Potential Drug-Drug Interactions in Elderly Patients Treated with Anti- Glaucoma Agents: A Cross-Sectional Study in North of Iran

Potential Drug-Drug Interactions in Elderly Patients Treated with Anti- Glaucoma Agents: A Cross-Sectional Study in North of Iran | 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 Potential Drug-Drug Interactions in Elderly Patients Treated with Anti- Glaucoma Agents: A Cross-Sectional Study in North of Iran Haleh Alizadeh, Azadeh Motavallian, Azadeh Eshraghi, Ehsan Amiri, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7933567/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 and aim: Glaucoma is one of the world's leading causes of blindness. This disease applies especially to older individuals who often require systemic drugs for other co-morbidities. In this age group, risks of polypharmacy can give rise to concerns over drug-drug interaction (DDI) that may compromise both safety and therapeutic efficacy for patients. The objective of this research is to study the DDI frequency and severity in older patients with glaucoma on systemic medications. Methods: A cross-sectional study involving glaucoma patients aged 60 years and above was conducted in Amiralmomenin Hospital, Rasht, Iran. The data regarding drugs was obtained during patient interviews and through their medical records. Potential DDIs were searched for Lexicomp and Micromedex software. Statistical analysis was also performed using SPP version 21 to determine the prevalence and severity of the interactions. Results: Among the studied 256 patients, 57%, with a mean age of 68.4 ± 4.2 years, were found to be taking at least 5 drugs. DDI was found in 75.8% of cases, while 23% of such cases involved systemic and ocular medications. Lexicomp identified significant DDI in 66.6% of cases while Micromedex found it in 75.8%, indicating sensitivity differences in detection. Most commonly involved drugs were Beta-blockers and carbonic anhydrase inhibitors, especially in combination with antihypertensive and diabetic medications. Independent risk factors for clinically significant DDIs were noted to be diabetes, heart disease, and age ≥70 years (p < 0.05). Conclusion: Many elderly patients encountered possible DDI experiences with glaucoma medications and systemic medications. Drugs for glaucoma may cause varying efficacy and safety in both topical and systemic therapies. The role of glaucoma drugs in the management of polypharmacy in the elderly is therefore important, as this can improve clinical outcomes and minimize the risk of adverse drug events. DDI Elderly Patients Glaucoma Ocular Pharmacology Polypharmacy Systemic Medications Introduction Polypharmacy and drug interactions are major noted issues observed in the elderly people( 1 ). Elderly patients are more predisposed to drug interactions due to age-related physiologic changes, and the increase in medication use specifically in those with chronic diseases like glaucoma( 1 , 2 ). Glaucoma is one of the most prevalent causes of irreversible blindness across the world( 3 ) .Glaucoma is defined by the gradual deterioration of the optic nerve, a decrease in ganglion cells, and a reduction in the thickness of retinal nerve fibers and progressively hinders patients in their daily activities as the disease worsens, resulting in reduced independence and adverse health consequences like falls and fractures ( 4 , 5 ). Glaucoma is associated with increased intraocular pressure (IOP) which requires a very lengthy course of treatment with multiple medications ( 3 ). Older adults with comorbidities are undoubtedly more at risk of harmful drug-drug interactions (DDIs), due to the fact that glaucoma treatment is complex and it encompasses both topical and systemic therapy ( 6 ). Such interactions can profoundly impact clinical outcomes, manifesting themselves as risks that could alter the efficacy and safety of the medications used. Elderly patients commonly have a variety of comorbidities, which may determine the use of a multitude of pharmacological agents, such as diabetes, cardiovascular diseases, hypertension chronic kidney disease (CKD), and stroke ( 7 ). Such drugs may interact with glaucoma medications and give rise to problems of decreased effectiveness and the likelihood of side effects, such as the use of brimonidine in patients treated with certain antidepressants ( 8 , 9 ). Concurrent use of other systemic antihypertensive medications with such classes as beta-blockers or prostaglandins for glaucoma may lead to increased or decreased effects resulting from pharmacokinetic or pharmacodynamic interactions ( 10 ). Improper handling of interactions has also contributed to the increasing morbidity and hospital admissions across healthcare systems. Though growing in recognition with polypharmacy and DDIs among the elderly, focused research on specific drug interactions pertaining to elderly glaucoma patients has remained sparse. While some studies have investigated the prevalence of DDIs in general elderly populations ( 11 ), the glaucoma patients may face specific hurdles owing to their treatment regimen, which might have escaped the attention of broader studies. Furthermore, elderly patients are at an even higher risk for the serious consequences of DDIs, with age-related alterations in pharmacokinetics and pharmacodynamics greatly influencing drug dispositions or actions. Moreover, DDIs have adverse effects in elderly individuals, not exclusively due to age, but also related to certain pharmacokinetic and pharmacodynamic changes, such as a decrease in hepatic and renal function to alter drug metabolism and elimination ( 12 ). The present study sought to overcome this knowledge gap by establishing the burden of DDI and clinical significance in elderly glaucoma patients attending Amiralmomenin Hospital in Rasht, Iran. To increase safety and efficacy in pharmacological management of this population, we analyzed the interaction types and their clinical implications, potential consequences of the interactions, and contributing factors. Furthermore, understanding these interactions may guide the clinician in developing safe and efficacious treatment regimens for elderly patients with glaucoma, thus improving patients' health and safety and minimizing the risks associated with polypharmacy. Methods Study Design This descriptive cross-sectional study aimed to evaluate potential drug-drug interactions (DDIs) in elderly glaucoma patients. The study was conducted at the glaucoma clinic of Amiralmomenin Hospital, the only tertiary eye center in Guilan province, north of Iran, in 2020. This study was conducted by the Declaration of Helsinki and the Good Clinical Practice Guidelines Study Population and Participants The study population comprised elderly patients aged 60 years and older who were receiving treatment at the glaucoma clinic of Amiralmomenin Hospital. All participants were also being treated concurrently with at least one systemic medication for non-ocular diseases. The selection of study participants was based on the following inclusion criteria: Patients diagnosed with glaucoma who are 60 years of age or older (≥ 60 years). Patients who are being treated with two or more medications, with at least one being an ocular medication. Exclusion criteria included: Patients who declined to participate in the study ,patient who are unable to respond to questions regarding their medication regimen during the initial follow-up Sampling Method and Sample Size Calculation Based on the methodology outlined in the study by Egger & Sabin (2017), it was determined that a minimum sample size of 256 participants would be necessary to detect potential drug-drug interactions among the elderly glaucoma patients. This calculation was based on a desired confidence level of 95% and a margin of error of 10%. Study Questionnaires and Software: Two questionnaires were used to collect and analyze the information. The first was intended to collect demographic information, medical history, and the number and type of medications (both ocular and non-ocular) that the patients were consistently taking. The second aimed to evaluate DDI using electronic databases Lexicomp and Micromedex. These software programs worked to show potential drug-drug interactions between ocular and systemic medications that were used by study participants. Data Collection: The Data were collected by a member of the research team, a Ph.D. student in pharmacy, under her direct supervision at the glaucoma clinic of Amiralmomenin Hospital. Patients fitting the criteria for selection were approached for informed consent. Informed consent was taken and entered onto the questionnaires about the participant's medications. Family members were asked for information if any patient had difficulty recalling his or her medications. The medications prescribed concerning the month before the study were clearly written. In addition, systemic conditions and comorbidities were also noted. Data were collected and entered weekly into the software for DDI evaluation inclusive of ocular, non-ocular, and ocular-non-ocular medication interactions. Data Analysis: Raw data collected were entered in SPSS statistical software Version 21 for analysis after selection. Descriptive statistics (frequency and 95% CI) were used to provide estimates on the occurrence of DDIs. The chi-square (χ²) test was used to compare the potential DDIs between demographic variables such as age, gender, and other types of education. The condition for statistical significance was set at p < 0.05. Ethical Considerations The researcher of this study was committed to, after registering the proposal in the Research Deputy of the faculty and completing the necessary arrangements, obtaining the relevant letters for introducing researchers regarding data collection and communication with patients. The study was approved by the ethics committee of Guilan University of Medical Sciences, Rasht, Iran (Ethics approval code: IR.GUMS.REC.1397.514). Results Demographic Characteristics Table 1 shows the frequency distribution of samples under study by age, gender, and educational status. A total of 256 eligible individuals were available for participation, the majority of whom (69.5%) were aged 60 to 70 years. Participants who were female (51.2%) and male (48.8%) were approximately equal in terms of gender. Regarding educational status, 24.6% of the patients were illiterate (could not read and write). History of Using Medications Table 1 presents the frequency distribution of the samples analyzed according to medication use, alcohol, smoking, drug, and other psychoactive substances. Most patients analyzed (77.3%) claimed self-medication. Most of the respondents (83.2%) did not claim smoking, alcohol, drug, or other psychoactive substance use. Table 1 shows the frequency distribution of samples examined by history of drug sensitivity. Most of the patients (95.3%) had no history of drug sensitivity. The findings also indicate the type of drugs implicated in drug sensitivity reported. History of Chronic Diseases Table 1 illustrates the frequency distribution of the studied samples in relation to the history of systemic diseases. It is evident from the findings that most of the patients studied (98.4%) had one or more histories of systemic diseases. Moreover, as indicated by Table 1 s, most of the patients had comorbid conditions, the most common being diabetes mellitus (82.4%), followed by hyperlipidemia (47.3%) and hypertension (46.5%), respectively. The other systemic diseases occurred at lower frequencies. Table 1 Distribution of Sample Frequency Based on sociodemograhpic, background diseases and associated indexes (Total = 256) Category Percentage Category Percentage Age Group Drug Sensitivity 60 to 70 years 69.5% Drug Sensitivity 4.7% Over 70 years 30.5% Sensitivity to Antibiotics 2.7% Gender Sensitivity to Anesthetic Drugs 0.8% Female 51.2% Sensitivity to Other Medications 1.2% Male 48.8% Chronic Systemic Disease Education Level Hepatitis 0.8% Illiterate (unable to read and write) 24.6% Cardiovascular Diseases (CVD) 82.4% Able to read and write 37.9% Gastrointestinal Diseases 5.5% High School Diploma 25.0% Hyperlipidemia 47.3% Higher than High School Diploma 12.5% Diabetes Mellitus 46.5% Method of Medication Use Prostate Disorders 6.3% Self-administration 77.3% Hyperthyroidism/Hypothyroidism 9.0% Assisted administration by caregiver 18.0% Osteoporosis 1.2% Administered by another person 4.7% Neurological Diseases 3.5% Consumption of Cigarettes, Alcohol, Drugs, and Other Psychoactive Substances Urinary Diseases 2.3% Alcohol Consumption 0.4% Blood Disorders 3.1% Smoking 5.9% Kidney Diseases 0.4% Drug and Other Psychoactive Substance Use 2.7% Other Diseases 7.8% History of Using Eye Drugs The frequency distribution of the analyzed samples according to the kind of eye medicine taken is shown in Table 2 . Dorzolamide-Timolol was the most often reported eye medicine among the patients in the study (59.8%), followed by prostaglandin analogues (56.6%). Notably, Dorzolamide-Timolol is commonly used in conjunction with surgical procedures such as Cobiosopt and Zilomole. Table 2 Frequency Distribution of Eye Medications Used Among the Samples. Eye Medication Used Number (percent) No Number (percent) Yes Total Timolol drop 226 (88.3) 30 (11.7) 256 Betaxolol drop 256 (100) 0 (0) 256 Dorzolamide drop 238 (93) 18 ( 7 ) 256 Latanoprost drop 111 (43.4) 145 (56.6) 256 Brimonidine drop 180 (70.3) 76(29.7%) 256 Timolol + Dorzolamide drop 103 (40.2) 153(59.8%) 256 Pilocarpine drop 256 (100) 0(0.0%) 256 Acetazolamide (PO) 254 (99.2) 2(0.8%) 256 Artificial Tears drop 250 (97.7) 6(2.3%) 256 Fluorometholone drop 255 (99.6) 1(0.4%) 256 Chloramphenicol drop 254 (99.2) 2(0.8%) 256 Homatropine drop 255 (99.6) 1(0.4%) 256 Betamethasone drop 251 (99) 5(2.0%) 256 NaCl 5% drop 254 (99.2) 2(0.8%) 256 Atropine 1% drop 255 (99.6) 1(0.4%) 256 Drug Interaction Based on Micromedex Software Table 3 shows the frequency distribution of drug interactions in the patients being studied based on the Micromedex software. As can be observed in them, in 33.6% of the cases, no drug interaction was found. In 15.2% of the samples, an ocular-non-ocular interaction existed, and in 19.9%, a non-ocular-non-ocular interaction was found. In 31.3% of the cases, both interactions were found. No ocular-ocular interactions were detected. Table 3 Frequency Distribution of Drug Interaction Categories, Classified by Examined Samples, based on Micromedex Software. Drug Interactions Category Frequency (%) 95% Confidence Interval)Lower Limit( 95% Confidence Interval )Upper Limit( No Drug Interactions 86 (33.6%) 28.0% 39.5% Ophthalmic - Non-Ophthalmic Interactions 39 (15.2%) 11.2% 20.0% Non-Ophthalmic - Non-Ophthalmic Interactions 51 (19.9%) 15.4% 25.1% Both Types of Drug Interactions 80 (31.3%) 25.8% 37.1% Ophthalmic - Ophthalmic Interactions 0 (0.0%) 0.0% 0.0% Total 256 (100.0%) Based on the observed interactions, non-ocular-ocular Interactions happened in 46.5% of the instances, whereas non-ocular-non-ocular Interactions occurred in 51.2% of the cases (according to Table 4 ). Table 4 Frequency Distribution of Drug Interaction based on Micromedex Software. Drug Interactions Category Frequency (%) 95% Confidence Interval )Lower Limit( 95% Confidence Interval )Upper Limit( Total P value No Drug Interactions With Drug Interactions No Drug Interactions With Drug Interactions No Drug Interactions With Drug Interactions Non-Ophthalmic - Ophthalmic Interactions 137 (53.5%) 119 (46.5%) 47.4% 40.4% 59.6% 52.6% 256 (100%) P = 0.289 Non-Ophthalmic - Non-Ophthalmic Interactions 125 (48.8%) 131 (51.2%) 42.7% 45.1% 54.9% 57.3% 256 (100%) Frequency Distribution of Drug Interaction Types Based on Micromedex Software Due to the analysis of Micromedex software, the prevalence of ocular-non-ocular interaction was type Moderate (C) 44.9%, and type Major (D) 2.7%, but none were reported for Minor (B) or Contraindicated (X). For non-ocular-non-ocular interactions among the test samples, type C interactions were observed with the maximum frequency (47.3%). Type D interactions were observed in 12.1% of the cases, and type B interactions in 2% of the cases. Type X interactions were observed in none of the cases for non-ocular-non-ocular interactions. Drug Interaction Based on Lexicomp Software Table 5 contains the frequency of drug interactions of the patients examined based on the Lexicomp drug interaction software. Based on the data, 22.7% (95% CI, 17.9%–28.1%) of the 256 samples examined had ocular-ocular interaction, 56.3% (95% CI, 50.1%–62.2%) had non-ocular-non-ocular interaction, and 48.4% (95% CI, 42.5%–54.5%) had non-ocular-non-ocular interaction. In total, according to this software, 42.4% of the samples (95% CI 39%–46%) possessed drug interactions. Table 5 Frequency Distribution of Drug Interaction based on Lexicomp Software. Drug Interactions Category Frequency (%) 95% Confidence Interval )Lower Limit( 95% Confidence Interval )Upper Limit( Total P value No Drug Interactions With Drug Interactions No Drug Interactions With Drug Interactions No Drug Interactions With Drug Interactions Ophthalmic - Ophthalmic Interactions 198 (77.3%) 58 (22.7%) 71.9% 17.9% 82.1% 28.1% 256 (100%) P < 0.001 Ophthalmic – Non-Ophthalmic Interactions 112 (43.8%) 144 (56.3%) 37.8% 50.1% 49.9% 62.2% 256 (100%) Non-Ophthalmic - Non-Ophthalmic Interactions 132 (51.6%) 124 (48.4%) 45.5% 42.4% 57.6% 54.5% 256 (100%) Frequency Distribution of Drug Interaction Types Based on Lexicomp Software Based on the Lexicomp software, the majority of ocular-ocular drug interactions (98.3%) were of type D, while 1.7% were of type X. A total of 22.3% and 0.4% of the samples being investigated suffered from drug interactions of type D and X, respectively. For ocular-non-ocular interactions, 65.9% were of type C, 32.4% of type D, and 1.6% of type X. Together, 23.4%, 47.5%, and 1.2% of the 256 patients had drug interactions of types C, D, and X, respectively. For non-ocular-non-ocular interactions, the table shows that 1% were type A, 16.6% were type B, 64.9% were type C, 10.9% were type D, and 2.1% were type X. Out of the 256 samples examined, 0.8% were type A, 12.5% were type B, 52.1% were type C, 8.2% were type D, and 1.6% were type X. In summary, the most frequent type of interactions was type C (58.7%), followed by type D (31.7%) and type B (7.3%). In logistic regression analysis of variables (age, gender, use of medication, smoking, alcohol and drug misuse, history of cardiovascular diseases, diabetes, thyroid diseases, gastrointestinal diseases, and hyperlipidemia), as per the Lexicomp Software, age, level of education, use method of medication, cardiovascular diseases, and diabetes showed statistically significant correlations with total drug interactions. In ocular-ocular interactions, only the use method of medication showed a statistically significant correlation. In non-ocular-non-ocular interactions, besides the application of medication, age, educational level, and history of diabetes were statistically significant. In ocular-non-ocular interactions, age and history of diabetes showed statistically significant relationships. Discussion Among different age groups, the elderly have been noted to constitute the largest group of both prescription and over-the-counter medication users ( 13 ). This is due to a larger number of chronic diseases facing this age group, more medication intake, and an increased chance of polypharmacy ( 14 ). It is estimated that approximately 6.5% of hospital admissions are due to adverse drug reactions (ADRs), with DDIs making up 16.6% of these ADRs( 15 ). Glaucoma, mostly of the first type, is more prevalent among the geriatric age group. Interactions among prescribed medications pose a bigger and more critical health issue that the healthcare sector is facing( 16 ). The essence of this study is to assess drug interaction, more importantly, in elderly patients who may have glaucoma for the first time, using electronic drug interaction detection software. The literature reports the prevalence of potential drug interactions in elderly patients ranging from 34.8% to 100% ( 17 ). The study conducted in 2002 in six European countries among outpatients older than 60 years found that the potential drug interactions were involved in 46% of patients( 18 ). Similarly, a multicenter observational study in hospitals in northwestern Ethiopia reported that 58.10% of elderly hospitalized patients experienced at least one DDI( 19 ). Differences in the prevalence of drug interactions may have arisen from differences in the definitions of polypharmacy (some studies required patients to take at least five medications for inclusion), differences in the populations being studied (inpatients versus outpatients), and different methods of interaction identification (software-based versus reference texts) ( 14 , 18 ). According to Micromede,x in our study, 66.4% of patients had at least one potential drug interaction. Most interactions identified were moderate (89.8%), followed were severe (9%). No potentially harmful contraindicated interactions were reported for either of the patients. In a study by Dr. Santos and colleagues from Brazil in 2017, a total of 934 outpatients aged 60 years and above (without targeting a specific disease) were analyzed using Micromedex, with 665 possible drug interactions found, 71% of which were moderate, 6.9% were major, and only 0.75% were contraindicated. In that study, polypharmacy was defined as the use of five or more medications, and they did not consider ophthalmic drops in their analysis ( 20 ). The discrepancies when compared with this study, including particularly for the frequency of contraindicated interactions, can be attributed to ethnic differences and polypharmacy, in which the studied group had to include at least five medications. None of the previous polypharmacy studies considered glaucoma drops under the umbrella of polypharmacy. However, a German study in a nursing home found that 6% of elderly residents had glaucoma and took systemic medications at the same time. The most commonly used drugs were Timolol (219), Prostaglandin analogues (101), and Carbonic anhydrase inhibitors (86). The mean number of medications recorded after the identification of a patient was 6.5. In 71.9% of cases, Timolol was taken together with at least one antihypertensive drug. Systemic and topical beta-blockers were used in 20% of patients, while 14% had a potentially cardiotoxic drug interaction ( 21 ). According to our study, 75.8% of patients had at least one potential interaction recorded using Lexicomp. 66% of the interactions fall under C-type; D and X got reported for 39.8% and 2.7% of patients, respectively. One in four patients had ophthalmic-ophthalmic interactions: 98.3% type D and 1.7% type X. An ophthalmic-systemic interaction was seen in 32.4%, or three times more often than systemic-systemic interaction at 10.6%. Any type X interaction was observed in 1.6% of ophthalmic-systemic interactions and 2.1% of systemic-systemic interactions. A study in Saudi Arabia conducted in 2018 on 310 outpatients above 65 years determined a possible drug interaction in 90.24% of their patients, with type C being the highest (87.7%), followed by type D (51.9%) and type X (16.4%). The average age of the patients in that study was 73.8 years; hence, the frequency of interaction in this study may be expected to differ. Other than in classes of specific diseases, over 91% of patients were on more than three medications, including certain ophthalmic medications ( 22 ). Several studies have established a fair-to-poor agreement between different software programs in detecting potential drug interactions with 18.5% to 20.5% agreement found in two studies comparing Lexicomp and Micromedex ( 23 , 24 ). Consistent with findings from other studies, Lexicomp identified more significant interactions, including more type X contraindications than Micromedex ( 25 ). In this study, ocular-ocular drug interactions identified by Lexicomp were reported at 22.7%, most of type D (98.3%). All type D interactions reported in this study were between an alpha-2 agonist (brimonidine) and a beta-blocker (timolol). Type X ocular-ocular interactions (1.7%) reported by this software were between oral acetazolamide and the ophthalmic drop dorzolamide. However, this drug interaction is considered less significant when using the ophthalmic drop form instead of the systemic form according to Lexicomp. In patients on topical beta-blockers concurrently with systemic beta-blockers, an additive effect occurs on AV node function; thus, these patients need careful heart-rate monitoring. When patients are maintained on long-term beta-blockers and alpha-2 agonists, withdrawing the alpha-2 agonist while continuing the beta-blocker greatly increases the risk of rebound hypertension ( 26 ). In addition, the patient should be thoroughly evaluated before beginning treatment with a systemic beta-blocker in conjunction with topical beta-blockers (i.e., timolol) for potential cardiovascular problems. Timolol, when instilled into the eye, enters systemic circulation via the nasolacrimal duct in at least 80% of cases; the bulk of it therefore circles around the body without undergoing any first-pass metabolism in the liver ( 27 ). This same mechanism pertains to other drugs particularly affecting the autonomic nervous system. Glaucoma and its associated comorbidities are more common among older patients than with accompanying comorbidities of cardiovascular, neurological, and psychiatric diseases. It is clear that cytochrome oxidase inhibitors are used extensively among elderly patients; these tend to slow the metabolism of timolol based on their inhibitory effect on CYP2D6, increasing its systemic effects ( 28 ). According to Lexicomp, using dorzolamide and acetazolamide increases the risk of metabolic acidosis; therefore, it is not recommended. They state that, generally, the literature shows that combinations of acetazolamide and dorzolamide do not appear to provide any additive therapeutic effects, as both alone can decrease the production of aqueous humor and intraocular pressure to similar extents or degrees of efficiency ( 29 ). Lastly, Lexicomp has flagged possible interactions with sulfonamide drugs when prescribing topical applications of carbonic anhydrase inhibitors, and they list topiramate and zonisamide ( 26 ). In our study, we found logistic analysis of the data (accounting for non-drug-related factors) among patients to reveal a significant association binding a higher probability of drug interactions with educational level, assistance from others in their medication use, and cardiovascular diseases, including hypertension. Educational background appears to enable meaningful medication use and intel supplied through intel to precisely mitigate adverse interactions. It is clear that several studies so far have illustrated the positive role of caregivers in reducing these potential drug interactions. The assistance of others improves the correct use of a given medication, leading to improved outcomes for the patient. Assistance from others can contribute to proper medication use, thereby increasing patient adherence to therapy and finally reducing the overall number of drugs used still ( 30 ). Many studies have noted that as one goes toward old age, the prevalence of cardiovascular diseases and diabetes increases. It means more treatments need to be prescribed, leading to the possibility of polypharmacy ( 31 ). In the logistic regression analysis of factors associated with ocular-ocular interactions, self-administration of medication was identified as the most significant factor increasing the likelihood of drug interactions (OR = 2.39), while educational level, unlike other studies, did not show significant influence in this regard ( 32 , 33 ). After conducting logistic regression analysis on drug-related variables, the number of medications was one of the most influential variables contributing to drug interactions. The odds of increased interactions with an addition of one extra ophthalmic medication and a non-ophthalmic medication approach were put at 1.93 and 1.22, respectively, confirming findings from other studies from across the globe ( 31 , 34 ). Conclusion Ocular-systemic interactions are responsible for 23% of high DDI prevalence in elderly glaucoma patients, which was 75.8%, according to this study. The drug classes most frequently involved were prostaglandin analogues, beta-blockers, and carbonic anhydrase inhibitors. The most important risk factors for pertinent DDI according to clinician importance were cardiovascular disease, age ≥ 70, and polypharmacy. These results highlight the imperative for routine drug reconciliation and multidisciplinary collaboration to maximize pharmacotherapy and avoid side effects in geriatric glaucoma patients. Declarations Funding This study received no specific funding from any public, commercial agencies. Ethics approval and consent to participate The study was approved by the ethics committee of Guilan University of Medical Sciences, Rasht, Iran (Ethics approval code: IR.GUMS.REC.1397.514). Informed consent was obtained from all participants. Consent for publication no individual patient data are included Availability of data and materials The datasets generated and analysed during the current study are available from the corresponding author on reasonable request . Competing interests The authors declare that they have no competing interests. Authors' contributions H.A., A.M., and A.E. conceptualized and designed the study. H.A., Y.A., R.S.M., E.A., and M.D. collected the data at Amiralmomenin Hospital. A.M. and A.E. performed the drug-drug interaction analysis using Lexicomp and Micromedex software. E.K.L. conducted the statistical analysis using SPSS version 21. H.A., A.M., A.E., and E.K.L. drafted the manuscript. Y.A., R.S.M., E.A., and M.D. contributed to data interpretation and critical revision of the manuscript. All authors reviewed and approved the final manuscript. Acknowledgements We thank the staff of Amiralmomenin Hospital for their support in data collection. References Sutanto H. Tackling polypharmacy in geriatric patients: Is increasing physicians’ awareness adequate? Archives Gerontol Geriatr Plus. 2025;2(3):100185. Tsoukalas N, Brito-Dellan N, Font C, Butler T, Rojas-Hernandez CM, Butler T, et al. Complexity and clinical significance of drug-drug interactions (DDIs) in oncology: challenging issues in the care of patients regarding cancer-associated thrombosis (CAT). 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Prevalence of the coprescription of clinically important interacting drug combinations involving oral anticancer agents in Singapore: a retrospective database study. Clin Ther. 2012;34(8):1696–704. Smithburger PL, Kane-Gill SL, Seybert AL. Drug–drug interactions in the medical intensive care unit: an assessment of frequency, severity and the medications involved. Int J Pharm Pract. 2012;20(6):402–8. Smithburger PL, Gill SLK, Benedict NJ, Falcione BA, Seybert AL. Grading the severity of drug-drug interactions in the intensive care unit: a comparison between clinician assessment and proprietary database severity rankings. Ann Pharmacother. 2010;44(11):1718–24. Palappalil DS, Sushama J, Kesavan KP. Drug Interactions as a cause of adverse drug reactions in a tertiary care hospital. Biomedical Pharmacol J. 2022;15(3):1637–45. Woreta FA, Gordon LK, O’Rese JK, Randolph JD, Zebardast N, Pérez-González CE. Enhancing diversity in the ophthalmology workforce. Ophthalmology. 2022;129(10):e127–36. Mäenpää J, Pelkonen O. Cardiac safety of ophthalmic timolol. Exp Opin Drug Saf. 2016;15(11):1549–61. Rosenberg LF, Krupin T, Tang L-Q, Hong PH, Ruderman JM. Combination of systemic acetazolamide and topical dorzolamide in reducing intraocular pressure and aqueous humor formation. Ophthalmology. 1998;105(1):88–93. Look KA, Stone JA. Medication management activities performed by informal caregivers of older adults. Res Social Administrative Pharm. 2018;14(5):418–26. Shetty V, Chowta MN, Chowta KN, Shenoy A, Kamath A, Kamath P. Evaluation of potential drug-drug interactions with medications prescribed to geriatric patients in a tertiary care hospital. J aging Res. 2018;2018(1):5728957. Domino FJ. Improving adherence to treatment for hypertension. Am Family Phys. 2005;71(11). Miller NH, Hill M, Kottke T, Ockene IS. The multilevel compliance challenge: recommendations for a call to action: a statement for healthcare professionals. Circulation. 1997;95(4):1085–90. Teka F, Teklay G, Ayalew E, Teshome T. Potential drug–drug interactions among elderly patients admitted to medical ward of Ayder Referral Hospital, Northern Ethiopia: a cross sectional study. BMC Res Notes. 2016;9:1–8. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7933567","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":545333831,"identity":"6e36b701-0596-45da-a52c-0906d6c4afb7","order_by":0,"name":"Haleh Alizadeh","email":"","orcid":"","institution":"Guilan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Haleh","middleName":"","lastName":"Alizadeh","suffix":""},{"id":545333832,"identity":"461af3c6-f2d2-4795-ad5a-b07b3ca6250c","order_by":1,"name":"Azadeh Motavallian","email":"","orcid":"","institution":"Guilan University of Medical 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Elderly patients are more predisposed to drug interactions due to age-related physiologic changes, and the increase in medication use specifically in those with chronic diseases like glaucoma(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Glaucoma is one of the most prevalent causes of irreversible blindness across the world(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) .Glaucoma is defined by the gradual deterioration of the optic nerve, a decrease in ganglion cells, and a reduction in the thickness of retinal nerve fibers and progressively hinders patients in their daily activities as the disease worsens, resulting in reduced independence and adverse health consequences like falls and fractures (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Glaucoma is associated with increased intraocular pressure (IOP) which requires a very lengthy course of treatment with multiple medications (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Older adults with comorbidities are undoubtedly more at risk of harmful drug-drug interactions (DDIs), due to the fact that glaucoma treatment is complex and it encompasses both topical and systemic therapy (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Such interactions can profoundly impact clinical outcomes, manifesting themselves as risks that could alter the efficacy and safety of the medications used.\u003c/p\u003e\u003cp\u003eElderly patients commonly have a variety of comorbidities, which may determine the use of a multitude of pharmacological agents, such as diabetes, cardiovascular diseases, hypertension chronic kidney disease (CKD), and stroke (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Such drugs may interact with glaucoma medications and give rise to problems of decreased effectiveness and the likelihood of side effects, such as the use of brimonidine in patients treated with certain antidepressants (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Concurrent use of other systemic antihypertensive medications with such classes as beta-blockers or prostaglandins for glaucoma may lead to increased or decreased effects resulting from pharmacokinetic or pharmacodynamic interactions (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Improper handling of interactions has also contributed to the increasing morbidity and hospital admissions across healthcare systems.\u003c/p\u003e\u003cp\u003eThough growing in recognition with polypharmacy and DDIs among the elderly, focused research on specific drug interactions pertaining to elderly glaucoma patients has remained sparse. While some studies have investigated the prevalence of DDIs in general elderly populations (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), the glaucoma patients may face specific hurdles owing to their treatment regimen, which might have escaped the attention of broader studies. Furthermore, elderly patients are at an even higher risk for the serious consequences of DDIs, with age-related alterations in pharmacokinetics and pharmacodynamics greatly influencing drug dispositions or actions. Moreover, DDIs have adverse effects in elderly individuals, not exclusively due to age, but also related to certain pharmacokinetic and pharmacodynamic changes, such as a decrease in hepatic and renal function to alter drug metabolism and elimination (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe present study sought to overcome this knowledge gap by establishing the burden of DDI and clinical significance in elderly glaucoma patients attending Amiralmomenin Hospital in Rasht, Iran. To increase safety and efficacy in pharmacological management of this population, we analyzed the interaction types and their clinical implications, potential consequences of the interactions, and contributing factors. Furthermore, understanding these interactions may guide the clinician in developing safe and efficacious treatment regimens for elderly patients with glaucoma, thus improving patients' health and safety and minimizing the risks associated with polypharmacy.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design\u003c/h2\u003e\u003cp\u003eThis descriptive cross-sectional study aimed to evaluate potential drug-drug interactions (DDIs) in elderly glaucoma patients. The study was conducted at the glaucoma clinic of Amiralmomenin Hospital, the only tertiary eye center in Guilan province, north of Iran, in 2020. This study was conducted by the Declaration of Helsinki and the Good Clinical Practice Guidelines\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003e\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003e\u003cb\u003eStudy Population and Participants\u003c/b\u003e\u003c/div\u003e\u003cp\u003eThe study population comprised elderly patients aged 60 years and older who were receiving treatment at the glaucoma clinic of Amiralmomenin Hospital. All participants were also being treated concurrently with at least one systemic medication for non-ocular diseases. The selection of study participants was based on the following inclusion criteria: Patients diagnosed with glaucoma who are 60 years of age or older (\u0026ge;\u0026thinsp;60 years). Patients who are being treated with two or more medications, with at least one being an ocular medication. Exclusion criteria included: Patients who declined to participate in the study ,patient who are unable to respond to questions regarding their medication regimen during the initial follow-up\u003c/p\u003e\n\u003ch3\u003eSampling Method and Sample Size Calculation\u003c/h3\u003e\n\u003cp\u003eBased on the methodology outlined in the study by Egger \u0026amp; Sabin (2017), it was determined that a minimum sample size of 256 participants would be necessary to detect potential drug-drug interactions among the elderly glaucoma patients. This calculation was based on a desired confidence level of 95% and a margin of error of 10%.\u003c/p\u003e\n\u003ch3\u003eStudy Questionnaires and Software:\u003c/h3\u003e\n\u003cp\u003eTwo questionnaires were used to collect and analyze the information. The first was intended to collect demographic information, medical history, and the number and type of medications (both ocular and non-ocular) that the patients were consistently taking. The second aimed to evaluate DDI using electronic databases Lexicomp and Micromedex. These software programs worked to show potential drug-drug interactions between ocular and systemic medications that were used by study participants.\u003c/p\u003e\n\u003ch3\u003eData Collection:\u003c/h3\u003e\n\u003cp\u003eThe Data were collected by a member of the research team, a Ph.D. student in pharmacy, under her direct supervision at the glaucoma clinic of Amiralmomenin Hospital. Patients fitting the criteria for selection were approached for informed consent. Informed consent was taken and entered onto the questionnaires about the participant's medications. Family members were asked for information if any patient had difficulty recalling his or her medications. The medications prescribed concerning the month before the study were clearly written. In addition, systemic conditions and comorbidities were also noted. Data were collected and entered weekly into the software for DDI evaluation inclusive of ocular, non-ocular, and ocular-non-ocular medication interactions.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eData Analysis:\u003c/h2\u003e\u003cp\u003eRaw data collected were entered in SPSS statistical software Version 21 for analysis after selection. Descriptive statistics (frequency and 95% CI) were used to provide estimates on the occurrence of DDIs. The chi-square (χ\u0026sup2;) test was used to compare the potential DDIs between demographic variables such as age, gender, and other types of education. The condition for statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003eThe researcher of this study was committed to, after registering the proposal in the Research Deputy of the faculty and completing the necessary arrangements, obtaining the relevant letters for introducing researchers regarding data collection and communication with patients. The study was approved by the ethics committee of Guilan University of Medical Sciences, Rasht, Iran (Ethics approval code: IR.GUMS.REC.1397.514).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eDemographic Characteristics\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the frequency distribution of samples under study by age, gender, and educational status. A total of 256 eligible individuals were available for participation, the majority of whom (69.5%) were aged 60 to 70 years. Participants who were female (51.2%) and male (48.8%) were approximately equal in terms of gender. Regarding educational status, 24.6% of the patients were illiterate (could not read and write).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eHistory of Using Medications\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the frequency distribution of the samples analyzed according to medication use, alcohol, smoking, drug, and other psychoactive substances. Most patients analyzed (77.3%) claimed self-medication. Most of the respondents (83.2%) did not claim smoking, alcohol, drug, or other psychoactive substance use.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the frequency distribution of samples examined by history of drug sensitivity. Most of the patients (95.3%) had no history of drug sensitivity. The findings also indicate the type of drugs implicated in drug sensitivity reported.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eHistory of Chronic Diseases\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the frequency distribution of the studied samples in relation to the history of systemic diseases. It is evident from the findings that most of the patients studied (98.4%) had one or more histories of systemic diseases. Moreover, as indicated by Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003es, most of the patients had comorbid conditions, the most common being diabetes mellitus (82.4%), followed by hyperlipidemia (47.3%) and hypertension (46.5%), respectively. The other systemic diseases occurred at lower frequencies.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of Sample Frequency Based on sociodemograhpic, background diseases and associated indexes (Total\u0026thinsp;=\u0026thinsp;256)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge Group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDrug Sensitivity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e60 to 70 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e69.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDrug Sensitivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOver 70 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSensitivity to Antibiotics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSensitivity to Anesthetic Drugs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSensitivity to Other Medications\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eChronic Systemic Disease\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation Level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHepatitis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIlliterate (unable to read and write)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCardiovascular Diseases (CVD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e82.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAble to read and write\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGastrointestinal Diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh School Diploma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHyperlipidemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e47.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigher than High School Diploma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDiabetes Mellitus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMethod of Medication Use\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProstate Disorders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-administration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e77.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHyperthyroidism/Hypothyroidism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAssisted administration by caregiver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOsteoporosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdministered by another person\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNeurological Diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eConsumption of Cigarettes, Alcohol, Drugs, and Other Psychoactive Substances\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUrinary Diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol Consumption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBlood Disorders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eKidney Diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrug and Other Psychoactive Substance Use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOther Diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eHistory of Using Eye Drugs\u003c/h2\u003e\u003cp\u003eThe frequency distribution of the analyzed samples according to the kind of eye medicine taken is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Dorzolamide-Timolol was the most often reported eye medicine among the patients in the study (59.8%), followed by prostaglandin analogues (56.6%). Notably, Dorzolamide-Timolol is commonly used in conjunction with surgical procedures such as Cobiosopt and Zilomole.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFrequency Distribution of Eye Medications Used Among the Samples.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEye Medication Used\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber (percent)\u003c/p\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber (percent)\u003c/p\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTimolol drop\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e226 (88.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (11.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e256\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBetaxolol drop\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e256 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e256\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDorzolamide drop\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e238 (93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e256\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLatanoprost drop\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e111 (43.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e145 (56.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e256\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBrimonidine drop\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e180 (70.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76(29.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e256\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTimolol\u0026thinsp;+\u0026thinsp;Dorzolamide drop\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e103 (40.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e153(59.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e256\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePilocarpine drop\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e256 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0(0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e256\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAcetazolamide (PO)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e254 (99.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2(0.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e256\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eArtificial Tears drop\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e250 (97.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6(2.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e256\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFluorometholone drop\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e255 (99.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1(0.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e256\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChloramphenicol drop\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e254 (99.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2(0.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e256\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHomatropine drop\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e255 (99.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1(0.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e256\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBetamethasone drop\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e251 (99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5(2.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e256\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNaCl 5% drop\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e254 (99.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2(0.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e256\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAtropine 1% drop\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e255 (99.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1(0.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e256\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eDrug Interaction Based on Micromedex Software\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the frequency distribution of drug interactions in the patients being studied based on the Micromedex software. As can be observed in them, in 33.6% of the cases, no drug interaction was found. In 15.2% of the samples, an ocular-non-ocular interaction existed, and in 19.9%, a non-ocular-non-ocular interaction was found. In 31.3% of the cases, both interactions were found. No ocular-ocular interactions were detected.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFrequency Distribution of Drug Interaction Categories, Classified by Examined Samples, based on Micromedex Software.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrug Interactions Category\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% Confidence Interval)Lower Limit(\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% Confidence Interval )Upper Limit(\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo Drug Interactions\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e86 (33.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOphthalmic - Non-Ophthalmic Interactions\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39 (15.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNon-Ophthalmic - Non-Ophthalmic Interactions\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51 (19.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBoth Types of Drug Interactions\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e80 (31.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e37.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOphthalmic - Ophthalmic Interactions\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e256 (100.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eBased on the observed interactions, non-ocular-ocular Interactions happened in 46.5% of the instances, whereas non-ocular-non-ocular Interactions occurred in 51.2% of the cases (according to Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFrequency Distribution of Drug Interaction based on \u003cem\u003eMicromedex\u003c/em\u003e Software.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDrug Interactions Category\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eFrequency (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e95% Confidence Interval )Lower Limit(\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e95% Confidence Interval )Upper Limit(\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo Drug Interactions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWith Drug Interactions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo Drug Interactions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWith Drug Interactions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo Drug Interactions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eWith Drug Interactions\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNon-Ophthalmic - Ophthalmic Interactions\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e137 (53.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e119 (46.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e59.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e52.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e256 (100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.289\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNon-Ophthalmic - Non-Ophthalmic Interactions\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e125 (48.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e131 (51.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e54.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e57.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e256 (100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eFrequency Distribution of Drug Interaction Types Based on Micromedex Software\u003c/h2\u003e\u003cp\u003eDue to the analysis of Micromedex software, the prevalence of ocular-non-ocular interaction was type Moderate (C) 44.9%, and type Major (D) 2.7%, but none were reported for Minor (B) or Contraindicated (X). For non-ocular-non-ocular interactions among the test samples, type C interactions were observed with the maximum frequency (47.3%). Type D interactions were observed in 12.1% of the cases, and type B interactions in 2% of the cases. Type X interactions were observed in none of the cases for non-ocular-non-ocular interactions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eDrug Interaction Based on Lexicomp Software\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e contains the frequency of drug interactions of the patients examined based on the Lexicomp drug interaction software. Based on the data, 22.7% (95% CI, 17.9%\u0026ndash;28.1%) of the 256 samples examined had ocular-ocular interaction, 56.3% (95% CI, 50.1%\u0026ndash;62.2%) had non-ocular-non-ocular interaction, and 48.4% (95% CI, 42.5%\u0026ndash;54.5%) had non-ocular-non-ocular interaction. In total, according to this software, 42.4% of the samples (95% CI 39%\u0026ndash;46%) possessed drug interactions.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFrequency Distribution of Drug Interaction based on \u003cem\u003eLexicomp\u003c/em\u003e Software.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDrug Interactions Category\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eFrequency (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e95% Confidence Interval )Lower Limit(\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e95% Confidence Interval )Upper Limit(\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo Drug Interactions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWith Drug Interactions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo Drug Interactions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWith Drug Interactions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo Drug Interactions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eWith Drug Interactions\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOphthalmic - Ophthalmic Interactions\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e198 (77.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58 (22.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e71.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e82.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e28.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e256 (100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOphthalmic \u0026ndash; Non-Ophthalmic Interactions\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e112 (43.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e144 (56.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e49.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e62.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e256 (100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNon-Ophthalmic - Non-Ophthalmic Interactions\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e132 (51.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e124 (48.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e42.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e57.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e54.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e256 (100%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eFrequency Distribution of Drug Interaction Types Based on Lexicomp Software\u003c/h2\u003e\u003cp\u003eBased on the Lexicomp software, the majority of ocular-ocular drug interactions (98.3%) were of type D, while 1.7% were of type X. A total of 22.3% and 0.4% of the samples being investigated suffered from drug interactions of type D and X, respectively. For ocular-non-ocular interactions, 65.9% were of type C, 32.4% of type D, and 1.6% of type X. Together, 23.4%, 47.5%, and 1.2% of the 256 patients had drug interactions of types C, D, and X, respectively.\u003c/p\u003e\u003cp\u003eFor non-ocular-non-ocular interactions, the table shows that 1% were type A, 16.6% were type B, 64.9% were type C, 10.9% were type D, and 2.1% were type X. Out of the 256 samples examined, 0.8% were type A, 12.5% were type B, 52.1% were type C, 8.2% were type D, and 1.6% were type X. In summary, the most frequent type of interactions was type C (58.7%), followed by type D (31.7%) and type B (7.3%).\u003c/p\u003e\u003cp\u003eIn logistic regression analysis of variables (age, gender, use of medication, smoking, alcohol and drug misuse, history of cardiovascular diseases, diabetes, thyroid diseases, gastrointestinal diseases, and hyperlipidemia), as per the Lexicomp Software, age, level of education, use method of medication, cardiovascular diseases, and diabetes showed statistically significant correlations with total drug interactions. In ocular-ocular interactions, only the use method of medication showed a statistically significant correlation. In non-ocular-non-ocular interactions, besides the application of medication, age, educational level, and history of diabetes were statistically significant. In ocular-non-ocular interactions, age and history of diabetes showed statistically significant relationships.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAmong different age groups, the elderly have been noted to constitute the largest group of both prescription and over-the-counter medication users (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). This is due to a larger number of chronic diseases facing this age group, more medication intake, and an increased chance of polypharmacy (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). It is estimated that approximately 6.5% of hospital admissions are due to adverse drug reactions (ADRs), with DDIs making up 16.6% of these ADRs(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Glaucoma, mostly of the first type, is more prevalent among the geriatric age group. Interactions among prescribed medications pose a bigger and more critical health issue that the healthcare sector is facing(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The essence of this study is to assess drug interaction, more importantly, in elderly patients who may have glaucoma for the first time, using electronic drug interaction detection software.\u003c/p\u003e\u003cp\u003eThe literature reports the prevalence of potential drug interactions in elderly patients ranging from 34.8% to 100% (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The study conducted in 2002 in six European countries among outpatients older than 60 years found that the potential drug interactions were involved in 46% of patients(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Similarly, a multicenter observational study in hospitals in northwestern Ethiopia reported that 58.10% of elderly hospitalized patients experienced at least one DDI(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Differences in the prevalence of drug interactions may have arisen from differences in the definitions of polypharmacy (some studies required patients to take at least five medications for inclusion), differences in the populations being studied (inpatients versus outpatients), and different methods of interaction identification (software-based versus reference texts) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAccording to Micromede,x in our study, 66.4% of patients had at least one potential drug interaction. Most interactions identified were moderate (89.8%), followed were severe (9%). No potentially harmful contraindicated interactions were reported for either of the patients. In a study by Dr. Santos and colleagues from Brazil in 2017, a total of 934 outpatients aged 60 years and above (without targeting a specific disease) were analyzed using Micromedex, with 665 possible drug interactions found, 71% of which were moderate, 6.9% were major, and only 0.75% were contraindicated. In that study, polypharmacy was defined as the use of five or more medications, and they did not consider ophthalmic drops in their analysis (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The discrepancies when compared with this study, including particularly for the frequency of contraindicated interactions, can be attributed to ethnic differences and polypharmacy, in which the studied group had to include at least five medications.\u003c/p\u003e\u003cp\u003eNone of the previous polypharmacy studies considered glaucoma drops under the umbrella of polypharmacy. However, a German study in a nursing home found that 6% of elderly residents had glaucoma and took systemic medications at the same time. The most commonly used drugs were Timolol (219), Prostaglandin analogues (101), and Carbonic anhydrase inhibitors (86). The mean number of medications recorded after the identification of a patient was 6.5. In 71.9% of cases, Timolol was taken together with at least one antihypertensive drug. Systemic and topical beta-blockers were used in 20% of patients, while 14% had a potentially cardiotoxic drug interaction (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e According to our study, 75.8% of patients had at least one potential interaction recorded using Lexicomp. 66% of the interactions fall under C-type; D and X got reported for 39.8% and 2.7% of patients, respectively. One in four patients had ophthalmic-ophthalmic interactions: 98.3% type D and 1.7% type X. An ophthalmic-systemic interaction was seen in 32.4%, or three times more often than systemic-systemic interaction at 10.6%. Any type X interaction was observed in 1.6% of ophthalmic-systemic interactions and 2.1% of systemic-systemic interactions.\u003c/p\u003e\u003cp\u003eA study in Saudi Arabia conducted in 2018 on 310 outpatients above 65 years determined a possible drug interaction in 90.24% of their patients, with type C being the highest (87.7%), followed by type D (51.9%) and type X (16.4%). The average age of the patients in that study was 73.8 years; hence, the frequency of interaction in this study may be expected to differ. Other than in classes of specific diseases, over 91% of patients were on more than three medications, including certain ophthalmic medications (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSeveral studies have established a fair-to-poor agreement between different software programs in detecting potential drug interactions with 18.5% to 20.5% agreement found in two studies comparing Lexicomp and Micromedex (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Consistent with findings from other studies, Lexicomp identified more significant interactions, including more type X contraindications than Micromedex (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this study, ocular-ocular drug interactions identified by Lexicomp were reported at 22.7%, most of type D (98.3%). All type D interactions reported in this study were between an alpha-2 agonist (brimonidine) and a beta-blocker (timolol). Type X ocular-ocular interactions (1.7%) reported by this software were between oral acetazolamide and the ophthalmic drop dorzolamide. However, this drug interaction is considered less significant when using the ophthalmic drop form instead of the systemic form according to Lexicomp.\u003c/p\u003e\u003cp\u003eIn patients on topical beta-blockers concurrently with systemic beta-blockers, an additive effect occurs on AV node function; thus, these patients need careful heart-rate monitoring. When patients are maintained on long-term beta-blockers and alpha-2 agonists, withdrawing the alpha-2 agonist while continuing the beta-blocker greatly increases the risk of rebound hypertension (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). In addition, the patient should be thoroughly evaluated before beginning treatment with a systemic beta-blocker in conjunction with topical beta-blockers (i.e., timolol) for potential cardiovascular problems. Timolol, when instilled into the eye, enters systemic circulation via the nasolacrimal duct in at least 80% of cases; the bulk of it therefore circles around the body without undergoing any first-pass metabolism in the liver (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). This same mechanism pertains to other drugs particularly affecting the autonomic nervous system.\u003c/p\u003e\u003cp\u003eGlaucoma and its associated comorbidities are more common among older patients than with accompanying comorbidities of cardiovascular, neurological, and psychiatric diseases. It is clear that cytochrome oxidase inhibitors are used extensively among elderly patients; these tend to slow the metabolism of timolol based on their inhibitory effect on CYP2D6, increasing its systemic effects (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). According to Lexicomp, using dorzolamide and acetazolamide increases the risk of metabolic acidosis; therefore, it is not recommended. They state that, generally, the literature shows that combinations of acetazolamide and dorzolamide do not appear to provide any additive therapeutic effects, as both alone can decrease the production of aqueous humor and intraocular pressure to similar extents or degrees of efficiency (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Lastly, Lexicomp has flagged possible interactions with sulfonamide drugs when prescribing topical applications of carbonic anhydrase inhibitors, and they list topiramate and zonisamide (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn our study, we found logistic analysis of the data (accounting for non-drug-related factors) among patients to reveal a significant association binding a higher probability of drug interactions with educational level, assistance from others in their medication use, and cardiovascular diseases, including hypertension.\u003c/p\u003e\u003cp\u003eEducational background appears to enable meaningful medication use and intel supplied through intel to precisely mitigate adverse interactions. It is clear that several studies so far have illustrated the positive role of caregivers in reducing these potential drug interactions. The assistance of others improves the correct use of a given medication, leading to improved outcomes for the patient. Assistance from others can contribute to proper medication use, thereby increasing patient adherence to therapy and finally reducing the overall number of drugs used still (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Many studies have noted that as one goes toward old age, the prevalence of cardiovascular diseases and diabetes increases. It means more treatments need to be prescribed, leading to the possibility of polypharmacy (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the logistic regression analysis of factors associated with ocular-ocular interactions, self-administration of medication was identified as the most significant factor increasing the likelihood of drug interactions (OR\u0026thinsp;=\u0026thinsp;2.39), while educational level, unlike other studies, did not show significant influence in this regard (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). After conducting logistic regression analysis on drug-related variables, the number of medications was one of the most influential variables contributing to drug interactions. The odds of increased interactions with an addition of one extra ophthalmic medication and a non-ophthalmic medication approach were put at 1.93 and 1.22, respectively, confirming findings from other studies from across the globe (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOcular-systemic interactions are responsible for 23% of high DDI prevalence in elderly glaucoma patients, which was 75.8%, according to this study. The drug classes most frequently involved were prostaglandin analogues, beta-blockers, and carbonic anhydrase inhibitors. The most important risk factors for pertinent DDI according to clinician importance were cardiovascular disease, age\u0026thinsp;\u0026ge;\u0026thinsp;70, and polypharmacy. These results highlight the imperative for routine drug reconciliation and multidisciplinary collaboration to maximize pharmacotherapy and avoid side effects in geriatric glaucoma patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received no specific funding from any public, commercial agencies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the ethics committee of Guilan University of Medical Sciences, Rasht, Iran (Ethics approval code: IR.GUMS.REC.1397.514). Informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eno individual patient data are included\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study are available from the corresponding author on reasonable request \u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.A., A.M., and A.E. conceptualized and designed the study. H.A., Y.A., R.S.M., E.A., and M.D. collected the data at Amiralmomenin Hospital. A.M. and A.E. performed the drug-drug interaction analysis using Lexicomp and Micromedex software. E.K.L. conducted the statistical analysis using SPSS version 21. H.A., A.M., A.E., and E.K.L. drafted the manuscript. Y.A., R.S.M., E.A., and M.D. contributed to data interpretation and critical revision of the manuscript. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the staff of Amiralmomenin Hospital for their support in data collection.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSutanto H. Tackling polypharmacy in geriatric patients: Is increasing physicians\u0026rsquo; awareness adequate? Archives Gerontol Geriatr Plus. 2025;2(3):100185.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTsoukalas N, Brito-Dellan N, Font C, Butler T, Rojas-Hernandez CM, Butler T, et al. 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Expert Opin Drug Metab Toxicol. 2020;16(4):297\u0026ndash;307.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ede Oliveira LM, Diel JAC, Nunes A, Dal Pizzol TS. Prevalence of drug interactions in hospitalised elderly patients: a systematic review. Eur J Hosp Pharm. 2021;28(1):4\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBj\u0026ouml;rkman IK, Fastbom J, Schmidt IK, Bernsten CB, Group PCotEiER. Drug\u0026mdash;drug interactions in the elderly. Ann Pharmacother. 2002;36(11):1675\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDagnew SB, Tadesse TY, Zeleke MM, Yiblet TG, Addis GT, Mekonnen GB, et al. Drug-drug interactions among hospitalized elderly in patients at medical wards of Northwest Ethiopia's Comprehensive Specialized Hospitals: A multicenter observational study. SAGE Open Med. 2022;10:20503121221135874.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSantos TRA, Silveira EA, Pereira LV, Provin MP, Lima DM, Amaral RG. Potential drug\u0026ndash;drug interactions in older adults: A population-based study. Geriatr Gerontol Int. 2017;17(12):2336\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuber M, K\u0026ouml;lzsch M, Stahlmann R, Hofmann W, Bolbrinker J, Dr\u0026auml;ger D, et al. Ophthalmic drugs as part of polypharmacy in nursing home residents with glaucoma. Drugs Aging. 2013;30:31\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAljadani R, Aseeri M. Prevalence of drug\u0026ndash;drug interactions in geriatric patients at an ambulatory care pharmacy in a tertiary care teaching hospital. BMC Res Notes. 2018;11:1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKo Y, Tan S-LD, Chan A, Wong Y-P, Yong W-P, Ng RC-H, et al. Prevalence of the coprescription of clinically important interacting drug combinations involving oral anticancer agents in Singapore: a retrospective database study. Clin Ther. 2012;34(8):1696\u0026ndash;704.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmithburger PL, Kane-Gill SL, Seybert AL. Drug\u0026ndash;drug interactions in the medical intensive care unit: an assessment of frequency, severity and the medications involved. Int J Pharm Pract. 2012;20(6):402\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmithburger PL, Gill SLK, Benedict NJ, Falcione BA, Seybert AL. Grading the severity of drug-drug interactions in the intensive care unit: a comparison between clinician assessment and proprietary database severity rankings. Ann Pharmacother. 2010;44(11):1718\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePalappalil DS, Sushama J, Kesavan KP. Drug Interactions as a cause of adverse drug reactions in a tertiary care hospital. Biomedical Pharmacol J. 2022;15(3):1637\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWoreta FA, Gordon LK, O\u0026rsquo;Rese JK, Randolph JD, Zebardast N, P\u0026eacute;rez-Gonz\u0026aacute;lez CE. Enhancing diversity in the ophthalmology workforce. Ophthalmology. 2022;129(10):e127\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eM\u0026auml;enp\u0026auml;\u0026auml; J, Pelkonen O. Cardiac safety of ophthalmic timolol. Exp Opin Drug Saf. 2016;15(11):1549\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRosenberg LF, Krupin T, Tang L-Q, Hong PH, Ruderman JM. Combination of systemic acetazolamide and topical dorzolamide in reducing intraocular pressure and aqueous humor formation. Ophthalmology. 1998;105(1):88\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLook KA, Stone JA. Medication management activities performed by informal caregivers of older adults. Res Social Administrative Pharm. 2018;14(5):418\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShetty V, Chowta MN, Chowta KN, Shenoy A, Kamath A, Kamath P. Evaluation of potential drug-drug interactions with medications prescribed to geriatric patients in a tertiary care hospital. J aging Res. 2018;2018(1):5728957.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDomino FJ. Improving adherence to treatment for hypertension. Am Family Phys. 2005;71(11).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMiller NH, Hill M, Kottke T, Ockene IS. The multilevel compliance challenge: recommendations for a call to action: a statement for healthcare professionals. Circulation. 1997;95(4):1085\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTeka F, Teklay G, Ayalew E, Teshome T. Potential drug\u0026ndash;drug interactions among elderly patients admitted to medical ward of Ayder Referral Hospital, Northern Ethiopia: a cross sectional study. BMC Res Notes. 2016;9:1\u0026ndash;8.\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":"DDI, Elderly Patients, Glaucoma, Ocular Pharmacology, Polypharmacy, Systemic Medications","lastPublishedDoi":"10.21203/rs.3.rs-7933567/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7933567/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground and aim:\u003c/strong\u003e Glaucoma is one of the world's leading causes of blindness. This disease applies especially to older individuals who often require systemic drugs for other co-morbidities. In this age group, risks of polypharmacy can give rise to concerns over drug-drug interaction (DDI) that may compromise both safety and therapeutic efficacy for patients. The objective of this research is to study the DDI frequency and severity in older patients with glaucoma on systemic medications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA cross-sectional study involving glaucoma patients aged 60 years and above was conducted in Amiralmomenin Hospital, Rasht, Iran. The data regarding drugs was obtained during patient interviews and through their medical records. Potential DDIs were searched for Lexicomp and Micromedex software. Statistical analysis was also performed using SPP version 21 to determine the prevalence and severity of the interactions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eAmong the studied 256 patients, 57%, with a mean age of 68.4 ± 4.2 years, were found to be taking at least 5 drugs. DDI was found in 75.8% of cases, while 23% of such cases involved systemic and ocular medications. Lexicomp identified significant DDI in 66.6% of cases while Micromedex found it in 75.8%, indicating sensitivity differences in detection. Most commonly involved drugs were Beta-blockers and carbonic anhydrase inhibitors, especially in combination with antihypertensive and diabetic medications. Independent risk factors for clinically significant DDIs were noted to be diabetes, heart disease, and age ≥70 years (p \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Many elderly patients encountered possible DDI experiences with glaucoma medications and systemic medications. Drugs for glaucoma may cause varying efficacy and safety in both topical and systemic therapies. The role of glaucoma drugs in the management of polypharmacy in the elderly is therefore important, as this can improve clinical outcomes and minimize the risk of adverse drug events.\u003c/p\u003e","manuscriptTitle":"Potential Drug-Drug Interactions in Elderly Patients Treated with Anti- Glaucoma Agents: A Cross-Sectional Study in North of Iran","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-19 09:47:36","doi":"10.21203/rs.3.rs-7933567/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":"c31a7165-916b-4fd3-8c81-694f3296aac8","owner":[],"postedDate":"November 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-17T01:39:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-19 09:47:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7933567","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7933567","identity":"rs-7933567","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}