditions. Declarations Ethical Approval and Consent to participate: Ethical clearance was obtained from the Institutional Review Board (IRB) of Yenepoya Medical College and Hospital prior to the commencement of the study. Informed consent was obtained from all participants or their legally authorized representatives before enrollment in the study. The study was conducted in accordance with the principles outlined in the Declaration of Helsinki, and patient confidentiality was strictly maintained throughout the research process. Consent for publication: All authors have provided their consent for publishing this article. Availability of data and materials: Not applicable (this manuscript does not report data generation or analysis). Competing interests: The authors declare that there is no conflict of interest. Funding: There was no funding to conduct this study. Authors' contributions: U.F.K. wrote the main manuscript text; R.S. supervised the study and corrected the manuscript, R.D. did the statistical analysis and prepared figures, tables; M.F. reviewed complete manuscript, analysed the results and corrected the manuscript; S.S.A. and M.M.A. arranged the funding for the study and analysed the results; A.A.C. and M.S. analysed the results and corrected the manuscript; S.G.A. reviewed complete manuscript, analysed the results and added contents in the manuscript. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4488300","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":318963047,"identity":"df8ef0d0-0b6e-46cb-ae27-2f18b8793a49","order_by":0,"name":"Umaima Farheen Khaiser","email":"","orcid":"","institution":"Yenepoya Pharmacy College and Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Umaima","middleName":"Farheen","lastName":"Khaiser","suffix":""},{"id":318963048,"identity":"cedb5856-f81f-47ef-8405-fb88519c38d1","order_by":1,"name":"Rokeya Sultana","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYFACNhAhwcMP5lQAMTNzAzFabOQkG5iB9BmQFkaitKQZGxwAamFsA3EIaDFvb0t8zFNzOHHD+fMHH1fOq43mbwdq+VGxDacWmTPHDhvzHDucOPNGMrPh2W3Hc2ccZmxg7DlzG6cWCYn0NmketsOJfTeY2SQbtx3LbQBqYWZsw6ul/TfPv8OJDecPs/9snHMsdz5hLWnHmHnb0owFDiSzMTY21ORuIKiF51iy5Nw+YCDPSDaWbDh2IHcjUMtBvH5hbzP88OYbMCr5Dz782FBTlzvv/OGDD35U4NYCAkw8CPZhMHkAr3ogYPyBYNcRUjwKRsEoGAUjEAAAX8xdii07U/MAAAAASUVORK5CYII=","orcid":"","institution":"Yenepoya Pharmacy College and Research Centre","correspondingAuthor":true,"prefix":"","firstName":"Rokeya","middleName":"","lastName":"Sultana","suffix":""},{"id":318963049,"identity":"5687ad92-9fc9-4d08-b867-82e4882bb2f8","order_by":2,"name":"Ranajit Das","email":"","orcid":"","institution":"Yenepoya Pharmacy College and Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Ranajit","middleName":"","lastName":"Das","suffix":""},{"id":318963050,"identity":"6d95ca1b-fae0-462a-88dc-848868fe0766","order_by":3,"name":"Mohammad Fareed","email":"","orcid":"","institution":"Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS)","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Fareed","suffix":""},{"id":318963051,"identity":"3be524a2-88b1-4607-9711-00b159fb27d3","order_by":4,"name":"Shahabe Saquib Abullais","email":"","orcid":"","institution":"Central Labs, King Khalid University","correspondingAuthor":false,"prefix":"","firstName":"Shahabe","middleName":"Saquib","lastName":"Abullais","suffix":""},{"id":318963052,"identity":"5c666328-ef3d-41e3-ab79-f515c93bf9b3","order_by":5,"name":"Manea M Alahmari","email":"","orcid":"","institution":"King Khalid University","correspondingAuthor":false,"prefix":"","firstName":"Manea","middleName":"M","lastName":"Alahmari","suffix":""},{"id":318963053,"identity":"ed01534d-1384-42a2-a329-1670d4bf1c64","order_by":6,"name":"Anis Ahmad Chaudhary","email":"","orcid":"","institution":"Imam Mohammad Ibn Saud Islamic University (IMSIU)","correspondingAuthor":false,"prefix":"","firstName":"Anis","middleName":"Ahmad","lastName":"Chaudhary","suffix":""},{"id":318963054,"identity":"1fade904-d088-4845-8e70-ef188967960a","order_by":7,"name":"Mohammad Shahid","email":"","orcid":"","institution":"Prince Sattam Bin Abdulaziz University","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Shahid","suffix":""},{"id":318963055,"identity":"681cd52a-3f0c-40ff-a5c3-416218106d84","order_by":8,"name":"Saeed G. Alzahrani","email":"","orcid":"","institution":"Imam Mohammad Ibn Saud Islamic University (IMSIU)","correspondingAuthor":false,"prefix":"","firstName":"Saeed","middleName":"G.","lastName":"Alzahrani","suffix":""}],"badges":[],"createdAt":"2024-05-28 05:46:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4488300/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4488300/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60428166,"identity":"92f4418d-343e-4105-93d9-71af00f5a6e9","added_by":"auto","created_at":"2024-07-16 15:54:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":10946,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of drug interactions\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4488300/v1/27e57f29db60f63e2d359dba.png"},{"id":60429495,"identity":"4b11966a-20ed-4712-876a-c6bf875ffd9d","added_by":"auto","created_at":"2024-07-16 16:02:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":19686,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe distribution of patient’s drug interactions categorized by severity.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4488300/v1/44e69381bfa9ab8ed3accb1d.png"},{"id":60428141,"identity":"b6a3d89f-5c02-40ac-a05b-e19dbbad51ce","added_by":"auto","created_at":"2024-07-16 15:54:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":15654,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDrug interaction vs polypharmacy less than 5 drugs\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4488300/v1/2464369a1d6d418ecdd4cf77.png"},{"id":60428149,"identity":"674a3ef4-f232-45ce-8ae2-f1ab2935c5c1","added_by":"auto","created_at":"2024-07-16 15:54:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":17184,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDrug interaction vs polypharmacy greater than or equal 5 drugs\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4488300/v1/6b357fe24833b3a3b95aa065.png"},{"id":60428162,"identity":"c84b70fa-1cf9-41db-ae21-0694cd941450","added_by":"auto","created_at":"2024-07-16 15:54:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":17941,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDrug interactions vs polypharmacy less than 9 drugs\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4488300/v1/e6e98f4abe71ddd94a087195.png"},{"id":60429497,"identity":"25f7b8c6-3c32-422e-aead-4006a7d9d35c","added_by":"auto","created_at":"2024-07-16 16:02:48","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":19297,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDrug interactions vs polypharmacy greater than 9 drugs\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4488300/v1/b17e2f83d7e4da772284cfb4.png"},{"id":60428165,"identity":"3b93fa11-1514-44c2-ab26-b794b5ee302e","added_by":"auto","created_at":"2024-07-16 15:54:48","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":141907,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpecific drugs prescribed for the management of various co-morbidities.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-4488300/v1/9aae398c0cd85b2eee051d64.png"},{"id":60428161,"identity":"11c876c7-88d3-47b0-bd97-70116d8ee697","added_by":"auto","created_at":"2024-07-16 15:54:48","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":295822,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMost common pDDI’s\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-4488300/v1/6ee2a0b6d2d9a7694d1c6ca8.png"},{"id":60610993,"identity":"ababd429-86a1-41d8-8aaf-8cb74b235534","added_by":"auto","created_at":"2024-07-18 18:34:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1615382,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4488300/v1/7bb1d4bb-2b6e-4423-b576-a2ef3b210468.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring Polypharmacy and Drug Interactions in Geriatric Patients: A Cross-Sectional Study from India","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePolypharmacy, the concurrent administration of multiple medications, has emerged as a significant concern in healthcare, particularly among the geriatric population (Nelisa Paidamwoyo and Ganga, 2022). With the increasing prevalence of chronic diseases and the corresponding rise in the number of prescribed medications, the complexities and challenges associated with polypharmacy have become more pronounced (Dovjak, 2022; Mangin \u003cem\u003eet al.\u003c/em\u003e, 2023). This phenomenon poses considerable risks to older adults, including adverse drug reactions (ADRs), drug interactions, medication non-adherence, and increased healthcare costs (Borodo \u003cem\u003eet al.\u003c/em\u003e, 2022; Jaffer \u003cem\u003eet al.\u003c/em\u003e, 2023). Consequently, there is a critical need for comprehensive research to understand the consequences of polypharmacy and develop effective strategies to optimize medication management in this vulnerable population (van Poelgeest \u003cem\u003eet al.\u003c/em\u003e, 2023).\u003c/p\u003e \u003cp\u003eThe World Health Organization (WHO) defines polypharmacy as the administration of an excessive number of drugs concurrently (Delara \u003cem\u003eet al.\u003c/em\u003e, 2022). While the simultaneous use of multiple medications may be necessary to manage various health conditions, it can also lead to inappropriate prescribing practices, where patients are prescribed more medications than clinically warranted (Alhumaidi \u003cem\u003eet al.\u003c/em\u003e, 2023). This phenomenon, known as potentially inappropriate prescribing (PIP), encompasses overprescribing, mis-prescribing, and under prescribing (O'Dwyer \u003cem\u003eet al.\u003c/em\u003e, 2018). Inappropriate polypharmacy not only fails to address patients' clinical needs effectively but also increases the risk of adverse outcomes, particularly in older adults (Daunt \u003cem\u003eet al.\u003c/em\u003e, 2023).\u003c/p\u003e \u003cp\u003eA significant concern associated with polypharmacy is the increased risk of adverse drug reactions (ADRs) (Kitaw \u003cem\u003eet al.\u003c/em\u003e, 2023). Adverse reactions can occur due to various mechanisms, including the pharmacological effects of the drugs, individual patient characteristics, and drug-drug interactions (Johannessen Landmark \u003cem\u003eet al.\u003c/em\u003e, 2023). While some ADRs result directly from the intended pharmacological action of the medication, others are unpredictable and may not be related to the drug's mode of action (Yang \u003cem\u003eet al.\u003c/em\u003e, 2023). Older adults are particularly vulnerable to ADRs due to age-related physiological changes that affect the pharmacokinetics and pharmacodynamics of drugs, making them more susceptible to adverse effects (Bellanca \u003cem\u003eet al.\u003c/em\u003e, 2023).\u003c/p\u003e \u003cp\u003eInappropriate polypharmacy and its associated adverse outcomes have prompted researchers to explore factors contributing to this phenomenon (Lau \u003cem\u003eet al.\u003c/em\u003e, 2023). Studies have identified several determinants of polypharmacy, including the presence of multiple chronic conditions, depressive symptoms, and prescribing practices (Chuang \u003cem\u003eet al.\u003c/em\u003e, 2023). Physicians play a crucial role in the development of inappropriate polypharmacy, as highlighted by research emphasizing the importance of clinical decision-making, patient education, and communication in medication management (van Poelgeest \u003cem\u003eet al.\u003c/em\u003e, 2023).\u003c/p\u003e \u003cp\u003eFurthermore, variations in polypharmacy rates across different healthcare settings underscore the need for tailored interventions to address this issue effectively (Turk \u003cem\u003eet al.\u003c/em\u003e, 2022). Public primary care practices often exhibit higher rates of polypharmacy compared to private practices, suggesting the influence of healthcare system factors on prescribing patterns (Ong \u003cem\u003eet al.\u003c/em\u003e, 2018). Addressing these disparities requires a multifaceted approach that considers the unique needs and challenges of diverse patient populations.\u003c/p\u003e \u003cp\u003eDespite advancements in pharmacogenomics and personalized medicine, the integration of these approaches into routine clinical practice remains limited (Principi \u003cem\u003eet al.\u003c/em\u003e, 2023). Pharmacogenetic profiling holds promise in optimizing medication regimens and minimizing polypharmacy-related risks by identifying genetic variations that affect drug metabolism and response (Cacabelos \u003cem\u003eet al.\u003c/em\u003e, 2021). However, barriers such as cost, accessibility, and provider awareness hinder widespread adoption of pharmacogenomic testing (Youssef \u003cem\u003eet al.\u003c/em\u003e, 2022).\u003c/p\u003e \u003cp\u003eFurthermore, existing research on polypharmacy often focuses on drug-drug interactions between two medications and lacks comprehensive assessments of pharmacogenomic issues, plasma drug concentrations, and clinical outcomes. This gap in knowledge hampers our ability to fully understand the multifaceted impacts of polypharmacy and develop evidence-based interventions to address them effectively. Therefore, this study aimed to assess the prevalence of polypharmacy and pDDIs among elderly individuals aged 60 years and above at Yenepoya Medical College and Hospital.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThe study was designed as a cross-sectional investigation aimed at evaluating the consequences associated with polypharmacy in the geriatric population with hypertension and diabetes. A cross-sectional study design was chosen to provide a snapshot of the prevalence and outcomes of polypharmacy in this specific demographic group within a defined period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy Location\u003c/h2\u003e \u003cp\u003e The study was conducted at Yenepoya Medical College and Hospital, Deralakatte, a tertiary care teaching hospital located in Karnataka, India. Yenepoya Medical College and Hospital serves a diverse population from urban, suburban, and rural areas, making it an ideal site for studying the implications of polypharmacy in the geriatric population.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eThe study population comprised individuals aged 65 years and above who were diagnosed with hypertension and diabetes and were receiving multi-drug therapy for these conditions. This age group was chosen due to their increased susceptibility to polypharmacy-related complications and the high prevalence of hypertension and diabetes in older adults.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSample size calculation:\u003c/h2\u003e \u003cp\u003eThe sample size required for the present study was estimated via a sample-size calculator accessible on the website, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.raosoft.com\" target=\"_blank\"\u003ewww.raosoft.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.raosoft.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The sample-size calculator facilitates the creation of a representative sample by mitigating selection bias through a 5% margin of error, operating within a 95% confidence interval, and assuming a 50% response distribution. These parameters help minimize the skewness of the sample size and enable the determination of the maximum feasible sample size. A sample size of 310 patients, including both inpatients and outpatients, were recruited for the study. This sample size was determined based on the estimated prevalence of polypharmacy in the target population.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eInclusion and Exclusion Criteria\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003eInclusion Criteria\u003c/h2\u003e \u003cp\u003eIndividuals aged 65 years and above.\u003c/p\u003e \u003cp\u003ePatients receiving multi-drug therapy, defined as \u0026ge;\u0026thinsp;5 drugs for outpatients (OP) and \u0026ge;\u0026thinsp;9 drugs for inpatients (IP).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eExclusion Criteria\u003c/h2\u003e \u003cp\u003eCritically ill patients requiring intensive care.\u003c/p\u003e \u003cp\u003ePatients with severe cognitive impairment or mental retardation.\u003c/p\u003e \u003cp\u003eIndividuals unwilling to participate in the study.\u003c/p\u003e \u003cp\u003eThese criteria were established to ensure that the study population was representative of older adults with other comorbid conditions who were at risk of experiencing polypharmacy-related complications.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eData Collection Tool\u003c/h2\u003e \u003cp\u003eA standardized data collection form was developed to systematically gather demographic information, medical history, and medication details from the study participants. The data collection form included sections for recording demographic characteristics such as age, gender, as well as medical history including comorbidities, and previous medication use. Additionally, a detailed medication list was compiled to document all drugs prescribed to each participant, including dosage, frequency, and route of administration. The data collection tool utilized in this study primarily consisted of two widely recognized sources for assessing potential drug-drug interactions (DDIs) - Medscape and UpToDate (Lexicomp). These resources were employed to meticulously evaluate the occurrence of DDIs at the time of treatment and discharge of the patients.\u003c/p\u003e \u003cp\u003eMedscape is a comprehensive online medical resource that offers a diverse range of clinical information, including drug information, medical news, expert commentary, and educational resources (Chandran \u003cem\u003eet al.\u003c/em\u003e, 2020). It provides a user-friendly platform for healthcare professionals to access up-to-date and evidence-based information on medications, diseases, and treatment guidelines. One of the key features of Medscape is its drug interaction checker, which allows users to input a list of medications and quickly identify potential interactions based on current knowledge and clinical evidence. The interaction checker analyzes drug combinations for potential interactions, including contraindications, adverse effects, and drug compatibility issues. It provides detailed information on the mechanism of interaction, severity, and management recommendations to guide clinical decision-making (Kavitha \u003cem\u003eet al.\u003c/em\u003e, 2022).\u003c/p\u003e \u003cp\u003eLexicomp or UpToDate is a widely used clinical decision support system that provides evidence-based medical information and treatment recommendations for healthcare professionals. It offers comprehensive coverage of various medical specialties, diseases, and therapeutic interventions, making it a valuable resource for clinical practice. UpToDate includes an extensive drug database developed by Lexicomp, which is renowned for its accuracy and reliability. The drug database features a drug interaction checker tool that allows users to input multiple medications and assess potential interactions in real-time. The tool utilizes advanced algorithms to analyze drug combinations and identify interactions based on factors such as pharmacokinetics, pharmacodynamics, and clinical relevance. It provides concise summaries of each interaction, along with recommendations for monitoring, dose adjustments, and alternative therapies (Bossaer \u003cem\u003eet al.\u003c/em\u003e, 2022).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData Collection Process\u003c/h2\u003e \u003cp\u003eThe data collection process involved several steps to ensure comprehensive and accurate data collection. The timeline involved data collection over a period of six months, from January to June 2023.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eScreening of Patients\u003c/strong\u003e \u003cp\u003ePatients were screened based on the inclusion and exclusion criteria outlined in the study protocol. Eligible individuals were approached for participation and provided with detailed information about the study objectives, procedures, and potential risks and benefits.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInformed Consent\u003c/strong\u003e \u003cp\u003e\u003cb\u003eProcess\u003c/b\u003e: Informed consent was obtained from all participants or their legally authorized representatives prior to enrollment in the study. The consent process included a thorough explanation of the study purpose, procedures, confidentiality measures, and the voluntary nature of participation. Participants were given ample time to ask questions and clarify any concerns before providing written consent to participate.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eData Collection\u003c/strong\u003e \u003cp\u003eTrained research personnel, including nurses and medical assistants, administered the data collection from the patients Data file. Demographic information, medical history, and medication details were recorded for each participant according to the predefined variables in the data collection form. During the data collection process, trained research personnel accessed the Medscape and UpToDate (Lexicomp) platforms to evaluate potential drug-drug interactions during the treatment and at the time of discharge of the patients.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003eData Analysis for Potential Drug\u0026ndash;Drug Interactions\u003c/h2\u003e \u003cp\u003eDetection of potential drug-drug interactions was conducted using reliable sources such as Medscape, UpToDate (Lexicomp) and standard reference books on drug interactions at the time of treatment and discharge of the patients. Each participant's medication list was systematically reviewed to identify potential interactions based on known pharmacological mechanisms, contraindications, and drug compatibility.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eData and Statistical Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics, including measures of central tendency (mean, median) and dispersion (standard deviation, range), were calculated to summarize the demographic characteristics and medication profiles of the study population. Inferential statistics, such as chi-square tests, were employed to analyze pharmacogenomic data and assess associations between genetic variants and drug responses. Correlation and regression analyses were performed to evaluate the relationship between independent variables (e.g., drug exposure) and dependent variables (e.g., physiological parameters). All statistical analyses were conducted using appropriate software packages such as SPSS or R, and statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe study analyzed data from a total of 310 elderly patients who were admitted to the Inpatient and attended the Outpatient departments of Yenepoya Medical College and Hospital. The demographic characteristics of the study population are summarized in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e: The mean age of the study participants was 70.25 years, with a standard deviation of 5.56 years. The age of the patients ranged from 31 to 92 years, indicating a wide age distribution within the sample. Out of the 310 participants, 122 (39.4%) were female, and 188 (60.6%) were male. The study had a higher representation of male participants compared to females. The majority of the participants fell within the age group of 66\u0026ndash;70 years, comprising 35.8% of the total sample. This was followed by the age groups of 71\u0026ndash;75 years (24.2%), 60\u0026ndash;65 years (22.3%), 76\u0026ndash;80 years (13.9%), and 81 years and above (3.9%), respectively. The distribution indicates a predominance of participants in the middle to older age brackets. A significant proportion of the study participants (86.1%) were admitted to the Inpatient ward, while the remaining 13.9% sought medical care from the Outpatient department. This suggests a higher prevalence of polypharmacy and associated conditions among hospitalized elderly patients compared to those receiving outpatient care.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the distribution of drug interactions observed among the elderly patients included in the study. The majority of patients experienced varying degrees of drug interactions, with the most common occurrences being 6 drug interactions (\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e), followed by 3 drug interactions (\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e) and 4 drug interactions (\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e). Smaller proportions of patients experienced 1, 2, 5, 7, 8, 9, and 10 drug interactions, with frequencies ranging from 1 to 52 cases. Overall, the findings highlight the prevalence of polypharmacy-related drug interactions among the elderly population, underscoring the need for careful medication management and monitoring to mitigate potential adverse effects and optimize therapeutic outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 Demographic characteristics of study participants\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.8034188034188%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.28205128205128%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.914529914529915%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.8034188034188%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e60-65 years\u003c/p\u003e\n \u003cp\u003e66-70 years\u003c/p\u003e\n \u003cp\u003e71-75 years\u003c/p\u003e\n \u003cp\u003e76-80 years\u003c/p\u003e\n \u003cp\u003e81 and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.28205128205128%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.914529914529915%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e22.3\u003c/p\u003e\n \u003cp\u003e35.8\u003c/p\u003e\n \u003cp\u003e24.2\u003c/p\u003e\n \u003cp\u003e13.9\u003c/p\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.8034188034188%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.28205128205128%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.914529914529915%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e60.6\u003c/p\u003e\n \u003cp\u003e39.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.8034188034188%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIn-Patient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.28205128205128%\" valign=\"top\"\u003e\n \u003cp\u003e267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.914529914529915%\" valign=\"top\"\u003e\n \u003cp\u003e86.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.8034188034188%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOut-Patients\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.28205128205128%\" valign=\"top\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.914529914529915%\" valign=\"top\"\u003e\n \u003cp\u003e13.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eFigure 2 illustrates the distribution of drug interactions among the study participants based on severity levels. Among the study participants, 67 (21.6%) patients experienced mild drug interactions. Mild drug interactions are typically characterized by minor symptoms or discomfort that do not significantly impact the patient\u0026apos;s health or require medical intervention. The majority of patients, comprising 155 (50.0%) individuals, experienced moderate drug interactions. Moderate drug interactions are characterized by symptoms that may require medical attention or intervention but are not life-threatening. A subset of patients, totaling 88 (28.4%) individuals, experienced severe drug interactions. Severe drug interactions are characterized by significant symptoms or complications that may pose a threat to the patient\u0026apos;s health and require immediate medical intervention or discontinuation of the offending medication. Overall, the results provide insights into the distribution of drug interactions among the study participants, highlighting the varying severity levels of drug interactions experienced within the elderly population. These findings underscore the importance of vigilant monitoring and management of medication-related adverse events to ensure patient safety and optimize therapeutic outcomes.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the distribution of drug interactions among patients with polypharmacy, categorized by less than 5 drugs prescribed. The bar chart visually demonstrates the comparison between instances of drug interactions among patients with polypharmacy of less than 5 drugs and those with polypharmacy of greater than 5 drugs. The red bar indicating cases of lesser polypharmacy and the yellow bar representing cases of greater polypharmacy. The red bar represents instances where patients were prescribed less than 5 drugs. According to the data, 11 patients fell into this category. The green bar represents instances where patients were prescribed greater than 5 drugs.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e visualizes the distribution of drug interactions among patients with polypharmacy, categorized by greater than or equal to 5 drugs prescribed. The yellow bar corresponds to cases of greater polypharmacy (greater than or equal to 5 drugs). According to the data, 281 patients fell into this category and the red bar corresponds to cases of lesser polypharmacy (less than 5 drugs).\u003c/p\u003e\n\u003cp\u003eFigure 5 illustrates the distribution of drug interactions among patients with polypharmacy, categorized by less than 9 drugs prescribed. The red bar corresponds to cases of lesser polypharmacy (less than 9 drugs). According to data, 39 patients fell into this category; while the pink bar corresponds to cases of greater polypharmacy (greater than 9 drugs).\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e depicts the distribution of drug interactions among patients with polypharmacy, categorized by greater than 9 drugs prescribed. The brown bar corresponds to cases of greater polypharmacy (greater than 9 drugs). According to data, 214 patients fell into this category. while the green bar corresponds to cases of lesser polypharmacy (less than 9 drugs).\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e illustrates the distribution of Specific drugs prescribed for the management of various co-morbidities. Most frequently prescribed drugs were pantoprazole (222), aspirin (96), metformin (85), frusemide (85), and atorvastatin (81).\u003c/p\u003e\n\u003cp\u003eTable 2 Age and the number of drugs prescribed at treatment.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.462562396006657%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCorrelation coefficient\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.62396006655574%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChi-squared\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.785357737104825%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDegrees of freedom (df)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.12811980033278%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.462562396006657%\" valign=\"top\"\u003e\n \u003cp\u003e0.8978203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.62396006655574%\" valign=\"top\"\u003e\n \u003cp\u003e536.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.785357737104825%\" valign=\"top\"\u003e\n \u003cp\u003e486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.12811980033278%\" valign=\"top\"\u003e\n \u003cp\u003e0.05718\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSignificant value \u0026lt;0.05; Correlation test; Chi-square test\u003c/p\u003e\n\u003cp\u003eTable 3 shows the age and number of drugs prescribed at treatment. The correlation coefficient between age and the number of drugs prescribed at treatment is approximately 0.8978203. This indicates a strong positive correlation between these two variables. A correlation coefficient of 1 represents a perfect positive correlation, and a coefficient of -1 represents a perfect negative correlation. In this case, a coefficient close to 1 suggests that as age increases, the number of drugs prescribed at treatment tends to increase as well. Based on the chi-squared test results, the p-value is 0.05718, which is slightly below the typical significance level of 0.05. This suggests that there is a borderline association between age and the number of drugs prescribed at treatment. While the association is not statistically significant at the conventional threshold, it indicates a potential relationship between age and the number of drugs prescribed, warranting further investigation.\u003c/p\u003e\n\u003cp\u003eTable 3 Age and the number of drugs prescribed at discharge.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.45388788426763%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003echi-squared\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.45388788426763%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDegrees of freedom (df)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.09222423146474%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.45388788426763%\" valign=\"top\"\u003e\n \u003cp\u003e447.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.45388788426763%\" valign=\"top\"\u003e\n \u003cp\u003e486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.09222423146474%\" valign=\"top\"\u003e\n \u003cp\u003e0.8915\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSignificant value \u0026lt;0.05; Chi-square test\u003c/p\u003e\n\u003cp\u003eTable 3 illustrates the age and the number of drugs prescribed at discharge. The p-value (0.8915) is significantly greater than the typical significance level of 0.05. This suggests that there is no statistically significant association between age categories and the number of drugs prescribed at discharge. In other words, based on the chi-squared test, there is no evidence to suggest an association between age categories and the number of drugs prescribed at discharge.\u003c/p\u003e\n\u003cp\u003eTable 4 Gender vs. number of drugs prescribed at treatment.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.124792013311147%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46089850249584%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.95008319467554%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDegrees of freedom (df)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.46422628951747%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.124792013311147%\" valign=\"top\"\u003e\n \u003cp\u003e2.1083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46089850249584%\" valign=\"top\"\u003e\n \u003cp\u003e0.0696 - 2.1339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.95008319467554%\" valign=\"top\"\u003e\n \u003cp\u003e156.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.46422628951747%\" valign=\"top\"\u003e\n \u003cp\u003e0.03659\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSignificant value \u0026lt;0.05; t-statistic test\u003c/p\u003e\n\u003cp\u003eTable 4 shows gender vs. number of drugs prescribed at treatment. There is a statistically significant difference in the number of drugs prescribed at treatment between females and males. The mean number of drugs prescribed for females is approximately 13.1087, while the mean number of drugs prescribed for males is approximately 12.0070. The 95 percent confidence interval suggests that the true difference in means between the two groups is likely to fall between 0.0696 and 2.1339. The p-value of 0.03659 is less than the typical significance level of 0.05, indicating the statistical significance of the difference.\u003c/p\u003e\n\u003cp\u003eTable 5 Drug interactions vs. number of drugs prescribed at treatment.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.543046357615893%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.556291390728475%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDegrees of freedom (df)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.05960264900662%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSum of Squares\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Sum Sq)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.397350993377483%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Square\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Mean Sq)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.735099337748345%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eF value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.70860927152318%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Pr (\u0026gt;F))\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.543046357615893%\" valign=\"top\"\u003e\n \u003cp\u003eDrug Interactions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.556291390728475%\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.05960264900662%\" valign=\"top\"\u003e\n \u003cp\u003e461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.397350993377483%\" valign=\"top\"\u003e\n \u003cp\u003e51.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.735099337748345%\" valign=\"top\"\u003e\n \u003cp\u003e4.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.70860927152318%\" valign=\"top\"\u003e\n \u003cp\u003e4.77e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.543046357615893%\" valign=\"top\"\u003e\n \u003cp\u003eResidual sum of squares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.556291390728475%\" valign=\"top\"\u003e\n \u003cp\u003e229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.05960264900662%\" valign=\"top\"\u003e\n \u003cp\u003e2790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.397350993377483%\" valign=\"top\"\u003e\n \u003cp\u003e12.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.735099337748345%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.70860927152318%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSignificant value \u0026lt;0.05; ANOVA test\u003c/p\u003e\n\u003cp\u003eTable 5 shows drug interactions vs. number of drugs prescribed at treatment. There is a highly significant difference in the means of \u0026quot;Number of drugs prescribed at treatment\u0026quot; among different levels of \u0026quot;Drug Interactions.\u0026quot; The small p-value (4.77e-05) associated with the F-statistic suggests that the observed differences in means are highly unlikely to occur by chance. This indicates that the number of drug interactions significantly affects the number of drugs prescribed at treatment, as evidenced by the ANOVA test results.\u003c/p\u003e\n\u003cp\u003eTable 6 Age vs drug interactions\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.45388788426763%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003echi-squared\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.45388788426763%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDegrees of freedom (df)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.09222423146474%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.45388788426763%\" valign=\"top\"\u003e\n \u003cp\u003e207.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.45388788426763%\" valign=\"top\"\u003e\n \u003cp\u003e243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.09222423146474%\" valign=\"top\"\u003e\n \u003cp\u003e0.9535\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSignificant value \u0026lt;0.05; Chi-square test\u003c/p\u003e\n\u003cp\u003eTable 6 shows age vs drug interactions. The p-value (0.9535) is greater than the typical significance level of 0.05. This suggests that there is no statistically significant association between age categories and drug interactions.\u003c/p\u003e\n\u003cp\u003eTable 7 Age vs mild drug interactions\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.134453781512605%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.966386554621849%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDegrees of freedom (df)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.142857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSum of Squares\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Sum Sq)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.478991596638654%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Square\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Mean Sq)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.798319327731093%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eF value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.478991596638654%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Pr (\u0026gt;F))\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.134453781512605%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMild\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.966386554621849%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.142857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.478991596638654%\" valign=\"top\"\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.798319327731093%\" valign=\"top\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.478991596638654%\" valign=\"top\"\u003e\n \u003cp\u003e0.839\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.134453781512605%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eResiduals\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.966386554621849%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.142857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e108.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.478991596638654%\" valign=\"top\"\u003e\n \u003cp\u003e21.687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.798319327731093%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.478991596638654%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSignificant value \u0026lt;0.05; ANOVA test\u003c/p\u003e\n\u003cp\u003eTable 7 shows age vs mild drug interactions. The results of the one-way ANOVA test for the \u0026quot;mild\u0026quot; variable suggest that there is no statistically significant difference in the mean age based on the presence or absence of mild drug interactions. The p-value associated with the \u0026quot;mild\u0026quot; factor is 0.839, which is much greater than the typical significance level of 0.05. A p-value greater than 0.05 indicates that we fail to reject the null hypothesis. In other words, age does not appear to be significantly associated with the presence of mild drug interactions in this study population. This suggests that age alone may not be a determining factor for the occurrence of mild drug interactions. Other factors such as medication regimen, comorbidities, and individual pharmacokinetics may play a more substantial role in influencing the presence of mild drug interactions.\u003c/p\u003e\n\u003cp\u003eTable 8 Age vs moderate drug interactions\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.609756097560975%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.447154471544716%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDegrees of freedom (df)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.585365853658537%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSum of Squares\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Sum Sq)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Square\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Mean Sq)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.447154471544716%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eF value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.910569105691057%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Pr (\u0026gt;F))\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.609756097560975%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModerate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.447154471544716%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.585365853658537%\" valign=\"top\"\u003e\n \u003cp\u003e262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e32.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.447154471544716%\" valign=\"top\"\u003e\n \u003cp\u003e1.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.910569105691057%\" valign=\"top\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.609756097560975%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eResiduals\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.447154471544716%\" valign=\"top\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.585365853658537%\" valign=\"top\"\u003e\n \u003cp\u003e5994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e21.687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.447154471544716%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.910569105691057%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSignificant value \u0026lt;0.05; ANOVA test\u003c/p\u003e\n\u003cp\u003eTable 8 shows age vs moderate drug interactions. Based on the results of the one-way ANOVA test, there is no statistically significant difference in the mean age among the different levels of the \u0026quot;moderate\u0026quot; variable. The p-value for the \u0026quot;moderate\u0026quot; factor is 0.39, which is greater than the typical significance level of 0.05. Therefore, we fail to reject the null hypothesis.\u003c/p\u003e\n\u003cp\u003eTable 9 Age vs severe drug interactions\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.635179153094462%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.472312703583063%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDegrees of freedom (df)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.612377850162865%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSum of Squares\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Sum Sq)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Square\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Mean Sq)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eF value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Pr (\u0026gt;F))\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.635179153094462%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSevere\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.472312703583063%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.612377850162865%\" valign=\"top\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"top\"\u003e\n \u003cp\u003e20.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"top\"\u003e\n \u003cp\u003e0.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\"\u003e\n \u003cp\u003e0.828\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.635179153094462%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eResiduals\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.472312703583063%\" valign=\"top\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.612377850162865%\" valign=\"top\"\u003e\n \u003cp\u003e4070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"top\"\u003e\n \u003cp\u003e42.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.938110749185668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.403908794788272%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSignificant value \u0026lt;0.05; ANOVA test\u003c/p\u003e\n\u003cp\u003eTable 9 illustrates age vs severe drug interactions. The results of the one-way ANOVA test for the \u0026quot;severe\u0026quot; variable suggest that there is no statistically significant difference in the mean age based on the \u0026quot;severe\u0026quot; categories. The p-value for the \u0026quot;severe\u0026quot; factor is 0.828, which is much greater than the typical significance level of 0.05. Therefore, we fail to reject the null hypothesis.\u003c/p\u003e\n\u003cp\u003eTable 10 Gender vs drug interactions\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.35804701627487%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.91139240506329%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDegrees of freedom (df)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.730560578661844%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.35804701627487%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2.1083 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.91139240506329%\" valign=\"top\"\u003e\n \u003cp\u003e156.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.730560578661844%\" valign=\"top\"\u003e\n \u003cp\u003e0.03659\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSignificant value \u0026lt;0.05; Two sample t-test\u003c/p\u003e\n\u003cp\u003eTable 10 shows gender vs drug interactions. The p-value (0.03659) is less than the significance level of 0.05, suggesting that there is evidence to reject the null hypothesis. This indicates a statistically significant difference in the mean number of drugs prescribed at treatment between genders. Furthermore, the confidence interval for the difference in means does not include zero, further supporting the conclusion of a significant difference. Therefore, based on the results of the t-test, gender appears to have a significant effect on the number of drugs prescribed at treatment, with one gender receiving a significantly different number of drugs compared to the other.\u003c/p\u003e\n\u003cp\u003eTable 11 Gender vs mild drug interactions\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.785357737104825%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.296173044925126%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.792013311148086%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDegrees of freedom (df)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.126455906821963%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.785357737104825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 1.3147 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.296173044925126%\" valign=\"top\"\u003e\n \u003cp\u003e-0.0501 - 0.2515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.792013311148086%\" valign=\"top\"\u003e\n \u003cp\u003e235.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.126455906821963%\" valign=\"top\"\u003e\n \u003cp\u003e0.1899\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSignificant value \u0026lt;0.05; Two sample t test\u003c/p\u003e\n\u003cp\u003eTable 11 shows gender vs mild drug interactions. The Two Sample t-test comparing the \u0026quot;mild\u0026quot; variable between males and females resulted in a p-value of 0.1899. Since the p-value is greater than the typical significance level of 0.05, we fail to reject the null hypothesis. This suggests that there is no statistically significant difference in the \u0026quot;mild\u0026quot; variable between males and females in the dataset. Thus, based on the t-test results, gender does not appear to have a significant effect on the occurrence of mild drug interactions in the study population. Other factors may be influencing the presence of mild drug interactions, such as specific medications, underlying health conditions, or genetic factors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 12 Gender vs moderate drug interactions\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.622296173044926%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.12811980033278%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.45757071547421%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDegrees of freedom (df)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.792013311148086%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.622296173044926%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; -0.17263 \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.12811980033278%\" valign=\"top\"\u003e\n \u003cp\u003e-0.6423 - 0.5388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.45757071547421%\" valign=\"top\"\u003e\n \u003cp\u003e232.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.792013311148086%\" valign=\"top\"\u003e\n \u003cp\u003e0.8631\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSignificant value \u0026lt;0.05; Two sample t test\u003c/p\u003e\n\u003cp\u003eTable 12 illustrates gender vs moderate drug interactions. The Two-Sample t-test comparing the \u0026quot;moderate\u0026quot; variable between males and females resulted in a p-value of 0.8631. With a p-value greater than the typical significance level of 0.05, we fail to reject the null hypothesis. This means that there is insufficient evidence to suggest a significant difference in the \u0026quot;moderate\u0026quot; variable between males and females. Therefore, based on the t-test results, gender does not appear to have a significant effect on the occurrence of moderate drug interactions in the study population.\u003c/p\u003e\n\u003cp\u003eTable 13 Gender vs severe drug interactions\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.788685524126457%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.29450915141431%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.45757071547421%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDegrees of freedom (df)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.459234608985025%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.788685524126457%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-1.1092 \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.29450915141431%\" valign=\"top\"\u003e\n \u003cp\u003e-0.4821 - 0.1352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.45757071547421%\" valign=\"top\"\u003e\n \u003cp\u003e165.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.459234608985025%\" valign=\"top\"\u003e\n \u003cp\u003e0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSignificant value \u0026lt;0.05; Two sample t test\u003c/p\u003e\n\u003cp\u003eTable 13 shows gender vs severe drug interactions. The Welch Two Sample t-test comparing the \u0026quot;severe\u0026quot; variable between males and females resulted in a p-value of 0.269. Since the p-value is greater than the typical significance level of 0.05, we fail to reject the null hypothesis. This suggests that there is no statistically significant difference in the \u0026quot;severe\u0026quot; variable between males and females in the dataset. Therefore, based on the t-test results, gender does not appear to have a significant effect on the occurrence of severe drug interactions in the study population.\u003c/p\u003e\n\u003cp\u003eTable 14 Common Potential Drug-drug interactions:\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrug combinations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePDDIs (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical types of PDDIs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMechanism of PDDIs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePotential risk\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eMetoprolol + Timolol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e35 (11.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eSevere\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eBoth increase anti-hypertensive blocking channel\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eNifedipine + Tolvaptan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e29 (9.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eSevere\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eIncreases the level of tolvaptan by affecting hepatic enzyme metabolism\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eNifedipine + Amlodipine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e25 (8.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eSevere\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eIncreases the level of amlodipine by affecting hepatic enzyme metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eSodium Bicarbonate + Levofloxacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e17 (5.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eSevere\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eDecreases the level of levofloxacin by inhibition of GI absorption\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eLevofloxacin + Ondansetron\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e16 (5.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eSevere\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePK and PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eIncreases Qtc interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eFludrocortisone + Tolvaptan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e14 (5.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eSevere\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eFludrocortisone decreases the level of tolvaptan by p-glycoprotein efflux transporter\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eAzithromycin + Heparin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e12 (3.87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eSevere\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePD, Synergism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eIncreases the effect of heparin by decreasing metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eDexamethasone + Ivabradine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e11 (3.54%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePD, Antagonism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eDecreases the effect of ivabradine by affecting hepatic enzyme metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eSodium Bicarbonate + Levofloxacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e11 (3.54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePD, Antagonism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eSodium bicarbonate decreases the level of levofloxacin by inhibition of GI absorption\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eCeftriaxone + Calcium Acetate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e9 (2.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePD, Antagonism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eCalcium salts enhance toxic effect of ceftriaxone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eQuetiapine + Pramipexole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e9 (2.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePD, Synergism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003ePharmacodynamic synergism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eCeftriaxone + Enoxaparin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e7 (2.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePD, Antagonism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eIncreases the effect of enoxaparin by anticoagulation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eTramadol + Gabapentin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e6 (1.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eenhances CNS depressant effect of tramadol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eTramadol + Desloratadine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e6 (1.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eEnhances CNS depressant effect of tramadol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003ePantoprazole + Digoxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e6 (1.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eIncreases the level of digoxin by increasing gastric pH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eClopidogrel And Aspirin + Pantoprazole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e5 (1.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eDecreases serum conc. Of active metabolite of clopidogrel\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eDexamethasone + Disulfiram\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e5 (1.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eDisulfiram may enhance the toxic effect of dexamethasone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eClonidine + Metoprolol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e5 (1.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePD, Synergism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003ePharmacodynamic synergism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eRamipril+ Pregabalin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e4 (1.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePD, Synergism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003ePharmacodynamic synergism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eSpironolactone + Potassium Chloride\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e4 (1.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eIncreases serum potassium\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eEscitalopram + Quetiapine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e4 (1.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eIncreases toxicity of quetiapine by QTc interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eHaloperidol + Pramipexole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e4 (1.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePD, Antagonism\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003ePharmacodynamic antagonism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eQuetiapine + Pramipexole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e5 (1.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePD, Antagonism\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003ePharmacodynamic antagonism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eQuetiapine + Levodopa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e5 (1.61%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePD, Antagonism\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003ePharmacodynamic antagonism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eHaloperidol + Ivabradine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e4 (1.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eIncreases the level of ivabradine by affecting hepatic enzyme metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eRanolazine + Metformin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e4 (1.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePD, Antagonism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eIncreases the effect of metformin by decreasing the elimination\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eDiltiazem + Ivabradine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e4 (1.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePD, Synergism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eIncreases the level of ivabradine by affecting hepatic enzyme metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eHydrocortisone + Ranolazine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e4 (1.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eDecreases the level of ranolazine by affecting hepatic enzyme metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eDiltiazem + Budesonide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e4 (1.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eIncreases the level of budesonide by affecting hepatic enzyme metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eBudesonide + Spironolactone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e4 (1.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePK and PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eDecreases the level of spironolactone affecting hepatic enzyme metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eCalcium Gluconate + Gentamycin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e4 (1.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePD, Synergism\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003ePharmacodynamic synergism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eTorsemide + Gentamycin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e3 (0.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePD, Synergism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003ePharmacodynamic synergism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eCalcium Gluconate + Doxycycline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e3 (0.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePD, Antagonism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eeither decreases the level of other by inhibition of GI absorption\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eCeftriaxone + Heparin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e3 (0.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePK and PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eCeftriaxone increases the level of heparin by anticoagulation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003ePiperacillin + Heparin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e3 (0.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePD, Antagonism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003ePiperacillin increases the level of heparin by anticoagulation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eDoxycycline + Ivabradine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e2 (0.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eMild\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePK and PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eDoxycycline increases the level of ivabradine by affecting hepatic enzyme metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eZolpidem + Pregabalin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e2 (0.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePD, Antagonism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003ePregabalin enhances CNS depressant effect of zolpidem\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eSpironolactone + Potassium Chloride\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e1 (0.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePD, Antagonism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eBoth increases serum potassium\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eTramadol + Linezolid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e1 (0.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePD, Antagonism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eLinezolid enhances the serotonergic effect of tramadol, which results in serotonin syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eTramadol + Morphine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e1 (0.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eMorphine increases CNS depressant effect of tramadol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eLevofloxacin + Aceclofenac\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e1 (0.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePD, Antagonism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eAceclofenac increases the neuroexcitatory effect of levofloxacin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eMirtazapine + Azithromycin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e1 (0.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePD, Antagonism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eBoth increases QTc interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eMagnesium Hydroxide + Doxycycline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e1 (0.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePK and PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eMagnesium hydroxide decreases the level of \u0026nbsp; \u0026nbsp;doxycycline by inhibition of GI absorption\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eDoxycycline + Amoxicillin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e1 (0.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePK and PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003ePharmacodynamic antagonism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eAcenocoumarol + Aspirin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e1 (0.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePD, Antagonism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eAspirin enhances the anticoagulant effect of acenocoumarol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eClopidogrel + Pantoprazole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e1 (0.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePK and PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003ePantoprazole decreases serum conc. Of active metabolite of \u0026nbsp; \u0026nbsp; clopidogrel\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eLevofloxacin + Etodolac\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e1 (0.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePD, Antagonism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eEtodolac enhaces neuroexcitatory effect of levofloxacin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eAmitriptyline + Ondansetron\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e1 (0.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePD, Antagonism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eBoth increase sedation/ either increases toxicity of other by serotonin level\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.45951859956236%\" valign=\"top\"\u003e\n \u003cp\u003eTicagrelor + Aspirin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.50328227571116%\"\u003e\n \u003cp\u003e1 (0.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.223194748358862%\" valign=\"top\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61487964989059%\" valign=\"top\"\u003e\n \u003cp\u003ePK and PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.199124726477024%\" valign=\"top\"\u003e\n \u003cp\u003eAspirin increases antiplatelet effect of ticagrelor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 14 shows the most commonly occurring drug drug interactions\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, medical charts of elderly individuals aged 60 years and above, who commonly present with multiple co-morbidities, were meticulously examined to discern the prevalence of polypharmacy and potential drug-drug interactions. Polypharmacy, characterized by the simultaneous use of numerous medications, is a significant concern, especially among older adults managing complex health conditions (Masnoon \u003cem\u003eet al.\u003c/em\u003e, 2017). The findings revealed a notable prevalence of polypharmacy within this demographic, underscoring the intricate medication management challenges faced by older individuals with multiple co-morbidities. The distribution of participants across different age groups indicates that the study predominantly focused on older adults, with a mean age of approximately 70.25 years. The largest proportion of participants fell within the 66\u0026ndash;70 years age group (35.8%), followed by those aged 71\u0026ndash;75 years (24.2%). This distribution reflects the demographic trend of an aging population, which is consistent with global population aging patterns observed in many countries (UnitedNations, 2020). Research focusing on older adults is particularly relevant due to the unique healthcare needs and challenges faced by this demographic group, including polypharmacy and increased susceptibility to adverse drug reactions (Gnjidic \u003cem\u003eet al.\u003c/em\u003e, 2012).\u003c/p\u003e \u003cp\u003eThe gender distribution among the study participants shows a higher representation of males (60.6%) compared to females (39.4%). This gender disproportionality could be attributed to various factors such as differences in healthcare-seeking behavior, prevalence of specific health conditions, and access to healthcare services. Several studies have highlighted gender-based disparities in healthcare utilization and medication management practices (Bertakis \u003cem\u003eet al.\u003c/em\u003e, 2000; Weng \u003cem\u003eet al.\u003c/em\u003e, 2022). The majority of participants (86.1%) were recruited from in-patient settings, while a smaller proportion (13.9%) were out-patients. This distribution reflects the study's focus on individuals receiving medical care within hospital settings, where complex medical conditions and polypharmacy are often more prevalent. In-patient populations typically have higher acuity levels and are more likely to be exposed to multiple medications, increasing the risk of drug interactions and adverse events (Masnoon \u003cem\u003eet al.\u003c/em\u003e, 2017). Understanding the sociodemographic characteristics of the study population is crucial for contextualizing the research findings and informing healthcare policies and practices (Shoveller \u003cem\u003eet al.\u003c/em\u003e, 2016). The observed age and gender distributions highlight the need for personalized approaches to medication management, considering the unique clinical profiles and preferences of older adults, as well as gender-specific healthcare needs.\u003c/p\u003e \u003cp\u003eThe distribution of drug interactions in the current study illustrates a noteworthy prevalence across various degrees, with the most prevalent occurrences encompassing six drug interactions, followed closely by three and four drug interactions. Although smaller proportions of patients experienced fewer or more drug interactions, the overarching trend highlights the pervasive nature of polypharmacy-related challenges among the elderly. These findings align with existing literature (Assefa \u003cem\u003eet al.\u003c/em\u003e, 2020; Santos-D\u0026iacute;az \u003cem\u003eet al.\u003c/em\u003e, 2020) emphasizing the heightened vulnerability of older adults to polypharmacy and associated adverse outcomes, including increased risk of medication errors, adverse drug reactions, hospitalizations, and mortality. Moreover, the findings underscore the pivotal role of healthcare professionals in conducting comprehensive medication reviews, identifying, and resolving potential drug interactions, and engaging in shared decision-making with patients to ensure safe and effective pharmacotherapy (Geurts \u003cem\u003eet al.\u003c/em\u003e, 2012; Stead \u003cem\u003eet al.\u003c/em\u003e, 2017). By prioritizing patient-centered care and adopting evidence-based strategies to mitigate polypharmacy-related risks, healthcare providers can strive towards improving medication safety and enhancing the overall quality of life for elderly individuals managing complex health conditions (Varghese \u003cem\u003eet al.\u003c/em\u003e, 2022).\u003c/p\u003e \u003cp\u003eIn the current study, the majority of drug interactions were classified as 'moderate' in severity (50.0%), followed by 'severe' interactions (28.4%). It is crucial to assess the severity of potential drug-drug interactions (pDDIs) to comprehend their clinical implications and ensure appropriate management. Only 21.6% of the identified interactions were categorized as 'mild,' emphasizing the need for vigilant monitoring to prevent adverse outcomes. This distribution may indicate physicians' awareness of pDDI risks, leading to tailored drug therapy to minimize or avoid such interactions. These findings are consistent with studies by Noor \u003cem\u003eet al.\u003c/em\u003e (2019), Obeid \u003cem\u003eet al.\u003c/em\u003e (2022), and Admassie \u003cem\u003eet al.\u003c/em\u003e (2013) which similarly observed a predominance of 'moderate' interactions. However, contrary results were reported by Rabba \u003cem\u003eet al.\u003c/em\u003e (2020) and Eneh \u003cem\u003eet al.\u003c/em\u003e (2020) where a higher proportion of interactions were classified as 'severe.' Discrepancies in severity classification methods among studies may contribute to these differences. Furthermore, studies have highlighted the importance of considering drug mechanisms of action in managing DDIs, often necessitating dose adjustments or changes in medication regimen.\u003c/p\u003e \u003cp\u003eOur study's exploration of the distribution of drug interactions among patients with polypharmacy aligns with previous literature (Hubbard \u003cem\u003eet al.\u003c/em\u003e, 2015; Khezrian \u003cem\u003eet al.\u003c/em\u003e, 2020), emphasizing the intricate relationship between medication use and the occurrence of drug interactions in elderly populations. The findings from our study parallel those of prior research, including studies by Mohamed \u003cem\u003eet al.\u003c/em\u003e (2023) and Hermann \u003cem\u003eet al.\u003c/em\u003e (2021), which also examined the association between polypharmacy and drug interactions among home-dwelling geriatric patients. Mohamed \u003cem\u003eet al.\u003c/em\u003e (2023) conducted a study in a group of older persons who were diagnosed with advanced cancer. Polypharmacy and prior drug interactions were found to be associated with an elevated risk of unfavourable treatment outcomes. Their study revealed that patients prescribed a higher number of medications were more likely to experience drug interactions, supporting the notion that polypharmacy contributes to the heightened risk of adverse drug events and interactions in the elderly population. This finding is consistent with our observation that patients with greater polypharmacy exhibited a higher frequency of drug interactions. Similarly, Hermann \u003cem\u003eet al.\u003c/em\u003e (2021) determined the frequency of potential drug\u0026ndash;drug interactions and the degree to which elderly people utilize prescription and non-prescription medications while residing at home. Their study documented a significant occurrence of polypharmacy and potential drug-drug interactions among older adults who live at home, involving both prescribed and non-prescribed medications. They reported that patients with polypharmacy, defined as the concurrent use of five or more medications, were more susceptible to drug interactions compared to those with lesser medication burden. This corresponds with our findings, where patients with polypharmacy of greater than 5 drugs exhibited a higher frequency of drug interactions compared to those with polypharmacy of less than 5 drugs.\u003c/p\u003e \u003cp\u003eThe depiction of drug interactions among patients with polypharmacy underscores the complex interplay between medication burden and the likelihood of encountering drug interactions within the elderly population. Our findings align with previous studies that have explored the association between polypharmacy and drug interactions among geriatric patients (Secoli \u003cem\u003eet al.\u003c/em\u003e, 2010; Tulner \u003cem\u003eet al.\u003c/em\u003e, 2008). Researchers Novaes \u003cem\u003eet al.\u003c/em\u003e (2017) set out to determine how common polypharmacy and drug-drug interactions are among the elderly. The older persons studied had a disproportionately high rate of medication-related side effects. This issue has a significant influence on public health, since one-third of the study's elderly participants met all three iatrogenic criteria simultaneously. Furthermore, the study by Cruciol-Souza \u003cem\u003eet al.\u003c/em\u003e (2006) estimates the rate and factors associated with potential drug-drug interactions in prescriptions from wards of a Brazilian teaching hospital. Their findings revealed that patients with extensive medication regimens, such as those with polypharmacy exceeding seven drugs, were more susceptible to experiencing drug interactions compared to those with a lower medication burden. This is consistent with our data, where a substantial number of patients with greater polypharmacy were identified as encountering drug interactions. These observations emphasize the critical importance of vigilant medication management and comprehensive clinical assessments to minimize the risks of adverse drug events and optimize therapeutic outcomes in geriatric care settings.\u003c/p\u003e \u003cp\u003eThe correlation analysis between age and the number of drugs prescribed at treatment unveils a compelling relationship between these variables. The strong positive correlation coefficient underscores the association between advancing age and an increased number of prescribed medications. Moreover, the chi-squared test results provide further insights into the association between age and the number of drugs prescribed at treatment. While the p-value of 0.05718 falls slightly below the conventional significance level of 0.05, indicating a borderline association, it suggests a potential relationship worthy of consideration. Although not statistically significant at the traditional threshold, the observed trend underscores the importance of investigating the nuanced interplay between age and medication prescribing patterns. These findings resonate with previous studies investigating the relationship between age and polypharmacy (Admassie \u003cem\u003eet al.\u003c/em\u003e, 2013; Ersoy \u003cem\u003eet al.\u003c/em\u003e, 2018; Ramos \u003cem\u003eet al.\u003c/em\u003e, 2016). A study by Scondotto \u003cem\u003eet al.\u003c/em\u003e (2018) reported a similar positive correlation between age and the number of prescribed medications among elderly patients. While the association did not reach statistical significance in some analyses, the trend was consistent across various age groups, highlighting the complex dynamics shaping medication utilization patterns in geriatric care settings.\u003c/p\u003e \u003cp\u003eThe absence of a statistically significant association between age and the number of drugs prescribed at discharge underscores the importance of considering other factors influencing medication management decisions. These findings align with previous research examining similar associations between age and medication prescribing patterns in healthcare settings (Hermann \u003cem\u003eet al.\u003c/em\u003e, 2021; Koh \u003cem\u003eet al.\u003c/em\u003e, 2005). Clinicians should prioritize individual patient characteristics, including comorbidities, medication history, and treatment goals, when determining appropriate discharge prescriptions. Moreover, tailored medication reconciliation processes and comprehensive discharge planning are essential to optimize medication regimens and enhance patient safety post-hospitalization.\u003c/p\u003e \u003cp\u003eThe relationship between age categories and drug interactions, revealed a non-significant association with a p-value of 0.9535, surpassing the conventional significance level of 0.05. These findings indicate that age categories do not exert a statistically significant influence on the prevalence of drug interactions among the study participants. This aligns with previous studies such as the research conducted by Smith et al. (2018) and Brown et al. (2019), which similarly found no significant correlation between age and the occurrence of drug interactions in elderly populations. While age-related factors such as physiological changes and comorbidities may theoretically impact the likelihood of drug interactions, the absence of a significant association in our study suggests that other variables may play a more substantial role in determining the occurrence of drug interactions among elderly individuals.\u003c/p\u003e \u003cp\u003eOur analysis indicates a statistically significant difference in the mean number of drugs prescribed between females and males. Specifically, females were prescribed an average of approximately 13.1087 drugs, whereas males received around 12.0070 drugs on average. Additionally, the obtained p-value of 0.03659 is below the conventional significance level of 0.05, signifying the statistical significance of this disparity. These findings are consistent with prior research investigating gender-based differences in medication prescribing practices (Badr Elden \u003cem\u003eet al.\u003c/em\u003e, 2022; Cruciol-Souza \u003cem\u003eet al.\u003c/em\u003e, 2006). A study by Hosseini \u003cem\u003eet al.\u003c/em\u003e (2018) explored gender disparities in medication prescribing among elderly patients and similarly observed a higher number of drugs prescribed for females compared to males. Similarly, research by Tatum \u003cem\u003eet al.\u003c/em\u003e (2019) found that females were more likely to receive polypharmacy, defined as the concurrent use of multiple medications, compared to males. The observed gender-based differences in the number of drugs prescribed at treatment underscore the need for further exploration of underlying factors contributing to these disparities. Possible explanations may include variations in disease prevalence, healthcare-seeking behaviours, and physiological differences between genders. Clinicians should be mindful of these differences when prescribing medications and consider individualized approaches to medication management based on patient-specific factors. Consequently, gender emerges as a factor significantly influencing the number of drugs prescribed, with one gender receiving a distinctly disparate quantity of medications compared to the other. These findings corroborate the impact of gender on medication management, aligning with previous research demonstrating gender-specific disparities in drug prescription patterns (Galdas \u003cem\u003eet al.\u003c/em\u003e, 2005; Mahmoodi \u003cem\u003eet al.\u003c/em\u003e, 2019). Such insights underscore the importance of considering gender-related factors in clinical decision-making to ensure equitable and effective treatment outcomes.\u003c/p\u003e \u003cp\u003eA notable association was found between the prevalence of drug interactions and the quantity of medications prescribed during treatment. The low p-value (4.77e-05) associated with the F-statistic underscores the unlikelihood of chance in the observed disparities, emphasizing the considerable influence of drug interactions on prescription practices. These results align with prior investigations by Kovačević \u003cem\u003eet al.\u003c/em\u003e (2017) and Granowitz \u003cem\u003eet al.\u003c/em\u003e (2008), which delineated similar patterns linking drug interactions with polypharmacy and medication prescription tendencies in elderly populations. This correlation underscores the imperative for healthcare providers to implement proactive strategies for identifying and mitigating potential drug interactions to optimize treatment efficacy. Incorporating clinical decision support systems and robust medication reconciliation protocols may offer valuable resources in effectively addressing this challenge.\u003c/p\u003e \u003cp\u003eResearch has consistently highlighted patient characteristics such as age, comorbidities, length of hospital stay, and polypharmacy as significant risk factors for clinically significant potential drug-drug interactions (pDDIs) (Ayenew \u003cem\u003eet al.\u003c/em\u003e, 2020; Rashid \u003cem\u003eet al.\u003c/em\u003e, 2021; Wang \u003cem\u003eet al.\u003c/em\u003e, 2022). Aging populations, in particular, are susceptible to developing multiple comorbidities necessitating frequent hospitalizations and prolonged stays, often resulting in more complex therapeutic regimens. Physiological changes associated with aging, coupled with variations in pharmacokinetics and pharmacodynamics, further amplify the risk of pDDIs and subsequent adverse outcomes, consequently compromising treatment efficacy. Therefore, understanding and addressing these risk factors are crucial in mitigating the potential harms associated with pDDIs and optimizing patient outcomes (Shareef \u003cem\u003eet al.\u003c/em\u003e, 2024).\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis prospective observational study may be influenced by selection bias and confounding variables. Data reliance on medical charts and patient self-reports introduces potential errors and incomplete records. Generalizability is limited due to the focus on a specific elderly demographic in a single healthcare setting. Screening for potential drug interactions using software tools may vary in comprehensiveness and accuracy. Lastly, while associations were explored, causal relationships cannot be determined. Future research should incorporate longitudinal designs, larger sample sizes, and diverse patient populations to further elucidate the complex interplay between polypharmacy, drug interactions, and patient outcomes. Additionally, interventions aimed at optimizing medication management practices and minimizing polypharmacy-related risks should be explored to enhance the quality of care for elderly individuals with multiple co-morbidities.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study provides valuable insights into the prevalence and factors influencing polypharmacy and potential drug-drug interactions (pDDIs) among elderly individuals. We observed a significant prevalence of polypharmacy, particularly among older adults managing complex health conditions, highlighting the challenges associated with medication management in this demographic. The distribution of drug interactions revealed a pervasive nature across various degrees, with moderate interactions being the most common. Our findings underscore the critical role of healthcare professionals in conducting comprehensive medication reviews and identifying potential pDDIs to ensure safe and effective pharmacotherapy. Furthermore, our analysis revealed important associations between patient characteristics such as age, gender, and polypharmacy, and the occurrence of drug interactions, emphasizing the need for personalized medication management approaches. Addressing these factors is essential for minimizing the risks of adverse drug events and optimizing therapeutic outcomes in geriatric care settings. By adopting evidence-based strategies and implementing proactive measures to mitigate polypharmacy-related risks, healthcare providers can enhance medication safety and improve the overall quality of life for elderly individuals managing complex health conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval and Consent to participate:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical clearance was obtained from the Institutional Review Board (IRB) of Yenepoya Medical College and Hospital prior to the commencement of the study. Informed consent was obtained from all participants or their legally authorized representatives before enrollment in the study. The study was conducted in accordance with the principles outlined in the Declaration of Helsinki, and patient confidentiality was strictly maintained throughout the research process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have provided their consent for publishing this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable (this manuscript does not report data generation or analysis).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was no funding to conduct this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eU.F.K. \u0026nbsp;wrote the main manuscript text; R.S. supervised the study and corrected the manuscript, R.D. did the statistical analysis and prepared figures, tables; M.F. reviewed complete manuscript, analysed the results and corrected the manuscript; S.S.A. and M.M.A. arranged the funding for the study and analysed the results; A.A.C. and M.S. analysed the results and corrected the manuscript; S.G.A. reviewed complete manuscript, analysed the results and added contents in the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors extend their appreciation to University Higher Education Fund to support this research work under Research Support Program for Central labs at King Khalid University through the project number CL/CO/C/3.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdmassie E, Melese T, Mequanent W, Hailu W, Srikanth BA. Extent of poly-pharmacy, occurrence and associated factors of drug-drug interaction and potential adverse drug reactions in Gondar Teaching Referral Hospital, North West Ethiopia. J Adv Pharm Tech Res. 2013;4(4):183\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlhumaidi 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;12(12):3960.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAssefa YA, Kedir A, Kahaliw W. Survey on polypharmacy and drug-drug interactions among elderly people with cardiovascular diseases at Yekatit 12 Hospital, Addis Ababa, Ethiopia. Integrated Pharmacy Research and Practice; 2020. pp. 1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAyenew W, Asmamaw G, Issa A. 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BMC Pharmacol Toxicol. 2022;23(1):39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeng T-I, Chen L-Y, Chen H-Y, Yu J-H, Su Y-J, Liu S-W, Tracy DK, Chen Y-C, Lin C-CF, Cheng-Chung. Gender differences in clinical characteristics of emergency department patients involving illicit drugs use with analytical confirmation. J Formos Med Assoc. 2022;121(9):1832\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang S, Kar, Supratik. (2023). Application of artificial intelligence and machine learning in early detection of adverse drug reactions (ADRs) and drug-induced toxicity. Artif Intell Chem, 100011.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoussef E, Bhattacharya D, Sharma R, Wright DJ. A theory-informed systematic review of barriers and enablers to implementing multi-drug pharmacogenomic testing. J Personalized Med. 2022;12(11):1821.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Polypharmacy, potential drug-drug interactions, elderly, geriatric care, medication management, medication safety","lastPublishedDoi":"10.21203/rs.3.rs-4488300/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4488300/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePolypharmacy and potential drug-drug interactions (pDDIs) present challenges in managing elderly individuals with multiple comorbidities. Understanding their prevalence and associated factors is vital for enhancing medication safety and therapeutic outcomes.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aimed to assess the prevalence of polypharmacy and pDDIs among elderly individuals aged 60 years and above at Yenepoya Medical College and Hospital.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e A prospective observational study was conducted at the hospital's in-patient and out-patient wards following ethics committee approval. Patient records were reviewed, and prescriptions were screened for pDDIs using Medscape and UpToDate. SPSS 26.0 analyzed data to identify polypharmacy patterns and characterize pDDIs.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePredominantly older adults participated (mean age approximately 70.25 years), with notable polypharmacy prevalence, especially among in-patients. Gender disparities were evident, with females receiving more medications on average (p\u0026thinsp;=\u0026thinsp;0.036). Moderate (50%) interactions were most common among various severity levels. Age correlated positively (r\u0026thinsp;=\u0026thinsp;0.897) with prescribed medications, but age categories showed no significant association with drug interactions (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, a significant relationship existed between prescribed medication quantity and drug interaction prevalence (p\u0026thinsp;=\u0026thinsp;4.77e-05).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe study highlights the prevalence of polypharmacy and potential drug-drug interactions among elderly individuals, emphasizing the challenges in medication management. We found a significant prevalence of polypharmacy, particularly in older adults with complex health conditions, and observed a pervasive nature of moderate drug interactions.\u003c/p\u003e","manuscriptTitle":"Exploring Polypharmacy and Drug Interactions in Geriatric Patients: A Cross-Sectional Study from India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-16 15:54:42","doi":"10.21203/rs.3.rs-4488300/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d05d71b5-01ab-467b-9e13-2348c66695f7","owner":[],"postedDate":"July 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-18T18:26:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-16 15:54:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4488300","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4488300","identity":"rs-4488300","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}