Management of possible drug-drug interactions in medical prescriptions received in pharmacies
NNANGA, C. D.; EMBOGO, D.; MINYEM NGOMBI AFUH, A. P.; NSEME ETOUCKEY, G. E.
Show abstract
IntroductionPharmacists and their community pharmacy are responsible for analysing medical prescriptions to identify possible drug-drug interactions. In Cameroon, drug-drug interactions have been examined on several occasions in the hospital settings, but rarely in the community pharmacies, especially from the point of view of management practices. The aim of this study was to describe the management of potential drug-drug interactions in prescriptions received in pharmacies in the Efoulan Health District, Yaounde, Cameroon. MethodologyA cross-sectional descriptive study with prospective data collection was conducted over a period of 07 months from November 2022 to May 2023 in all pharmacies in the Efoulan Health District. All legible prescriptions containing at least two drugs were included. Data were collected using a pre-established and pre-tested form. Interactions were identified using the THERIAQUE(R) drug database. Data analysis was performed using IBM-SPSS(R) Version 23.0 software. ResultsThe study involved 67 prescriptions containing 124 drug interactions, averaging 1.8 interactions per prescription. The interactions were mainly pharmacodynamic (74.2%) versus pharmacokinetic (25.8%) in terms of type, and to be taken into account in terms of risk level (52.5%). They were mainly formed by synergism for pharmacodynamic interactions (89.1%) and complexation for pharmacokinetic interactions (59.4%). Prescribers were mainly cardiologists (42.9%) and general practitioners (42.9%). These pharmacological reactions occurred mainly between two non-steroidal anti-inflammatory drugs. The detection rate for interactions was low (21.8%) due to the use of personal knowledge (85.2%) and physical documents (14.8%) for detection. The interactions mainly detected were pharmacokinetic (13.7%) and the combinations to be used with caution (13.7%). The attitudes adopted were, in descending order: advise the patient (55.5%), do nothing (40.8%) and refer the patient back to the prescriber (3.7%). ConclusionMany drug interactions can occur as a result of medical prescriptions. Few of them are detected by dispensers, and the resulting attitudes vary.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Medication Clusters at Hospital Discharge and Risk of Adverse Drug Events at 30-days Post-Discharge: A Population-based Cohort Study of Older Adults 92%
- A comprehensive assessment of statin discontinuation among patients who concurrently initiate statins and CYP3A4-inhibitor drugs; a multistate transition model 92%
- Investigation on the interaction between nifedipine and ritonavir containing antivirus regimens: a physiologically-based pharmacokinetic/pharmacodynamic analysis 91%
Similar papers in this journal
- Determinants of cardiac adverse events of chloroquine and hydroxychloroquine in 20 years of drug safety surveillance reports 93%
- High rate of major drug-drug interactions of lopinavir-ritonavir for COVID-19 treatment 92%
- The use of angiotensin-converting enzyme inhibitors but not angiotensin receptor blockers in hospitalized patients with COVID-19 is associated with a lower risk of mortality 92%
Similar papers in this journal
- Pharmacogenomics implementation training improve self-efficacy and competency to drive adoption in clinical practice 93%
- Real-world observation on response to cholinesterase inhibitors or selective serotonin reuptake inhibitors prescribed to outpatients with dementia using electronic medical records 90%
- Innovative, rapid, high throughput method for drug repurposing in a pandemic - a case study of SARS-CoV-2 and COVID-19 90%
Similar papers in this journal
- Methods and computational techniques for predicting adherence to treatment: a scoping review 92%
- Predicting the physiological effects of multiple drugs using electronic health record 91%
- MACI: A machine learning-based approach to identify drug classes of antibiotic resistance genes from metagenomic data 89%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.