Determination of potential drug–drug interactions in prescription orders dispensed in a community pharmacy setting using Micromedex® and Lexicomp®: a retrospective observational study

Abbas, Afraa; Al-Shaibi, Samaher; Sankaralingam, Sowndramalingam; Awaisu, Ahmed; Kattezhathu, Vyas S.; Wongwiwatthananukit, Supakit; Owusu, Yaw

doi:10.1007/s11096-021-01346-8

Cited by 11 publications

(10 citation statements)

References 34 publications

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“…There were no studies of consistency analysis between Lexicomp ® and Micromedex ® in elderly patients. In other population research, the drug combinations evaluated for consistency were fewer and showed fair or moderate consistency of Lexicomp ® and Micromedex ® ( 31 - 35 ). In our study, a total of 21,527 drug combinations in elderly outpatient pharmaceuticals were examined.…”

Section: Discussionmentioning

confidence: 91%

Prevalence and associated factors of drug-drug interactions in elderly outpatients in a tertiary care hospital: a cross-sectional study based on three databases

Liu¹,

Wang²,

Gong³

et al. 2023

Ann Transl Med

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Background Drug-drug interactions (DDIs) are factors of adverse drug reactions and are more common in elderly patients. Identifying potential DDIs can prevent the related risks. Fewer studies of potential DDIs in prescribing for elderly patients in outpatient clinics. This study aimed to investigate the prevalence and associated factors with potential DDIs and potentially clinically significant DDIs (csDDIs) among elderly outpatients based on 3 DDIs databases. Methods A cross-sectional study was carried out on outpatients (≥65 years old) of a tertiary care hospital in China between January and March 2022. Patients’ prescriptions, including at least 1 systemic drug, were consecutively collected. The potential DDIs were identified by Lexicomp ® , Micromedex ® , and DDInter. Patient-related clinical parameter recorded at the prescriptions and DDIs with higher risk rating was analyzed. Variables showing association in univariate analysis (P<0.2) were included in logistic regression analysis. Weighted kappa analysis was used to analyze the consistencies of different databases. Results A total of 19,991 elderly outpatients were involved in the study, among whom 21,527 drug combinations including 486 drugs occurred. Lexicomp ® , Micromedex ® , and DDInter respectively identified 32.22%, 32.93%, and 22.62% of patients have at least one potential DDIs, meanwhile, 9.16%, 14.53%, and 4.56% of patients have at least one potential csDDIs. Under any evaluation criteria, polypharmacy and neurology visits were risk factors for csDDIs. Lexicomp ® has the highest coverage rate (87.86%) for drugs. Micromedex ® identified the most csDDIs (740 drug combinations). Drugs used in diabetes and psycholeptics were frequently found in the csDDIs of 2 commercial databases. The consistency between Lexicomp ® and Micromedex ® was moderate (weighted kappa 0.473). DDInter had fair consistencies with the other databases. Conclusions This study showed the prevalence of potential DDIs is high in elderly outpatients and potential csDDIs were prevalent. Considering the relative risk, pre-warning of potential DDIs before outpatient prescribing is necessary. As the consistencies among identification criteria are not good, more research is needed to focus on actual adverse outcomes to promote accurate prevention of csDDIs.

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Section: Discussionmentioning

confidence: 91%

Prevalence and associated factors of drug-drug interactions in elderly outpatients in a tertiary care hospital: a cross-sectional study based on three databases

Liu¹,

Wang²,

Gong³

et al. 2023

Ann Transl Med

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“…Micromedex, recognized for its credibility and expansive database, has long been a cornerstone for healthcare professionals seeking comprehensive and up-to-date drug-related information. 16 Its robustness positioned it as an apt benchmark for assessing the performance of ChatGPT. By comparing the outputs generated by ChatGPT-3.5 and ChatGPT-4 with the assessments provided by Micromedex, valuable insight was gained regarding ChatGPT's ability to discern medication safety in individuals with kidney disease.…”

Section: Discussionmentioning

confidence: 99%

Evaluating ChatGPT's efficacy in assessing the safety of non-prescription medications and supplements in patients with kidney disease

Sheikh,

Barreto,

Miao

et al. 2024

DIGITAL HEALTH

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Background This study investigated the efficacy of ChatGPT-3.5 and ChatGPT-4 in assessing drug safety for patients with kidney diseases, comparing their performance to Micromedex, a well-established drug information source. Despite the perception of non-prescription medications and supplements as safe, risks exist, especially for those with kidney issues. The study's goal was to evaluate ChatGPT's versions for their potential in clinical decision-making regarding kidney disease patients. Method The research involved analyzing 124 common non-prescription medications and supplements using ChatGPT-3.5 and ChatGPT-4 with queries about their safety for people with kidney disease. The AI responses were categorized as “generally safe,” “potentially harmful,” or “unknown toxicity.” Simultaneously, these medications and supplements were assessed in Micromedex using similar categories, allowing for a comparison of the concordance between the two resources. Results Micromedex identified 85 (68.5%) medications as generally safe, 35 (28.2%) as potentially harmful, and 4 (3.2%) of unknown toxicity. ChatGPT-3.5 identified 89 (71.8%) as generally safe, 11 (8.9%) as potentially harmful, and 24 (19.3%) of unknown toxicity. GPT-4 identified 82 (66.1%) as generally safe, 29 (23.4%) as potentially harmful, and 13 (10.5%) of unknown toxicity. The overall agreement between Micromedex and ChatGPT-3.5 was 64.5% and ChatGPT-4 demonstrated a higher agreement at 81.4%. Notably, ChatGPT-3.5's suboptimal performance was primarily influenced by a lower concordance rate among supplements, standing at 60.3%. This discrepancy could be attributed to the limited data on supplements within ChatGPT-3.5, with supplements constituting 80% of medications identified as unknown. Conclusion ChatGPT's capabilities in evaluating the safety of non-prescription drugs and supplements for kidney disease patients are modest compared to established drug information resources. Neither ChatGPT-3.5 nor ChatGPT-4 can be currently recommended as reliable drug information sources for this demographic. The results highlight the need for further improvements in the model's accuracy and reliability in the medical domain.

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“…For example, some pharmacists did not recognize the interaction between warfarin and nonsteroidal anti-inflammatory drugs (NSAIDs), which can increase the risk of bleeding. The study suggests that further training and continuing education may be necessary to improve pharmacists’ ability to manage DDIs ( Abbas et al, 2022 ). Similarly, a study found that community pharmacists in the United States had a limited understanding of potential DDIs, particularly between nonprescription analgesics and prescription medications.…”

Section: Discussionmentioning

confidence: 99%

Assessment of community pharmacists’ knowledge about drug-drug interactions in Jeddah, Saudi Arabia

2023

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Background: Drug-drug interactions (DDIs) have the potential to result in severe adverse drug events and profoundly affect patient outcomes. The pivotal role community pharmacists assume in recognizing and effectively managing these interactions necessitates a comprehensive understanding and heightened awareness of their implications. Such knowledge and awareness among community pharmacists are fundamental for ensuring the delivery of safe and efficacious care to patients.Aim: This study aimed to assess the knowledge of community pharmacists in Jeddah, Saudi Arabia, regarding drug-drug interactions (DDIs).Method: A cross-sectional survey was administered to a cohort of 147 community pharmacists through the utilization of a self-administered questionnaire. The questionnaire encompassed a comprehensive range of 30 multiple-choice questions, encompassing various facets pertaining to drug-drug interactions (DDIs).Results: A total of 147 community pharmacists working in Jeddah City, Saudi Arabia, completed the survey. The majority of them were male (89.1%, n = 131), and had bachelor’s degrees in pharmacy. Results showed that the lowest correct response of DDIs was between Theophylline/Omeprazole, while the highest was between amoxicillin and acetaminophen. Results revealed that among the 28 drug pairs, only six pairs were determined correctly by most participants. The study found that majority of the studied community pharmacist could not determine the correct answer on drug-drug interaction knowledge, as also seen with the measured below half mean DDIs knowledge of 38.22 ± 22.0 (min = 0, max = 89.29, median = 35.71).Conclusion: The study highlights the need for ongoing training and education programs for community pharmacists in Saudi Arabia to enhance their knowledge and understanding of DDIs, ultimately leading to improved patient care and safety.

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Determination of potential drug–drug interactions in prescription orders dispensed in a community pharmacy setting using Micromedex® and Lexicomp®: a retrospective observational study

Cited by 11 publications

References 34 publications

Prevalence and associated factors of drug-drug interactions in elderly outpatients in a tertiary care hospital: a cross-sectional study based on three databases

Prevalence and associated factors of drug-drug interactions in elderly outpatients in a tertiary care hospital: a cross-sectional study based on three databases

Evaluating ChatGPT's efficacy in assessing the safety of non-prescription medications and supplements in patients with kidney disease

Assessment of community pharmacists’ knowledge about drug-drug interactions in Jeddah, Saudi Arabia

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