Iranian Journal of Medical Sciences

Document Type : Original Article(s)

Authors

1 Department of Pharmaceutics, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran

2 Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran

3 Department of Clinical Pharmacy, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran

4 Department of Artificial Intelligence in Medical Sciences, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran

5 Department of Emergency Medicine, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran

10.30476/ijms.2025.106551.4081

Abstract

Background: Drug-drug interactions (DDIs) are among the most important medical errors that can lead to adverse effects, increased toxicity, or reduced treatment efficacy. The frequency and severity of DDIs vary across specialties. However, studies covering multiple specialties in Iran are few and not up-to-date. This study aims to fill this gap by offering a large-scale, multi-specialty analysis of DDIs in Iran using real-world e-prescription data.
Methods: This study analyzed pharmacological DDIs in 1,049,769 e-prescription records from Shiraz, Iran, spanning from November 2021 to February 2024. We used Lexicomp® DDI checker software and Python programming language to identify the most prevalent DDIs overall, the top contributing drug specialties for each of those DDIs, the specialties with the highest rates of potential DDIs, and the most prevalent DDI within each specialty.
Results: The analysis revealed that 38.77% of prescriptions contained at least one C, D, or X DDI. Dexamethasone, ketorolac, quetiapine, and aspirin were the drugs most commonly involved. The most frequent DDIs occurred between aprepitant and dexamethasone, ketorolac, and naproxen, aprepitant and doxorubicin, prednisolone, and tacrolimus, and diclofenac sodium and ketorolac. The medical specialties with the highest incidence of D or X level DDIs were rheumatology, endocrinology, orthopedics, oncology, internal medicine, emergency services, and psychiatry. The average counts of D or X DDIs per prescription were 0.53, 0.41, 0.40, 0.40, 0.26, 0.24, and 0.23, respectively.
Conclusion: This study underscores the need for provider vigilance and proactive measures, such as training and e-prescription alerts, to ensure patient safety.

Highlights

Pedram Porbaha (Google Scholar
Mehrdad Sharifi (Google Scholar

Keywords

  1. Farooqui R, Hoor T, Karim N, Muneer M. Potential Drug-Drug Interactions among Patients prescriptions collected from Medicine Out-patient Setting. Pak J Med Sci. 2018;34:144-8. doi: 10.12669/pjms.341.13986. PubMed PMID: 29643896; PubMed Central PMCID: PMC5857000.
  2. Moura CS, Acurcio FA, Belo NO. Drug-drug interactions associated with length of stay and cost of hospitalization. J Pharm Pharm Sci. 2009;12:266-72. doi: 10.18433/j35c7z. PubMed PMID: 20067703.
  3. Alinezhad A, Babaei MH, Gholami K, Khoei SH. Studying the Prevalence of Medical Interventions in the Recipes. Open Access Maced J Med Sci. 2019;7:1879-83. doi: 10.3889/oamjms.2019.314. PubMed PMID: 31316677; PubMed Central PMCID: PMC6614277.
  4. Namazi S, Pourhatami S, Borhani-Haghighi A, Roosta S. Incidence of Potential Drug-Drug Interaction and Related Factors in Hospitalized Neurological Patients in two Iranian Teaching Hospitals. Iran J Med Sci. 2014;39:515-21. PubMed PMID: 25429173; PubMed Central PMCID: PMC4242985.
  5. Moradi O, Karimzadeh I, Davani-Davari D, Shafiekhani M, Sagheb MM, Raees-Jalali GA. Drug-Drug Interactions among Kidney Transplant Recipients in The Outpatient Setting. Int J Organ Transplant Med. 2020;11:185-95. PubMed PMID: 33335699; PubMed Central PMCID: PMC7726842.
  6. Kheshti R, Aalipour M, Namazi S. A comparison of five common drug-drug interaction software programs regarding accuracy and comprehensiveness. J Res Pharm Pract. 2016;5:257-63. doi: 10.4103/2279-042X.192461. PubMed PMID: 27843962; PubMed Central PMCID: PMC5084483.
  7. Marcath LA, Xi J, Hoylman EK, Kidwell KM, Kraft SL, Hertz DL. Comparison of Nine Tools for Screening Drug-Drug Interactions of Oral Oncolytics. J Oncol Pract. 2018;14:e368-e74. doi: 10.1200/JOP.18.00086. PubMed PMID: 29787332; PubMed Central PMCID: PMC9797246.
  8. Bhandari B, Lamichhane P, Yadav D, Bajracharya SR. Potential Drug-drug Interactions among Hospital Discharge Prescriptions in a Tertiary Care Centre of Nepal: A Descriptive Cross-sectional Study. JNMA J Nepal Med Assoc. 2022;60:146-50. doi: 10.31729/jnma.7065. PubMed PMID: 35210638; PubMed Central PMCID: PMC9200007.
  9. Abdelkawy K, Kharouba M, Shendy K, Abdelmagged O, Galal N, Tarek M, et al. Prevalence of Drug-Drug Interactions in Primary Care Prescriptions in Egypt: A Cross-Sectional Retrospective Study. Pharmacy (Basel). 2023;11. doi: 10.3390/pharmacy11030106. PubMed PMID: 37368432; PubMed Central PMCID: PMC10301217.
  10. Lalagkas PN, Poulentzas G, Tsiolis L, Berberoglou E, Hadjipavlou-Litina D, Douros A, et al. Investigating Potential Drug-Drug Interactions from Greek e-Prescription Data. Curr Drug Saf. 2022;17:114-20. doi: 10.2174/1574886316666210816115811. PubMed PMID: 34397333.
  11. Leal Rodriguez C, Kaas-Hansen BS, Eriksson R, Biel JH, Belling KG, Andersen SE, et al. Drug interactions in hospital prescriptions in Denmark: Prevalence and associations with adverse outcomes. Pharmacoepidemiol Drug Saf. 2022;31:632-42. doi: 10.1002/pds.5415. PubMed PMID: 35124852; PubMed Central PMCID: PMC9303679.
  12. Jazbar J, Locatelli I, Horvat N, Kos M. Clinically relevant potential drug-drug interactions among outpatients: A nationwide database study. Res Social Adm Pharm. 2018;14:572-80. doi: 10.1016/j.sapharm.2017.07.004. PubMed PMID: 28716467.
  13. Teramura-Gronblad M, Raivio M, Savikko N, Muurinen S, Soini H, Suominen M, et al. Potentially severe drug-drug interactions among older people and associations in assisted living facilities in Finland: a cross-sectional study. Scand J Prim Health Care. 2016;34:250-7. doi: 10.1080/02813432.2016.1207142. PubMed PMID: 27428445; PubMed Central PMCID: PMC5036014.
  14. Johnell K, Klarin I. The relationship between number of drugs and potential drug-drug interactions in the elderly: a study of over 600,000 elderly patients from the Swedish Prescribed Drug Register. Drug Saf. 2007;30:911-8. doi: 10.2165/00002018-200730100-00009. PubMed PMID: 17867728.
  15. Al Mamun K, Islam MA, Sharmin T, Biswas K. Incidence of drug-drug interactions in prescriptions of general practitioners and specialists in Bangladesh. Asian Journal of Medicine and Health. 2021;19:60-6. doi: 10.9734/ajmah/2021/v19i830358.
  16. Shafiekhani M, Karimi S, ali Davarpanah M, Vazin A. Evaluating drug interactions, adverse drug reactions, and level of adherence to highly active antiretroviral therapy regimen amongst HIV-positive patients who referred to an AIDS healthcare center in Fars, southern Iran: the first multifaceted study from Iran. HIV & AIDS Review International Journal of HIV-Related Problems. 2017;16:24-31. doi: 10.5114/hivar.2017.65334.
  17. Namazi S, Pourhatami S, Borhani-Haghighi A, Roosta S. Incidence of Potential Drug-Drug Interaction and Related Factors in Hospitalized Neurological Patients in two Iranian Teaching Hospitals. Iran J Med Sci. 2014;39:515-21. PubMed PMID: 25429173; PubMed Central PMCID: PMC4242985.
  18. Ahmadizar F, Soleymani F, Abdollahi M. Study of drug-drug interactions in prescriptions of general practitioners and specialists in iran 2007-2009. Iran J Pharm Res. 2011;10:921-31. PubMed PMID: 24250431; PubMed Central PMCID: PMC3813067.
  19. Sharma A. Clinically Relevant Drug-Drug Interactions and Management Strategies: A Hospital based Study. Europasian Journal of Medical Sciences. 2020;2:58-63. doi: 10.46405/ejms.v2i2.246.
  20. Chowdhury K, Hazra A, Ghosh S, Choudhury S. Drug use survey to identify significant drug-drug interactions and assess clinical importance in the outpatient setting of a tertiary care hospital. Indian J Pharmacol. 2024;56:172-7. doi: 10.4103/ijp.ijp_483_23. PubMed PMID: 39078180; PubMed Central PMCID: PMC11286091.
  21. Pandas Development Team T. pandas-dev/pandas: Pandas. Zenodo. 2020;21:1-9.
  22. Harris CR, Millman KJ, van der Walt SJ, Gommers R, Virtanen P, Cournapeau D, et al. Array programming with NumPy. Nature. 2020;585:357-62. doi: 10.1038/s41586-020-2649-2. PubMed PMID: 32939066; PubMed Central PMCID: PMC7759461.
  23. Waskom ML. Seaborn: statistical data visualization. Journal of open source software. 2021;6:3021. doi: 10.21105/joss.03021.
  24. Hunter JD. Matplotlib: A 2D graphics environment. Computing in science & engineering. 2007;9:90-5. doi: 10.1109/MCSE.2007.55.
  25. Minaei H, Peikanpour M, Yousefi N, Peymani P, Peiravian F, Shobeiri N, et al. Country Pharmaceutical Situation on Access, Quality, and Rational Use of Medicines: An Evidence from a middle-income country. Iran J Pharm Res. 2019;18:2191-203. doi: 10.22037/ijpr.2019.111636.13273. PubMed PMID: 32184884; PubMed Central PMCID: PMC7059036.
  26. Masoud A, Noori Hekmat S, Dehnavieh R, Haj-Akbari N, Poursheikhali A, Abdi Z. An Investigation of Prescription Indicators and Trends Among General Practitioners and Specialists From 2005 to 2015 in Kerman, Iran. Int J Health Policy Manag. 2018;7:818-27. doi: 10.15171/ijhpm.2018.28. PubMed PMID: 30316230; PubMed Central PMCID: PMC6186487.
  27. Bourdin V, Bigot W, Vanjak A, Burlacu R, Lopes A, Champion K, et al. Drug-Drug Interactions Involving Dexamethasone in Clinical Practice: Myth or Reality? J Clin Med. 2023;12. doi: 10.3390/jcm12227120. PubMed PMID: 38002732; PubMed Central PMCID: PMC10672071.
  28. Fendrick AM, Pan DE, Johnson GE. OTC analgesics and drug interactions: clinical implications. Osteopath Med Prim Care. 2008;2:2. doi: 10.1186/1750-4732-2-2. PubMed PMID: 18257920; PubMed Central PMCID: PMC2257951.
  29. van Roon EN, van den Bemt PM, Jansen TL, Houtman NM, van de Laar MA, Brouwers JR. An evidence-based assessment of the clinical significance of drug-drug interactions between disease-modifying antirheumatic drugs and non-antirheumatic drugs according to rheumatologists and pharmacists. Clin Ther. 2009;31:1737-46. doi: 10.1016/j.clinthera.2009.08.009. PubMed PMID: 19808132.
  30. Huri HZ, Ling DY, Ahmad WA. Association between glycemic control and antidiabetic drugs in type 2 diabetes mellitus patients with cardiovascular complications. Drug Des Devel Ther. 2015;9:4735-49. doi: 10.2147/DDDT.S87294. PubMed PMID: 26316711; PubMed Central PMCID: PMC4547657.
  31. Shabani F, Farrier AJ, Krishnaiyan R, Hunt C, Uzoigwe CE, Venkatesan M. Common contra-indications and interactions of drugs in orthopaedic practice. Bone Joint J. 2015;97:434-41. doi: 10.1302/0301-620X.97B4.35230. PubMed PMID: 25820879.
  32. Wolters Kluwer. UpToDate, v2023. Connor RF (editors). Riverwoods: Wolters Kluwer; 2023.
  33. Di Lauro L, Vici P, Belli F, Tomao S, Fattoruso SI, Arena MG, et al. Docetaxel, oxaliplatin, and capecitabine combination chemotherapy for metastatic gastric cancer. Gastric Cancer. 2014;17:718-24. doi: 10.1007/s10120-013-0321-3. PubMed PMID: 24318671.
  34. Phansalkar S, Desai A, Choksi A, Yoshida E, Doole J, Czochanski M, et al. Criteria for assessing high-priority drug-drug interactions for clinical decision support in electronic health records. BMC Med Inform Decis Mak. 2013;13:65. doi: 10.1186/1472-6947-13-65. PubMed PMID: 23763856; PubMed Central PMCID: PMC3706355.
  35. Elhaddad M, Hamam S. AI-Driven Clinical Decision Support Systems: An Ongoing Pursuit of Potential. Cureus. 2024;16:e57728. doi: 10.7759/cureus.57728. PubMed PMID: 38711724; PubMed Central PMCID: PMC11073764.