Recommendations for selecting drug-drug interactions for clinical decision support

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.

Design, development, implementation, and evaluation of a severe drug-drug interaction alert system in the ICU: An analysis of acceptance and override rates

BACKGROUND AND OBJECTIVE

The override rate of Drug-Drug Interaction Alerts (DDIA) in Intensive Care Units (ICUs) is very high. Therefore, this study aimed to design, develop, implement, and evaluate a severe Drug-Drug Alert System (DDIAS) in a system of ICUs and measure the override rate of this system.

METHODS

This is a cross-sectional study that details the design, development, implementation, and evaluation of a DDIAS for severe interactions into a Computerized Provider Order Entry (CPOE) system in the ICUs of Nemazee general teaching hospitals in 2021. The patients exposed to the volume of DDIAS, acceptance and overridden of DDIAS, and usability of DDIAS have been collected. The study was approved by the local Institutional Review Board (IRB) and; the ethics committee of Shiraz University of Medical Science on date: 2019-11-23 (Approval ID: IR.SUMS.REC.1398.1046).

RESULTS

The knowledge base of the DDIAS contains 9,809 severe potential drug-drug interactions (pDDIs). A total of 2672 medications were prescribed in the population study. The volume and acceptance rate for the DDIAS were 81 % and 97.5 %, respectively. The override rate was 2.5 %. The mean System Usability Scale (SUS) score of the DDIAS was 75.

CONCLUSION

This study demonstrates that implementing high-risk DDIAS at the point of prescribing in ICUs improves adherence to alerts. In addition, the usability of the DDIAS was reasonable. Further studies are needed to investigate the establishment of severe DDIAS and measure the prescribers' response to DDIAS on a larger scale.

Barriers to Adoption of Tailored Drug-Drug Interaction Clinical Decision Support

OBJECTIVE

Despite the benefits of the tailored drug-drug interaction (DDI) alerts and the broad dissemination strategy, the uptake of our tailored DDI alert algorithms that are enhanced with patient-specific and context-specific factors has been limited. The goal of the study was to examine barriers and health care system dynamics related to implementing tailored DDI alerts and identify the factors that would drive optimization and improvement of DDI alerts.

METHODS

We employed a qualitative research approach, conducting interviews with a participant interview guide framed based on Proctor's taxonomy of implementation outcomes and informed by the Theoretical Domains Framework. Participants included pharmacists with informatics roles within hospitals, chief medical informatics officers, and associate medical informatics directors/officers. Our data analysis was informed by the technique used in grounded theory analysis, and the reporting of open coding results was based on a modified version of the Safety-Related Electronic Health Record Research Reporting Framework.

RESULTS

Our analysis generated 15 barriers, and we mapped the interconnections of these barriers, which clustered around three entities (i.e., users, organizations, and technical stakeholders). Our findings revealed that misaligned interests regarding DDI alert performance and misaligned expectations regarding DDI alert optimizations among these entities within health care organizations could result in system inertia in implementing tailored DDI alerts.

CONCLUSION

Health care organizations primarily determine the implementation and optimization of DDI alerts, and it is essential to identify and demonstrate value metrics that health care organizations prioritize to enable tailored DDI alert implementation. This could be achieved via a multifaceted approach, such as partnering with health care organizations that have the capacity to adopt tailored DDI alerts and identifying specialists who know users' needs, liaise with organizations and vendors, and facilitate technical stakeholders' work. In the future, researchers can adopt the systematic approach to study tailored DDI implementation problems from other system perspectives (e.g., the vendors' system).

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.

Implementation of a Pharmacovigilance System in a Resources-Limited Country in the Context of COVID-19: Lebanon's Success Story

Implementation of Pharmacovigilance (PV) systems in resource-limited countries is a real challenge. The objective of this paper is to describe the implementation of an effective national PV system in Lebanon in the context of COVID-19, within a limited resources setting and with the absence of a guaranteed funding. In 2018, the PV center hosted at the Lebanese University, Faculty of Pharmacy under the supervision of the Quality Assurance of Pharmaceutical Products Program within the Lebanese Ministry of Public Health became an associate member of the World Health Organization (WHO) Program of International Drug Monitoring and recognized as Full member in 2021.This analysis highlights the requirements of the WHO that were met in Lebanon to create an effective PV system. The Lebanese experience shows that it is not only possible, but also crucial to implement a PV system in low to middle-income countries with limited resources and with the absence of a guaranteed funding in order to be able to promote patients' safety. Support from organizations like WHO and World Bank, skilled leadership, hard work and dedicated staff with efficient training, and finally mass awareness initiatives were all considered as key elements to implement a successful PV System. In the midst of a turbulent political, economic and health context, Lebanon has been able to develop one of the most active and rapidly evolving PV systems in the Middle East.

The curse and blessing of abundance-the evolution of drug interaction databases and their impact on drug network analysis

BACKGROUND

Widespread bioinformatics applications such as drug repositioning or drug-drug interaction prediction rely on the recent advances in machine learning, complex network science, and comprehensive drug datasets comprising the latest research results in molecular biology, biochemistry, or pharmacology. The problem is that there is much uncertainty in these drug datasets-we know the drug-drug or drug-target interactions reported in the research papers, but we cannot know if the not reported interactions are absent or yet to be discovered. This uncertainty hampers the accuracy of such bioinformatics applications.

RESULTS

We use complex network statistics tools and simulations of randomly inserted previously unaccounted interactions in drug-drug and drug-target interaction networks-built with data from DrugBank versions released over the plast decade-to investigate whether the abundance of new research data (included in the latest dataset versions) mitigates the uncertainty issue. Our results show that the drug-drug interaction networks built with the latest dataset versions become very dense and, therefore, almost impossible to analyze with conventional complex network methods. On the other hand, for the latest drug database versions, drug-target networks still include much uncertainty; however, the robustness of complex network analysis methods slightly improves.

CONCLUSIONS

Our big data analysis results pinpoint future research directions to improve the quality and practicality of drug databases for bioinformatics applications: benchmarking for drug-target interaction prediction and drug-drug interaction severity standardization.

Potential Drug-Drug Interactions Among Hospitalised Elderly Patients in Northern Sri Lanka, A Lower Middle-Income Country: A Retrospective Analysis

BACKGROUND AND OBJECTIVES

Elderly individuals are more vulnerable to potential drug-drug interactions (pDDIs) as age-related physiological changes, polypharmacy and hospitalisations are known to increase the risk of pDDIs. The aims of this study were to assess the impact of hospitalisation and other associated factors on pDDIs in elderly patients, in a resource-limited setting.

METHODS

This is a retrospective analysis of data of elderly patients (aged ≥ 65 years) admitted to the medical units of Jaffna Teaching Hospital. Preadmission and post-admission data were collected from clinic and hospital records, respectively. The British National Formulary was used to identify and categorise pDDIs. Point prevalence of pDDIs in elderly patients and the total number of pDDIs before and after hospitalisation were estimated. Factors contributing to pDDIs were determined by univariate and multivariable logistic regression.

RESULTS

Two hundred and eighty-eight hospitalised elderly patients with a median age of 71 years (interquartile range 67-76 years) showed a significant increase in the prevalence of pDDIs post-admission compared with the preadmission values (77.1% vs 61.5%; p < 0.001) associated with an increase in total pDDIs (377 vs 488; p < 0.001) where the majority (> 75%) were potential pharmacodynamic interactions. An unadjusted analysis showed a significant association between pDDI and polypharmacy [taking five or more medications] (odds ratio [OR] = 14.17; 95% confidence interval [CI] 7.41-27.10), the presence of more than three underlying medical conditions (OR 4.14; 95% CI 1.70-10.06), ischaemic heart disease (OR 3.25; 95% CI 1.78-5.94) and asthma (OR 8.14; 95% CI 2.46-26.88). However, when adjusted for confounders only polypharmacy (OR 14.10; 95% CI 6.50-30.60) and the presence of underlying asthma (OR 11.61; 95% CI 2.82-47.85) were associated with pDDIs.

CONCLUSIONS

The prevalence of pDDIs among elderly patients was high and increased with hospital admissions. Polypharmacy and relevant comorbidities were contributory factors. Increased awareness of the potential for pDDIs through appropriate training and simple measures including a proper drug history, creating a bespoke pDDI list and frequent medication reviews by healthcare professionals would help to mitigate pDDIs in resource-limited and technology-limited settings.

TLC-Act: A Novel Tool for Managing Drug Interactions

Background

Clinical decision support systems (CDSS) are used by pharmacists to assist in managing drug-drug interactions (DDIs). However, previous research suggests that such systems may perform suboptimally in providing clinically relevant information in practice.

Objectives

The primary objective of this study was to develop a novel DDI management tool to reflect the clinical thought process that a pharmacist uses when assessing a DDI. The secondary objective was to investigate practitioners' perceptions of this tool.

Methods

This study was conducted in 3 phases: development of the DDI management tool, implementation of the tool in clinical practice, and collection of practitioners' opinions of the tool through an online qualitative survey (although because of circumstances related to the COVID-19 pandemic, the study population for the survey phase included only pharmacy residents). A comprehensive literature search and analysis by an expert panel provided underlying context for the DDI management tool. The tool was validated through simulation against a known list of DDIs before implementation into practice by hospital pharmacists and pharmacy residents. Participating pharmacy residents were invited to provide feedback on the tool. Survey results were analyzed using descriptive statistics.

Results

The novel tool that was developed in this study (called TLC-Act) consisted of components important to a pharmacist when assessing a DDI, including the duration of concomitant use of the interacting medications and patient-specific risk factors. Study participants implemented the tool in clinical practice for a total of 6 weeks. Of the 28 pharmacy residents surveyed, 15 (54%) submitted a response, of whom 11 (73%) found the TLC-Act tool to be slightly more useful for assessing a DDI than usual care with the CDSS alone.

Conclusions

The TLC-Act tool maps out a pharmacist's clinical thought process when assessing a DDI in practice. This novel tool may be more useful than a CDSS alone for managing DDIs, as it takes into account other important factors pertinent to the assessment of a DDI.

A comparison of four drug-drug interaction databases for patients undergoing haematopoietic stem cell transplantation

WHAT IS KNOWN AND OBJECTIVE

Patients who have undergone haematopoietic stem cell transplantation are prone to drug-drug interactions due to polypharmacy. Drug-drug interaction databases are essential tools for identifying interactions in this patient group. However, drug-drug interaction checkers, which help manage interactions, may have disagreements about assessing the existence or severance of the interactions. The study aimed to determine differences among popular drug-drug interaction databases from several angles for patients who underwent haematopoietic stem cell transplantation.

METHODS

The 21-day treatment sheets of one hundred patients who underwent haematopoietic stem cell transplantation were examined in two subscription-based (Uptodate and Micromedex) and two open-access databases (Drugs.com and Epocrates) in terms of several categories two years in a row. Statistical analysis was utilized to understand the compatibility of databases in terms of severity scores, evidence levels, given references, and word counts in interaction reports. Fleiss' and Cohen's kappa statistics were used to analyse the databases' agreement levels.

RESULTS AND DISCUSSION

A total of 1393 and 1382 different drug-drug interactions were detected in subsequent versions of the databases, namely the 2021 and 2022 versions. The Fleiss kappa overall agreement among databases was slight. Uptodate and Micromedex showed fair agreement, and other database pairs showed slight agreement in severity ratings.

CONCLUSION

There was a poor agreement among databases for interactions seen in bone marrow transplantation patients. Therefore, it would be safer to use more than one database in daily practice. Further work needs to be done to understand the agreement level of databases for different types of interactions.

Translational drug-interaction corpus

The discovery of drug-drug interactions (DDIs) that have a translational impact among in vitro pharmacokinetics (PK), in vivo PK and clinical outcomes depends largely on the quality of the annotated corpus available for text mining. We have developed a new DDI corpus based on an annotation scheme that builds upon and extends previous ones, where an abstract is fragmented and each fragment is then annotated along eight dimensions, namely, focus, polarity, certainty, evidence, directionality, study type, interaction type and mechanism. The guideline for defining these dimensions has undergone refinement during the annotation process. Our DDI corpus comprises 900 positive DDI abstracts and 750 that are not directly relevant to DDI. The abstracts in corpus are separated into eight categories of DDI or non-DDI evidence: DDI with pharmacokinetic (PK) mechanism, in vivo DDI PK, DDI clinical, drug-nutrition interaction, single drug, not drug related, in vitro pharmacodynamic (PD) and case report. Seven annotators, three annotators with drug-interaction research experience and four annotators with less drug-interaction research experience independently annotated the DDI corpus, where two researchers independently annotated each abstract. After two rounds of annotations with additional training in between, agreement improved from (0.79, 0.96, 0.86, 0.70, 0.91, 0.65, 0.78, 0.90) to (0.93, 0.99, 0.96, 0.94, 0.95, 0.93, 0.96, 0.97) for focus, certainty, evidence, study type, interaction type, mechanisms, polarity and direction, respectively. The novice-level annotators improved from 0.83 to 0.96, while the expert-level annotators stayed in high performance with some improvement, from 0.90 to 0.96. In summary, we achieved 96% agreement among each pair of annotators with regard to the eight dimensions. The annotated corpus is now available to the community for inclusion in their text-mining pipelines. Database URL https://github.com/zha204/DDI-Corpus-Database/tree/master/DDI%20corpus.

Drug Interactions Affecting Antiarrhythmic Drug Use

Antiarrhythmic drugs (AAD) play an important role in the management of arrhythmias. Drug interactions involving AAD are common in clinical practice. As AADs have a narrow therapeutic window, both pharmacokinetic as well as pharmacodynamic interactions involving AAD can result in serious adverse drug reactions ranging from arrhythmia recurrence, failure of device-based therapy, and heart failure, to death. Pharmacokinetic drug interactions frequently involve the inhibition of key metabolic pathways, resulting in accumulation of a substrate drug. Additionally, over the past 2 decades, the P-gp (permeability glycoprotein) has been increasingly cited as a significant source of drug interactions. Pharmacodynamic drug interactions involving AADs commonly involve additive QT prolongation. Amiodarone, quinidine, and dofetilide are AADs with numerous and clinically significant drug interactions. Recent studies have also demonstrated increased morbidity and mortality with the use of digoxin and other AAD which interact with P-gp. QT prolongation is an important pharmacodynamic interaction involving mainly Vaughan-Williams class III AAD as many commonly used drug classes, such as macrolide antibiotics, fluoroquinolone antibiotics, antipsychotics, and antiemetics prolong the QT interval. Whenever possible, serious drug-drug interactions involving AAD should be avoided. If unavoidable, patients will require closer monitoring and the concomitant use of interacting agents should be minimized. Increasing awareness of drug interactions among clinicians will significantly improve patient safety for patients with arrhythmias.

Contextualized Drug-Drug Interaction Management Improves Clinical Utility Compared With Basic Drug-Drug Interaction Management in Hospitalized Patients

Drug–drug interactions (DDIs) frequently trigger adverse drug events or reduced efficacy. Most DDI alerts, however, are overridden because of irrelevance for the specific patient. Basic DDI clinical decision support (CDS) systems offer limited possibilities for decreasing the number of irrelevant DDI alerts without missing relevant ones. Computerized decision tree rules were designed to context‐dependently suppress irrelevant DDI alerts. A crossover study was performed to compare the clinical utility of contextualized and basic DDI management in hospitalized patients. First, a basic DDI‐CDS system was used in clinical practice while contextualized DDI alerts were collected in the background. Next, this process was reversed. All medication orders (MOs) from hospitalized patients with at least one DDI alert were included. The following outcome measures were used to assess clinical utility: positive predictive value (PPV), negative predictive value (NPV), number of pharmacy interventions (PIs)/1,000 MOs, and the median time spent on DDI management/1,000 MOs. During the basic DDI management phase 1,919 MOs/day were included, triggering 220 DDI alerts/1,000 MOs; showing 57 basic DDI alerts/1,000 MOs to pharmacy staff; PPV was 2.8% with 1.6 PIs/1,000 MOs costing 37.2 minutes/1,000 MOs. No DDIs were missed by the contextualized CDS system (NPV 100%). During the contextualized DDI management phase 1,853 MOs/day were included, triggering 244 basic DDI alerts/1,000 MOs, showing 9.6 contextualized DDIs/1,000 MOs to pharmacy staff; PPV was 41.4% (P < 0.01), with 4.0 PIs/1,000 MOs (P < 0.01) and 13.7 minutes/1,000 MOs. The clinical utility of contextualized DDI management exceeds that of basic DDI management.

Similarity and Consistency Assessment of Three Major Online Drug-Drug Interaction Resources

AIM

To explore the level of agreement on drug-drug interaction (DDI) information listed in three major online drug information resources (DIRs) in terms of: (1) interacting drug pairs; (2) severity rating; (3) evidence rating and (4) clinical management recommendations.

METHODS

We extracted information from the British National Formulary (BNF), Thesaurus, and Micromedex. Following drug name normalisation, we estimated the overlap of the DIRs in terms of DDI. We annotated clinical management recommendations either manually, where possible, or through application of a machine learning algorithm.

RESULTS

The DIRs contained 51,481 (BNF), 38,037 (Thesaurus), and 65,446 (Micromedex) drug pairs involved in DDIs. The number of common DDIs across the three DIRs was 6,970 (13.54% of BNF, 18.32% of Thesaurus, and 10.65% of Micromedex). Micromedex and Thesaurus overall showed higher levels of similarity in their severity ratings, while the BNF agreed more with Micromedex on the critical severity ratings and with Thesaurus on the least significant ones. Evidence rating agreement between BNF and Micromedex was generally poor. Variation in clinical management recommendations was also identified, with some categories (i.e. Monitor and Adjust dose) showing higher levels of agreement compared to others (i.e. Use with caution, Wash-out, Modify administration).

CONCLUSIONS

There is considerable variation in the DDIs included in the examined DIRs, together with variability in categorisation of severity and clinical advice given. DDIs labelled as critical were more likely to appear in multiple DIRs. Such variability in information could have deleterious consequences for patient safety, and there is a need for harmonisation and standardisation.

Linking Technology to Address the Social and Medical Determinants of Health for Safe Medicines Use

OBJECTIVES

Both social and medical factors can negatively affect health outcomes, especially in vulnerable populations. To address these 2 types of factors in a postdischarge population, 2 nonprofit organizations collaborated to combine their novel decision support programs and address the question: Could combined programs have greater potential for improved health outcomes?

METHODS

HomeMeds, a social health program in which trained social services staff make home visits to vulnerable clients, was combined with MedSafety Scan, a medical health, clinical decision support tool. Data captured in the home visits were entered into the HomeMeds and MedSafety Scan programs to detect those patients at the greatest risk of adverse health outcomes because of medications.

RESULTS

Patients (n = 108; mean age, 77 years; multiple comorbidities and LACE+ (length of stay, acuity, comorbidities, emergency department visits [hospital index]; score >29) received a postdischarge home visit by trained social services staff. The number of drugs reported as being taken was 10.4 ± 5.1 (range, 1-26), which was less than prescribed at discharge in 62% of patients (range, 1-8). Both programs detected a serious risk of medication-induced harm, mostly from different causes such as drug-drug interactions or for use not recommended in the elderly.

CONCLUSIONS

Combined analysis of data from 2 novel decision support programs yielded complementary findings that together address both medical and social determinants of health. These have the potential to reduce medication-induced harm, costly rehospitalization, and/or emergency department visits and support the further evaluation of this combined approach in other vulnerable populations such as the seriously mentally ill, frail, those confined to home, opioid dependent, or otherwise impaired.

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.

Improving the specificity of drug-drug interaction alerts: Can it be done?

PURPOSE

Inaccurate and nonspecific medication alerts contribute to high override rates, alert fatigue, and ultimately patient harm. Drug-drug interaction (DDI) alerts often fail to account for factors that could reduce risk; further, drugs that trigger alerts are often inconsistently grouped into value sets. Toward improving the specificity of DDI alerts, the objectives of this study were to (1) highlight the inconsistency of drug value sets for triggering DDI alerts and (2) demonstrate a method of classifying factors that can be used to modify the risk of harm from a DDI.

METHODS

This was a proof-of-concept study focused on 15 well-known DDIs. Using 3 drug interaction references, we extracted 2 drug value sets and any available order- and patient-related factors for each DDI. Fleiss' kappa was used to measure the consistency of value sets among references. Risk-modifying factors were classified as order parameters (eg, route and dose) or patient characteristics (eg, comorbidities and laboratory results).

RESULTS

Seventeen value sets (56%) had nonsignificant agreement. Agreement among the remaining 13 value sets was on average moderate. Thirty-three factors that could reduce risk in 14 of 15 DDIs (93%) were identified. Most risk-modifying factors (67%) were classified as order parameters.

CONCLUSION

This study demonstrates the importance of increasing the consistency of drug value sets that trigger DDI alerts and how alert specificity and usefulness can be improved with risk-modifying factors obtained from drug references. It may be difficult to operationalize certain factors to reduce unnecessary alerts; however, factors can be used to support decisions by providing contextual information.

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.

A mixed methods study on medicines information needs and challenges in New Zealand general practice

BACKGROUND

Medicines are central to healthcare in aging populations with chronic multi-morbidity. Their safe and effective use relies on a large and constantly increasing knowledge base. Despite the current era of unprecedented access to information, there is evidence that unmet information needs remain an issue in clinical practice. Unmet medicines information needs may contribute to sub-optimal use of medicines and patient harm. Little is known about medicines information needs in the primary care setting. The aim of this study was to investigate the nature of medicines information needs in routine general practice and understand the challenges and influences on the information-seeking behaviour of general practitioners.

METHODS

A mixed methods study involving 18 New Zealand general practitioner participants was undertaken. Quantitative data were collected to characterize the medicines information needs arising during 642 consultations conducted by the participants. Qualitative data regarding participant views on their medicines information needs, resources used, challenges to meeting the needs and potential solutions were collected by semi-structured interview. Integration occurred by comparison of results from each method.

RESULTS

Of 642 consultations, 11% (n = 73/642) featured at least one medicines information need. The needs spanned 14 different categories with dosing the most frequent (26%) followed by side effects (15%) and drug interactions (14%). Two main themes describing the nature of general practitioners' medicines information needs were identified from the qualitative data: a 'common core' related to medicine dose, side effects and interactions and a 'perplexing periphery'. Challenges in the perplexing periphery were the variation in information needs, complexity, 'known unknowns' and 'unknown unknowns'. Key factors affecting general practitioners' strategies for meeting medicines information needs were trust in a resource, presence of the patient, how the information was presented, scarcity of time, awareness of the existence of a resource, and its accessibility.

CONCLUSIONS

General practitioners face challenges in meeting wide-ranging medicines information needs in patients with increasingly complex care needs. Recognising the challenges and factors that influence resource use in practice can inform optimisation of medicines information support resources. Resources for general practitioners must take into account the complexity and time constraints of real-world practice. An individually responsive approach involving greater collaboration with pharmacists and specialist medicines information support services may provide a potential solution.

Comparison of Clinical Importance of Drug Interactions Identified by Hospital Pharmacists and a Local Clinical Decision Support System

Background

Drug-drug interactions (DDIs) may cause adverse drug events, potentially leading to hospital admission. Clinical decision support systems (CDSSs) can improve decision-making by clinicians as well as drug safety. However, previous research has suggested that pharmacists are concerned about discrepancies between CDSSs and common clinical practice in terms of severity ratings and recommended actions for DDIs.

Objectives

The primary objective was to characterize the level of agreement in terms of DDI severity ranking and actions recommended between the local CDSS and pharmacists. The secondary objectives were to determine the level of agreement among pharmacists concerning DDI severity, to determine the influence of the CDSS on clinicians' decision-making, and to review the literature supporting the severity rankings of DDIs identified in the study institution's database.

Methods

This 2-part survey study involved pharmacists and pharmacy residents working at 1 of 4 health organizations within the Lower Mainland Pharmacy Services, British Columbia, who were invited to participate by email. Participants were first asked to rank the severity of 15 drug pairs (representing potential DDIs) on a 5-point Likert scale and to select an action to manage each interaction. Participants were then given the CDSS severity classification for the same 15 pairs and again asked to select an appropriate management action.

Results

Of the estimated 500 eligible pharmacists, a total of 73 pharmacists participated, for a response rate of about 15%. For DDIs of moderate severity, most participants chose to monitor. For severe and contraindicated interactions, the severity ranking and action proposed by participants varied, despite the same severity classification by the CDSS. There was poor agreement among respondents about the severity of the various DDIs. Moreover, knowledge of the CDSS severity ranking did not seem to change the actions proposed by most respondents.

Conclusion

This study identified a gap between the local CDSS and clinical practice. There were discrepancies in terms of severity rankings and actions proposed to manage DDIs, particularly for severe and contraindicated DDIs. The current CDSS did not appear to have a large impact on clinical decision-making, which suggests that it may not be functioning to its full potential.

Potential drug-drug interactions of antiretrovirals and antimicrobials detected by three databases

Standard treatment for HIV infection involves a combination of antiretrovirals. Additionally, opportunistic infections in HIV infected patients require further antimicrobial medications that might cause drug-drug interactions (DDIs). The objective of this study was to to compare the recognition of DDIs between antiretrovirals and antimicrobials by three proprietary databases and evaluate their concordance. 114 items of antiretrovirals and antimicrobials from the National List of Essential Medicines of Thailand 2018 were used in the study. However, 21 items were not recognised by Micromedex, Drugs.com, and Liverpool HIV interactions. Only 93 items were available for the detection of potential DDIs by the three databases. Potential DDIs detected from the three databases included 292 pairs. Liverpool showed the highest number of DDIs with 285 pairs compared with 259 pairs by drugs.com and 133 pairs by Micromedex. Regarding the severity classifications, Liverpool reported 10% Contraindicated; Micromedex reported 14% contraindicated and 59% major; Drugs.com reported 21% major. The Fleiss’ kappa agreements were fair to poor among the three databases, higher agreement was observed for DDIs classified as severe. This study highlights the need to harmonize the evaluation and interpretation of DDI risk in order to produce standardized information to support prescribers.

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.

Clinical decision support systems-based interventions to improve medication outcomes: A systematic literature review on features and effects

Background: Clinical decision support systems (CDSSs) interventions were used to improve the life quality and safety in patients and also to improve practitioner performance, especially in the field of medication. Therefore, the aim of the paper was to summarize the available evidence on the impact, outcomes and significant factors on the implementation of CDSS in the field of medicine. Methods: This study is a systematic literature review. PubMed, Cochrane Library, Web of Science, Scopus, EMBASE, and ProQuest were investigated by 15 February 2017. The inclusion requirements were met by 98 papers, from which 13 had described important factors in the implementation of CDSS, and 86 were medicated-related. We categorized the system in terms of its correlation with medication in which a system was implemented, and our intended results were examined. In this study, the process outcomes (such as; prescription, drug-drug interaction, drug adherence, etc.), patient outcomes, and significant factors affecting the implementation of CDSS were reviewed. Results: We found evidence that the use of medication-related CDSS improves clinical outcomes. Also, significant results were obtained regarding the reduction of prescription errors, and the improvement in quality and safety of medication prescribed. Conclusion: The results of this study show that, although computer systems such as CDSS may cause errors, in most cases, it has helped to improve prescribing, reduce side effects and drug interactions, and improve patient safety. Although these systems have improved the performance of practitioners and processes, there has not been much research on the impact of these systems on patient outcomes.

Assisted prescribing: Clinical decision support with MedSafety Scan now available

Too often, adverse events due to prescription medications are a cause of death and disability. Many of these events could be prevented, but most efforts to do so have had limited success, mainly due to the challenges of having the information that is necessary for safe prescribing available at the time when prescriptions are being written. Hospital-based Clinical Decision Support (CDS) systems are being developed to manage this information, identify at- risk patients, and help mitigate their risk of medication-induced harm. AZCERT, a non-profit created in 1999 with federal funding has helped hospitals develop these systems and has released an internet-based CDS program to assist in the safe prescribing of medications. This CDS program, MedSafety Scan, can be customized for any clinical venue and is available as an open-source program for all healthcare providers at www.medsafetyscan.org.

Appropriateness of Overridden Alerts in Computerized Physician Order Entry: Systematic Review

BACKGROUND

The clinical decision support system (CDSS) has become an indispensable tool for reducing medication errors and adverse drug events. However, numerous studies have reported that CDSS alerts are often overridden. The increase in override rates has raised questions about the appropriateness of CDSS application along with concerns about patient safety and quality of care.

OBJECTIVE

The aim of this study was to conduct a systematic review to examine the override rate, the reasons for the alert override at the time of prescribing, and evaluate the appropriateness of overrides.

METHODS

We searched electronic databases, including Google Scholar, PubMed, Embase, Scopus, and Web of Science, without language restrictions between January 1, 2000 and March 31, 2019. Two authors independently extracted data and crosschecked the extraction to avoid errors. The quality of the included studies was examined following Cochrane guidelines.

RESULTS

We included 23 articles in our systematic review. The range of average override alerts was 46.2%-96.2%. An average of 29.4%-100% of the overrides alerts were classified as appropriate, and the rate of appropriateness varied according to the alert type (drug-allergy interaction 63.4%-100%, drug-drug interaction 0%-95%, dose 43.9%-88.8%, geriatric 14.3%-57%, renal 27%-87.5%). The interrater reliability for the assessment of override alerts appropriateness was excellent (kappa=0.79-0.97). The most common reasons given for the override were "will monitor" and "patients have tolerated before."

CONCLUSIONS

The findings of our study show that alert override rates are high, and certain categories of overrides such as drug-drug interaction, renal, and geriatric were classified as inappropriate. Nevertheless, large proportions of drug duplication, drug-allergy, and formulary alerts were appropriate, suggesting that these groups of alerts can be primary targets to revise and update the system for reducing alert fatigue. Future efforts should also focus on optimizing alert types, providing clear information, and explaining the rationale of the alert so that essential alerts are not inappropriately overridden.

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.

Prevalence of drug interactions in hospitalised elderly patients: a systematic review

BACKGROUND

The prevalence of drug-drug interactions (DDIs) in hospital settings is variable, and elderly patients are considered a high risk population for DDIs. There are no systematic reviews describing the prevalence of DDIs in hospitalised elderly patients.

OBJECTIVES

To assess and summarise the available data on the prevalence of DDIs in hospitalised elderly patients and to describe which drugs, drug classes and drug combinations are most commonly involved in DDIs.

DATA SOURCE

A systematic electronic literature search was conducted on Medline/PubMed, Embase, Lilacs, SciElo, Web of Science, Cinahl, Scopus, Cochrane, OpenGrey, Capes Thesis Bank, OasisBR, OpenAire and abstracts from scientific events, without limitation on language or period of publication. Study selection was completed on 21 September 2018.

STUDY ELIGIBILITY CRITERIA, PARTICIPANTS AND INTERVENTIONS

Original observational studies that reported the prevalence of actual or potential DDIs during hospitalisation in patients aged 60 years or older were included. The main outcome measure was prevalence of DDIs and number of DDIs per patient. Subgroup analysis was performed in studies that reported the prevalence of DDIs in geriatric units.

STUDY APPRAISAL AND SYNTHESIS METHODS

Study quality was assessed using the Agency for Healthcare Research and Quality methodological checklist for cross sectional and prevalence studies.

RESULTS

34 studies were included, involving 9577 patients. The prevalence of DDIs ranged from 8.34% to 100%. In studies conducted in geriatric units, the prevalence ranged from 80.5% to 90.5%. The number of DDIs per patient ranged from 1.2 to 30.6. Single drugs most commonly involved in DDIs were furosemide, captopril, warfarin and dipyrone. Drug classes mostly involved were potassium sparing diuretics and angiotensin converting enzyme inhibitors.

LIMITATIONS

The main limitation is the heterogeneity between the included studies that precluded a meta-analysis. Several different methods were used to identify DDIs, majorly, and potential DDIs. Few studies have reported measures to control the quality of the collected data.

CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS

The prevalence of DDIs ranged widely, and the variation may reflect differences in the conditions of the elderly patients and level of attention (or complexity of care), as well as methodological differences, especially the methods and/or software used to identify DDIs.

SYSTEMATIC REVIEW REGISTRATION NUMBER

CRD42018096720.

Drug interaction resources: mind the gaps

Drug interactions can lead to significant toxicity or loss of clinical effect. The risks increase with the number of drugs the patient takes General and specialised drug interaction resources are available. Access to up-to-date electronic resources is encouraged There are gaps in the information on interactions for new drugs, those with complicated metabolism and drugs with limited use. It may be necessary to use multiple resources to find the information When assessing information about interactions, clinicians should evaluate the relevance for each patient. In high-risk situations, expert advice can be valuable Clinicians should report new or unusual drug interactions to the Therapeutic Goods Administration

A systematic approach to optimize electronic health record medication alerts in a health system

PURPOSE

The effectiveness of a systematic, streamlined approach to optimize drug-drug interaction alerts in an electronic health record for a health system was studied.

METHODS

An 81-week quasi-experimental study was conducted to evaluate interventions made to medication-related clinical decision-support (CDS) alerts. Medication-related CDS alerts were systematically reduced using a multi disciplinary healthcare committee. The primary endpoint was weekly overall, modification, and acknowledgement rates of medication alerts after drug-drug interaction reclassification. Secondary endpoints included sub analysis of types of medication alerts (drug-drug interaction and duplicate therapy alerts) and alert use by providers (pharmacist and prescribers). Data was analyzed using interrupted time series regression analysis.

RESULTS

After implementation of the new alert system, total number of weekly inpatient alerts decreased from 68,900 (66,300-70,900) and 50,300 (48,600-53,600) in the postintervention period (p < 0.001). The perentage of alerts acknowledged weekly increased from 11.8% (IQR, 11.4-12.1%) in the preintervention period to 13.7% (IQR, 13.3-14.0%) in the postintervention period (p < 0.001). The percentage of alerts that were modified also increased from 5.0% (IQR, 4.9-5.3%) in the preintervention period to 7.3% (IQR, 7.0-7.6%) in the postintervention period (p < 0.001). Both increases were primarily seen with pharmacists versus other healthcare professionals (p < 0.001).

CONCLUSION

A committee-led systematic approach to optimizing drug-drug interactions facilitated a significant decrease in the overall number of alerts and an increase in both medication alert acknowledgement and modification rates.

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.

The Effect of Eliminating Intermediate Severity Drug-Drug Interaction Alerts on Overall Medication Alert Burden and Acceptance Rate

OBJECTIVE

This study aimed to determine the effects of reducing the number of drug-drug interaction (DDI) alerts in an order entry system.

METHODS

Retrospective pre-post analysis at an urban medical center of the rates of medication alerts and alert acceptance during a 5-month period before and 5-month period after the threshold for firing DDI alerts was changed from "intermediate" to "severe." To ensure that we could determine varying response to each alert type, we took an in-depth look at orders generating single alerts.

RESULTS

Before the intervention, 241,915 medication orders were placed, of which 25.6% generated one or more medication alerts; 5.3% of the alerts were accepted. During the postintervention period, 245,757 medication orders were placed of which 16.0% generated one or more medication alerts, a 37.5% relative decrease in alert rate (95% confidence interval [CI]: -38.4 to -36.8%), but only a 9.6% absolute decrease (95% CI: -9.4 to -9.9%). 7.4% of orders generating alerts were accepted postintervention, a 39.6% relative increase in acceptance rate (95% CI: 33.2-47.2%), but only a 2.1% absolute increase (95% CI: 1.8-2.4%). When only orders generating a single medication alert were considered, there was a 69.1% relative decrease in the number of orders generating DDI alerts, and an 85.7% relative increase in the acceptance rate (95% CI: 58.6-126.2%), though only a 1.8% absolute increase (95% CI: 1.3-2.3%).

CONCLUSION

Eliminating intermediate severity DDI alerts resulted in a statistically significant decrease in alert burden and increase in the rate of medication alert acceptance, but alert acceptance remained low overall.

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.

Adverse drug reactions caused by drug-drug interactions in cardiovascular disease patients: introduction of a simple prediction tool using electronic screening database items

Objective: Cardiovascular disease (CVD) drugs have been frequently implicated in adverse drug reaction (ADR)-related hospitalizations. Drug-drug interactions (DDIs) are common preventable cause of ADRs, but the impact of DDIs in the CVD population has not been investigated. Hence, the primary aim of the study was to identify DDIs associated with ADRs in CVD patients at hospital admission. The second aim was to develop a simple tool to identify high-risk patients for DDI-related adverse events. Methods: An observational study was conducted on the Cardiology Ward of University Clinical Hospital Center. Data were obtained from medical charts. A clinical panel identified DDIs implicated in ADRs, using LexiInteract database and Drug Interaction Probability Scale. Statistics were performed using PASW 22 (SPSS Inc.). Results: DDIs contributed to hospital admission with a total prevalence of 9.69%. DDI-related ADRs affected mainly cardiac function (heart rate or rhythm, 41.07%); bleeding and effect on blood pressure were equally distributed (17.86%). Non-cardiovascular ADRs were found in 23.21% of DDIs. After admission, 73% of the identified DDIs led to changes in prescription. Prediction ability of calculated DDI adverse event probability scores was rated as good (AUC = 0.80, p < .001). Conclusions: CVD patients are highly exposed to adverse DDIs; about one in ten patients hospitalized with CVD might have a DDI contributing to the hospitalization. Given the high prevalence of CVD, DDI-related harm might be a significant burden worldwide. Identification of patients with high DDI adverse event risk might ease the recognition of DDI-related harm and improve the use of electronic databases in clinical practice.

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.

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.

A retrospective comparison of inappropriate prescribing of psychotropics in three Norwegian nursing homes in 2000 and 2016 with prescribing quality indicators

Background

Inappropriate prescribing of psychotropics is a persistent and prevalent problem in nursing homes. The present study compared inappropriate prescribing of psychotropics in nursing homes 16 years apart with prescribing quality indicators. The purpose was to identify any change in inappropriate prescribing of relevance for medical informatics.

Methods

Three Norwegian nursing homes were audited in 2000 and 2016 with regard to prescribing quality. Psychotropics among 386 patients in 2000, and 416 patients in 2016, included combinations of antidepressants, antipsychotics, anxiolytics-hypnotics, and antiepileptics. Prescribing quality indicators included psychotropic polypharmacy (defined as concurrent use of three or more psychotropics) and potential inappropriate psychotropic substances or combinations. Furthermore, potential clinically relevant psychotropic interactions were classified as pharmacodynamic or pharmacokinetic using an interaction database. The first ranked (most important) interaction in each patient was selected with the following importance of categories in the database; recommended action > documentation > severity. Three levels (from low to high) within each category were used for ranking.

Results

From 2000 to 2016, psychotropic polypharmacy increased from 6.2 to 29.6%, potential inappropriate psychotropic substances was reduced from 17.9 to 11.3% and potential inappropriate psychotropic combinations increased from 7.8 to 27.9%. Changes in polypharmacy and combinations were predominantly associated with prescribing of anxiolytics-hypnotics. Sixty-three patients (16.3%) had psychotropic interactions in 2000 increasing to 146 patients (35.1%) in 2016. The increase in interactions was associated with prescribing of antidepressants. First ranked interactions, more than 60% of all interactions in both years, were increasingly pharmacodynamic, from 69.9 to 91.0%. Interactions in 2016 were associated with a lower level of recommended action and documentation, but not severity compared to 2000. The inappropriate prescribing of antipsychotics and antiepileptics was reduced in 2016 compared to 2000.

Conclusions

Using prescribing quality indicators we observed the importance of antidepressants and anxiolytics-hypnotics for inappropriate prescribing in 2016 while the role of antipsychotics and antiepileptics were reduced compared to 2000. A change to mainly pharmacodynamic interactions that lack good documentation was also observed. The present findings can be used for medical informatics-based approaches to address specific problems with prescribing, and prescribing quality indicators, in Norwegian nursing homes.

The Case-Crossover Design for Drug-Drug Interactions: Considerations for Implementation

BACKGROUND

The case-crossover design may be useful for evaluating the clinical impact of drug-drug interactions in electronic healthcare data; however, experience with the design in this context is limited.

METHODS

Using US healthcare claims data (1994-2013), we evaluated two examples of interacting drugs with prior evidence of harm: (1) cytochrome P450 (CYP)3A4-metabolized statins + clarithromycin or erythromycin and rhabdomyolysis; and (2) clopidogrel + fluoxetine or fluvoxamine and ischemic events. We conducted case-crossover analyses with (1) a three-parameter model with a product term and a six-parameter saturated model that distinguished initiation order of the two drugs; and (2) with or without active comparators.

RESULTS

In the statin example, the three-parameter model produced estimates consistent with prior evidence with the active comparator (product term odds ratio [OR] = 2.05, 95% confidence interval [CI] = 1.00, 4.23) and without (OR = 1.99, 95% CI = 1.04, 3.81). In the clopidogrel example, this model produced results opposite of expectation (OR = 0.78, 95% = 0.68, 0.89), but closer to what was observed in prior studies when active comparator was used (OR = 1.03, 95% CI = 0.90, 1.19). The saturated model revealed heterogeneity of estimates across strata and considerable confounding; strata with concordant clopidogrel exposure likely produced the least biased estimates.

CONCLUSION

The three-parameter model assumes a common drug-drug interaction effect, whereas the saturated model is useful for identifying potential effect heterogeneity or differential confounding across strata. Restriction to certain strata or use of an active comparator may be necessary in the presence of within-person confounding.

A comparison of potential psychiatric drug interactions from six drug interaction database programs

Harmful drug-drug interactions (DDI) frequently include psychiatric drugs. Drug interaction database programs are viewed as a primary tool to alert physicians of potential DDI, but may provide different results as there is no standard to define DDI. This study compared the category of potential DDI provided by 6 commercial drug interaction database programs (3 subscription, 3 open access) for 100 drug interaction pairs. The pairs involved 94 different drugs; 67 included a psychiatric and non-psychiatric drug, and 33 included two psychiatric drugs. The category assigned to the potential DDI by the 6 programs was compared using percent agreement and Fleiss' kappa interrater reliability measure. The overall percent agreement for the category of potential DDI for the 100 drug interaction pairs was 66%. The Fleiss kappa overall interrater agreement was fair. The kappa agreement was substantial for interaction pairs with any severe category rating, and fair for interaction pairs with any major category rating. The category of potential DDI for drug interaction pairs including psychiatric drugs often differs among drug interaction database programs. Modern technology allows easy access to several interaction database programs. When assistance from a drug interaction database program is needed, the physician should check more than one program.

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.

Optimizing Drug-Drug Interaction Alerts Using a Multidimensional Approach

OBJECTIVES

Excessive alerts are a common concern associated with clinical decision support systems that monitor drug-drug interactions (DDIs). To reduce the number of low-value interruptive DDI alerts at our hospital, we implemented an iterative, multidimensional quality improvement effort, which included an interdisciplinary advisory group, alert metrics, and measurement of perceived clinical value.

METHODS

Alert data analysis indicated that DDIs were the most common interruptive medication alert. An interdisciplinary alert advisory group was formed to provide expert advice and oversight for alert refinement and ongoing review of alert data. Alert data were categorized into drug classes and analyzed to identify DDI alerts for refinement. Refinement strategies included alert suppression and modification of alerts to be contextually aware.

RESULTS

On the basis of historical analysis of classified DDI alerts, 26 alert refinements were implemented, representing 47% of all alerts. Alert refinement efforts resulted in the following substantial decreases in the number of interruptive DDI alerts: 40% for all clinicians (22.9-14 per 100 orders) and as high as 82% for attending physicians (6.5-1.2 per 100 orders). Two patient safety events related to alert refinements were reported during the project period.

CONCLUSIONS

Our quality improvement effort refined 47% of all DDI alerts that were firing during historical analysis, significantly reduced the number of DDI alerts in a 54-week period, and established a model for sustained alert refinements.

Detection of drug-drug interactions through data mining studies using clinical sources, scientific literature and social media

Drug-drug interactions (DDIs) constitute an important concern in drug development and postmarketing pharmacovigilance. They are considered the cause of many adverse drug effects exposing patients to higher risks and increasing public health system costs. Methods to follow-up and discover possible DDIs causing harm to the population are a primary aim of drug safety researchers. Here, we review different methodologies and recent advances using data mining to detect DDIs with impact on patients. We focus on data mining of different pharmacovigilance sources, such as the US Food and Drug Administration Adverse Event Reporting System and electronic health records from medical institutions, as well as on the diverse data mining studies that use narrative text available in the scientific biomedical literature and social media. We pay attention to the strengths but also further explain challenges related to these methods. Data mining has important applications in the analysis of DDIs showing the impact of the interactions as a cause of adverse effects, extracting interactions to create knowledge data sets and gold standards and in the discovery of novel and dangerous DDIs.

Involvement of CYP4F2 in the Metabolism of a Novel Monophosphate Ester Prodrug of Gemcitabine and Its Interaction Potential In Vitro

Compound-3 is an oral monophosphate prodrug of gemcitabine. Previous data showed that Compound-3 was more potent than gemcitabine and it was orally active in a tumor xenograft model. In the present study, the metabolism of Compound-3 was investigated in several well-known in vitro matrices. While relatively stable in human and rat plasma, Compound-3 demonstrated noticeable metabolism in liver and intestinal microsomes in the presence of NADPH and human hepatocytes. Compound-3 could also be hydrolyzed by alkaline phosphatase, leading to gemcitabine formation. Metabolite identification using accurate mass- and information-based scan techniques revealed that Compound-3 was subjected to sequential metabolism, forming alcohol, aldehyde and carboxylic acid metabolites, respectively. Results from reaction phenotyping studies indicated that cytochrome P450 4F2 (CYP4F2) was a key CYP isozyme involved in Compound-3 metabolism. Interaction assays suggested that CYP4F2 activity could be inhibited by Compound-3 or an antiparasitic prodrug pafuramidine. Because CYP4F2 is a key CYP isozyme involved in the metabolism of eicosanoids and therapeutic drugs, clinical relevance of drug-drug interactions mediated via CYP4F2 inhibition warrants further investigation.

Updating the Evidence of the Interaction Between Clopidogrel and CYP2C19-Inhibiting Selective Serotonin Reuptake Inhibitors: A Cohort Study and Meta-Analysis

INTRODUCTION

We previously found that patients who initiate clopidogrel while treated with a cytochrome P450 (CYP) 2C19-inhibiting selective serotonin reuptake inhibitor (SSRI) have a higher risk of subsequent ischemic events than patients treated with other SSRIs. It is not known whether initiating an inhibiting SSRI while treated with clopidogrel will also increase risk of ischemic events.

OBJECTIVE

The aim of this study was to assess clinical outcomes following initiation of a CYP2C19-inhibiting SSRI versus initiation of other SSRIs among patients treated with clopidogrel and to update existing evidence on the clinical impact of clopidogrel-SSRI interaction.

METHODS

Using five US databases (1998-2013), we conducted a cohort study of clopidogrel initiators who encountered treatment with SSRI during their clopidogrel therapy. Patients were matched by propensity score (PS) and followed for as long as they were exposed to both clopidogrel and index SSRI group. Outcomes were a composite ischemic event (myocardial infarction, ischemic stroke, or a revascularization procedure, whichever came first) and a composite major bleeding event (gastrointestinal bleed or hemorrhagic stroke, whichever came first). Results were combined via random-effects meta-analysis with previous evidence from subjects initiating clopidogrel while on SSRI therapy.

RESULTS

The PS-matched cohort comprised 2346 clopidogrel users starting CYP2C19-inhibiting SSRI therapy and 16,115 starting other SSRIs (mean age 61 years; 59% female). Compared with those treated with a non-inhibiting SSRI, the hazard ratio (HR) for patients treated with a CYP2C19-inhibiting SSRI was 1.07 (95% confidence interval [CI] 0.82-1.40) for the ischemic outcome and 1.00 (95% CI 0.42-2.36) for bleeding. The pooled estimates were 1.11 (95% CI 1.01-1.22) for ischemic events and 0.80 (95% CI 0.55-1.18) for bleeding.

CONCLUSIONS

We observed similar estimates of association between the two studies. The updated evidence still indicates a small decrease in clopidogrel effectiveness associated with concomitant exposure to clopidogrel and CYP2C19-inhibiting SSRIs.

Comparison of three commercial knowledge bases for detection of drug-drug interactions in clinical decision support

Objective

To compare 3 commercial knowledge bases (KBs) used for detection and avoidance of potential drug-drug interactions (DDIs) in clinical practice.

Methods

Drugs in the DDI tables from First DataBank (FDB), Micromedex, and Multum were mapped to RxNorm. The KBs were compared at the clinical drug, ingredient, and DDI rule levels. The KBs were evaluated against a reference list of highly significant DDIs from the Office of the National Coordinator for Health Information Technology (ONC). The KBs and the ONC list were applied to a prescription data set to simulate their use in clinical decision support.

Results

The KBs contained 1.6 million (FDB), 4.5 million (Micromedex), and 4.8 million (Multum) clinical drug pairs. Altogether, there were 8.6 million unique pairs, of which 79% were found only in 1 KB and 5% in all 3 KBs. However, there was generally more agreement than disagreement in the severity rankings, especially in the contraindicated category. The KBs covered 99.8-99.9% of the alerts of the ONC list and would have generated 25 (FDB), 145 (Micromedex), and 84 (Multum) alerts per 1000 prescriptions.

Conclusion

The commercial KBs differ considerably in size and quantity of alerts generated. There is less variability in severity ranking of DDIs than suggested by previous studies. All KBs provide very good coverage of the ONC list. More work is needed to standardize the editorial policies and evidence for inclusion of DDIs to reduce variation among knowledge sources and improve relevance. Some DDIs considered contraindicated in all 3 KBs might be possible candidates to add to the ONC list.

Potential Drug-Drug Interactions Among Critically Ill Pediatric Patients in a Tertiary Pulmonary Center

Patients in the pediatric intensive care unit (PICU) are at increased risk of potential drug-drug interactions (pDDIs) because of the complexity of pharmacotherapy. The current study aimed to assess the rate, pattern, risk factors, and management of pDDIs in the PICU of an academic pulmonary hospital. A prospective observational study was conducted for 6 months. Pharmacotherapy data of PICU-admitted patients were evaluated by a clinical pharmacologist. Interacting drugs, reliability, mechanism, potential outcome, and clinical management of pDDIs were identified using the Lexi-Interact database. Logistic regression was applied to analyze the risk factors that could be associated with the interactions. One hundred and twenty-three medication profiles were evaluated during the study period. Diseases of the respiratory system were the main diagnoses among intensive care unit (ICU)-admitted patients (56.1%). A total of 38.6% of the patients exposed to at least 1 major and/or contraindicated interaction during ICU admission. Most pDDIs occurred through metabolic (35.4%) and additive (34.8%) mechanisms. The existence of pDDIs was significantly associated with the number of prescribed medications. Exposure to pDDIs is frequent in critically ill pediatric patients and related to the number of medications. Daily and close cooperation between clinicians and clinical pharmacologists is recommended to prevent harmful outcomes of DDIs.