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

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.

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.

Drug-drug interactions in patients with acute coronary syndrome across phases of treatment

The objective of this study is to evaluate potential drug-drug interactions (pDDIs) and risk factors for pDDIs in three phases of an acute coronary syndrome (ACS) treatment: from the point of first medical contact to the coronary angiography (first phase), after coronary angiography to the last day of hospitalization (second phase), and at discharge from hospital (third phase). This retrospective observational cohort clinical study was conducted at the Clinic for Cardiology of the Clinical Centre Kragujevac, a public tertiary care hospital in Kragujevac, Serbia. Micromedex^® interaction checker was used to detect pDDIs. This study included 245 ACS patients. All patients were exposed to at least one pDDI in all the phases of treatment. Mean total number of pDDIs was 9.47 ± 6.07, 10.11 ± 6.92, and 6.29 ± 3.66 in first, second, and third phases, respectively. Age, > 6 h from the beginning of the symptoms to admission, primary PCI, STE-ACS, COPD, delirium, hyperlipidemia, hypertension, obesity, systolic blood pressure at admission, TIMI risk score at admission, ALT, LDL, number of physicians who prescribed drugs to a single patient, number of prescribed drugs, and various pharmacological classes increased risk of pDDIs. Mechanical ventilation, dementia, and drug allergy noted in the medical documentation protected against them. Effects of heart failure, diabetes, and aPTT depended on phase of treatment and severity of pDDI. In conclusion, physicians should be vigilant to the possibility of pDDIs in patients harbouring factors that may increase their rate.

A Case-Crossover-Based Screening Approach to Identifying Clinically Relevant Drug-Drug Interactions in Electronic Healthcare Data

We sought to develop a semiautomated screening approach using electronic healthcare data to identify drug-drug interactions (DDIs) that result in clinical outcomes. Using a case-crossover design with 30-day hazard and referent windows, we evaluated codispensed drugs (potential precipitants) in 7,801 patients who experienced rhabdomyolysis while on cytochrome P450 (CYP)3A4-metabolized statins and in 15,147 who experienced bleeding while on dabigatran. Estimates of direct associations between precipitant drugs and outcomes were used to adjust for bias and precipitants' direct effects. The P values were adjusted for multiple testing using the false discovery rate (FDR). From among 460 drugs codispensed with statins, 1 drug (clarithromycin) generated an alert (adjusted odds ratio (OR) 5.83, FDR < 0.05). From among 485 drugs codispensed with dabigatran, 2 drugs (naproxen and enoxaparin, ORs 2.50 and 2.75; FDR < 0.05) generated an alert. All three signals reflected known pharmacologic interactions, confirming the potential of case-crossover-based approaches for DDI screening in electronic healthcare data.

PharmActa: Empowering Patients to Avoid Clinical Significant Drug⁻Herb Interactions

Herbal medicinal products (HMPs) are the subject of increasing interest regarding their benefits for health. However, a serious concern is the potential appearance of clinically significant drug⁻herb interactions in patients. This work provides an overview of drug⁻herb interactions and an evaluation of their clinical significance. We discuss how personalized health services and mobile health applications can utilize tools that provide essential information to patients to avoid drug⁻HMP interactions. There is a specific mention to PharmActa, a dedicated mobile app for personalized pharmaceutical care with information regarding drug⁻HMPs interactions. Several studies over the years have shown that for some HMPs, the potential to present clinically significant interactions is evident, especially for many of the top selling HMPs. Towards that, PharmActa presents how we can improve the way that information regarding potential drug⁻herb interactions can be disseminated to the public. The utilization of technologies focusing on medical information and context awareness introduce a new era in healthcare. The exploitation of eHealth tools and pervasive mobile monitoring technologies in the case of HMPs will allow the citizens to be informed and avoid potential drug⁻HMPs interactions enhancing the effectiveness and ensuring safety for HMPs.

Drug interaction screening in SWOG clinical trials

PURPOSE

The frequency and process for drug interaction (DI) screening at sites enrolling patients into SWOG clinical trials were studied.

METHODS

Survey invitations were e-mailed to 180 SWOG head clinical research associates to determine the frequency of and personnel involved in DI assessment in subjects who were screened for and enrolled in clinical trials at their sites. Descriptive statistics were performed to evaluate the data.

RESULTS

A total of 83 surveys recorded a response to at least 1 question, yielding an overall response rate of 46.1%. At least 72 completed surveys were submitted, for a completion rate of 40.0%. The majority of sites (51%) reported that DI screening only occurred during eligibility assessment when a DI was included in the protocol exclusion criteria. The pharmacist was "always" involved in DI screening during eligibility assessment at 17% of sites. Clinical research coordinators (56%) and research nurses (45%) were the predominant personnel who performed DI screening to assess eligibility for trial enrollment. A subset of sites (3-6%) reported not having access to a pharmacist. Fewer than 10% of sites reported that they "always" use drug information services, websites, resources, or literature searches, though many tools were used "often" or "sometimes" by more than 20% of sites.

CONCLUSION

A survey revealed that DI screening was not being systematically conducted within SWOG clinical trials. When DI screening did occur, it was primarily conducted by clinical research coordinators or study nurses. Pharmacist-led DI screening was not the current practice within SWOG sites surveyed and was precluded by a lack of pharmacists' availability or involvement.

Identifying Common Methods Used by Drug Interaction Experts for Finding Evidence About Potential Drug-Drug Interactions: Web-Based Survey

Background

Preventing drug interactions is an important goal to maximize patient benefit from medications. Summarizing potential drug-drug interactions (PDDIs) for clinical decision support is challenging, and there is no single repository for PDDI evidence. Additionally, inconsistencies across compendia and other sources have been well documented. Standard search strategies for complete and current evidence about PDDIs have not heretofore been developed or validated.

Objective

This study aimed to identify common methods for conducting PDDI literature searches used by experts who routinely evaluate such evidence.

Methods

We invited a convenience sample of 70 drug information experts, including compendia editors, knowledge-base vendors, and clinicians, via emails to complete a survey on identifying PDDI evidence. We created a Web-based survey that included questions regarding the (1) development and conduct of searches; (2) resources used, for example, databases, compendia, search engines, etc; (3) types of keywords used to search for the specific PDDI information; (4) study types included and excluded in searches; and (5) search terms used. Search strategy questions focused on 6 topics of the PDDI information—(1) that a PDDI exists; (2) seriousness; (3) clinical consequences; (4) management options; (5) mechanism; and (6) health outcomes.

Results

Twenty participants (response rate, 20/70, 29%) completed the survey. The majority (17/20, 85%) were drug information specialists, drug interaction researchers, compendia editors, or clinical pharmacists, with 60% (12/20) having >10 years’ experience. Over half (11/20, 55%) worked for clinical solutions vendors or knowledge-base vendors. Most participants developed (18/20, 90%) and conducted (19/20, 95%) search strategies without librarian assistance. PubMed (20/20, 100%) and Google Scholar (11/20, 55%) were most commonly searched for papers, followed by Google Web Search (7/20, 35%) and EMBASE (3/20, 15%). No respondents reported using Scopus. A variety of subscription and open-access databases were used, most commonly Lexicomp (9/20, 45%), Micromedex (8/20, 40%), Drugs@FDA (17/20, 85%), and DailyMed (13/20, 65%). Facts and Comparisons was the most commonly used compendia (8/20, 40%). Across the 6 attributes of interest, generic drug name was the most common keyword used. Respondents reported using more types of keywords when searching to identify the existence of PDDIs and determine their mechanism than when searching for the other 4 attributes (seriousness, consequences, management, and health outcomes). Regarding the types of evidence useful for evaluating a PDDI, clinical trials, case reports, and systematic reviews were considered relevant, while animal and in vitro data studies were not.

Conclusions

This study suggests that drug interaction experts use various keyword strategies and various database and Web resources depending on the PDDI evidence they are seeking. Greater automation and standardization across search strategies could improve one’s ability to identify PDDI evidence. Hence, future research focused on enhancing the existing search tools and designing recommended standards is needed.

Determination of Potential Drug-Drug Interactions Using Various Software Programs in a Community Pharmacy Setting

Objectives

The aim of the present study was to compare various software programs in detecting potential drug-drug interactions in a community pharmacy setting.

Materials and Methods

Details of prescriptions were collected from 50 community pharmacies located in İstanbul in March and April 2015 (two days per week). From each pharmacy, the first 20 prescriptions that included more than one drug were collected to evaluate potential drug-drug interactions. The following software programs were utilized to detect potential drug-drug interactions: micromedexsolutions.com, medscape.com, and drugs.com. The number of potential interactions detected by the software programs was determined.

Results

At least one potential drug-drug interaction was detected in 39.2% of the 1000 prescriptions by one of the software programs. According to the rates of total drug-drug interactions gathered from various software programs, these programs gave the following results: medscape.com 33.3%, drugs.com 31.3%, and micromedexsolutions.com 21.2%.

Conclusion

After comparing different software programs, the potential drug-drug interactions found by the programs proved to be different. Therefore, we recommend that pharmacists confirm with a different program before making a decision when they detect clinically significant potential drug-drug interactions.

Evaluation of Clinical Relevance of Drug-Drug Interaction Alerts Prior to Implementation

INTRODUCTION

Drug-drug interaction (DDI) alerts are often implemented in the hospital computerized provider order entry (CPOE) systems with limited evaluation. This increases the risk of prescribers experiencing too many irrelevant alerts, resulting in alert fatigue. In this study, we aimed to evaluate clinical relevance of alerts prior to implementation in CPOE using two common approaches: compendia and expert panel review.

METHODS

After generating a list of hypothetical DDI alerts, that is, alerts that would have been triggered if DDI alerts were operational in the CPOE, we calculated the agreement between multiple drug interaction compendia with regards to the severity of these alerts. A subset of DDI alerts (n = 13), with associated patient information, were presented to an expert panel to reach a consensus on whether each alert should be included in the CPOE.

RESULTS

There was poor agreement between compendia in their classifications of DDI severity (Krippendorff's α: 0.03; 95% confidence interval: -0.07 to 0.14). Only 10% of DDI alerts were classed as severe by all compendia. On the other hand, the panel reached consensus on 12 of the 13 alerts that were presented to them regarding whether they should be included in the CPOE.

CONCLUSION

Using an expert panel and allowing them to discuss their views openly likely resulted in high agreement on what alerts should be included in a CPOE system. Presenting alerts in the context of patient cases allowed panelists to identify the conditions under which alerts were clinically relevant. The poor agreement between compendia suggests that this methodology may not be ideal for the evaluation of DDI alerts. Performing preimplementation review of DDI alerts before they are enabled provides an opportunity to minimize the risk of alert fatigue before prescribers are exposed to false-positive alerts.

Preferences of Patients and Pharmacists with Regard to the Management of Drug-Drug Interactions: A Choice-Based Conjoint Analysis

Introduction

The management of drug–drug interactions (DDIs) is a complex process in which risk–benefit assessments should be combined with the patient’s perspective.

Objective

The aim of this study was to determine patients’ and pharmacists’ preferences regarding DDI management.

Methods

We conducted a choice-based conjoint survey about a fictitious DDI concerning the combination of a cardiovascular drug and an antibiotic for pneumonia. Patients and pharmacists had to choose 12 times between two management options. The options were described by five attributes, including risk, benefit and practical consequences. Each attribute could have two different levels, which were varied over the choice tasks. Latent class analysis was used to identify potential classes of respondents with distinct patterns of similar preferences.

Results

In total, 298 patients and 178 pharmacists completed the questionnaire. The latent class model for both patients and pharmacists resulted in three classes. For patients, in one class the most importance was attached to avoiding switch of medication (class probability 20%), in a second class to fewer adverse events (41%), and in a third class to blood sampling (39%). For pharmacists, again one class attached the highest importance to avoiding switch of medication (31%). The other classes gave priority to curing pneumonia (31%) and avoiding blood sampling (38%).

Conclusion

The results showed diverging preferences regarding DDI management among both patients and pharmacists. Different groups attached different value to risk and benefit versus practical considerations. Awareness of existing variability in preferences among and between pharmacists and patients is a step towards shared decision making in DDI management.

Electronic supplementary material

The online version of this article (doi:10.1007/s40264-017-0601-7) contains supplementary material, which is available to authorized users.

Predicting potential drug-drug interactions on topological and semantic similarity features using statistical learning

Drug-drug interaction (DDI) is a change in the effect of a drug when patient takes another drug. Characterizing DDIs is extremely important to avoid potential adverse drug reactions. We represent DDIs as a complex network in which nodes refer to drugs and links refer to their potential interactions. Recently, the problem of link prediction has attracted much consideration in scientific community. We represent the process of link prediction as a binary classification task on networks of potential DDIs. We use link prediction techniques for predicting unknown interactions between drugs in five arbitrary chosen large-scale DDI databases, namely DrugBank, KEGG, NDF-RT, SemMedDB, and Twosides. We estimated the performance of link prediction using a series of experiments on DDI networks. We performed link prediction using unsupervised and supervised approach including classification tree, k-nearest neighbors, support vector machine, random forest, and gradient boosting machine classifiers based on topological and semantic similarity features. Supervised approach clearly outperforms unsupervised approach. The Twosides network gained the best prediction performance regarding the area under the precision-recall curve (0.93 for both random forests and gradient boosting machine). The applied methodology can be used as a tool to help researchers to identify potential DDIs. The supervised link prediction approach proved to be promising for potential DDIs prediction and may facilitate the identification of potential DDIs in clinical research.

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.

The prevalence of major drug-drug interactions in older adults with cancer and the role of clinical decision support software

OBJECTIVES

Drug-drug interactions (DDIs) represent an escalating concern for older adults attributed to polypharmacy, multi-morbidity and organ dysfunction. Few studies have evaluated the prevalence of major DDIs and the variability between DDI detection software which confuses management.

MATERIALS AND METHODS

Prevalence of major DDIs was examined as a secondary analysis of outpatients aged ≥65 years. Demographic and clinical information was collected from electronic health records including age, sex, race, cancer type, comorbidities, and medications. All DDIs were screened by a clinical pharmacist using Lexi-Interact® and Micromedex®. Major DDIs were defined as Lexi-Interact® category D or X and/or Micromedex® category major or contraindication. Summary statistics of patient characteristics and DDIs were computed.

RESULTS

Our cohort included 142 patients (mean age, 77.7 years; 56% women, 73% Caucasian). The mean medications was 9.8 including 6.7 prescriptions, 2.6 non-prescriptions, and 0.5 herbals. Lexi-Interact® identified 310 major DDIs in 69% of patients (n = 98) with an average of 2.2 DDIs per patient. Micromedex® identified 315 major DDIs in 61% of patients (n = 87) with an average of 2.2 DDIs per patient. DDIs mostly involved opioids, antiplatelets, electrolyte supplements, antiemetics, and antidepressants. Variability existed with the severity rating reporting of the clinical decision support software.

CONCLUSIONS

There was a high prevalence of major DDIs in older adults with cancer. Utilizing clinical decision support software was beneficial for detecting DDIs however, variability existed with severity reporting. Future studies need to identify the relevant DDIs with clinical implications in order to optimize medication safety in this population.

Aspects influencing patients' preferences for the management of drug-drug interactions: A focus group study

OBJECTIVE

The management of drug-drug interactions (DDIs) involves a complex risk-benefit assessment, in which patients' preferences should be taken into account. The aim of this study was to examine the aspects influencing patients' preferences with regard to DDI management options.

METHODS

A qualitative study consisting of five focus groups with patients chronically using cardiovascular drugs was conducted. Key questions concerned preferences regarding DDI management options for a provided fictitious DDI. Thematic analysis of the verbatim transcripts was performed.

RESULTS

Despite their limited knowledge with respect to DDIs, patients easily chose a management option for the presented DDI. When additional information was provided, preferences showed to be fluid. Ten interdependent aspects influencing preferences were derived from patients' argumentations: risk perception, fear, acceptance of uncertainty, openness to change, willingness to take risk, trust in health care professional, financial & practical burdens, health condition, experience, and knowledge & assumptions.

CONCLUSION

Patients' preferences regarding DDI management options were often determined by provided information. Preferences were dependent on an interplay of diverse aspects.

PRACTICE IMPLICATIONS

Tailored provision of information and individualized counseling is needed for active patient involvement in DDI decision making.

Consumer Mobile Apps for Potential Drug-Drug Interaction Check: Systematic Review and Content Analysis Using the Mobile App Rating Scale (MARS)

BACKGROUND

General consumers can now easily access drug information and quickly check for potential drug-drug interactions (PDDIs) through mobile health (mHealth) apps. With aging population in Canada, more people have chronic diseases and comorbidities leading to increasing numbers of medications. The use of mHealth apps for checking PDDIs can be helpful in ensuring patient safety and empowerment.

OBJECTIVE

The aim of this study was to review the characteristics and quality of publicly available mHealth apps that check for PDDIs.

METHODS

Apple App Store and Google Play were searched to identify apps with PDDI functionality. The apps' general and feature characteristics were extracted. The Mobile App Rating Scale (MARS) was used to assess the quality.

RESULTS

A total of 23 apps were included for the review-12 from Apple App Store and 11 from Google Play. Only 5 of these were paid apps, with an average price of $7.19 CAD. The mean MARS score was 3.23 out of 5 (interquartile range 1.34). The mean MARS scores for the apps from Google Play and Apple App Store were not statistically different (P=.84). The information dimension was associated with the highest score (3.63), whereas the engagement dimension resulted in the lowest score (2.75). The total number of features per app, average rating, and price were significantly associated with the total MARS score.

CONCLUSIONS

Some apps provided accurate and comprehensive information about potential adverse drug effects from PDDIs. Given the potentially severe consequences of incorrect drug information, there is a need for oversight to eliminate low quality and potentially harmful apps. Because managing PDDIs is complex in the absence of complete information, secondary features such as medication reminder, refill reminder, medication history tracking, and pill identification could help enhance the effectiveness of PDDI apps.

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.

The prevalence and preventability of potentially relevant drug-drug interactions in patients admitted for cardiovascular diseases: A cross-sectional study

AIM

The aim was to describe the type and prevalence of potentially relevant drug-drug interactions (pDDIs) in a population of patients admitted for cardiovascular diseases (CVD), and management strategies for reducing the occurrence of pDDIs.

METHODS

A retrospective cross-sectional study was performed on Cardiology ward of University Clinical Hospital Center in Belgrade, Serbia. A total of 527 patients, with more than one prescription during hospital stay, were enrolled in this study. Data were obtained from medical records. LexiInteract was used as the screening tool.

RESULTS

At least one potentially relevant pDDI was identified in 83.9% of patients. Occurrence was significantly more prevalent in patients with higher number of drugs, multimorbidity, longer length of stay, arrhythmia, heart failure, infectious and respiratory disease. About 13% of pDDIs exposures were accompanied with concurrent renal or liver disease, as an additional risk for DDI manifestation. Among CVD, patients with a history of myocardial infarction possessed the highest additional risk. The most common potential clinical outcome was the effect on cardiovascular system 48.5%, renal function and/or potassium 22.3%, bleeding 9.5%, impaired glucose control 6.8% and digoxin toxicity 4.6%. Main management strategies to avoid X or D class included using paracetamol instead of NSAID or alternative NSAID (38%), alternative antibiotic or antifungal (20.4%), H₂ receptor antagonist instead of PPI (8.3%), avoiding therapeutic duplication (7.3%), and alternative HMG-CoA reductase inhibitor (7%). Heart rate, blood pressure, electrolytes/potassium and blood glucose could have been employed in monitoring for potential consequence of 72.2% C class pDDIs.

CONCLUSIONS

Use of drug interaction screening tools can be beneficial risk mitigation strategy for potentially relevant pDDIs in CVD patients. DDI screening software could be linked to the patient's laboratory results or clinical data regarding renal or liver function, as an approach to reinforce DDIs alert quality.

Potential Drug-Drug and Herb-Drug Interactions in Patients With Cancer: A Prospective Study of Medication Surveillance

Purpose:

Patients with cancer frequently use herbal supplements and concomitant medications along with antineoplastic agents. These patients are at high risk of herb-drug interactions (HDIs) and drug-drug interactions (DDIs). We aimed to determine clinically relevant DDIs and HDIs leading to pharmaceutical intervention.

Methods:

Patients starting a new anticancer therapy were asked to complete a questionnaire to identify concomitant use of any over-the-counter drug or herbal supplement. Potential DDIs and HDIs were identified using two different databases. If a potentially clinically relevant DDI was recognized by the clinical pharmacist, a notification was sent to the prescribing oncologist, who decided whether to carry out a suggested intervention. Regression analyses were performed to identify variables associated with clinically relevant DDIs.

Results:

A total of 149 patients were included in this study, with 36 potentially clinically relevant DDIs identified in 26 patients (17.4%; 95% CI, 11.3% to 23.5%), all of them leading to therapy modifications. In total, four patients (2.7%; 95% CI, 0.1% to 5.3%) had experienced clinical consequences from DDIs at the time of pharmacist notification. Additionally, 84 patients (56.4%; 95% CI, 48.4% to 64.4%) reported using concurrent herbal supplements, and 122 possible HDIs were detected. Concomitant use of two or more drugs was independently associated with high risk of a clinically significant DDI (odds ratio, 2.53; 95% CI, 1.08 to 5.91; P = .03).

Conclusion:

Potentially clinically relevant DDIs and possible HDIs were frequently detected in this prospective study. A multidisciplinary approach is required to identify and avoid potentially harmful combinations with anticancer therapy.

Information needs for making clinical recommendations about potential drug-drug interactions: a synthesis of literature review and interviews

BACKGROUND

Drug information compendia and drug-drug interaction information databases are critical resources for clinicians and pharmacists working to avoid adverse events due to exposure to potential drug-drug interactions (PDDIs). Our goal is to develop information models, annotated data, and search tools that will facilitate the interpretation of PDDI information. To better understand the information needs and work practices of specialists who search and synthesize PDDI evidence for drug information resources, we conducted an inquiry that combined a thematic analysis of published literature with unstructured interviews.

METHODS

Starting from an initial set of relevant articles, we developed search terms and conducted a literature search. Two reviewers conducted a thematic analysis of included articles. Unstructured interviews with drug information experts were conducted and similarly coded. Information needs, work processes, and indicators of potential strengths and weaknesses of information systems were identified.

RESULTS

Review of 92 papers and 10 interviews identified 56 categories of information needs related to the interpretation of PDDI information including drug and interaction information; study design; evidence including clinical details, quality and content of reports, and consequences; and potential recommendations. We also identified strengths/weaknesses of PDDI information systems.

CONCLUSIONS

We identified the kinds of information that might be most effective for summarizing PDDIs. The drug information experts we interviewed had differing goals, suggesting a need for detailed information models and flexible presentations. Several information needs not discussed in previous work were identified, including temporal overlaps in drug administration, biological plausibility of interactions, and assessment of the quality and content of reports. Richly structured depictions of PDDI information may help drug information experts more effectively interpret data and develop recommendations. Effective information models and system designs will be needed to maximize the utility of this information.

User-centered design improves the usability of drug-drug interaction alerts: Experimental comparison of interfaces

Clinical Decision Support Systems can alert health professionals about drug interactions when they prescribe medications. The Hospital Italiano de Buenos Aires in Argentina developed an electronic health record with drug-drug interaction alerts, using traditional software engineering techniques and requirements. Despite enhancing the drug-drug interaction knowledge database, the alert override rate of this system was very high. We redesigned the alert system using user-centered design (UCD) and participatory design techniques to enhance the drug-drug interaction alert interface. This paper describes the methodology of our UCD. We used crossover method with realistic, clinical vignettes to compare usability of the standard and new software versions in terms of efficiency, effectiveness, and user satisfaction. Our study showed that, compared to the traditional alert system, the UCD alert system was more efficient (alerts faster resolution), more effective (tasks completed with fewer errors), and more satisfying. These results indicate that UCD techniques that follow ISO 9241-210 can generate more usable alerts than traditional design.

Recommendations for selecting drug-drug interactions for clinical decision support

PURPOSE

Recommendations for including drug-drug interactions (DDIs) in clinical decision support (CDS) are presented.

SUMMARY

A conference series was conducted to improve CDS for DDIs. A work group consisting of 20 experts in pharmacology, drug information, and CDS from academia, government agencies, health information vendors, and healthcare organizations was convened to address (1) the process to use for developing and maintaining a standard set of DDIs, (2) the information that should be included in a knowledge base of standard DDIs, (3) whether a list of contraindicated drug pairs can or should be established, and (4) how to more intelligently filter DDI alerts. We recommend a transparent, systematic, and evidence-driven process with graded recommendations by a consensus panel of experts and oversight by a national organization. We outline key DDI information needed to help guide clinician decision-making. We recommend judicious classification of DDIs as contraindicated and more research to identify methods to safely reduce repetitive and less-relevant alerts.

CONCLUSION

An expert panel with a centralized organizer or convener should be established to develop and maintain a standard set of DDIs for CDS in the United States. The process should be evidence driven, transparent, and systematic, with feedback from multiple stakeholders for continuous improvement. The scope of the expert panel's work should be carefully managed to ensure that the process is sustainable. Support for research to improve DDI alerting in the future is also needed. Adoption of these steps may lead to consistent and clinically relevant content for interruptive DDIs, thus reducing alert fatigue and improving patient safety.

Formalizing Evidence Type Definitions for Drug-Drug Interaction Studies to Improve Evidence Base Curation

In this research we aim to demonstrate that an ontology-based system can categorize potential drug-drug interaction (PDDI) evidence items into complex types based on a small set of simple questions. Such a method could increase the transparency and reliability of PDDI evidence evaluation, while also reducing the variations in content and seriousness ratings present in PDDI knowledge bases. We extended the DIDEO ontology with 44 formal evidence type definitions. We then manually annotated the evidence types of 30 evidence items. We tested an RDF/OWL representation of answers to a small number of simple questions about each of these 30 evidence items and showed that automatic inference can determine the detailed evidence types based on this small number of simpler questions. These results show proof-of-concept for a decision support infrastructure that frees the evidence evaluator from mastering relatively complex written evidence type definitions.

Feasibility of Prioritizing Drug-Drug-Event Associations Found in Electronic Health Records

Background and Objective

Several studies have demonstrated the ability to detect adverse events potentially related to multiple drug exposure via data mining. However, the number of putative associations produced by such computational approaches is typically large, making experimental validation difficult. We theorized that those potential associations for which there is evidence from multiple complementary sources are more likely to be true, and explored this idea using a published database of drug–drug-adverse event associations derived from electronic health records (EHRs).

Methods

We prioritized drug–drug-event associations derived from EHRs using four sources of information: (1) public databases, (2) sources of spontaneous reports, (3) literature, and (4) non-EHR drug–drug interaction (DDI) prediction methods. After pre-filtering the associations by removing those found in public databases, we devised a ranking for associations based on the support from the remaining sources, and evaluated the results of this rank-based prioritization.

Results

We collected information for 5983 putative EHR-derived drug–drug-event associations involving 345 drugs and ten adverse events from four data sources and four prediction methods. Only seven drug–drug-event associations (<0.5 %) had support from the majority of evidence sources, and about one third (1777) had support from at least one of the evidence sources.

Conclusions

Our proof-of-concept method for scoring putative drug–drug-event associations from EHRs offers a systematic and reproducible way of prioritizing associations for further study. Our findings also quantify the agreement (or lack thereof) among complementary sources of evidence for drug–drug-event associations and highlight the challenges of developing a robust approach for prioritizing signals of these associations.

Electronic supplementary material

The online version of this article (doi:10.1007/s40264-015-0352-2) contains supplementary material, which is available to authorized users.

Free software to analyse the clinical relevance of drug interactions with antiretroviral agents (SIMARV®) in patients with HIV/AIDS

BACKGROUND

Highly active antiretroviral therapy has extended the expected lifespan of patients with HIV/AIDS. However, the therapeutic benefits of some drugs used simultaneously with highly active antiretroviral therapy may be adversely affected by drug interactions.

OBJECTIVE

The goal was to design and develop a free software to facilitate analysis, assessment, and clinical decision making according to the clinical relevance of drug interactions in patients with HIV/AIDS.

METHODS

A comprehensive Medline/PubMed database search of drug interactions was performed. Articles that recognized any drug interactions in HIV disease were selected. The publications accessed were limited to human studies in English or Spanish, with full texts retrieved. Drug interactions were analyzed, assessed, and grouped into four levels of clinical relevance according to gravity and probability. Software to systematize the information regarding drug interactions and their clinical relevance was designed and developed.

RESULTS

Overall, 952 different references were retrieved and 446 selected; in addition, 67 articles were selected from the citation lists of identified articles. A total of 2119 pairs of drug interactions were identified; of this group, 2006 (94.7%) were drug-drug interactions, 1982 (93.5%) had an identified pharmacokinetic mechanism, and 1409 (66.5%) were mediated by enzyme inhibition. In terms of clinical relevance, 1285 (60.6%) drug interactions were clinically significant in patients with HIV (levels 1 and 2). With this information, a software program that facilitates identification and assessment of the clinical relevance of antiretroviral drug interactions (SIMARV^®) was developed.

CONCLUSIONS

A free software package with information on 2119 pairs of antiretroviral drug interactions was designed and developed that could facilitate analysis, assessment, and clinical decision making according to the clinical relevance of drug interactions in patients with HIV/AIDS.

Clinically important drug-drug interactions in poly-treated elderly outpatients: a campaign to improve appropriateness in general practice

AIMS

The aim was to assess the impact of a campaign for general practitioners (GPs) to reduce clinically-important drug-drug interactions (DDIs) in poly-treated elderly patients.

METHODS

We compiled a list of 53 DDIs and analyzed reimbursed prescriptions dispensed to poly-treated (≥four drugs) elderly (>65 years) patients in the Emilia Romagna region during January 2011-June 2011 (first pre-intervention period), January 2012-June 2012 (second pre-intervention period) and January 2013-June 2013 (post-intervention period). Educational initiatives to GPs were completed in July 2012-December 2012. Pre-test/post-test analysis (2013 vs. 2012) was performed, also using predicted 2013 data (P < 0.01 for statistical significance).

RESULTS

Despite the slight increase in poly-therapy rate (16% in 2013, +1.5% from 2011), we found a stable or slightly declining number of potential DDIs for each elderly poly-treated patient (~1.5). In 2013, 11 DDIs exceeded 5% of prevalence rate: antidiabetics-β-adrenoceptor blockers ranked first (20.3%), followed by ACE Inhibitors (ACEIs)/sartans-non steroidal anti-inflammatory drugs (NSAIDs) (16.4%), diuretics-NSAIDs (13.6%), selective serotonin re-uptake inhibitors (SSRIs)-NSAIDs/acetyl salicylic acid (ASA) (12.7%) and corticosteroids-NSAIDs/ASA (9.7%). A remarkable reduction emerged for NSAID-related DDIs (diuretics-NSAIDs peaked -14.5%; P < 0.01), whereas prevalence of antidiabetics-β-adrenoceptor blockers increased (+7.9%; P < 0.01). When using predicted values, the statistical significance disappeared for antidiabetics-β-adrenoceptor blockers (+1.3%; P = 0.04), whereas it persisted for almost all NSAIDs-related DDIs: ACEIs/sartans-NSAIDs (-3.0%), diuretics-NSAIDs (-6.0%), SSRIs-NSAIDs/ASA (-5.9%).

CONCLUSIONS

This campaign contained the burden of DDIs in poly-treated elderly patients by 1) reducing most prevalent DDIs, especially NSAIDs-related DDIs and 2) balancing the observed rise in poly-therapy rate with stable rate in overall prescriptions of potentially interacting drugs per patient.

Drug interaction databases in medical literature: transparency of ownership, funding, classification algorithms, level of documentation, and staff qualifications. A systematic review

PURPOSE

It is well documented that drug-drug interaction databases (DIDs) differ substantially with respect to classification of drug-drug interactions (DDIs). The aim of this study was to study online available transparency of ownership, funding, information, classifications, staff training, and underlying documentation of the five most commonly used open access English language-based online DIDs and the three most commonly used subscription English language-based online DIDs in the literature.

METHODS

We conducted a systematic literature search to identify the five most commonly used open access and the three most commonly used subscription DIDs in the medical literature. The following parameters were assessed for each of the databases: Ownership, classification of interactions, primary information sources, and staff qualification. We compared the overall proportion of yes/no answers from open access databases and subscription databases by Fisher's exact test-both prior to and after requesting missing information.

RESULTS

Among open access DIDs, 20/60 items could be verified from the webpage directly compared to 24/36 for the subscription DIDs (p = 0.0028). Following personal request, these numbers rose to 22/60 and 30/36, respectively (p < 0.0001). For items within the "classification of interaction" domain, proportions were 3/25 versus 11/15 available from the webpage (P = 0.0001) and 3/25 versus 15/15 (p < 0.0001) available upon personal request.

CONCLUSION

Available information on online available transparency of ownership, funding, information, classifications, staff training, and underlying documentation varies substantially among various DIDs. Open access DIDs had a statistically lower score on parameters assessed.

Toward a complete dataset of drug-drug interaction information from publicly available sources

Although potential drug-drug interactions (PDDIs) are a significant source of preventable drug-related harm, there is currently no single complete source of PDDI information. In the current study, all publically available sources of PDDI information that could be identified using a comprehensive and broad search were combined into a single dataset. The combined dataset merged fourteen different sources including 5 clinically-oriented information sources, 4 Natural Language Processing (NLP) Corpora, and 5 Bioinformatics/Pharmacovigilance information sources. As a comprehensive PDDI source, the merged dataset might benefit the pharmacovigilance text mining community by making it possible to compare the representativeness of NLP corpora for PDDI text extraction tasks, and specifying elements that can be useful for future PDDI extraction purposes. An analysis of the overlap between and across the data sources showed that there was little overlap. Even comprehensive PDDI lists such as DrugBank, KEGG, and the NDF-RT had less than 50% overlap with each other. Moreover, all of the comprehensive lists had incomplete coverage of two data sources that focus on PDDIs of interest in most clinical settings. Based on this information, we think that systems that provide access to the comprehensive lists, such as APIs into RxNorm, should be careful to inform users that the lists may be incomplete with respect to PDDIs that drug experts suggest clinicians be aware of. In spite of the low degree of overlap, several dozen cases were identified where PDDI information provided in drug product labeling might be augmented by the merged dataset. Moreover, the combined dataset was also shown to improve the performance of an existing PDDI NLP pipeline and a recently published PDDI pharmacovigilance protocol. Future work will focus on improvement of the methods for mapping between PDDI information sources, identifying methods to improve the use of the merged dataset in PDDI NLP algorithms, integrating high-quality PDDI information from the merged dataset into Wikidata, and making the combined dataset accessible as Semantic Web Linked Data.

Toward standardized reporting of drug interactions: the READI checklist for anecdotal reports

Anecdotal reports contribute 30% of the literature on adverse drug reactions and interactions. However, the quality of such reports has not been uniformly high. Standardized reporting of clinical studies is of increasing interest, including the CARE guidelines on reporting anecdotal cases in general. Although there are guidelines on evaluating and managing drug-drug interactions, there are none recommending methods for reporting suspected drug interactions. Here, based on published guidelines for reporting suspected adverse drug reactions, I propose a checklist for reporting details of suspected drug interactions, the REporting Anecdotal Drug Interactions (READI) checklist, hoping to stimulate discussion and improve reporting of suspected drug interactions. The checklist includes items relating, among others, to the patient affected, the drugs involved, and the outcome.