Prescribing Pattern of Drugs Used in Myocardial Infarction with Comorbidity and Assessment of Complexity Using Medication Regimen Complexity Index (MRCI): Prospective, Cross Sectional Observational Study

Myocardial infarction (MI) is one of the leading manifestations of cardiovascular disease (CVD) and remains a major cause of mortality and morbidity worldwide. According to the World Health Organization (WHO), cardiovascular diseases account for approximately 17.9 million deaths annually, with ischemic heart disease representing the largest proportion of these deaths. Early diagnosis and prompt evidence-based treatment are essential to reduce myocardial damage, prevent complications, and improve survival outcomes in patients with MI (1,2).

Myocardial infarction occurs due to prolonged myocardial ischemia resulting from an abrupt reduction or complete cessation of coronary blood flow, most commonly caused by rupture of an atherosclerotic plaque followed by thrombus formation. Depending on electrocardiographic findings, MI is classified into ST-elevation myocardial infarction (STEMI) and non-ST-elevation myocardial infarction (NSTEMI), each requiring specific pharmacological and interventional management strategies (3,4).

The burden of myocardial infarction is increasing rapidly in developing countries such as India due to urbanization, sedentary lifestyle, unhealthy dietary habits, smoking, obesity, diabetes mellitus, and hypertension. Furthermore, the age of onset of coronary artery disease among the Indian population is considerably younger than that observed in Western countries, making cardiovascular disease a significant public health concern (5,6).

Patients admitted with myocardial infarction frequently present with multiple comorbid conditions, particularly hypertension, diabetes mellitus, chronic kidney disease (CKD), dyslipidemia, and heart failure. These comorbidities substantially increase disease severity and complicate therapeutic management because each condition requires additional pharmacological treatment. Consequently, most patients receive multiple medications simultaneously, resulting in polypharmacy and increased treatment burden (7,8).

Polypharmacy, generally defined as the concurrent use of five or more medications, has become increasingly common among patients with myocardial infarction. Although evidence-based cardiovascular pharmacotherapy involving antiplatelets, statins, beta-blockers, angiotensin-converting enzyme inhibitors (ACEIs), angiotensin receptor blockers (ARBs), anticoagulants, and other supportive medications has significantly improved survival, the addition of medications for associated comorbidities substantially increases medication burden (9,10). Excessive polypharmacy has been associated with poor medication adherence, drug-drug interactions, adverse drug reactions, medication errors, increased healthcare costs, prolonged hospitalization, and higher rates of hospital readmission (11,12).

Current international guidelines from the American College of Cardiology (ACC), American Heart Association (AHA), and European Society of Cardiology (ESC) strongly recommend evidence-based pharmacotherapy for the management of myocardial infarction. However, several studies have demonstrated considerable variations in prescribing practices due to physician preference, institutional protocols, patient-specific characteristics, socioeconomic factors, and healthcare resource availability. Therefore, evaluating real-world prescribing patterns is essential to determine whether current treatment practices are consistent with established clinical guidelines (13–15).

Drug utilization research plays an important role in evaluating the quality of prescribing practices and promoting rational drug use. Prescription pattern analysis provides valuable information regarding the selection of medications, frequency of drug use, combination therapy, and adherence to standard treatment guidelines. Such studies help identify inappropriate prescribing practices and provide opportunities to optimize pharmacotherapy, improve clinical outcomes, and reduce unnecessary medication use (16,17).

Medication regimen complexity has emerged as another important determinant of successful pharmacotherapy. The complexity of a medication regimen depends not only on the number of prescribed medications but also on dosage forms, dosing frequency, administration schedules, and additional instructions. Increasing medication regimen complexity is strongly associated with poor medication adherence, reduced treatment effectiveness, increased medication errors, and poorer clinical outcomes (18).

The Medication Regimen Complexity Index (MRCI), developed by George et al., is a validated and reliable tool designed to objectively quantify medication regimen complexity. Unlike simple medication counts, the MRCI evaluates three major components of the prescription: dosage forms, dosing frequency, and additional administration instructions. The cumulative score provides a comprehensive assessment of the overall complexity of an individual's medication regimen (19).

Several studies have demonstrated that higher MRCI scores are significantly associated with poor medication adherence, increased hospitalization, greater healthcare utilization, and lower quality of life among patients with chronic diseases, including cardiovascular disorders (20,21). Therefore, assessment of medication regimen complexity provides clinicians with an opportunity to identify patients at higher risk of non-adherence and implement strategies such as medication review, regimen simplification, patient counseling, and pharmacist-led interventions.

Despite the growing recognition of medication regimen complexity, limited evidence is available regarding prescribing patterns and MRCI assessment among Indian patients with myocardial infarction, particularly those with multiple comorbidities. Most available studies have been conducted in Western populations, where healthcare delivery systems, prescribing practices, and patient characteristics differ substantially from those observed in India. Consequently, there remains a need for region-specific data to better understand real-world prescribing practices and medication complexity in Indian clinical settings (22,23).

Therefore, the present prospective cross-sectional observational study was undertaken to evaluate the prescribing pattern of drugs used in patients with myocardial infarction and associated comorbidities and to assess medication regimen complexity using the Medication Regimen Complexity Index (MRCI). The findings of this study are expected to provide valuable evidence regarding cardiovascular polypharmacy, identify factors associated with high medication regimen complexity, promote rational prescribing practices, and support the development of interventions aimed at improving medication adherence and clinical outcomes among patients with myocardial infarction.

Need of Study

 The high incidence of myocardial infarction (MI), along with the frequent presence of comorbidities such as type 2 diabetes mellitus (T2DM), hypertension (HTN), chronic kidney disease (CKD), and acute kidney injury (AKI), significantly increases the complexity of clinical management. These conditions often require multiple pharmacological therapies, leading to polypharmacy. Polypharmacy, while often necessary, is associated with an increased risk of drug drug and drug disease interactions, adverse drug reactions, poor medication adherence, and therapeutic duplication. Such complexities can compromise treatment effectiveness and result in suboptimal clinical outcomes. Therefore, evaluating and optimizing medication regimens in MI patients is essential. Understanding polypharmacy can help improve treatment strategies, enhance patient safety, reduce healthcare costs, and ultimately improve clinical outcomes.

OBJECTIVES

• To determine the prescription patterns for MI to avoid the irrational use of drugs
• To assess the medication regimen complexity, using the MRCI to identify the type of action with the highest potential for reducing complexity (according to the three MRCI sections) and contribute to rational use of medication in population.
• To identify cardiovascular polypharmacy (drugs more than 5)
• To determine the factors associated with the high medication regimen complexity for patients with MI and related comorbidities.

MATERIALS AND METHODS

Study Design and Setting

A prospective, cross-sectional observational study was carried out to quantify medication regimen complexity using the Medication Regimen Complexity Index (MRCI) and to examine the prescription patterns of drugs used in patients with myocardial infarction (MI). The study was conducted at Sahyadri Hospital, a tertiary care multispecialty facility offering specialist cardiovascular care, in Nashik, Maharashtra, India. The investigation was carried out between November 2024 and April 2025, a span of six months. The Institutional Ethics Committee examined and approved the study protocol prior to its start, and all operations were carried out in compliance with the ethical guidelines for biomedical research involving human subjects.

Study Population

Inclusion Criteria

Patients with a confirmed diagnosis of myocardial infarction who were admitted to the cardiology department were included in the study. During the study period, 150 patients who fit the eligibility requirements were enrolled. Individuals of any gender who were diagnosed with ST-Elevation Myocardial Infarction (STEMI), Non-ST-Elevation Myocardial Infarction (NSTEMI), In order to assess their influence on prescription patterns and medication complexity, patients with related comorbidities such as diabetes mellitus, hypertension, chronic kidney disease.

Exclusion Criteria

Patients with serious mental disease, those under the age of eighteen, pregnant or nursing women, and those with insufficient medical records were not allowed to participate in the study. Additionally, patients who refused to give informed consent were not allowed to participate.

Study Materials

Patient case records, treatment charts, laboratory investigation reports, drug administration records, discharge summaries, and a specifically created data collection form were among the resources used in the study. The complexity of the drug regimens given to research participants was evaluated using the drug Regimen Complexity Index (MRCI) tool. Data entry, administration, and statistical analysis were done using Microsoft Excel and the Statistical Package for Social Sciences (SPSS) version 29.0.

Data Collection Procedure

Prospective data collection was carried out by examining patient medical records and conducting patient interviews as needed. Prior to data collection, each subject provided written informed consent. A verified and organized data collecting form created especially for the study was used to gather information.

Age, gender, height, weight, body mass index (BMI), and social history were among the demographic information gathered. Clinical data was recorded, including the diagnosis, type of myocardial infarction, length of hospital stay, prior history of cardiovascular illness, and related comorbid diseases. Additionally included were specifics of laboratory tests, including cardiac biomarkers, serum creatinine, blood glucose levels, lipid profiles, and other pertinent biochemical characteristics.

Medication-related data was gathered from discharge prescriptions and inpatient treatment charts. The medication's name, dosage form, dose, frequency of administration, method of administration, length of therapy, and therapeutic category were all recorded. Antiplatelet agents, anticoagulants, statins, beta-blockers, angiotensin-converting enzyme inhibitors (ACE inhibitors), angiotensin receptor blockers (ARBs), vasodilators, calcium channel blockers, antidiabetic agents, antihypertensive drugs, proton pump inhibitors, and other supportive medications were among the various therapeutic categories into which prescribed medications were divided.

Evaluation of Prescription Patterns

The use of different drug classes administered to patients with myocardial infarction was assessed using prescription pattern analysis. Calculations were made for the frequency and percentage of specific medications and therapeutic classes. Monotherapy and combination therapy patterns were evaluated. Analysis was done on the prescribing patterns in patients with various comorbid diseases, such as diabetes mellitus, hypertension, and chronic kidney disease. The degree of polypharmacy among patients was also assessed in the study.

The concurrent use of five or more drugs was referred to as polypharmacy. Each patient's number of prescribed cardiovascular drugs was taken into account when evaluating cardiovascular polypharmacy. Standard treatment guidelines for managing myocardial infarction were used to assess the appropriateness of prescribed drugs.

Assessment of Medication Regimen Complexity

Medication regimen complexity was assessed using the Medication Regimen Complexity Index (MRCI), a validated and reliable tool designed to quantify the complexity of medication regimens. Unlike simple medication counts, the MRCI evaluates multiple aspects of a prescription that may influence patient adherence and treatment outcomes.

The MRCI consists of three sections:

Section A: Dosage Forms

This section assigns scores based on the type of dosage form prescribed, such as tablets, capsules, injections, inhalers, topical preparations, and other formulations.

Section B: Frequency of Dosage

This section evaluates how frequently medications are administered. Higher scores are assigned to regimens requiring multiple daily doses compared to once-daily medications.

Section C: Additional Directions

This section evaluates unique administration guidelines that may complicate a regimen, such as breaking pills, taking drugs with food, or using different dose schedules.

Individual scores from all three sections were summed to obtain the total MRCI score for each patient. Higher scores indicated greater medication regimen complexity. Patients were categorized based on their complexity scores to evaluate factors associated with increased treatment burden.

Outcome Measures

The primary result of the study was to evaluate prescription patterns in patients with myocardial infarction and to assess the complexity of medication regimens by the MRCI tool. Secondary outcomes were factors associated with high MRCI scores and the impact of comorbidities and polypharmacy on medication complexity.

Statistical Analysis

The collected data were checked for completeness, coded, and entered into Microsoft Excel before being exported to SPSS version 29.0 for statistical analysis. Descriptive statistics were used to summarize demographic and clinical characteristics. Continuous variables were expressed as mean ± standard deviation, while categorical variables were presented as frequencies and percentages.

Mean MRCI scores were compared between different patient groups using the independent sample t-test. Chi-square test was used to evaluate the associations between categorical variables. Binary logistic regression was used to identify predictors of increased medication regimen complexity. Variables included in the regression model were age, gender, diabetes mellitus, hypertension, chronic kidney disease, number of prescribed medications and cardiovascular polypharmacy. The level of statistical significance was p < 0.05.

This methodological approach enabled an in-depth assessment of prescribing practices and complexity of medication regimens in patients with myocardial infarction and concomitant comorbidities, which in turn provided meaningful insights for improving pharmacotherapy and enhancing patient care outcomes.

RESULTS AND DISCUSSION

Results

A total of 150 patients diagnosed with myocardial infarction (MI) with or without associated comorbidities were included in the study. The mean age of the study population was 60.92 ± 10.05 years, indicating that MI predominantly affected older adults in the present study (Table 1).

GENDER	Frequency	Percent
MALE	103	68.67
FEMALE	47	31.33
TOTAL	150	100

                               Graph 1. Distribution of the patients according to gender.

DISCUSSION

This study evaluated the prescribing patterns of myocardial infarction (MI) patients with associated comorbidities and the complexity of medication regimens using the Medication Regimen Complexity Index (MRCI). The mean age of the study population was 60.92 ± 10.05 years, with a predominance of male patients (68.67%).  These findings are consistent with previous studies reporting a higher prevalence of MI among older adults and males, highlighting the influence of age and gender on cardiovascular disease burden (34,35).

The most common co-morbidities in the study population were diabetes mellitus (49.33%), hypertension (43.33%) and chronic kidney disease (25.33%). Similar observations were also reported in previous studies where hypertension and diabetes were found to be major risk factors associated with myocardial infarction (38). The combination of these comorbidities raises cardiovascular risk, and often requires multiple medications, increasing treatment complexity.

Prescription pattern analysis showed common use of statins, antiplatelets, beta-blockers, vasodilators and antianginal agents. These results reflect the use of current guideline-directed medical therapy for the treatment of MI. The widespread use of antiplatelets and lipid-lowering agents in MI patients has also been described previously and indicates their importance in secondary prevention (34,35,38).

Combination treatments, especially those including statins, antiplatelets, and antihypertensive drugs, were frequently administered. To obtain the best possible control of cardiovascular disease and related comorbidities, combined therapy is frequently required. However, especially in patients receiving numerous treatment agents, a growing medication load may lead to adverse drug events and medication non-adherence (3,28,29).

A moderate-to-high degree of pharmaceutical regimen complexity is indicated by the study's mean overall MRCI score of 16.96 ± 7.13. The overall complexity was significantly influenced by all three MRCI domains, including dosage forms, dosing frequency, and supplementary administration instructions. Similar results have been seen in cardiovascular patients, where high MRCI scores were found to be significantly influenced by dose frequency (36). Increased medication complexity has been associated with reduced adherence and poorer clinical outcomes (5,7).

Diabetes mellitus and the intricacy of drug regimens were shown to be significantly correlated (p = 0.003). Diabetes mellitus was also found to be an independent predictor of high MRCI scores using logistic regression analysis. The necessity for several antidiabetic medications, regular dosage schedules, and extra monitoring needs in diabetic patients may be the cause of this discovery. There have been prior reports of similar links between diabetes and more complicated regimens (36).

42% of patients had cardiovascular polypharmacy, which was significantly correlated with the complexity of the treatment regimen (p < 0.001). MRCI scores were significantly higher in patients using five or more cardiovascular medicines. Drug-drug interactions, adverse drug responses, and poor medication adherence are all linked to polypharmacy, which is acknowledged as a significant factor in regimen complexity (3,5,28,29).

The results of this study highlight the significance of routinely evaluating medication complexity in patients with MI who have comorbidities. drug burden reduction techniques that preserve therapeutic efficacy include drug evaluation, the use of fixed-dose combinations, patient counseling, and clinical pharmacist interventions (30, 32, 33).

Overall, the study shows that complicated drug regimens are often encountered by MI patients who had diabetes mellitus, hypertension, and chronic kidney disease. The complexity of drug regimens was found to be significantly influenced by diabetes mellitus and cardiovascular polypharmacy. In this patient population, routine examination using the MRCI tool may help optimize pharmacotherapy, increase drug adherence, and improve clinical outcomes (5,7,36).

CONCLUSION

MI patients with comorbidities commonly received moderate to highly complex medication regimens. Diabetes mellitus and cardiovascular polypharmacy (≥5 cardiovascular drugs) were significant predictors of increased medication regimen complexity, whereas age, gender, hypertension, and CKD showed no significant association. These findings emphasize the need for regimen simplification and greater clinical pharmacist involvement to improve medication adherence and clinical outcomes.

REFERENCES

1. Thygesen K, Alpert JS, Jaffe AS, Chaitman BR, Bax JJ, Morrow DA, et al. Fourth Universal Definition of Myocardial Infarction (2018). J Am Coll Cardiol. 2018 Oct 30;72(18):2231–64.
2. Anderson JL, Morrow DA. Acute Myocardial Infarction. N Engl J Med. 2017 May 25;376(21):2053–64.
3. Maher RL, Hanlon J, Hajjar ER. Clinical consequences of polypharmacy in elderly. Expert Opin Drug Saf. 2014 Jan;13(1):57–65.
4. Krumholz HM, Radford MJ, Wang Y, Chen J, Heiat A, Marciniak TA. National use and effectiveness of beta-blockers for the treatment of elderly patients after acute myocardial infarction: National Cooperative Cardiovascular Project. JAMA. 1998 Aug 19;280(7):623–9.
5. George J, Phun YT, Bailey MJ, Kong DCM, Stewart K. Development and validation of the medication regimen complexity index. Ann Pharmacother. 2004 Sep;38(9):1369–76.
6. Libby AM, Fish DN, Hosokawa PW, Linnebur SA, Metz KR, Nair KV, et al. Patient-level medication regimen complexity across populations with chronic disease. Clin Ther. 2013 Apr;35(4):385-398.e1.
7. Wimmer BC, Cross AJ, Jokanovic N, Wiese MD, George J, Johnell K, et al. Clinical Outcomes Associated with Medication Regimen Complexity in Older People: A Systematic Review. J Am Geriatr Soc. 2017 Apr;65(4):747–53.
8. American Diabetes Association Professional Practice Committee. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2022. Diabetes Care. 2022 Jan 1;45(Suppl 1):S17–38.
9. Kidney Disease: Improving Global Outcomes (KDIGO) Blood Pressure Work Group. KDIGO 2021 Clinical Practice Guideline for the Management of Blood Pressure in Chronic Kidney Disease. Kidney Int. 2021 Mar;99(3S):S1–87.
10. Patel V, Parikh R, Nandraj S, Balasubramaniam P, Narayan K, Paul VK, et al. Assuring health coverage for all in India. Lancet Lond Engl. 2015 Dec 12;386(10011):2422–35.
11. Ingersoll KS, Cohen J. The impact of medication regimen factors on adherence to chronic treatment: a review of literature. J Behav Med. 2008 Jun;31(3):213–24.
12. Cardiovascular diseases (CVDs) [Internet]. [cited 2025 May 31]. Available from: https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)
13. Prabhakaran D, Jeemon P, Roy A. Cardiovascular Diseases in India: Current Epidemiology and Future Directions. Circulation. 2016 Apr 19;133(16):1605–20.
14. Vos T, Lim SS, Abbafati C, Abbas KM, Abbasi M, Abbasifard M, et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet. 2020 Oct 17;396(10258):1204–22.
15. Morrish NJ, Wang SL, Stevens LK, Fuller JH, Keen H. Mortality and causes of death in the WHO Multinational Study of Vascular Disease in Diabetes. Diabetologia. 2001 Sep;44 Suppl 2:S14-21.
16. Kannel WB. Blood pressure as a cardiovascular risk factor: prevention and treatment. JAMA. 1996 May 22;275(20):1571–6.
17. Palmer SC, Navaneethan SD, Craig JC, Johnson DW, Tonelli M, Garg AX, et al. Meta-analysis: erythropoiesis-stimulating agents in patients with chronic kidney disease. Ann Intern Med. 2010 Jul 6;153(1):23–33.
18. Promoting rational use of medicines [Internet]. [cited 2025 May 31]. Available from: https://www.who.int/activities/promoting-rational-use-of-medicines
19. Routledge PA, O’Mahony MS, Woodhouse KW. Adverse drug reactions in elderly patients. Br J Clin Pharmacol. 2004 Feb;57(2):121–6.
20. Amsterdam EA, Wenger NK, Brindis RG, Casey DE, Ganiats TG, Holmes DR, et al. 2014 AHA/ACC Guideline for the Management of Patients with Non-ST-Elevation Acute Coronary Syndromes: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2014 Dec 23;64(24):e139–228.
21. Levine GN, Bates ER, Bittl JA, Brindis RG, Fihn SD, Fleisher LA, et al. 2016 ACC/AHA Guideline Focused Update on Duration of Dual Antiplatelet Therapy in Patients With Coronary Artery Disease: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines: An Update of the 2011 ACCF/AHA/SCAI Guideline for Percutaneous Coronary Intervention, 2011 ACCF/AHA Guideline for Coronary Artery Bypass Graft Surgery, 2012 ACC/AHA/ACP/AATS/PCNA/SCAI/STS Guideline for the Diagnosis and Management of Patients With Stable Ischemic Heart Disease, 2013 ACCF/AHA Guideline for the Management of ST-Elevation Myocardial Infarction, 2014 AHA/ACC Guideline for the Management of Patients With Non–ST-Elevation Acute Coronary Syndromes, and 2014 ACC/AHA Guideline on Perioperative Cardiovascular Evaluation and Management of Patients Undergoing Noncardiac Surgery. Circulation [Internet]. 2016 Sep 6 [cited 2025 Feb 19];134(10). Available from: https://www.ahajournals.org/doi/10.1161/CIR.0000000000000404
22. Ibanez B, James S, Agewall S, Antunes MJ, Bucciarelli-Ducci C, Bueno H, et al. 2017 ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation. Eur Heart J. 2018 Jan 7;39(2):119–77.
23. (PDF) Rosuvastatin, inflammation, C-reactive Protein, JUPITER, and primary prevention of cardiovascular disease–a perspective. ResearchGate [Internet]. [cited 2025 May 31]; Available from: https://www.researchgate.net/publication/49785834_Rosuvastatin_inflammation_C-reactive_Protein_JUPITER_and_primary_prevention_of_cardiovascular_disease-a_perspective
24. Yusuf S, Islam S, Chow CK, Rangarajan S, Dagenais G, Diaz R, et al. Use of secondary prevention drugs for cardiovascular disease in the community in high-income, middle-income, and low-income countries (the PURE Study): a prospective epidemiological survey. Lancet Lond Engl. 2011 Oct 1;378(9798):1231–43.
25. El-Menyar AA, Albinali HA, Bener A, Mohammed I, Al Suwaidi J. Prevalence and impact of diabetes mellitus in patients with acute myocardial infarction: a 10-year experience. Angiology. 2009 Jan;60(6):683–8.
26. Mansur N, Weiss A, Beloosesky Y. Looking beyond polypharmacy: quantification of medication regimen complexity in the elderly. Am J Geriatr Pharmacother. 2012 Aug;10(4):223–9.
27. Kassaw AT, Sendekie AK, Minyihun A, Gebresillassie BM. Medication regimen complexity and its impact on medication adherence in patients with multimorbidity at a comprehensive specialized hospital in Ethiopia. Front Med. 2024 May 27;11:1369569.
28. Jokanovic N, Tan ECK, van den Bosch D, Kirkpatrick CM, Dooley MJ, Bell JS. Clinical medication review in Australia: A systematic review. Res Soc Adm Pharm RSAP. 2016;12(3):384–418.
29. Rochon PA, Gurwitz JH. Optimising drug treatment for elderly people: the prescribing cascade. BMJ. 1997 Oct 25;315(7115):1096–9.
30. Dakappa A, Narayanareddy M. A cross-sectional study to assess the rationality of fixed dose combinations prescribed in geriatric patients in a tertiary care hospital. Int J Basic Clin Pharmacol. 2016;5(4):1441–7.
31. Holloway KA, Ivanovska V, Wagner AK, Vialle-Valentin C, Ross-Degnan D. Have we improved use of medicines in developing and transitional countries and do we know how to? Two decades of evidence. Trop Med Int Health TM IH. 2013 Jun;18(6):656–64.
32. Mekonnen AB, McLachlan AJ, Brien JAE. Pharmacy-led medication reconciliation programmes at hospital transitions: a systematic review and meta-analysis. J Clin Pharm Ther. 2016 Apr;41(2):128–44.
33. Scott P, Ma H, Viriyakosol S, Terkeltaub R, Liu-Bryan R. Engagement of CD14 mediates the inflammatory potential of monosodium urate crystals. J Immunol. 2006;177(9):6370–8.
34. Mehra A, Bhat NK, Sharma SK, Khajuria K. Drug prescribing pattern in patients of myocardial infarction in a tertiary care teaching hospital of North India. Int J Basic Clin Pharmacol. 2020 Aug 25;9(9):1357.
35. Kuthiala G, Chaudhary G. Ropivacaine: A review of its pharmacology and clinical use. Indian J Anaesth. 2011;55(2):104–10.
36. Tinoco MS, Groia-Veloso RCDS, Santos JNDD, Cruzeiro MGM, Dias BM, Reis AMM. Medication regimen complexity of coronary artery disease patients. Einstein São Paulo. 2021 Mar 5;19:eAO5565.
37. Abdelbary A, Kaddoura R, Balushi SA, Ahmed S, Galvez R, Ahmed A, et al. Implications of the medication regimen complexity index score on hospital readmissions in elderly patients with heart failure: a retrospective cohort study. BMC Geriatr. 2023 Jun 19;23(1):377.
38. Palli K, Chidrawar V, Veerendra U, Chenchu S, Devangam M, Shiromwar S, et al. Drug Prescribing Pattern in Myocardial Infarction Patients at a Tertiary Care Hospital in South India. Int J Pharm Investig. 2023 Sep 21;13(4):883–8.
39. Keche Y, Gaikwad NR, Wasnik PN, Nagpure K, Siddiqui MS, Joshi A, et al. Analysis of Drugs Prescribed to Elderly Patients in a Tertiary Health Care Center in Raipur, Central India: An Observational Study. Cureus [Internet]. 2024 Jan 23 [cited 2025 May 31]; Available from: https://www.cureus.com/articles/222397-analysis-of-drugs-prescribed-to-elderly-patients-in-a-tertiary-health-care-center-in-raipur-central-india-an-observational-study
40. .ResearchGate [Internet]. 2025 [cited 2025 May 31]. (PDF) Prescribing Pattern in Coronary Artery Disease: A Prospective Study. Available from: https://www.researchgate.net/publication/281685707_Prescribing_Pattern_in_Coronary_Artery_Disease_A_Prospective_Study