Potential drug-drug interactions in inpatients treated at the Internal Medicine ward of Tikur Anbessa Specialized Hospital

Determinants t of patient satisfaction with pharmacy services at Felege Hiwot comprehensive specialized hospital, Bahir Dar, Ethiopia

Background

Patients have explicit desire for quality services when they visit health institutions. Patient satisfaction is a primary means of measuring the effectiveness of healthcare delivery. However, inadequate discovery of their needs may result in patient dissatisfaction. Patients who are satisfied with pharmaceutical care are likely to adherence, seeking for medical attentions and take medications properly. Providing better access to quality pharmacies is a way to improve patient satisfaction with healthcare services.

Study objective

The study was aimed to assess patient satisfaction with pharmacy services at Felege Hiwot Comprehensive Specialized Hospital (FHCSH).

Methods and materials

An institution-based cross-sectional study design was conducted at FHCSH from 1 May 2021 to 30 October 2021. Data were collected by interview with structured questionnaires. Then the data were coded, entered, and then analyzed by SPSS version 23. The association between predictors and outcome was measured using bivariate logistic regression.

Result

In this study, 384 study participants were included. Majority of the participants were males (55.37%, N=227). The overall satisfaction score of the participants of this study was 65.37%. In our study, clients showed greater satisfaction towards pharmacists' commitment to correct myths (92%), pharmacist availability (88.78), and cleanliness (87.8%). Multivariate analysis indicated physical restriction [adjusted odds ratio (AOR)=6:95%; 95% CI (2.98, 25.9)] and rural residence [AOR=2.43; 95% CI (1.71, 9.6)] had significant association with pharmacy service dissatisfaction. Among the quality pharmacy service indicators, counselling on how medications work [AOR=9; 95% CI (1.48, 8.85)], keeping to dosage regimen [AOR=7.3; 95% CI (5.49, 11.06)], and advice on the current medical condition had greater odds of client dissatisfaction.

Conclusion

The findings of the current study showed that patients' satisfaction towards outpatient pharmacy services provided by FHCSH is very low as it is indicated in their perception towards revealing written information about medication use, medication availability, medication storage, and instructions about medication side effects.

Potential drug-drug interactions in outpatients with depression of a psychiatry department

Objective

This study aims to explore the prevalence and associated risk factors for potential drug-drug interactions (pDDIs) in prescriptions among outpatients with depression, and report the widespread relevant drug interactions.

Methods

The cross-sectional retrospective study was conducted on outpatients in a psychiatric hospital. We included prescriptions of outpatients with a principal diagnosis of depression from April 1st to June 30th in 2021. The patients were ≥ 18 years old and treated with two or more drugs including at least one psychotropic drug. pDDIs were detected and identified mainly using Medscape's drug interactions checker. Gender, the number of concomitant drugs, age and diagnosis were analysed as potential risk factors for the occurrence of pDDIs by logistic regression.

Results

A total of 13,617 prescriptions were included in the present analysis, and 4222 prescriptions (31.0%) were at risk of 8557 pDDIs. The risk of pDDIs in patients who were prescribed 4-6 drugs (OR: 3.49, 95% CI: 3.11-3.91, p < 0.001) or 7 or more drugs simultaneously (OR: 7.86, 95% CI: 1.58-39.04, p < 0.05) increased compared with patients prescribed 2-3 drugs. Patients with recurrent depressive disorders (OR: 1.18, 95% CI: 1.02-1.36, p < 0.05) had an increased risk of pDDIs compared with patients with depressive episodes. In terms of severity of pDDIs identified by Medscape's drug interactions checker, 0.7%, 16.4%, 77.5% and 5.4% of pDDIs were classified as contraindicated, serious, monitor closely and minor, respectively. The most common pDDI was escitalopram + quetiapine (374 prescriptions), which was classified as serious and monitor closely due to different mechanisms of interaction. Increased central nervous system (CNS)-depressant effect was the most frequent potential clinical adverse outcome of the identified pDDIs.

Conclusions

pDDIs in outpatients with depression were prevalent in this retrospective study. The number of concomitant drugs and severity of the disease were important risk factors for pDDIs. The pDDIs of the category monitor closely were the most common, and the CNS-depressant effect was the most frequent potential clinical adverse outcome.

A retrospective evaluation of drug–drug interactions in patients admitted to Internal Medicine Departments in Palestinian Hospitals

Objective:

To measure the prevalence and identify risk factors associated with drug–drug interactions among patients admitted to internal medicine departments in Palestinian hospitals

Methods:

A retrospective cross-sectional observational study was conducted. Data were obtained from patient files from the internal medicine departments in Palestinian hospitals from 1 September 2017, to 31 March 2018. The data collected included patient gender, age, length of hospitalization, medications prescribed, and the number of medications. The digital clinical decision support system IBM Micromedex® was used to assess potential drug–drug interactions.

Results:

The number of patients included in this study is 513. The total number of potential drug–drug interactions detected in study participants is 1558. The average number of potential drug–drug interactions per patient was found to be 3 ± 3.9. Among study participants, 66.1% (n = 339) were found to have potential drug–drug interactions in their current medications. The most commonly encountered drug–drug interactions type was “major” drug–drug interaction, which was encountered in 43.6% (n = 681) of total detected drug–drug interactions. Other types of drug–drug interactions were encountered in 42% (n = 647), 14% (n = 224), and 0.4% (n = 6) which were moderate, minor, and contraindicated drug–drug interactions, respectively. Patients’ age, number of medications, and length of hospitalization were associated with the increased risk of potential drug–drug interactions.

Conclusion:

The results indicated a high prevalence of potential drug–drug interactions in Palestinian hospitals, associated with polypharmacy, increased age, and increased length of hospitalization. Therefore, managing patient medication by a drug expert such as a clinical pharmacist to identify and resolve potential drug–drug interactions will possibly decrease the high prevalence of drug–drug interactions, prevent patient harm, and decrease the cost of hospitalization.

Drug–drug interactions among hospitalized elderly in patients at medical wards of Northwest Ethiopia’s Comprehensive Specialized Hospitals: A multicenter observational study

Objective:

Drug–drug interactions are of major concern due to links to untoward drug effects, hospitalizations, and serious health impacts. Elderly patients are more predisposed to drug interactions than younger patients. The present study aimed to find out the prevalence of drug–drug interactions at North West Ethiopian compressive specialized hospitals’ Internal Medicine wards.

Methods:

From 30 April to 30 July 2021 GC, a multicenter prospective observational study was conducted at north Ethiopian specialized hospitals. Data was collected by using a structured questionnaire adapted from different literature and medical records at the North West Ethiopian Comprehensive Specialized Hospitals’ Internal Medicine wards during the study period. Thereafter checked the completeness of the collected data was checked drug–drug interactions by using Medscape. Epi data version 4.6.2 software was used as data clearance and STATA version 14.1 was used for further data analysis.

Result:

A total of 389 subjects participated in the study of which more than half (55.53%) of them were female with a mean (SD) age of 68.9 ± 7.46 years. A total of 641 drug–drug interactions were detected in this investigation of which, 225(35.1%) were major, 299(46.6%) were significant interactions, and 117(18.3%) were minor interactions. Hospital stay (AOR = 5.95 CI: 3.49–10.12), retire (AOR = 6.71 CI: 1.26–35.78), 5–9 drugs (AOR = 5.30 CI: 2.91–9.67) and more than 10 drugs (AOR = 8.03 CI: 2.47–26.07) were important risk factors for drug–drug interactions.

Conclusion:

The findings of this study suggest that drug–drug interactions were high among hospitalized elderly patients. The presence of polypharmacy, to be retired, and hospital stayed were all found to be strongly linked with drug–drug interactions.

Potential Drug-Drug Interactions in Hospitalized Medical Patients: Data From Low Resource Settings

Introduction

Adverse events related to Drug-Drug Interactions (DDIs) are among the few common reasons for hospitalization worldwide; however, they can be prevented with an efficient patient-centered system. Different mechanisms have successfully limited the prevalence of DDIs in developed countries. There are limited data regarding DDIs from limited-resource settings. Furthermore, there is no cost-effective system that has shown promising results in preventing them in this setting. This study aims to assess the frequency of potential DDIs in a low-resource setting and to check its association with different factors such as poly-pharmacy and demographics.

Methods

Through this cross-sectional study, drug charts of patients admitted to a medical unit in November 2019 were analyzed using a structured questionnaire. A list of drugs co-prescribed to each patient was entered into the Medscape Drug Interaction checker to calculate the frequency and severity of potential DDIs.

Results

The mean age of patients was 49 years, and on average, seven drugs were prescribed to each patient. Among 100 analyzed prescriptions, 400 potential DDIs were identified with a mean of 4±5.42 per patient. According to Medscape interaction checker classification, 2 DDIs were contraindicated, 28 were serious, 246 required close monitoring, and 124 were minor. The most frequently encountered drug interaction was "spironolactone with furosemide." There was a significant correlation of the occurrence of potential DDIs with increased numbers of prescribed drugs.

Conclusion

Our patient population was prescribed more drugs per patient than calculated in other settings. Poly-pharmacy is an independent risk factor for DDIs. Lastly, advancing age exposes patients to poly-pharmacy, and therefore, they are at a higher risk of developing DDIs.

Pediatric Drug-Drug Interaction Evaluation: Drug, Patient Population, and Methodological Considerations

Hospitalized pediatric patients and those with complex or chronic conditions treated on an outpatient basis are commonly prescribed multiple drugs, resulting in increased risk for drug-drug interactions (DDIs). Although dedicated DDI evaluations are routinely performed in healthy adult volunteers during drug development, they are rarely performed in pediatric patients because of ethical, logistical, and methodological challenges. In the absence of pediatric DDI evaluations, adult DDI data are often extrapolated to pediatric patients. However, the magnitude of a DDI in pediatric patients may differ from adults because of age-dependent physiological changes that can impact drug disposition or response and because of other factors related to the drug (eg, dose, formulation) and the patient population (eg, disease state, obesity). Therefore, the DDI magnitude needs to be assessed in children separately from adults, although a lack of clinical DDI data in pediatric populations makes this evaluation challenging. As a result, pediatric DDI assessment relies on the predictive performance of the pharmacometric approaches used, such as population and physiologically based pharmacokinetic modeling. Therefore, careful consideration needs to be given to adequately account for the age-dependent physiological changes in these models to build high confidence for such untested DDI scenarios. This review article summarizes the key considerations related to the drug, patient population, and methodology, and how they can impact DDI evaluation in the pediatric population.

Assessment of Potential Drug–Drug Interactions and Their Predictors in Chronic Outpatient Department of Dessie Referral Hospital, Dessie, Northeast Ethiopia

Objective

To assess the prevalence and predictors of Potential drug–drug interactions (DDIs) at the chronic outpatient department of Dessie Referral Hospital, Dessie, Northeast Ethiopia.

Patients and Methods

A cross-sectional study was carried out on the medical records of patients treated in the chronic ambulatory department of Dessie Referral Hospital (DRH), from March 1/2019 to May 30/2019. Ethical clearance was granted from the department of pharmacy, college of medicine, and health sciences, Wollo University. Lexi-comp computer program database was used to detect pDDIs. SPSS version 22 was used to produce a descriptive analysis of the background data and logistic regression to identify predictors of pDDIs.

Results

In this study, the medical record of 300 patients has been reviewed and 489 pDDIs have been identified. The prevalence of pDDIs per patient was 1.63. Of all the identified pDDIs, the moderate severity interactions were the majority, 88.55% (n=433) followed by 8.38% (n=41) of minor, 2.66% (n=13) of major, and 0.41% (n=2) of contraindicated drug interactions. Taking three or more drugs at a time has been found as a statistically significant predictor of the occurrence of pDDIs.

Conclusion

A high rate of moderate severity pDDIs have been recorded. A system of checks and balances should be developed and executed for all those who are involved in prescribing, dispensing, and administration of medications for effective identification and prevention of pDDIs.

Adverse drug reactions related to potential serious drug interactions in patients with cardiovascular diseases

OBJECTIVE

To assess the occurrence of adverse drug reactions associated with potential serious drug interactions identified in prescriptions of hospitalized patients with cardiovascular disease.

METHOD

A documentary, quantitative, and cross-sectional research study. Between August and September 2016, ninety-nine prescriptions of patients hospitalized for more than 48 hours in the cardiology ward of a hospital in Rio de Janeiro were analyzed. Drug interactions were evaluated by Micromedex®, and adverse events were identified through trackers and analyzed by specialists using the Naranjo Algorithm, by means of descriptive statistics.

RESULTS

Eighteen potential serious interactions were detected in 22 drug pairs, mainly simvastatin x anlodipine (18%) and enoxaparin x clopidogrel (18%). Of the 18 medical records investigated, four trackers were found and three probable adverse events (16.6%) were defined due to hemorrhagic changes in patients.

CONCLUSION

Drug interactions are likely to cause harm to the patient, which requires implementing barriers for the safety of the medication system.

Prevalence of potential drug-drug interactions and associated factors among outpatients and inpatients in Ethiopian hospitals: a systematic review and meta-analysis of observational studies

BACKGROUND

Drug-drug interaction is an emerging threat to public health. Currently, there is an increase in comorbid disease, polypharmacy, and hospitalization in Ethiopia. Thus, the possibility of drug-drug interaction occurrence is high in hospitals. This study aims to summarize the prevalence of potential drug-drug interactions and associated factors in Ethiopian hospitals.

METHODS

A literature search was performed by accessing legitimate databases in PubMed/MEDLINE, Google Scholar, and Research Gate for English-language publications. To fetch further related topics advanced search was also applied in Science Direct and HINARI databases. The search was conducted on August 3 to 25, 2019. All published articles available online until the day of data collection were considered. Outcome measures were analyzed with Open Meta Analyst and CMA version statistical software. Der Simonian and Laird's random effect model, I² statistics, and Logit event rate were also performed.

RESULTS

A total of 14 studies remained eligible for inclusion in systematic review and meta-analysis. From the included studies, around 8717 potential drug-drug interactions were found in 3259 peoples out of 5761 patients. The prevalence of patients with potential drug-drug interactions in Ethiopian hospitals was found to be 72.2% (95% confidence interval: 59.1, 85.3%). Based on severity, the prevalence of major, moderate, and minor potential drug-drug interaction was 25.1, 52.8, 16.9%, respectively, also 1.27% for contraindications. The factors associated with potential drug-drug interactions were related to patient characteristics such as polypharmacy, age, comorbid disease, and hospital stay.

CONCLUSIONS

There is a high prevalence of potential drug-drug interactions in Ethiopian hospitals. Polypharmacy, age, comorbid disease, and hospital stay were the risk factors associated with potential drug-drug interactions.

Are patients with a nasally placed feeding tube at risk of potential drug-drug interactions? A multicentre cross-sectional study

Aims

The primary aims were to determine the rate of potential drug-drug interactions (pDDIs) in patients with nasally placed feeding tubes (NPFT) and the factors significantly associated with pDDIs. The secondary aim was to assess the change in pDDIs for patients between admission and discharge.

Material and methods

This multicentre study applied a cross-sectional design and was conducted in six Brazilian hospitals, from October 2016 to July 2018. Data from patients with NPFT were collected through electronic forms. All regular medications prescribed were recorded. Medications were classified according to the World Health Organization (WHO) Anatomical Therapeutic Chemical code. Drug-drug interaction screening software was used to screen patients’ medications for pDDIs. Negative binomial regression was used to account for the over dispersed nature of the pDDI count. Since the number of pDDIs was closely related to the number of prescribed medications, we modelled the rate of pDDIs with the count of pDDIs as the numerator and the number of prescribed medications as the denominator; six variables were considered for inclusion: time (admission or discharge), patient age, patient gender, age-adjusted Charlson Comorbidity Index (CCI) score, type of prescription (electronic or handwritten) and patient care complexity. To account for correlation within the two time points (admission and discharge) for each patient a generalised estimating equations approach was used to adjust the standard error estimates. To test the change in pDDI rate between admission and discharge a full model of six variables was fitted to generate an adjusted estimate.

Results

In this study, 327 patients were included. At least one pDDI was found in more than 91% of patients on admission and discharge and most of these pDDIs were classified as major severity. Three factors were significantly associated with the rate of pDDIs per medication: patient age, patient care complexity and prescription type (handwritten vs electronic). There was no evidence of a difference in pDDI rate between admission and discharge.

Conclusion

Patients with a NPFT are at high risk of pDDIs. Drug interaction screening tools and computerized clinical decision support systems could be effective risk mitigation strategies for this patient group.

Preventing potential drug-drug interactions through alerting decision support systems: A clinical context based methodology

BACKGROUND

The effectiveness of the clinical decision support systems (CDSSs) is hampered by frequent workflow interruptions and alert fatigue because of alerts with little or no clinical relevance. In this paper, we reported a methodology through which we applied knowledge from the clinical context and the international recommendations to develop a potential drug-drug interaction (pDDI) CDSS in the field of kidney transplantation.

METHODS

Prescriptions of five nephrologists were prospectively recorded through non-participatory observations for two months. The Medscape multi-drug interaction checker tool was used to detect pDDIs. Alongside the Stockley's drug interactions reference, our clinicians were consulted with respect to the clinical relevance of detected pDDIs. We performed semi-structured interviews with five nephrologists and one informant nurse. Our clinically relevant pDDIs were checked with the Dutch "G-Standard". A multidisciplinary team decided the design characteristics of pDDI-alerts in a CDSS considering the international recommendations and the inputs from our clinical context. Finally, the performance of the CDSS in detecting DDIs was evaluated iteratively by a multidisciplinary research team.

RESULTS

Medication data of 595 patients with 788 visits were collected and analyzed. Fifty-two types of interactions were most common, comprising 90% of all pDDIs. Among them 33 interactions (comprising 77% of all pDDIs) were rated as clinically relevant and were included in the CDSS's knowledge-base. Of these pDDIs, 73% were recognized as either pseudoduplication of drugs or not a pDDI when checked with the Dutch G-standard. Thirty-three alerts were developed and physicians were allowed to customize the appearance of pDDI-alerts based on a proposed algorithm.

CONCLUSION

Clinical practice contexts should be studied to understand the complexities of clinical work and to learn the type, severity and frequency of pDDIs. In order to make the alerts more effective, clinicians' points of view concerning the clinical relevance of pDDIs are critical. Moreover, flexibility should be built into a pDDI-CDSS to allow clinicians to customize the appearance of pDDI-alerts based on their clinical context.

Risk of potential drug-drug interactions in the cardiac intensive care units. A comparative analysis between 2 tertiary care hospitals

OBJECTIVES

To identify and compare the prevalence of drug-drug interactions (DDIs) in the intensive cardiac care units (CCUs) of 2 tertiary care hospitals and analyze their association with various predictors. Methods: This one-year prospective cross-sectional study was conducted in 2 tertiary care hospitals of Peshawar, Khyber Teaching Hospital (KTH) and Hayatabad Medical Complex (HMC), Peshawar, Pakistan, between January 2014 to Janury 2015. The patient medication profiles from the respective CCUs were evaluated for potential DDIs (PDDIs) using Micromedex DrugReax and Drug interaction facts. Results: The prevalence of PDDIs was 96.5% and  95.7% in the 2 hospitals, with over 1200 PDDIs in total. A significant association was found between the number of prescribed drugs and PDDIs in both  hospitals.  Conclusion: The knowledge of PDDIs is either lacking among the clinicians or is not taken into consideration. Monitoring PDDIs and timely interventions are required to minimize the adverse outcomes.