Consensus recommendations for systematic evaluation of drug-drug interaction evidence for clinical decision support

Clinical Trial Data-Driven Risk Assessment of Drug-Drug Interactions: A Rapid and Accurate Decision-Making Tool

BACKGROUND

In clinical practice, the vast array of potential drug combinations necessitates swift and accurate assessments of pharmacokinetic drug-drug interactions (DDIs), along with recommendations for adjustments. Current methodologies for clinical DDI evaluations primarily rely on basic extrapolations from clinical trial data. However, these methods are limited in accuracy owing to their lack of a comprehensive consideration of various critical factors, including the inhibitory potency, dosage, and type of the inhibitor, as well as the metabolic fraction and intestinal availability of the substrate.

OBJECTIVE

This study aims to propose an efficient and accurate clinical pharmacokinetic-mediated DDI assessment tool, which comprehensively considers the effects of inhibitory potency and dosage of inhibitors, intestinal availability and fraction metabolized of substrates on DDI outcomes.

METHODS

This study focuses on DDIs caused by cytochrome P450 3A4 enzyme inhibition, utilizing extensive clinical trial data to establish a methodology to calculate the metabolic fraction and intestinal availability for substrates, as well as the concentration and inhibitory potency for inhibitors ( K i ork inact / K I ). These parameters were then used to predict the outcomes of DDIs involving 33 substrates and 20 inhibitors. We also defined the risk index for substrates and the potency index for inhibitors to establish a clinical DDI risk scale. The training set for parameter calculation consisted of 73 clinical trials. The validation set comprised 89 clinical DDI trials involving 53 drugs. which was used to evaluate the reliability of in vivo values of K i andk inact / K I , the accuracy of DDI predictions, and the false-negative rate of risk scale.

RESULTS

First, the reliability of the in vivo K i andk inact / K I values calculated in this study was assessed using a basic static model. Compared with values obtained from other methods, this study values showed a lower geometric mean fold error and root mean square error. Additionally, incorporating these values into the physiologically based pharmacokinetic-DDI model facilitated a good fitting of the C-t curves when the substrate's metabolic enzymes are inhibited. Second, area under the curve ratio predictions of studied drugs were within a 1.5 × margin of error in 81% of cases compared with clinical observations in the validation set. Last, the clinical DDI risk scale developed in this study predicted the actual risks in the validation set with only a 5.6% incidence of serious false negatives.

CONCLUSIONS

This study offers a rapid and accurate approach for assessing the risk of pharmacokinetic-mediated DDIs in clinical practice, providing a foundation for rational combination drug use and dosage adjustments.

Polypharmacy and drug interactions in older patients with cancer receiving chemotherapy: associated factors

BACKGROUND

Polypharmacy in older adults with cancer receiving chemotherapy leads to increased risks of drug interactions, translating in potential hazardous health outcomes. This study aims to assess the prevalence of polypharmacy, drug-drug interactions (DDIs), and severe-drug interactions (SDIs) in older patients with cancer. Antineoplastic agents (ANAs) involvement and possible risk contexts (comorbidities with cardiac risk, and high-risk medications) were also analysed.

METHODS

Observational study with older adults (≥ 65 years) diagnosed with cancer, who were treated with antineoplastic agents (ANAs); it was conducted in three hospitals from the north of Portugal. Data collection was obtained using self-reports and medical records. DDIs were identified and classified using Micromedex® software. Descriptive and association analyze statistics were performed. Statistical hypothesis tests with p value less than 0.05 were considered significant. All statistical procedures and analysis were performed with R version 4.1.3.

RESULTS

We enrolled 552 patients. Polypharmacy prevalence was 88.40%; 76.45% and 56.16% of the patients presented with DDIs and SDIs, respectively. SDIs with ANAs were found in 21.20% of the patients. High-risk medications were associated with a higher risk of polypharmacy, DDIs, and SDIs. Polypharmacy and DDIs were higher in patients with hypertension or diabetes. SDIs were higher in patients with diabetes.

CONCLUSION

Polypharmacy, potential DDIs and SDIs were highly prevalent in older adults with cancer. A careful review of the medication administered is necessary to decrease it. These findings warrant further research to optimize medication in this population and decrease problems related to medication, which may lead to emergency room visits and hospitalisations, compromising patient safety and/or ongoing treatments.

Potentially Life-Threatening Interaction between Opioids and Intrathecal Baclofen in Individuals with a Childhood-Onset Neurological Disorder: A Case Series and Review of the Literature

BACKGROUND

 Spasticity and dystonia are movement impairments that can occur in childhood-onset neurological disorders. Severely affected individuals can be treated with intrathecal baclofen (ITB). Concomitant use of ITB and opioids has been associated with central nervous system (CNS) depression. This study aims to describe the clinical management of this interaction, based on a case series and review of literature.

METHODS

 Four individuals with childhood-onset CNS disorders (age 8-24) and CNS-depressant overdose symptoms after the concomitant use of ITB and opioids are described. The Drug Interaction Probability Scale (DIPS) was calculated to assess the cause-relationship (doubtful <2, possible 2-4, probable 5-8, and highly probable >8) of the potential drug-drug interaction. A literature review of similar previously reported cases and the possible pharmacological mechanisms of opioid-baclofen interaction is provided.

RESULTS

 After ITB and opioid co-administration, three out of four patients had decreased consciousness, and three developed respiratory depression. DIPS scores indicated a possible cause-relationship in one patient (DIPS: 4) and a probable cause-relationship in the others (DIPS: 6, 6, and 8). Discontinuation or adjusting ITB or opioid dosages resulted in clinical recovery. All patients recovered completely. In the literature, two articles describing nine unique cases were found.

CONCLUSION

 Although the opioid-ITB interaction is incompletely understood, concomitant use may enhance the risk of symptoms of CNS-depressant overdose, which are potentially life-threatening. If concomitant use is desirable, we strongly recommend to closely monitor these patients to detect interaction symptoms early. Awareness and monitoring of the potential opioid-ITB interaction is essential to reduce the risk of severe complications.

An Assessment of Different Decision Support Software from the Perspective of Potential Drug-Drug Interactions in Patients with Chronic Kidney Diseases

Chronic kidney disease (CKD) is a multifaceted disorder influenced by various factors. Drug-drug interactions (DDIs) present a notable risk factor for hospitalization among patients with CKD. This study aimed to assess the frequency and attributes of potential DDIs (pDDIs) in patients with CKD and to ascertain the concordance among different Clinical Decision Support Software (CDSS). A cross-sectional study was conducted in a nephrology outpatient clinic at a university hospital. The pDDIs were identified and evaluated using Lexicomp® and Medscape®. The patients' characteristics, comorbidities, and medicines used were recorded. The concordance of different CDSS were evaluated using the Kendall W coefficient. An evaluation of 1121 prescribed medications for 137 patients was carried out. The mean age of the patients was 64.80 ± 14.59 years, and 41.60% of them were male. The average year with CKD was 6.48 ± 5.66. The mean number of comorbidities was 2.28 ± 1.14. The most common comorbidities were hypertension, diabetes, and coronary artery disease. According to Medscape, 679 pDDIs were identified; 1 of them was contraindicated (0.14%), 28 (4.12%) were serious-use alternative, and 650 (9.72%) were interventions that required closely monitoring. According to Lexicomp, there were 604 drug-drug interactions. Of these interactions, 9 (1.49%) were in the X category, 60 (9.93%) were in the D category, and 535 (88.57%) were in the C category. Two different CDSS systems exhibited statistically significant concordance with poor agreement (W = 0.073, p < 0.001). Different CDSS systems are commonly used in clinical practice to detect pDDIs. However, various factors such as the operating principles of these programs and patient characteristics can lead to incorrect guidance in clinical decision making. Therefore, instead of solely relying on programs with lower reliability and consistency scores, multidisciplinary healthcare teams, including clinical pharmacists, should take an active role in identifying and preventing pDDIs.

Drug-drug interactions with oral anticoagulants: information consistency assessment of three commonly used online drug interactions databases in Switzerland

Background: Toxicity or treatment failure related to drug-drug interactions (DDIs) are known to significantly affect morbidity and hospitalization rates. Despite the availability of numerous databases for DDIs identification and management, their information often differs. Oral anticoagulants are deemed at risk of DDIs and a leading cause of adverse drug events, most of which being preventable. Although many databases include DDIs involving anticoagulants, none are specialized in them. Aim and method: This study aims to compare the DDIs information content of four direct oral anticoagulants and two vitamin K antagonists in three major DDI databases used in Switzerland: Lexi-Interact, Pharmavista, and MediQ. It evaluates the consistency of DDIs information in terms of differences in severity rating systems, mechanism of interaction, extraction and documentation processes and transparency. Results: This study revealed 2'496 DDIs for the six anticoagulants, with discrepant risk classifications. Only 13.2% of DDIs were common to all three databases. Overall concordance in risk classification (high, moderate, and low risk) was slight (Fleiss' kappa = 0.131), while high-risk DDIs demonstrated a fair agreement (Fleiss' kappa = 0.398). The nature and the mechanism of the DDIs were more consistent across databases. Qualitative assessments highlighted differences in the documentation process and transparency, and similarities for availability of risk classification and references. Discussion: This study highlights the discrepancies between three commonly used DDI databases and the inconsistency in how terminology is standardised and incorporated when classifying these DDIs. It also highlights the need for the creation of specialised tools for anticoagulant-related interactions.

Serotonin syndrome and neuroleptic malignant syndrome: A case report of intersecting symptomatology

Introduction

Serotonin syndrome and neuroleptic malignant syndrome are caused by 2 distinct pathologies; however, the clinical presentation associated with both syndromes share many features.

Methods

We describe a 56-year-old male patient who presented to our facility with seizures, leukocytosis, fevers, extremity hyperreflexia, and signs of autonomic dysfunction as evidenced by cardiovascular instability. The patient was noted to be taking vortioxetine, trazodone, lamotrigine, lurasidone, and carbidopa-levodopa as outpatient medications for his depression, an unspecified mood disorder, and Parkinson disease. Following a robust workup and failure of other therapies, all serotonergic and dopaminergic medications were held, and the patient was tried on cyproheptadine for serotonin syndrome, which led to the cessation of fevers. Bromocriptine was added to the regimen, which led to the resolution of the remainder of the patient's symptoms.

Results

The overlapping symptomatology of several key diagnostic criteria for both serotonin syndrome and neuroleptic malignant syndrome as well as their nature as diagnoses of exclusion require an evaluation of the patient's aggregate improvement following targeted pharmacologic strategies for both syndromes. The efficacy of both cyproheptadine and bromocriptine when administered concomitantly support the concurrent pathologies.

Discussion

Clinicians at the bedside must be cognizant of the potential for clinically relevant drug-drug interactions that may present with overlapping pathologies.

Potential drug-drug interaction and its determinants among patients with cancer receiving chemotherapy in oncology centres of Northwest Ethiopia: an institutional-based cross-sectional study

OBJECTIVE

The study was conducted to assess potential drug-drug interactions (PDDIs) and its determinants among patients with cancer receiving chemotherapy.

DESIGN AND SETTING

An institutional-based cross-sectional study was used. This study was conducted from 1 June 2021 to 15 December 2021, in Northwest Ethiopia oncology centres.

PARTICIPANTS

All eligible patients with cancer received a combination of chemotherapy.

OUTCOMES

The prevalence and severity of PDDIs were evaluated using three drug interaction databases. Characteristics of participants were presented, arranged and summarised using descriptive statistics. The predictors and outcome variables were examined using logistic regression. The cut-off point was a p value of 0.05.

RESULTS

Of 422 patients included in the study, 304 patients were exposed to at least one PDDI with a prevalence of 72.1% (95 % CI: 68% to 76%) using three drug interaction databases. There were varied reports of the severity of PDDI among databases, but the test agreement using the kappa index was 0.57 (95% CI: 0.52 to 0.62, p=0.0001) which is interpreted as a moderate agreement among three databases. Patients aged ≥50 years old had the risk to be exposed to PDDI by odds of 3.1 times (adjusted OR (AOR)=3.1, 95% CI (1.8 to 5.3); p=0.001) as compared with patients <50 years old. Similarly, patients with polypharmacy and comorbidity were more likely to be exposed to PDDI than their counterparts (AOR=2.4, 95% CI (1.4 to 4.1); p=0.002 and AOR=1.9, 95% CI (1.1 to 3.4); p=0.02, respectively).

CONCLUSION

The main finding of this study is the high prevalence of PDDI, signifying the need for strict patient monitoring for PDDIs among patients with cancer receiving chemotherapy. We suggest the use of at least three drug databases for quality screening. Patients with an age ≥50 years old, polypharmacy and comorbidity were significantly associated with PDDIs. The establishment of oncology clinical pharmacists and computerised reminder mechanisms for PDDIs through drug utilisation review is suggested.

Potentially Harmful Drug-Drug Interactions and Their Associated Factors Among Hospitalized Cardiac Patients: A Cross-Sectional Study

BACKGROUND

Cardiovascular diseases are responsible for a significant proportion of mortalities worldwide. Elderly patients are the most affected by cardiovascular diseases, and because of factors such as polypharmacy, multimorbidity, and age-related changes in drug availability and metabolism, they are highly susceptible to the occurrence of drug-drug interactions. Drug-drug interactions are among the many drug-related problems leading to negative outcomes among inpatients and outpatients. Thus, it is important to investigate the prevalence, involved drugs, and factors related to potential drug-drug interactions (pDDIs) to properly optimize pharmacotherapy regimens for these patients.

OBJECTIVE

We aimed to determine the prevalence of pDDIs, drugs most frequently implicated, and significant predictors associated with these interactions among hospitalized patients in the Cardiology Unit at Sultan Qaboos University Hospital in Muscat, Oman.

METHODS

This retrospective cross-sectional study included 215 patients. Micromedex Drug-Reax® was used to identify pDDIs. Data extracted from patients' medical records were collected and analyzed. Univariable and multivariable linear regression was applied to determine the predictors associated with the observed pDDIs.

RESULTS

A total of 2057 pDDIs were identified, with a median of nine (5-12) pDDIs per patient. Patients with at least one pDDI accounted for 97.2% of all the included patients. The majority of pDDIs were of major severity (52.6%), fair level of documentation (45.5%), and pharmacodynamic basis (55.9%). Potential drug-drug interactions between atorvastatin and clopidogrel were the most frequently observed (9%). Of all the detected pDDIs, around 79.6% of them included at least one antiplatelet drug. Having diabetes mellitus as a comorbidity (B = 2.564, p < 0.001) and the number of drugs taken during the hospitalization period (B = 0.562, p < 0.001) were factors positively associated with the frequency of pDDIs.

CONCLUSIONS

Potential drug-drug interactions were highly prevalent among hospitalized cardiac patients at Sultan Qaboos University Hospital, Muscat, Oman. Patients having diabetes as a comorbidity and with a high number of administered drugs were at a higher risk of an increased number of pDDIs.

Antibiotics and Lipid-Modifying Agents: Potential Drug-Drug Interactions and Their Clinical Implications

Evidence-based prescribing requires taking into consideration the many aspects of optimal drug administration (e.g., dosage, comorbidities, co-administered drugs, etc.). A key issue is the administration of drugs for acute disorders that may potentially interfere with previously prescribed long-term medications. Initiating an antibiotic for an acute bacterial infection constitutes a common example. Hence, appropriate knowledge and awareness of the potential DDIs of antibiotics would lead to proper adjustments, thus preventing over- or under-treatment. For example, some statins, which are the most prescribed lipid-modifying agent (LMA), can lead to clinically important drug-drug interactions (DDIs) with the concurrent administration of antibiotics, e.g., macrolides. This review discusses the clinically significant DDIs of antibiotics associated with co-administrated lipid-lowering therapy and highlights common cases where regimen modifications may or may not be necessary.

Risk Factors for Potential Drug-Drug Interactions in Outpatients with Dyslipidemia

Background

Patients with dyslipidemia are usually multimorbid and require polypharmacy. Therefore, it is important to identify potential drug-drug interactions (pDDIs) in time to prevent their consequences. We aimed to identify and analyze risk factors contributing to their occurrence to guide health professionals.

Methods

A prospective cross-sectional study of 216 outpatients with dyslipidemia was conducted from May 2021 to April 2022 in Podgorica, the capital of Montenegro. pDDIs were identified using Medscape, Epocrates, and Drugs online interaction checkers. Multivariate regression analysis was performed to evaluate the potential predictors of interactions.

Results

pDDIs were detected in 212 (98.1%) participants, whereas pDDIs with high clinical significance were detected in 25.46%, 40.74%, and 58.8% of subjects by Drugs, Epocrates, and Medscape, respectively. Polypharmacy emerged as a risk factor for the occurrence of pDDIs in all three checkers in each category of clinical significance. The use of non-steroidal anti-inflammatory drugs and antiplatelet drugs contributes to the incidence of severe pDDIs B=1.014, 95%CI 0.681-1.346, P=0.000 and B=0.492, 95%CI 0.286-0.698, P=0.000, by Epocrates and Medscape respectively. The number of prescribers per patient was a protective factor against moderate pDDI B= -0.858, 95%CI -1.572-(-0.144), P=0.019 and B= -0.956, 95%CI -1.671-(-0.241), P=0.009, by Medscape and Epocrates, respectively, but a risk factor for the occurrence of minor pDDIs B=0.373, 95%CI 0.033-0.712 P=0.032 and B=0.143, 95%CI 0.042-0.244, P=0.006, by the same checkers.

Conclusion

Knowledge of the risk factors contributing to the occurrence of pDDIs is important for the development and implementation of strategies for their prevention, and given the high prevalence of dyslipidemia, understanding these factors seems crucial nowadays.

Drug-drug interactions with CFTR modulator therapy in cystic fibrosis: Focus on Trikafta®/Kaftrio®

The combination of CFTR modulators ivacaftor, tezacaftor and elexacaftor (Trikafta®, Kaftrio®) significantly improve outcomes, including survival in a broad range of cystic fibrosis patients. These drugs have complicated metabolic profiles that make the potential for drug interactions an important consideration for prescribers, care providers and patients. Prolonged survival also increases risk of age-related disease and their associated pharmacotherapy, further increasing the risk of drug interactions and the need for increased vigilance amongst care providers. We systematically searched the literature for studies identifying and evaluating pharmacokinetic and pharmacodynamic drug interactions involving the components of Trikafta®/Kaftrio®. We also searched electronic databases of drugs for possible drug interactions based on metabolic profiles. We identified 86 potential drug interactions of which 13 were supported by 14 studies. There is a significant need for research to describe the likelihood, magnitude and clinical impact of the drug interactions proposed here.

Drug-Drug Interactions: How to Manage the Risk-A Stakeholder Approach

Drug-drug interactions (DDIs) are well-recognized, chronic, multidimensional issues that have defied efforts to make substantial reductions in the burden of effects on patients and the health care system. This Commentary offers a stakeholder approach to characterizing the problem and identifying potential ways to address the risks posed by DDIs. Stakeholders may comprise 2 groups: a triad consisting of the patient, the prescriber, and the pharmacist and a pentad of institutional stakeholders consisting of institutions of education and training for prescribers and pharmacists, drug development sector companies, regulatory agencies, payer institutions, and publishing companies of journals on healthcare topics. Suggested strategic opportunities to mitigate the risk of harm from DDIs include the following: (1) identify critical leadership to set the agenda, manage the process, and mark progress; (2) enhance self-advocacy skills, particularly for patients; (3) create more opportunities for patients to learn, understand, and participate in the process; (4) establish and enhance partnerships between and among stakeholders; (5) seek broader use of regulatory machinery; (6) institutionalize principles of conservative prescribing and deprescribing in medical education and professional training; (7) establish, highlight, and promote DDI in HEDIS quality metrics, key performance indicators, and balanced scorecards; (8) encourage publishers to engage the issue more deeply by developing dedicated specialty journals for physicians, pharmacists, and cross-professional audiences and encourage journal editors to dedicate sections in pharmacy and clinical journals; and (9) involve the political process to include markers for DDI mitigation and to set performance goals in US Food and Drug Administration-directed legislation.

Assessment of risk factors associated with potential drug-drug interactions among patients suffering from chronic disorders

Patients suffering from chronic diseases are more likely to experience pDDIs due to older age, prolonged treatment, severe illness and greater number of prescribed drugs. The objective of the current study was to assess the prevalence of pDDIs and risk factors associated with occurrence of pDDIs in chronic disease patients attending outpatient clinics for regular check-ups. Patients suffering from diabetes, chronic obstructive pulmonary disease (COPD), stroke and osteoporosis were included in the study. This study was a cross sectional, observational, prospective study that included 337 patients from outpatient clinics of respiratory ward, cardiac ward and orthopedic ward of Nishter Hospital Multan, Pakistan. The mean number of interactions per patient was 1.68. A greater risk for occurrence of pDDI was associated with older age ≥ 60 years (OR = 1.95, 95% CI = 1.44-2.37, p<0.001); polypharmacy (≥ 5 drugs) (OR = 3.74, 95% CI 2.32-4.54, p<0.001); overburden (OR = 2.23, 95% CI = 1.64-3.16, p<0.01); CCI score (OR = 1.28, 95% CI = 1.04-1.84, p<0.001); multiple prescribers to one patient (OR = 1.18, 95% CI = 1.06-1.41, p<0.01); and trainee practitioner (OR = 1.09, 95% CI = 1.01-1.28, p<0.01). Old age, polypharmacy, overburden healthcare system, higher comorbidity index, multiple prescribers to one patient and trainee practitioner were associated with increased risk of occurrence of pDDIs in chronic disease patients.

Potential drug-drug interactions and associated factors among admitted patients with psychiatric disorders at selected hospitals in Northwest Ethiopia

BACKGROUND

Prescribing medications without potential drug-drug interactions (pDDIs) is one of the components of the rational use of medications. However, taking combined medications has resulted in life-threatening pDDIs, which are causing severe clinical outcomes for patients. This study was aimed at assessing the prevalence of pDDIs and associated factors in admitted patients with psychiatric disorders.

METHODS

An institution-based multicenter cross-sectional study was conducted among patients with psychiatric disorders admitted to a selected hospital in Northwest Ethiopia. Samples were approached through a systematic sampling method. The Statistical Package for the Social Sciences (SPSS) version 26 was used to analyze the data. Logistic regression was applied to determine the association of variables with pDDIs. A p-value of < 0.05 was statistically significant.

RESULTS

Out of 325 study participants, more than half (52.9%) were females, with a median age of 61 years. Overall, more than two-thirds (68.9%) were exposed to at least one clinically significant, either significant or serious level of pDDIs. Nearly one-fourth (23.2%) of participants had at least one serious level of pDDIs. Older patients were found more likely to have pDDIs compared to younger patients (p = 0.043). Similarly, patients with a higher number of prescribed medications (p = 0.035) and patients with longer hospital admissions (p = 0.004) were found more likely to be exposed to pDDIs than their counterparts.

CONCLUSION

In this study, a significant number of admitted patients with psychiatric problems encountered clinically significant pDDIs. As a result, healthcare providers could assess and follow patients with a combination of medications that potentially have a drug-drug interaction outcome.

Alerts in Clinical Decision Support Systems (CDSS): A Bibliometric Review and Content Analysis

A clinical decision support system (CDSS) informs or generates medical recommendations for healthcare practitioners. An alert is the most common way for a CDSS to interact with practitioners. Research about alerts in CDSS has proliferated over the past ten years. The research trend is ongoing with new emerging terms and focus. Bibliometric analysis is ideal for researchers to understand the research trend and future directions. Influential articles, institutes, countries, authors, and commonly used keywords were analyzed to grasp a comprehensive view on our topic, alerts in CDSS. Articles published between 2011 and 2021 were extracted from the Web of Science database. There were 728 articles included for bibliometric analysis, among which 24 papers were selected for content analysis. Our analysis shows that the research direction has shifted from patient safety to system utility, implying the importance of alert usability to be clinically impactful. Finally, we conclude with future research directions such as the optimization of alert mechanisms and comprehensiveness to enhance alert appropriateness and to reduce alert fatigue.

Potential drug-drug interactions of immunosuppressants in kidney transplant recipients: comparison of drug interaction resources

Background Drug-drug interactions are frequently observed in kidney transplant recipients due to polypharmacy and use of immunosuppressants. However, there is only one study evaluating clinically relevant potential drug-drug interactions of immunosuppressants specially in kidney transplant recipients by means of online databases and Stockleys Drug Interactions, as a gold standard. Aim This study aimed to compare four online databases used frequently to determined clinically relevant potential drug-drug interactions of immunosuppressants in kidney transplant recipients according to the Renal Drug Handbook. Method This was a descriptive cross-sectional study conducted between October 1, 2019, and March 18, 2020, in the nephrology ward of Ankara University School of the Medicine, Ibn-i Sina Hospital. In total, 52 adult patients' discharge prescriptions were retrieved from their medical records and analyzed retrospectively. Micromedex®, Lexicomp®, Medscape, and Drugs.com databases were used to evaluate drug interactions. The Renal Drug Handbook was used as a gold standard to do specificity and sensitivity analysis. Results A total of 127 potential drug-drug interactions between the immunosuppressants and co-medications were detected by at least one online database. 32 (25.2%) of these were approved as clinically relevant potential drug-drug interactions by the Renal Drug Handbook. Lexicomp® and Drugs.com have exhibited the highest sensitivity (0.72 and 0.75) while Micromedex® has shown the highest specifity (0.83). Furthermore, the highest positive predictive value has been observed in Micromedex® (0.53). Micromedex® and Medscape had the highest negative predictive value (0.83 and 0.82). However, the kappa value of all was low. The values of inter-rater agreement (Kappa index) between online databases and the Renal Drug Handbook were weak (range 0.05-0.36). In addition, only 11 (8.7%) of potential drug-drug interactions were identified by all online databases. Conclusion This study showed that there was a weak compatibility between each database examined and the Renal Drug Handbook to detect clinically relevant potential drug-drug interactions for immunosuppressants in kidney transplant recipients. Therefore, we suggest that although databases might be practical to take a quick glance in detection of potential drug-drug interactions between immunosuppressants and co-medications, the data should be evaluated in detail and interpreted with caution in combination with a reference book like Renal Drug Handbook.

Predictors of drug-drug interactions of medications prescribed to patients admitted due to suicidal behavior

INTRODUCTION

Drug-drug interactions among people with suicidal behavior is a challenging topic, considering the harm it poses for patients already vulnerable and the lack of literature on the thematic. This aspect must not be neglected in research and clinical practice, and thus requires thorough investigation.

OBJECTIVE

to investigate predictors of drug-drug interaction of prescribed drugs and the prescription of two or more drugs for people admitted due to suicidal behavior in a psychiatric emergency department (short-stay hospital ward).

METHOD

A cross-sectional study with retrospective approach, carried out in a Brazilian psychiatric emergency unit in 2015. Data about first and last medical prescriptions were collected from 127 patients' files. Descriptive statistics and the Zero Adjusted Logarithmic Distribution (ZALG) model were adopted, with the significance level α = 0.05.

RESULTS

Potential drug-drug interactions were found in most of the first and last prescriptions. The sample majority were female, with previous suicide attempts, being discharged from the hospital with three drugs (or more) prescribed, and without referral to any health service. Age and comorbidities were predictors of more drug prescriptions and the amount of prescribed drugs was the most important predictor of drug-drug interactions (quantity and severity).

CONCLUSIONS

the variables associated with drug-drug interactions and prescription of two or more drugs among people with suicidal behavior needs to be investigated in different contexts and addressed in interventions with the aim to promote patient safety.

Investigating Potential Drug-Drug Interactions in Pediatric and Adolescent Patients Receiving Chemotherapy

INTRODUCTION

Pediatric and adolescent oncology patients admitted to receive chemotherapy are at risk for drug-drug interactions (DDI). While adult literature has quoted this risk to be as high as 95% of encounters, the literature in pediatrics is limited. This is a single-center, retrospective chart review of DDI in hospitalized pediatric oncology patients.

METHODS

All patients admitted to Texas Children's Hospital for chemotherapy were included. Medications ordered during the hospitalization were evaluated by Lexicomp® Drug Interactions Tool. Interactions classified as D or X or interactions rated a C including a chemotherapeutic agent were independently reviewed by three clinicians for clinical relevance. Medications associated with central nervous system (CNS) depression or QTc prolongation were counted separately.

RESULTS

Of 100 admissions evaluated, 100% had a flagged interaction. There were a total of 12 X-rated interactions, 8 D-rated interactions, and 12 C-rated interactions with a chemotherapeutic agent found to be clinically relevant. Thirty-three percent of admissions had 4 or more QTc prolonging medications ordered. Twenty-four percent of admissions had 3 or more prescribed CNS depressants. In total 49% of admissions were found to have at least 1 clinically-significant DDI.

CONCLUSIONS

This study exemplifies the risk of drug-drug interactions in children and young adults admitted to the hospital for chemotherapy. We demonstrated a high rate of flagged interactions with about half of admissions found to have a potentially clinically-significant DDI. Concomitant use of multiple QTc prolonging and CNS depressant medications was also prevalent, indicating a need to evaluate monitoring practices.

Racial and ethnic disparities associated with the measure for drug-drug interactions among Medicare beneficiaries

BACKGROUND

Drug-drug interactions (DDIs) cause many preventable hospitalizations and admissions. Efforts have been made to raise DDI awareness and reduce DDI occurrence; for example, Medicare Part D Star Ratings, a health plan quality assessment program, included a DDI measure. Previous research reported racial and ethnic disparities in health services utilization and that racial and ethnic minorities, compared with non-Hispanic whites (whites), may be less likely to be targeted for a similar measure, a Star Ratings adherence measure for diabetes medications.

OBJECTIVE

This study aimed to investigate whether any racial and ethnic disparities are associated with the DDI measure in Part D Star Ratings among Medicare populations with diabetes, hypertension, and hyperlipidemia.

METHODS

This cross-sectional study analyzed a 2017 Medicare Part D data sample, including 3,960,813 beneficiaries. Because the inclusion in the denominator of the Star Ratings DDI measure was determined by the use of a list of target medications, the likelihood of using a listed target medication was compared between racial and ethnic minorities and whites. Individuals with diabetes, hypertension, and hyperlipidemia were included in the analysis owing to the high prevalence of these conditions. Patient- and community-level characteristics were adjusted by logistic regression.

RESULTS

Of the entire study sample, 26.2% used a target medication. Compared with whites, most racial and ethnic minorities were less likely to use a target medication. For example, among individuals with diabetes, blacks, Hispanics, Asians/Pacific Islanders, and others had, respectively, 14% (odds ratio 0.86 [95% CI 0.84-0.88]), 5% (0.95 [0.93-0.98]), 12% (0.88 [0.84-0.92]), and 10% (0.90 [0.87-0.93]) lower odds compared with whites. Findings were similar among hypertension and hyperlipidemia cohorts, except that Hispanics had similar odds of use as whites.

CONCLUSION

Most racial and ethnic minorities may have lower likelihood of being targeted for the DDI measure compared with whites. Future studies should examine whether these disparities affect health outcomes and devise new DDI measures for racial and ethnic minorities.

Drug Interactions for Patients with Respiratory Diseases Receiving COVID-19 Emerged Treatments

Pandemic of coronavirus disease (COVID-19) is still pressing the healthcare systems worldwide. Thus far, the lack of available COVID-19-targeted treatments has led scientists to look through drug repositioning practices and exploitation of available scientific evidence for potential efficient drugs that may block biological pathways of SARS-CoV-2. Till today, several molecules have emerged as promising pharmacological agents, and more than a few medication protocols are applied during hospitalization. On the other hand, given the criticality of the disease, it is important for healthcare providers, especially those in COVID-19 clinics (i.e., nursing personnel and treating physicians), to recognize potential drug interactions that may lead to adverse drug reactions that may negatively impact the therapeutic outcome. In this review, focusing on patients with respiratory diseases (i.e., asthma or chronic obstructive pulmonary disease) that are treated also for COVID-19, we discuss possible drug interactions, their underlying pharmacological mechanisms, and possible clinical signs that healthcare providers in COVID-19 clinics may need to acknowledge as adverse drug reactions due to drug-drug interactions.

Comparison of potential psychiatric drug interactions in six drug interaction database programs: A replication study after 2 years of updates

OBJECTIVE

Drug interaction database programs are a fundamental clinical tool. In 2018, we compared the category of potential drug-drug interaction (DDI) provided by six drug interaction database programs for 100 drug interaction pairs including psychiatric drugs, and found the category often differed. This study replicated the comparison in 2020 after 2 years of updates to all six drug interaction database programs.

METHODS

The 100 drug pairs included 94 different drugs: 67 pairs with a psychiatric and non-psychiatric drug, and 33 pairs with two psychiatric drugs. The assigned category of potential DDI for the drug pairs was compared using percent agreement and Fleiss kappa statistic of interrater reliability.

RESULTS

Despite 67 updates involving 46 of the 100 drug pairs, differences remained. The overall percent agreement among the six drug interaction database programs for the category of potential DDI was 67%. The interrater agreement results did not change. The Fleiss kappa overall interrater agreement was fair. The kappa agreement for a drug pair with any severe category rating was substantial, and the kappa agreement for a drug pair with any major category rating was fair.

CONCLUSIONS

Physicians should be aware of the inconsistency among drug interaction database programs in the category of potential DDI for drug pairs including psychiatric drugs. Additionally, the category of potential DDI for a drug pair may change over time. This study highlights the importance of ongoing international efforts to standardize methods used to define and classify potential DDI.

Prevalence of drug-drug interactions in older people before and after hospital admission: analysis from the OPERAM trial

Background

Drug-drug interactions (DDIs) are highly prevalent in older patients but little is known about prevalence of DDIs over time. Our main objective was to assess changes in the prevalence and characteristics of drug-drug interactions (DDIs) during a one-year period after hospital admission in older people, and associated risk factors.

Methods

We conducted a sub-study of the European OPERAM trial (OPtimising thERapy to prevent Avoidable hospital admissions in Multimorbid older people), which assessed the effects of a structured medication review (experimental arm) compared to usual care (control arm) on reducing drug-related hospital readmissions. All OPERAM patients (≥70 years, with multimorbidity and polypharmacy, hospitalized in four centers in Bern, Brussels, Cork and Utrecht between December 2016 and October 2018, followed over 1 year) who were alive at hospital discharge and had full medication data during the index hospitalization (at baseline i.e., enrolment at admission, and at discharge) were included. DDIs were assessed using an international consensus list of potentially clinically significant DDIs in older people. The point-prevalence of DDIs was evaluated at baseline, discharge, and at 2, 6 and 12 months after hospitalization. Logistic regression models were performed to assess independent variables associated with changes in DDIs 2 months after baseline.

Results

Of the 1950 patients (median age 79 years) included, 1045 (54%) had at least one potentially clinically significant DDI at baseline; point-prevalence rates were 58, 57, 56 and 57% at discharge, and 2, 6 and 12 months, respectively. The prevalence increased significantly from baseline to discharge (P < .001 [significant only in the control group]), then remained stable over time (P for trend .31). The five most common DDIs –all pharmacodynamic in nature– accounted for 80% of all DDIs and involved drugs that affect potassium concentrations, centrally-acting drugs and antithrombotics. At 2 months, DDIs had increased in 459 (27%) patients and decreased in 331 (19%). The main factor predictive of a change in the prevalence of DDIs was hyperpolypharmacy (≥10 medications).

Conclusions

DDIs were very common; their prevalence increased during hospitalization and tended to remain stable thereafter. Medication review may help control this increase and minimize the risk of adverse drug events.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-021-02532-z.

Pharmacokinetic Profile of Gilteritinib: A Novel FLT-3 Tyrosine Kinase Inhibitor

BACKGROUND AND OBJECTIVE

Gilteritinib is a novel, highly selective tyrosine kinase inhibitor approved in the USA, Canada, Europe, Brazil, Korea, and Japan for the treatment of FLT3 mutation-positive acute myeloid leukemia. This article describes the clinical pharmacokinetic profile of gilteritinib.

METHODS

The pharmacokinetic profile of gilteritinib was assessed from five clinical studies.

RESULTS

Dose-proportional pharmacokinetics was observed following once-daily gilteritinib administration (dose range 20-450 mg). Median maximum concentration was reached 2-6 h following single and repeat dosing of gilteritinib; mean elimination half-life was 113 h. Elimination was primarily via feces. Exposure to gilteritinib was comparable under fasted and fed conditions. Gilteritinib is primarily metabolized via cytochrome P450 (CYP) 3A4; coadministration of gilteritinib with itraconazole (a strong P-glycoprotein inhibitor and CYP3A4 inhibitor) or rifampicin (a strong P-glycoprotein inducer and CYP3A inducer) significantly affected the gilteritinib pharmacokinetic profile. No clinically relevant interactions were observed when gilteritinib was coadministered with midazolam (a CYP3A4 substrate) or cephalexin (a multidrug and toxin extrusion 1 substrate). Unbound gilteritinib exposure was similar between subjects with hepatic impairment and normal hepatic function.

CONCLUSIONS

Gilteritinib exhibits a dose-proportional pharmacokinetic profile in healthy subjects and in patients with relapsed/refractory acute myeloid leukemia. Gilteritinib exposure is not significantly affected by food. Moderate-to-strong CYP3A inhibitors demonstrated a significant effect on gilteritinib exposure. Coadministration of gilteritinib with CYP3A4 or multidrug and toxin extrusion 1 substrates did not impact substrate concentrations. Unbound gilteritinib was comparable between subjects with hepatic impairment and normal hepatic function; dose adjustment is not warranted for patients with hepatic impairment.

CLINICAL TRIAL REGISTRATION

NCT02014558, NCT02456883, NCT02571816.

Possible inhibitory effects of terbinafine on aripiprazole metabolism: Two case reports

Aripiprazole, an atypical antipsychotic, is a metabolic substrate for cytochrome P450 (CYP)3A4 and 2D6. Terbinafine, an antifungal agent used for onychomycosis, is a CYP2D6 inhibitor and could theoretically reduce the metabolism of aripiprazole. However, there are no published reports describing this interaction. We present 2 female patients hospitalized in a psychiatric unit who were both taking aripiprazole 15 mg daily and terbinafine 250 mg daily prior to admission. The first patient was a 58-year-old female who was prescribed aripiprazole and terbinafine concomitantly for approximately 5 months prior to admission. A commercial pharmacogenetic testing platform classified this patient as a normal metabolizer for CYP3A4 and 2D6. The first patient's serum trough aripiprazole concentration at steady-state concentration (Css) was 207.5 ng/mL. The second patient was a 43-year-old female who was taking aripiprazole and terbinafine concomitantly for approximately 2 weeks prior to admission who had a Css aripiprazole concentration of 278.9 ng/mL. Aripiprazole has a wide therapeutic range (100 to 350 ng/mL) and a reference dose-related drug concentration of 11.7 (mean) ± 5.6 (SD) ng/mL/mg/d. Our patients had Css aripiprazole concentrations 18% and 59% higher than guideline-supported dose-related drug concentrations. Through the use of therapeutic drug monitoring, pharmacogenetic data, electronic pharmaceutical claims data, and the Drug Interaction Probability Scale, we suggest terbinafine possibly increases aripiprazole concentrations 18% to 59%. Further reports are needed to confirm these findings prior to using this information in clinical practice.

Real-life drug-drug and herb-drug interactions in outpatients taking oral anticancer drugs: comparison with databases

PURPOSE

Due to polypharmacy and the rising popularity of complementary and alternative medicines (CAM), oncology patients are particularly at risk of drug-drug interactions (DDI) or herb-drug interactions (HDI). The aims of this study were to assess DDI and HDI in outpatients taking oral anticancer drug.

METHOD

All prescribed and non-prescribed medications, including CAM, were prospectively collected by hospital pharmacists during a structured interview with the patient. DDI and HDI were analyzed using four interaction software programs: Thériaque^®, Drugs.com^®, Hédrine, and Memorial Sloan Kettering Cancer Center (MSKCC) database. All detected interactions were characterized by severity, risk and action mechanism. The need for pharmaceutical intervention to modify drug use was determined on a case-by-case basis.

RESULTS

294 patients were included, with a mean age of 67 years [55-79]. The median number of chronic drugs per patient was 8 [1-29] and 55% of patients used at least one CAM. At least 1 interaction was found for 267 patients (90.8%): 263 (89.4%) with DDI, 68 (23.1%) with HDI, and 64 (21.7%) with both DDI and HDI. Only 13% of the DDI were found in Thériaque^® and Drugs.com^® databases, and 125 (2.5%) were reported with similar level of risk on both databases. 104 HDI were identified with only 9.5% of the interactions found in both databases. 103 pharmaceutical interventions were performed, involving 61 patients (20.7%).

CONCLUSION

Potentially clinically relevant drug interaction were frequently identified in this study, showing that several databases and structured screening are required to detect more interactions and optimize medication safety.

High-priority drug-drug interaction clinical decision support overrides in a newly implemented commercial computerized provider order-entry system: Override appropriateness and adverse drug events

OBJECTIVE

The study sought to determine frequency and appropriateness of overrides of high-priority drug-drug interaction (DDI) alerts and whether adverse drug events (ADEs) were associated with overrides in a newly implemented electronic health record.

MATERIALS AND METHODS

We conducted a retrospective study of overridden high-priority DDI alerts occurring from April 1, 2016, to March 31, 2017, from inpatient and outpatient settings at an academic health center. We studied highest-severity DDIs that were previously designated as "hard stops" and additional high-priority DDIs identified from clinical experience and literature review. All highest-severity alert overrides (n = 193) plus a stratified random sample of additional overrides (n = 371) were evaluated for override appropriateness, using predetermined criteria. Charts were reviewed to identify ADEs for overrides that resulted in medication administration. A chi-square test was used to compare ADE rate by override appropriateness.

RESULTS

Of 16 011 alerts presented to providers, 15 318 (95.7%) were overridden, including 193 (87.3%) of the highest-severity DDIs and 15 125 (95.8%) of additional DDIs. Override appropriateness was 45.4% overall, 0.5% for highest-severity DDIs and 68.7% for additional DDIs. For alerts that resulted in medication administration (n = 423, 75.0%), 29 ADEs were identified (6.9%, 5.1 per 100 overrides). The rate of ADEs was higher with inappropriate vs appropriate overrides (9.4% vs 4.3%; P = .038).

CONCLUSIONS

The override rate was nearly 90% for even the highest-severity DDI alerts, indicating that stronger suggestions should be made for these alerts, while other alerts should be evaluated for potential suppression.

A Minimal Information Model for Potential Drug-Drug Interactions

Despite the significant health impacts of adverse events associated with drug-drug interactions, no standard models exist for managing and sharing evidence describing potential interactions between medications. Minimal information models have been used in other communities to establish community consensus around simple models capable of communicating useful information. This paper reports on a new minimal information model for describing potential drug-drug interactions. A task force of the Semantic Web in Health Care and Life Sciences Community Group of the World-Wide Web consortium engaged informaticians and drug-drug interaction experts in in-depth examination of recent literature and specific potential interactions. A consensus set of information items was identified, along with example descriptions of selected potential drug-drug interactions (PDDIs). User profiles and use cases were developed to demonstrate the applicability of the model. Ten core information items were identified: drugs involved, clinical consequences, seriousness, operational classification statement, recommended action, mechanism of interaction, contextual information/modifying factors, evidence about a suspected drug-drug interaction, frequency of exposure, and frequency of harm to exposed persons. Eight best practice recommendations suggest how PDDI knowledge artifact creators can best use the 10 information items when synthesizing drug interaction evidence into artifacts intended to aid clinicians. This model has been included in a proposed implementation guide developed by the HL7 Clinical Decision Support Workgroup and in PDDIs published in the CDS Connect repository. The complete description of the model can be found at https://w3id.org/hclscg/pddi.

Automatically classifying the evidence type of drug-drug interaction research papers as a step toward computer supported evidence curation

A longstanding issue with knowledge bases that discuss drug-drug interactions (DDIs) is that they are inconsistent with one another. Computerized support might help experts be more objective in assessing DDI evidence. A requirement for such systems is accurate automatic classification of evidence types. In this pilot study, we developed a hierarchical classifier to classify clinical DDI studies into formally defined evidence types. The area under the ROC curve for sub-classifiers in the ensemble ranged from 0.78 to 0.87. The entire system achieved an F1 of 0.83 and 0.63 on two held-out datasets, the latter consisting focused on completely novel drugs from what the system was trained on. The results suggest that it is feasible to accurately automate the classification of a sub-set of DDI evidence types and that the hierarchical approach shows promise. Future work will test more advanced feature engineering techniques while expanding the system to classify a more complex set of evidence types.

Breast cancer, placing drug interactions in the spotlight: is polypharmacy the cause of everything?

PURPOSE

Breast cancer is the most prevalent and lethal cancer among women. Forty-one percent of cases occur in people ≥ 70 years, hindering their treatment given its comorbidities and polypharmacy (PP). Potential drug-drug interactions (PDDI) were analyzed in elderly breast cancer patients between daily and oncospecific treatments and their associations with Age, BMI, Mini Nutritional Assessment (MNA), Frailty categorization, PP, and adverse effects.

PATIENTS/METHODS

A cohort of 77 patients ≥ 70 years with breast cancer who underwent a Comprehensive Geriatric Assessment (CGA) were included. Clinical characteristics were collected using medical records. PDDI between treatments were analyzed using two databases. Data were assessed using linear regression, Chi-square, Mann-Whitney U, and Kruskal-Wallis tests. Finally, a multivariate logistic regression model was built and tested to predict adverse effects.

RESULTS

From 719 PDDI, 530 (74%) were moderate (r2 = 0.72) and the median number of drugs during oncospecific treatment (r2 = 0.73) was 9 (range 3-26). Overall, 59 patients (77%) had adverse effects associated with Frailty categorization and MNA (p < 0.05). The distribution of major, moderate, minor, and total PDDI was associated with PP at CGA and during oncospecific treatment (p < 0.05). Moreover, it was verified that Frailty categorization protects from adverse effects given the intervention made at CGA.

CONCLUSIONS

CGA should be applied in oncologic elderly patients to assess clinical outcomes and categorize them according to their frailty but also to analyze PDDI. Furthermore, we encourage the use of the model in clinical practice for predicting the occurrence of adverse effects, improving therapeutic conciliation.

The effect of different intensivist staffing patterns on the rate of potential drug-drug interactions in adult trauma intensive care units

BACKGROUND

Drug-drug interactions (DDIs) have created alarming challenges for public health, especially in those admitted to intensive care units (ICUs). Many studies have shown that involvement of intensivists in the ICUs improves the outcome and decreases the treatment costs. The effect of academic versus non-academic (therapeutic) intensivist as well as hours of coverage and attendance of intensivist on potential DDIs (pDDIs) was evaluated in six adult trauma ICUs of a level one trauma center.

METHODS

In this 6-month cross-sectional study, 200 patients were included. The DDIs were classified into five groups, including type A, B, C, D, and X. pDDIs were defined as interactions belonged to C, D and X categories. Patients in six adult ICUs with three different patterns of intensivist staffing models including type A (once-daily therapeutic intensivist visit followed by 24 h on-call), B (twice-daily academic intensivist visit, 8 h of attendance in ICU and 16 h on-call) and C (all criteria just like ICU type B, except for the presence of therapeutic instead of academic intensivist) were screened for pDDIs.

RESULTS

In total, 3735 drug orders and 3869 drugs (193 different types) were screened and 1826 pDDIs were identified. Type C, D and X interactions accounted for 60.6%, 35.5%, and 3.9% of all pDDIs, respectively. The mean of pDDI per patient was significantly higher (p-value < 0.001) in the ICU type A than ICU types C and B. The frequency of pDDIs was the highest in the type A ICUs. A statistically significant relationship was observed between the number of prescribed drugs and ICU length of stay (p-value < 0.001 and p = 0.009, respectively).

CONCLUSION

Different patterns of intensivist staffing affect pDDIs to varying degrees. In the studied ICUs academic versus therapeutic intensivist, twice versus once-daily visit, and 8 h attendance with16 h on-call versus 24 h on-call were associated with more reductions in pDDIs.

PLAIN LANGUAGE SUMMARY

The impact of different intensivist staffing patterns in ICUs on the rate of potential drug-drug interactionsDrug-drug interactions (DDIs) have created alarming challenges for public health, especially in patients admitted to intensive care units (ICUs). Many studies have shown that involvement of intensivists in the ICUs improves the outcome and limits the costs. Considering the high incidence of potential DDIs (pDDIs) occurring for critically ill patients and the importance of ADRs caused by pDDIs in ICUs, the effect of the presence of an academic versus therapeutic intensivist, as well as the hour of coverage of intensivist on prevalence of pDDIs was evaluated in six adult trauma ICUs of a level one trauma center in Shiraz, Iran. We also determined the prevalence of pDDIs and their associated risk factors. To the best of our knowledge, this is the first study that has assessed the effect of various ICU physician staffing models on the incidence and pattern of pDDIs.

Risk Factors Associated With Medication Errors Among Patients Suffering From Chronic Disorders

Introduction: Medication error is unintentional and can be reduced by reducing the risk factors. Patients suffering from chronic diseases are at an increased risk of medication errors.

Objective: This work aims to assess the risk factors associated with medication errors among patients suffering from chronic disorders in hospitals of South Punjab, Pakistan.

Methodology: Multiple logistic regression analysis was used to assess the impact of different risk factors on the prevalence of medication errors in patients suffering from chronic diseases.

Results: A greater risk for the occurrence of medication errors was associated with age ≥60 years (odds ratio, OR = 1.9; 95% CI = 1.3–3.1; p = 0.001), overburdened healthcare system (OR = 2.2; 95% CI = 1.64–3.56; p < 0.000), number of prescribed drugs ≥5 (OR = 1.74; 95% CI = 1.02–2.64; p < 0.000), comorbidities (OR = 2.6; 95% CI = 1.72–3.6; p = 0.003), Charlson comorbidity index (OR = 1.31; 95% CI = 0.49–1.84; p = 0.004), and multiple prescribers to one patient (OR = 1.12; 95% CI = 0.64–1.76; p = 0.001).

Conclusion: Older age, overburdened healthcare system, number of prescribed drugs, comorbidities, Charlson comorbidity index, and multiple prescribers to one patient are significant risk factors for the occurrence of medication errors.

Drug-drug interactions with direct oral anticoagulants associated with adverse events in the real world: A systematic review

BACKGROUND

Direct oral anticoagulants (DOACs) have emerged as safe and effective alternatives to Vitamin-K antagonists for treatment and prevention of arterial and venous thrombosis. Due to their novelty, pharmacokinetic DOAC drug-drug interactions (DDIs) that result in clinical adverse events have not been well-documented.

OBJECTIVE

This study aims to systematically review reported pharmacokinetic DDIs resulting in clinical adverse events through documented observational evidence to better inform clinicians in clinical practice.

METHODS

A comprehensive literature review of EMBASE, MEDLINE, and Ovid HealthStar was conducted through March 10th, 2020. Two independent reviewers screened and extracted data from eligible articles according to pre-established inclusion and exclusion criteria. Articles reporting bleeding or thrombotic outcomes in non-controlled (observational) settings resulting from suggested pharmacokinetic DOAC DDIs were included.

RESULTS

A total of 5567 citations were reviewed, of which 24 were included following data extraction. The majority were case reports (n = 21) documenting a single adverse event resulting from a suspected DOAC DDI, while the remaining papers were a case series (n = 1) and cohort studies (n = 2). The most commonly reported interacting drugs were amiodarone and ritonavir (bleeding), and phenobarbital, phenytoin, and carbamazepine (thrombosis). Bleeding events more often resulted from a combined mechanism (P-glycoprotein AND CYP3A4 inhibition), whereas thrombotic events resulted from either combined OR single P-glycoprotein/CYP3A4 induction.

CONCLUSION

Current literature evaluating the real-world risk of DOAC DDIs is limited to few case reports and retrospective observational analyses. Clinicians are encouraged to continue to report suspected drug interactions resulting in adverse events.

A computational approach to predict multi-pathway drug-drug interactions: A case study of irinotecan, a colon cancer medication

Drug-drug interactions (DDIs) are a potentially distressing corollary of drug interventions, and may result in discomfort, debilitating illness, or even death. Existing research predominantly considers only a single level of interaction; however, serious health complications may result from multi-pathway DDIs, and so new methods are needed to enable predicting and preventing complex DDIs. This article introduces a novel method for the prediction of DDIs at two pharmacological levels (metabolic and transporter interactions) by means of a rule-based model implemented with Semantic Web technologies. The chemotherapy agent irinotecan is used as a case study for demonstrating the validity of this approach. Mechanistic and interaction data were mined from available sources and then used to predict interactors of irinotecan, including potential DDIs mediated by previously unidentified mechanisms. The findings also draw attention to the profound variation between DDI resources, indicating that clinical practice would see significant value from the development of an evidence-based resource to support DDI identification.

Herb-Drug Interaction: Application of a UPLC-MS/MS Method to Determine the Effect of Polygonum capitatum Extract on the Tissue Distribution and Excretion of Levofloxacin in Rats

Polygonum capitatum has unique curative effects on the urinary system. In fact, many Polygonum capitatum-based preparations are currently used in the clinic. In China, the combination of levofloxacin (LVFX) with a Chinese herbal preparation derived from Polygonum capitatum has been used for the clinical treatment of urinary system diseases, which can improve the curative effects and reduce the side effects of LVFX. However, the herb-drug interaction (HDI) between these drugs has not been reported and the effect of Polygonum capitatum on the in vivo process of LVFX is unclear. In this article, a sensitive ultraperformance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS) method was developed to evaluate the effects of the combined application of LVFX and the Polygonum capitatum extract on tissue distribution and excretion. Thereafter, the method was validated for selectivity, accuracy, precision, linearity, lower limit of quantification (LLOQ), dilution integrity, recovery, and matrix effect. Based on tissue distribution, LVFX could diffuse into all of the tested tissues, with significant differences in the content of each tissue between the coadministration group and single administration group. At 48 h after the combination was orally administered, the urinary cumulative excretion of LVFX decreased from 20.69% to 11.84% while its fecal cumulative excretion decreased from 26.08% to 13.28%. Our results suggest that a drug interaction exists between the two drugs in the process of distribution and excretion. This study provides important experimental evidence for further studies on the clinical efficacy and mechanism of the Polygonum capitatum extract and LVFX.

Potential drug-drug interactions in ICU patients: a retrospective study

Objectives A "potential drug-drug interaction" (pDDI) is the possibility one drug has to alter the effects of another when both are administered simultaneously. Intensive care unit (ICU) patients are especially prone to these pDDIs. This study aimed to determine the frequency and severity of pDDIs during the hospitalization of patients in the ICU. Methods This study was conducted retrospectively in three hospitals, including both governmental and non-governmental hospitals in Nablus, Palestine, over the course of six months; starting in January 2018 and ending in June 2018. The sample size included 232 ICU patients, and medications prescribed during the hospitalization of these patients were evaluated for pDDIs using the drugs.com application. Results A total of 167 patients (72%) were found to have at least one pDDI, while the total number of pDDIs in the study was 422, resulting in an average of 1.82 pDDIs per patient. Out of the total identified pDDIs, 41 interactions (9.7%) were major interactions, 281 (66.6%) were moderate interactions and 100 (23.7%) were minor interactions. The past medical history of these patients showed that many had hypertension (29%), diabetes mellitus (25%) and ischemic heart disease (10%). A serious combination, enoxaparin and aspirin, was found in six patients. Furthermore, as the number of administered drugs increased, the number of interactions increased as well. Conclusions The pDDIs are common in ICU patients. The most common and clinically most important pDDIs require special attention. Polypharmacy significantly increases the number and level of pDDIs, especially in patients with multiple chronic illnesses. Adequate knowledge regarding the most common pDDIs is necessary to enable healthcare professionals to implement ICU strategies that ensure patient safety.

Probable Drug Interaction Between Etanercept and Cyclosporine Resulting in Clinically Unexpected Low Trough Concentrations: First Case Report

Preclinical studies have shown that anti-cytokine therapies could alter drug dispositions through affecting cytochrome P450 synthesis; however, evidence and case reports evaluating clinical relevance of this interaction are scarce. This is the first reported case of interaction between cyclosporine (CsA) and etanercept in a 42-year-old male patient with ankylosing spondylitis and immunoglobulin A nephropathy in whom cytokine levels were monitored both before and after CsA initiation. The initiation of etanercept led to at least 2.5-folds increase in total clearance of CsA. After comprehensive assessment and stepwise exclusion of alternative causes, it was considered that inflammation resolution with etanercept administration has highly induced clearance of CsA, probably mediated by interleukin-2. The case has shown that co-administration of CsA and anti-cytokine therapies such as etanercept needs close monitoring of trough levels. Physicians and pharmacists should be aware of similar interactions especially when the biological therapy is initiated or discontinued and for patients undergoing acute inflammation phase. Monitoring cytokine levels should be considered when drug-cytokine interaction is suspected.

Potential Drug interactions with Drugs used for Bipolar Disorder: A Comparison of 6 Drug Interaction Database Programs

Background Patients with bipolar disorder frequently experience polypharmacy, putting them at risk for clinically significant drug-drug interactions (DDI). Online drug interaction database programs are used to alert physicians, but there are no internationally recognized standards to define DDI. This study compared the category of potential DDI returned by 6 commercial drug interaction database programs for drug interaction pairs involving drugs commonly prescribed for bipolar disorder.

Methods The category of potential DDI provided by 6 drug interaction database programs (3 subscription, 3 open access) was obtained for 125 drug interaction pairs. The pairs involved 103 drugs (38 psychiatric, 65 nonpsychiatric); 88 pairs included a psychiatric and nonpsychiatric drug; 37 pairs included 2 psychiatric drugs. Every pair contained at least 1 mood stabilizer or antidepressant. The category provided by 6 drug interaction database programs was compared using percent agreement and Fleiss kappa statistic of interrater reliability.

Results For the 125 drug pairs, the overall percent agreement among the 6 drug interaction database programs was 60%; the Fleiss kappa agreement was slight. For drug interaction pairs with any category rating of severe (contraindicated), the kappa agreement was moderate. For drug interaction pairs with any category rating of major, the kappa agreement was slight.

Conclusion There is poor agreement among drug interaction database programs for the category of potential DDI involving psychiatric drugs. Drug interaction database programs provide valuable information, but the lack of consistency should be recognized as a limitation. When assistance is needed, physicians should check more than 1 drug interaction database program.

Association between Concomitant Use of Hydrochlorothiazide and Adverse Chemotherapy-Related Events among Older Women with Breast Cancer Treated with Cyclophosphamide

BACKGROUND

The pharmacy reference database, Micromedex, lists concomitant hydrochlorothiazide and cyclophosphamide use as a potential, major drug-drug interaction (DDI), although only one small, single-center study supports this claim. Our objective was to estimate associations between this potential DDI and two adverse chemotherapy-related events, neutropenia-related hospitalizations and treatment regimen discontinuation, among a cohort of women with breast cancer initiating adjuvant chemotherapy containing cyclophosphamide.

METHODS

Using linked Surveillance, Epidemiology, and End Results Program (SEER)-Medicare data, we included women 66 years and older with breast cancer diagnosis between 2007 and 2011, who initiated a regimen containing cyclophosphamide. Risk ratios (RR) and 95% confidence intervals for adverse outcomes comparing women exposed versus unexposed to the potential DDI were assessed using modified multivariable Poisson regression adjusting for potential confounders.

RESULTS

In total, 27% of women receiving cyclophosphamide treatment were exposed to concomitant hydrochlorothiazide, of which 11% experienced a neutropenia-related hospitalization and 21% discontinued their chemotherapy regimen prior to completion. Adjusted risks of both adverse events were similar between those exposed and unexposed to the potential DDI [neutropenia-related hospitalization: adjusted RR (aRR) = 0.92 (0.70-1.21); treatment discontinuation: aRR = 1.00 (0.96-1.05)].

CONCLUSIONS

Our results do not support an association between concomitant hydrochlorothiazide use and two clinically relevant adverse chemotherapy-related events.

IMPACT

Our results support reassessing and potentially lowering severity of this potential interaction in drug reference databases.

Testing the face validity and inter-rater agreement of a simple approach to drug-drug interaction evidence assessment

Low concordance between drug-drug interaction (DDI) knowledge bases is a well-documented concern. One potential cause of inconsistency is variability between drug experts in approach to assessing evidence about potential DDIs. In this study, we examined the face validity and inter-rater reliability of a novel DDI evidence evaluation instrument designed to be simple and easy to use.

METHODS

A convenience sample of participants with professional experience evaluating DDI evidence was recruited. Participants independently evaluated pre-selected evidence items for 5 drug pairs using the new instrument. For each drug pair, participants labeled each evidence item as sufficient or insufficient to establish the existence of a DDI based on the evidence categories provided by the instrument. Participants also decided if the overall body of evidence supported a DDI involving the drug pair. Agreement was computed both at the evidence item and drug pair levels. A cut-off of ≥ 70% was chosen as the agreement threshold for percent agreement, while a coefficient > 0.6 was used as the cut-off for chance-corrected agreement. Open ended comments were collected and coded to identify themes related to the participants' experience using the novel approach.

RESULTS

The face validity of the new instrument was established by two rounds of evaluation involving a total of 6 experts. Fifteen experts agreed to participate in the reliability assessment, and 14 completed the study. Participant agreement on the sufficiency of 22 of the 34 evidence items (65%) did not exceed the a priori agreement threshold. Similarly, agreement on the sufficiency of evidence for 3 of the 5 drug pairs (60%) was poor. Chance-corrected agreement at the drug pair level further confirmed the poor interrater reliability of the instrument (Gwet's AC1 = 0.24, Conger's Kappa = 0.24). Participant comments suggested several possible reasons for the disagreements including unaddressed subjectivity in assessing an evidence item's type and study design, an infeasible separation of evidence evaluation from the consideration of clinical relevance, and potential issues related to the evaluation of DDI case reports.

CONCLUSIONS

Even though the key findings were negative, the study's results shed light on how experts approach DDI evidence assessment, including the importance situating evidence assessment within the context of consideration of clinical relevance. Analysis of participant comments within the context of the negative findings identified several promising future research directions including: novel computer-based support for evidence assessment; formal evaluation of a more comprehensive evidence assessment approach that requires consideration of specific, explicitly stated, clinical consequences; and more formal investigation of DDI case report assessment instruments.

Drug-Drug Interactions in Acute Coronary Syndrome Patients: Systematic Review

Drug-drug interaction (DDI) is defined as a clinically significant change in the exposure and/or response to a drug caused by co-administration of another drug which may result in a precipitation of an adverse event or alteration of its therapeutic effects. The aim of this systematic review was to provide an overview of DDIs that were actually observed or evaluated in acute coronary syndrome (ACS) patients with particular focus on DDIs with clinical relevance. Electronic searches of the literature were conducted in the following databases: MEDLINE, EBSCO, Scopus, Google Scholar and SCIndeks. A total of 117 articles were included in the review. This review showed that ACS patients can be exposed to a variety of DDIs with diverse outcomes which include decreased efficacy of antiplatelet drugs, thrombolytics or anticoagulants, increased risk of bleeding, rhabdomyolysis, hepatotoxicity, adverse effects on cardiovascular system (e.g. QT interval prolongation, arrhythmias, excessive bradycardia, severe hypotension), serotonin syndrome and drug-induced fever. Majority of the DDIs involved antiplatelet drugs (e.g. aspirin, clopidogrel and ticagrelor). Evidence of some of the reported DDIs is inconclusive as some of the studies have shown conflicting results. There is a need for additional post-marketing and population-based studies to evaluate the true effects of disease states and other factors on the clinical outcomes of DDIs. Clinicians should be attentive to the potential for DDIs and their associated harm in order to minimize or, if possible, avoid medication-related adverse events in ACS patients.

Clinically relevant drug interactions between newer antidepressants and oral anticoagulants

Introduction: This is a review of the drug interactions (DIs) between newer antidepressants and oral anticoagulants (OACs): vitamin K antagonists (VKAs) and direct-acting OACs (DOACs).Areas covered: Articles were obtained from PubMed searches performed for each of the newer antidepressants and oral anticoagulants. The basic pharmacokinetic and pharmacodynamic mechanisms for DIs with these drugs were summarized. Some newer antidepressants are inhibitors of a number of cytochrome P450 (CYP) isoforms and many antidepressants appear to have potential to impair serotonin platelet function and increase bleeding risk.Expert opinion: Clinicians should not forget that the DIs between newer antidepressants and VKAs can be potentially lethal. Among SSRIs, fluoxetine and fluvoxamine appear to be associated with the highest DI risk with warfarin, the most commonly prescribed VKA worldwide. Case reports featuring duloxetine, mirtazapine and trazadone suggested potential for interaction with warfarin. As CYP3A4 is an important metabolic pathway for all DOACs except dabigatran, it appears reasonable to recommend avoiding the co-prescription of fluoxetine and fluvoxamine (weak to moderate CYP3A4 inhibitors) and St John's wort (CYP3A4 inducer). Many package inserts for the newer antidepressants include a warning regarding an increased risk of bleeding events with concomitant use of these agents with OACs.

Merits, features, and desiderata to be considered when developing electronic health records with embedded clinical decision support systems in Palestinian hospitals: a consensus study

BACKGROUND

Electronic health records (EHRs) with embedded clinical decision support systems (CDSSs) have the potential to improve healthcare delivery. This study was conducted to explore merits, features, and desiderata to be considered when planning for, designing, developing, implementing, piloting, evaluating, maintaining, upgrading, and/or using EHRs with CDSSs.

METHODS

A mixed-method combining the Delphi technique and Analytic Hierarchy Process was used. Potentially important items were collected after a thorough search of the literature and from interviews with key contact experts (n = 19). Opinions and views of the 76 panelists on the use of EHRs were also explored. Iterative Delphi rounds were conducted to achieve consensus on 122 potentially important items by a panel of 76 participants. Items on which consensus was achieved were ranked in the order of their importance using the Analytic Hierarchy Process.

RESULTS

Of the 122 potentially important items presented to the panelists in the Delphi rounds, consensus was achieved on 110 (90.2%) items. Of these, 16 (14.5%) items were related to the demographic characteristics of the patient, 16 (14.5%) were related to prescribing medications, 16 (14.5%) were related to checking prescriptions and alerts, 14 (12.7%) items were related to the patient's identity, 13 (11.8%) items were related to patient assessment, 12 (10.9%) items were related to the quality of alerts, 11 (10%) items were related to admission and discharge of the patient, 9 (8.2%) items were general features, and 3 (2.7%) items were related to diseases and making diagnosis.

CONCLUSIONS

In this study, merits, features, and desiderata to be considered when planning for, designing, developing, implementing, piloting, evaluating, maintaining, upgrading, and/or using EHRs with CDSSs were explored. Considering items on which consensus was achieved might promote congruence and safe use of EHRs. Further studies are still needed to determine if these recommendations can improve patient safety and outcomes in Palestinian hospitals.

Clinically relevant drug interactions between statins and antidepressants

WHAT IS KNOWN AND OBJECTIVE

Statins, also known as 3-hydroxy-3-methylglutaryl-CoA reductase inhibitors, and antidepressant drugs are frequently used in combination due to the high and growing incidence of cardiovascular diseases and psychiatric disorders worldwide. Several aspects on management, the risk of adverse events (AEs) occurrence and the potential clinically relevant pharmacokinetic (PK) and pharmacodynamic (PD) drug-drug interactions (DDIs) between these two classes have not been well investigated. The aim of the present review was to describe the PK and PD interactions, of clinical relevance, between statins and antidepressant drugs and provide a comprehensive overview of their pharmacological features for appropriate multiple drug regimens.

METHODS

Relevant studies were identified through a literature search of PubMed and the Cochrane databases focusing on clinically relevant DDIs between statins and antidepressants. Only papers in English were included in the search.

RESULTS AND DISCUSSION

Pharmacodynamic (PD) drug-drug interactions (DDIs) are unlikely to occur as statins are highly selective inhibitors of HMG-CoA reductase with no relevant effect on other enzymes or receptor systems. Despite the numerous PK studies on individual drugs belonging to statins and antidepressant agents, only a few case reports regarding specific DDIs are present in the literature and no clinical studies have been performed. PK data allow to speculate on potential DDIs, comparing the metabolic pathways, intestinal and liver transporters and elimination routes. Overall, second-generation antidepressants, in particular citalopram, escitalopram, mirtazapine, reboxetine and venlafaxine, have weak inhibitory effects on various cytochrome (CYP) isozymes and seem to have a more advantageous DDIs profile in vivo. Conversely, nefazodone, fluoxetine, paroxetine and fluvoxamine influence considerably CYPs activity with potential effects on statins plasma levels, although pravastatin, pitavastatin and rosuvastatin are not susceptible to inhibition by any CYP. Albeit no studies have been performed on P-glycoprotein (P-gp), interactions of clinical relevance are unlikely.

WHAT IS NEW AND CONCLUSION

Although DDIs with antidepressants are potentially, but rarely clinically significant, the use of antidepressants with a more favourable drug interaction profile is advisable. An evaluation on DDIs between these drugs can be useful for future PK/PD studies on drug-drug interaction to provide clinicians with more data for appropriate multiple drug regimens.

Analysis of consensus among drug interaction databases with regard to combinations of psychotropics

Drug interaction databases are important tools in today's clinical decision support. However, there is great variation with regard to classification and presentation of interactions among databases. The present study aimed to investigate consensus among databases with regard to combinations of psychotropics. A database integrated in Norwegian computerised clinical decision support systems and three international recommended subscription databases were compared. Combinations of psychotropics (two or more) prescribed to patients 65 years or older on a single day from three nursing homes in Bergen, Norway 16 years apart (2000 and 2016) were studied. The databases were compared in a common analysis with the following questions: interaction (no, not contraindicated or contraindicated), type (pharmacodynamic or pharmacokinetic), the total number of interactions, and the first ranked interaction among several in each patient. Consensus among the four drug interaction databases was associated with pharmacokinetic interactions involving mainly older psychotropics in the common analysis. The qualities that best characterised interactions with consensus was primarily the evidence including a description of manageability. There was a surprising lack of consensus with regard to contraindicated interactions, even when older psychotropics were involved. Lack of consensus decreased with the number of psychotropics in the combinations. This was mainly because the highest ranked interactions in the respective databases involved different drugs. We propose evidence and manageability as core factors when ranking and presenting interactions in clinical decision support.

Semi-Supervised Learning Algorithm for Identifying High-Priority Drug-Drug Interactions Through Adverse Event Reports

Identifying drug-drug interactions (DDIs) is a critical enabler for reducing adverse drug events and improving patient safety. Generating proper DDI alerts during prescribing workflow has the potential to prevent DDI-related adverse events. However, the implementation of DDI alerting system remains a challenge as users are experiencing alert overload which causes alert fatigue. One strategy to optimize the current system is to establish a list of high-priority DDIs for alerting purposes, though it is a resource-intensive task. In this study, we propose a machine learning framework to extract useful features from the FDA adverse event reports and then identify potential high-priority DDIs using an autoencoder-based semi-supervised learning algorithm. The experimental results demonstrate the effectiveness of using adverse event feature representations in differentiating high- and low-priority DDIs. Additionally, the proposed algorithm utilizes stacked autoencoders and weighted support vector machine for boosting classification performance, which outperforms other competing methods in terms of F-measure and AUC score. This framework integrates multiple information sources, leverages domain knowledge and clinical evidence, and provides a practical approach for pre-screening high-priority DDI candidates for medication alerts.

The Effect of Patient-Specific Drug-Drug Interaction Alerting on the Frequency of Alerts: A Pilot Study

Background: False-positive drug-drug interaction alerts are frequent and result in alert fatigue that can result in prescribers bypassing important alerts. Development of a method to present patient-appropriate alerts is needed to help restore alert relevance. Objective: The purpose of this study was to assess the potential for patient-specific drug-drug interaction (DDI) alerts to reduce alert burden. Methods: This project was conducted at a tertiary care medical center. Seven of the most frequently encountered DDI alerts were chosen for developing patient-specific, algorithm-based DDI alerts. For each of the DDI pairs, 2 algorithms featuring different values for modifying factors were made. DDI alerts from the 7 drug pairs were collected over 30 days. Outcome measures included the number of DDI alerts generated before and after patient-specific algorithm application to the same patients over the same time period. Results: A total of 14 algorithms were generated, and each was evaluated by comparing the number of alerts generated by our existing, customized clinical decision support (CDS) software and the patient-specific algorithms. The CDS DDI alerting software generated an average of 185.3 alerts per drug pair over the 30-day study period. Patient-specific algorithms reduced the number of alerts resulting from the algorithms by 11.3% to 93.5%. Conclusion and Relevance: Patient-specific DDI alerting is an innovative and effective approach to reduce the number of DDI alerts, may potentially increase the appropriateness of alerts, and may decrease the potential for alert fatigue.

Countrywide prevalence of critical drug interactions in Hungarian outpatients: a retrospective analysis of pharmacy dispensing data

Background

Drug-drug interactions (DDIs) present a significant source of adverse drug reactions. Despite being one of the commonly cited risks to patient safety, prevention of DDIs still poses a challenge to healthcare systems. The prevalence of DDIs can be used as a quality indicator for the safety of prescribing. With the analysis of drug utilization databases, real-world data on critical DDIs can be obtained. The aim of this study was to establish a list of critical DDIs and estimate their prevalence in the Hungarian outpatient population.

Methods

Since there is no conclusive and generally accepted repository of high-risk DDIs, a systematic search of the literature for consensus-based lists was performed. Based on these results and their analysis with 5 interaction compendia, we propose a simple methodology to identify critical combinations. Present study focused on DDIs which are (1) of high clinical importance thus being most likely to cause significant harm if not detected, (2) well-supported by available evidence and (3) affect drugs which are routinely dispensed in the community pharmacy setting. A retrospective analysis of prescriptions filled between 2013 and 2016 was performed. The source of drug utilization data was the IQVIA’s national prescription fill database. The number of interacting drug pairs dispensed at the same time to the same patient was established.

Results

After excluding drugs with low dispensing rates, the analysis covered 39 DDIs. The distribution of risk categories of the analysed DDIs was inconsistent among different drug interaction compendia. The total number of prescriptions filled varied between 173924449 and 176368468 per year. The prevalence of the selected potential DDIs ranged from 0.00 to 355.89 per 100000 prescriptions per year. There was significant variation between how the number of cases had changed for each DDI throughout the study period, no general tendency could have been described.

Conclusions

There were 1.8 million cases of co-dispensing each year, where prescribers’ and community pharmacists’ role in recognizing and managing potentially serious interactions was or would have been critical. The method presented to identify high-risk DDIs can serve as a starting point for the much-needed improvement of routine interaction screening.

Electronic supplementary material

The online version of this article (10.1186/s40360-019-0311-0) contains supplementary material, which is available to authorized users.