Clinically Significant Drug–Drug Interactions Between Oral Anticancer Agents and Nonanticancer Agents: Profiling and Comparison of Two Drug Compendia

Impact of the use of a drug-drug interaction checker on pharmacist interventions involving well-known strong interactors

OBJECTIVES

Several drug-drug interaction (DDI) checkers such as DDI-Predictor have been developed to detect and grade DDIs. DDI-Predictor gives an estimate of the magnitude of an interaction based on the ratio of areas under the curve. The objective of the present study was to analyse the frequencies of DDIs involving well-known strong interactors such as rifampicin and selective serotonin reuptake inhibitors (SSRIs), as reported by a clinical pharmacy team using DDI-Predictor, and the pharmacist intervention acceptance rate.

METHODS

The pharmacist intervention rate and the physician acceptance rate were calculated for DDIs involving rifampicin or the SSRIs fluoxetine, paroxetine, duloxetine and sertraline. The rates were compared with a bilateral χ2 test or Fisher's exact test.

RESULTS

Of the 284 DDIs recorded, 38 (13.4%) involved rifampicin and 78 (27.5%) involved SSRIs. The pharmacist intervention rate differed significantly (68.4% for rifampicin vs 48.8% for SSRIs; p=0.045) but the physician acceptance rate did not (84.6% for rifampicin vs 81.6% for SSRIs; p=1). Pharmaceutical interventions for SSRIs were more frequent when the ratio of the area under the drug concentration versus time curve in DDI-Predictor was >2. Pharmacists were more likely to issue a pharmacist intervention for DDIs involving rifampicin because of a high perceived risk of treatment failure and were less likely to issue a pharmacist intervention for DDIs involving an SSRI, except when the suspected interaction was strong.

CONCLUSIONS

DDI checkers can help pharmacists to manage DDIs involving strong interactors. DDIs involving strong inhibitors versus a strong inducer differ with regard to their intervention and acceptance rates, notably due to the estimation of the magnitude of the DDI.

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

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

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

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

Potential Drug Interactions in Hospitalized Hematologic Cancer Patients: New Update with New Chemotherapy Regimens

Objective

This cross-sectional study aimed to assess the frequency of potential drug-drug interactions (DDIs) and demographic correlates of moderate and major DDIs among patients with hematologic cancer at a referral hematology hospital in Iran.

Methods

In this study, for 6 months, all patients suffering from hematologic cancers admitted to the tertiary oncology hospital, Omid, Isfahan, were considered. Data from all medications prescribed to patients during hospitalization were analyzed using the online Lexicomp® drug interaction checker, recording all interactions classified by risk level: C, D, or X.

Findings

A total of 674 DDIs were detected in 109 patients. The prevalence of treatments with at least one clinically relevant interaction was 95%, being 57.9% for those at level C and 31.5% for levels D and X. According to the frequency, the main interaction was between aprepitant and corticosteroids, followed by the interaction between aprepitant and vincristine. The most common interaction between antineoplastic agents was between doxorubicin and cyclophosphamide. In terms of mechanism, most of DDIs (54.9%) were pharmacodynamics. Only the number of administered medications was associated with DDI occurrence.

Conclusion

Potential DDIs of moderate to major severity are common among patients with hematologic malignancies. This underscores the importance of implementing different strategies to mitigate this clinically significant risk.

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

PURPOSE

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

METHOD

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

RESULTS

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

CONCLUSION

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

Assessing Consistency of Drug-Drug Interaction-Related Information Across Various Drug Information Resources

Background

Information related to drug-drug interactions (DDIs) varies significantly from one drug information (DI) resource to another. These variations pose challenges for healthcare professionals in making the right decisions regarding using some of the drug combinations in needy patients. The objective of this study was to review eight different DI resources for scope, completeness, and consistency of information related to DDIs.

Methodology

A total of eight DI resources, namely, Micromedex^®, Portable Electronic Physician Information Database^©, UpToDate^®, Medscape.com drug interaction checker, Drugs.com drug interaction checker, Stockley’s Drug Interactions (ninth edition, 2010), Drug Interactions Analysis & Management: Facts and Comparisons 2014 (ninth edition, 2014), and the drug interaction appendix of the British National Formulary-76, were compared. Each DI resource was scored for scope by calculating the percentage of interactions that had an entry in each resource. A completeness score was calculated for each resource describing severity, clinical effects, mechanism, and DDI management. The consistency of the information was assessed using Fleiss Kappa (k) score estimated using ReCal3 0.1 (alpha) web service and Statistical Package for the Social Sciences version 24.

Results

The scope score was the highest (100%) for UpToDate^® and Portable Electronic Physician Information Database^©, whereas the completeness score was the highest (100%) for Drug Interaction Analysis & Management: Facts and comparisons 2014. The inter-source reliability scores among the eight different DI sources were poor (k < 0.20, p < 0.05) for documentation of information related to severity, clinical effects, mechanism, and management of DDIs.

Conclusions

Variations in the information cause uncertainty among healthcare professionals concerning interacting drug pairs in clinical practice. This may also increase the possibility of adverse drug outcomes when interacting drug pairs are used in at-risk patients. We recommend comprehensive preventive and management strategies for DDIs depending on a uniform scale of severity and clinical effects across various DI resources.

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 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.

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.

International core outcome set for clinical trials of medication review in multi-morbid older patients with polypharmacy

BACKGROUND

Comparisons of clinical trial findings in systematic reviews can be hindered by the heterogeneity of the outcomes reported. Moreover, the outcomes that matter most to patients might be underreported. A core outcome set can address these issues, as it defines a minimum set of outcomes that should be reported in all clinical trials in a particular area of research. The objective in this study was to develop a core outcome set for clinical trials of medication review in multi-morbid older patients with polypharmacy.

METHODS

Firstly, eligible outcomes were identified through a systematic review of trials of medication review in older patients (≥65 years) and interviews with 15 older patients. Secondly, an international three-round Delphi survey in four countries involving patients, healthcare professionals, and experts was conducted to validate outcomes to be included in the core outcome set. Consensus meetings were conducted to validate the results.

RESULTS

Of the 164 participants invited to take part in the Delphi survey, 150 completed Round 1, including 55 patients or family caregivers, 55 healthcare professionals, and 40 experts. A total of 129 participants completed all three rounds. Sixty-four eligible outcomes were extracted from 47 articles, 32 clinical trial protocols, and patient interviews. Thirty outcomes were removed and one added after Round 1, 18 outcomes were removed after Round 2, and seven after Round 3. Results were discussed during consensus meetings. Consensus was reached on seven outcomes, which constitute the core outcome set: drug-related hospital admissions; drug overuse; drug underuse; potentially inappropriate medications; clinically significant drug-drug interactions; health-related quality of life; pain relief.

CONCLUSIONS

We developed a core outcome set of seven outcomes which should be used in future trials of medication review in multi-morbid older patients with polypharmacy.

Translational Biomedical Informatics and Pharmacometrics Approaches in the Drug Interactions Research

Drug interaction is a leading cause of adverse drug events and a major obstacle for current clinical practice. Pharmacovigilance data mining, pharmacokinetic modeling, and text mining are computation and informatic tools on integrating drug interaction knowledge and generating drug interaction hypothesis. We provide a comprehensive overview of these translational biomedical informatics methodologies with related databases. We hope this review illustrates the complementary nature of these informatic approaches and facilitates the translational drug interaction research.

Drug-drug interaction potential in men treated with enzalutamide: Mind the gap

AIMS

Metastatic castration-resistant prostate cancer (mCRPC) patients are generally older patients with several co-morbidities and are therefore at increased risk of complications due to drug-drug interactions (DDIs). We assessed the prevalence of potential DDIs in a cohort of mCRPC patients treated with enzalutamide.

METHODS

We conducted a retrospective review of pharmacy records to retrieve individual drug histories of mCRPC patients who started enzalutamide therapy in a tertiary care setting. Potential DDIs were analysed using two international drug interaction compendia: Lexicomp^® and Micromedex^® , and the Dutch drug database. Two potential pharmacodynamic DDIs were analysed.

RESULTS

A total of 105 records were evaluated for potential DDIs with enzalutamide. Of 205 different co-medications, 56 were flagged by at least one of the three compendia: Lexicomp, Micromedex and the Dutch drug database flagged for potential DDIs in 85%, 54% and 32%, respectively. Eighty-five per cent of DDIs were classified as major. The median number of co-medications per patient was 11 (range 1-26). The median (range) number of interactions per patient was 4 (0-10), 1 (0-5) and 0 (0-2) for Lexicomp, Micromedex and the Dutch drug database, respectively. In 23% and 45% of all patients, a potential DDI was found with PPIs and CNS depressants, respectively.

CONCLUSIONS

A high prevalence of potential DDIs was found. The inclusion and grading of potential DDIs was highly variable between the three drug interaction compendia. Physicians, nurses and pharmacists should be aware of this potential problem, which might require intensive monitoring or alternative treatment strategies to prevent suboptimal treatment of the co-morbidities in patients treated with enzalutamide.

Prevalence of QT interval prolonging drug-drug interactions (QT-DDIs) in psychiatry wards of tertiary care hospitals in Pakistan: a multicenter cross-sectional study

Background QT prolongation and associated arrhythmias, torsades de pointes (TdP), are considerable negative outcomes of many antipsychotic and antidepressant agents frequently used by psychiatric patients. Objective To identify the prevalence, levels, and predictors of QT prolonging drug-drug interactions (QT-DDIs), and AZCERT (Arizona Center for Education and Research on Therapeutics) classification of drugs involved in QT-DDIs. Setting Psychiatry wards of three major tertiary care hospitals of Khyber-Pakhtunkhwa, Pakistan. Method This was a multicenter cross-sectional study. Micromedex DrugReax was used for identification of QT-DDIs. TdP risks were identified by the AZCERT classification. Multivariate logistic regression analysis was performed to identify predictors of QT-DDIs. Main outcome measure Prevalence of QT-DDIs (overall, age-wise and gender-wise) and their levels of severity and documentation; AZCERT classes of drugs involved in QT-DDIs; and odds ratios for predictors of QT-DDIs. Results Of 600 patients, 58.5% were female. Median age was 25 years (IQR = 20-35). Overall 51.7% patients had QT-DDIs. Of total 698 identified QT-DDIs, most were of major-severity (98.4%) and fair-documentation (93.7%). According to the AZCERT classification, 36.4% of the interacting drugs were included in list-1 (known risk of TdP), 26.9% in list-2 (possible risk of TdP) and 27.5% in list-3 (conditional risk of TdP). Drugs commonly involved in QT-DDI were olanzapine (n = 146), haloperidol (138), escitalopram (122), risperidone (91), zuclopenthixol (87), quetiapine (n80) and fluoxetine (74). In multivariate logistic regression analysis, QT-DDIs were significantly associated with 6-7 prescribed medications (p = 0.04) and >7 medications (p = 0.03). Similarly, there was significant association of occurrence of QT-DDIs with 2-3 QT drugs (p < 0.001) and >3 QT drugs (p < 0.001). Conclusion A considerable number of patients are exposed to QT-DDIs in psychiatry. There is a need to implement protocol for monitoring the outcomes of QT-DDIs.

Definition of variables required for comprehensive description of drug dosage and clinical pharmacokinetics

PURPOSE

Electronic clinical decision support systems (CDSS) require drug information that can be processed by computers. The goal of this project was to determine and evaluate a compilation of variables that comprehensively capture the information contained in the summary of product characteristic (SmPC) and unequivocally describe the drug, its dosage options, and clinical pharmacokinetics.

METHODS

An expert panel defined and structured a set of variables and drafted a guideline to extract and enter information on dosage and clinical pharmacokinetics from textual SmPCs as published by the European Medicines Agency (EMA). The set of variables was iteratively revised and evaluated by data extraction and variable allocation of roughly 7% of all centrally approved drugs.

RESULTS

The information contained in the SmPC was allocated to three information clusters consisting of 260 variables. The cluster "drug characterization" specifies the nature of the drug. The cluster "dosage" provides information on approved drug dosages and defines corresponding specific conditions. The cluster "clinical pharmacokinetics" includes pharmacokinetic parameters of relevance for dosing in clinical practice. A first evaluation demonstrated that, despite the complexity of the current free text SmPCs, dosage and pharmacokinetic information can be reliably extracted from the SmPCs and comprehensively described by a limited set of variables.

CONCLUSION

By proposing a compilation of variables well describing drug dosage and clinical pharmacokinetics, the project represents a step forward towards the development of a comprehensive database system serving as information source for sophisticated CDSS.

Antipsychotic prescribing patterns during and after critical illness: a prospective cohort study

Background

Antipsychotics are used to treat delirium in the intensive care unit (ICU) despite unproven efficacy. We hypothesized that atypical antipsychotic treatment in the ICU is a risk factor for antipsychotic prescription at discharge, a practice that might increase risk since long-term use is associated with increased mortality.

Methods

After excluding patients on antipsychotics prior to admission, we examined antipsychotic use in a prospective cohort of ICU patients with acute respiratory failure and/or shock. We collected data on medication use from medical records and assessed patients for delirium using the Confusion Assessment Method for the ICU. Using multivariable logistic regression, we analyzed whether age, delirium duration, atypical antipsychotic use, and discharge disposition (each selected a priori) were independent risk factors for discharge on an antipsychotic. We also examined admission Acute Physiology and Chronic Health Evaluation (APACHE) II score, haloperidol use, and days of benzodiazepine use in post hoc analyses.

Results

After excluding 18 patients due to prior antipsychotic use and three who withdrew, we included 500 patients. Among 208 (42%) treated with an antipsychotic, median (interquartile range) age was 59 (49–69) years and APACHE II score was 26 (22–32), characteristics that were similar among antipsychotic nonusers. Antipsychotic users were more likely than nonusers to have had delirium (93% vs. 61%, p < 0.001). Of the 208 antipsychotic users, 172 survived to hospital discharge, and 42 (24%) of these were prescribed an antipsychotic at discharge. Treatment with an atypical antipsychotic was the only independent risk factor for antipsychotic prescription at discharge (odds ratio 17.6, 95% confidence interval 4.9 to 63.3; p < 0.001). Neither age, delirium duration, nor discharge disposition were risk factors (p = 0.11, 0.38, and 0.12, respectively) in the primary regression model, and post hoc analyses found APACHE II (p = 0.07), haloperidol use (p = 0.16), and days of benzodiazepine use (p = 0.31) were also not risk factors for discharge on an antipsychotic.

Conclusions

In this study, antipsychotics were used to treat nearly half of all antipsychotic-naïve ICU patients and were prescribed at discharge to 24% of antipsychotic-treated patients. Treatment with an atypical antipsychotic greatly increased the odds of discharge with an antipsychotic prescription, a practice that should be examined carefully during medication reconciliation since these drugs carry “black box warnings” regarding long-term use.

An evaluation of the completeness of drug-drug interaction-related information in package inserts

PURPOSE

The project aimed to evaluate the completeness of drug-drug interaction (DDI)-related information in package inserts (PIs) and develop a systematic approach to conduct the evaluation.

METHODS

DDI-related information in the branded PIs of statins, macrolides, protease inhibitors and selected drugs of narrow therapeutic index (DNTI) were evaluated against the criteria distilled from the Food and Drug Administration (FDA) labelling recommendation guidance document. Decision trees were crafted and employed in the evaluation process. Scores were computed to give each PI an overall completeness score and individual criterion completeness score. The Kruskal-Wallis test and Dunn's multiple comparison test were used to assess the differences in the completeness scores.

RESULTS

The mean overall completeness score of the 21 PIs was 35.7 ± 13.4 % (range 12.2-62 %). Eight out of the 11 individual evaluation criterion had a mean completeness score below 50 %. A subclass analysis conducted revealed that PIs from the different drug classes differed in the type of DDI-related information, such that they are more complete or less complete.

CONCLUSION

The completeness score of DDI-related information in the PIs varied extensively amongst and within drug classes. A consensus between the authorities and drug companies on the type and quality of DDI-related information to be included could improve their completeness in PIs and make PIs a valuable source of DDI reference. Decision trees, albeit not validated yet, lay the groundwork for a valuable tool to evaluate DDI-related or other drug information.

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.

Drug interactions involving antiepileptic drugs: assessment of the consistency among three drug compendia and FDA-approved labels

Interactions of antiepileptic drugs (AEDs) with other substances may lead to adverse effects and treatment failure. To avoid such interactions, clinicians often rely on drug interaction compendia. Our objective was to compare the concordance for twenty-two AEDs among three drug interaction compendia (Micromedex, Lexi-Interact, and Clinical Pharmacology) and the US Food and Drug Administration-approved product labels. For each AED, the overall concordance among data sources regarding existence of interactions and their classification was poor, with less than twenty percent of interactions listed in all four sources. Concordance among the three drug compendia decreased with the fraction of the drug excreted unchanged and was greater for established inducers of hepatic drug-metabolizing enzymes than for the drugs that are not inducers (R-square=0.83, P<0.01). For interactions classified as contraindications, major, and severe, concordance among the four data sources was, in most cases, less than 30%. Prescribers should be aware of the differences between drug interaction sources of information for both older AEDs and newer AEDs, in particular for those AEDs which are not involved in hepatic enzyme-mediated interactions.

Potential drug-drug interactions in infant, child, and adolescent patients in children's hospitals

BACKGROUND AND OBJECTIVES

Hospitalized infants, children, and adolescents are typically exposed to numerous distinct medications during inpatient admissions, increasing their risk of potential drug-drug interactions (PDDIs). We assessed the prevalence and characteristics of PDDI exposure of pediatric patients treated in children's hospitals.

METHODS

This retrospective cohort study included patients <21 years old hospitalized in children's hospitals throughout the United States. PDDIs were identified by using the MicroMedex DRUG-REAX system. We calculated the patients exposed to PDDIs, stratified according to the seriousness of the interaction; daily and cumulative counts of PDDI exposures; and characterization of the cited potential adverse effects.

RESULTS

Of 498 956 hospitalizations in 2011, 49% were associated with ≥1 PDDI, with a "contraindicated" PDDI occurring in 5% of all hospitalizations, a "major" PDDI present in 41%, a "moderate" PDDI in 28%, and a "minor" PDDI in 11%. Opioids were involved in 25% of all PDDIs, followed by antiinfective agents (17%), neurologic agents (15%), gastrointestinal agents (13%), and cardiovascular agents (13%). One-half of all PDDI exposures were due to specific drug pairs occurring in ≤3% of patients per hospital day. The most common potential adverse drug events included additive respiratory depression (in 21% of PDDIs), bleeding risk (5%), QT interval prolongation (4%), reduced iron absorption/availability (4%), central nervous system depression (4%), hyperkalemia (3%), and altered diuretic effectiveness (3%).

CONCLUSIONS

Exposure to PDDIs is common among hospitalized children. Empirical data are needed to determine the probability and magnitude of the actual harm for each specific PDDI, particularly for less common drug pairs.

A novel feature-based approach to extract drug-drug interactions from biomedical text

MOTIVATION

Knowledge of drug-drug interactions (DDIs) is crucial for health-care professionals to avoid adverse effects when co-administering drugs to patients. As most newly discovered DDIs are made available through scientific publications, automatic DDI extraction is highly relevant.

RESULTS

We propose a novel feature-based approach to extract DDIs from text. Our approach consists of three steps. First, we apply text preprocessing to convert input sentences from a given dataset into structured representations. Second, we map each candidate DDI pair from that dataset into a suitable syntactic structure. Based on that, a novel set of features is used to generate feature vectors for these candidate DDI pairs. Third, the obtained feature vectors are used to train a support vector machine (SVM) classifier. When evaluated on two DDI extraction challenge test datasets from 2011 and 2013, our system achieves F-scores of 71.1% and 83.5%, respectively, outperforming any state-of-the-art DDI extraction system.

AVAILABILITY AND IMPLEMENTATION

The source code is available for academic use at http://www.biosemantics.org/uploads/DDI.zip.

Potential drug interactions and chemotoxicity in older patients with cancer receiving chemotherapy

PURPOSE

Increased risk of drug interactions due to polypharmacy and aging-related changes in physiology among older patients with cancer is further augmented during chemotherapy. No previous studies examined potential drug interactions (PDIs) from polypharmacy and their association with chemotherapy tolerance in older patients with cancer.

METHODS

This study is a retrospective medical chart review of 244 patients aged 70+ years who received chemotherapy for solid or hematological malignancies. PDI among all drugs, supplements, and herbals taken with the first chemotherapy cycle were screened for using the Drug Interaction Facts software, which classifies PDIs into five levels of clinical significance with level 1 being the highest. Descriptive and correlative statistics were used to describe rates of PDI. The association between PDI and severe chemotoxicity was tested with logistic regressions adjusted for baseline covariates.

RESULTS

A total of 769 PDIs were identified in 75.4% patients. Of the 82 level 1 PDIs identified among these, 32 PDIs involved chemotherapeutics. A large proportion of the identified PDIs were of minor clinical significance. The risk of severe non-hematological toxicity almost doubled with each level 1 PDI (OR=1.94, 95% CI: 1.22-3.09), and tripled with each level 1 PDI involving chemotherapeutics (OR=3.08, 95% CI: 1.33-7.12). No association between PDI and hematological toxicity was found.

CONCLUSIONS

In this convenience sample of older patients with cancer receiving chemotherapy we found notable rates of PDI and a substantial adjusted impact of PDI on risk of non-hematological toxicity. These findings warrant further research to optimize chemotherapy outcomes.

Frequency of use of QT-interval prolonging drugs in psychiatry in Belgium

INTRODUCTION

Drug-induced QT-prolongation is an established risk factor for Torsade de pointes and sudden cardiac death. The list of QT-prolonging drugs is extensive and includes many drugs commonly used in psychiatry.

AIM

In this study we performed a cross-sectional analysis of medication profiles to assess the prevalence of drug interactions potentially leading to QT-prolongation.

SETTING

6 psychiatric hospitals in Flanders, Belgium.

METHODS

For each patient, the full medication list was screened for the presence of interactions, with special attention to those with an increased risk for QT-prolongation. Current practice on QT monitoring and prevention of drug-induced arrhythmia was assessed.

MAIN OUTCOME MEASURE

Number of drug interactions with risk of QT-prolongation.

RESULTS

592 patients (46 % female; mean age 55.7 ± 17.1 years) were included in the analysis. 113 QT-prolonging interactions were identified in 43 patients (7.3 %). QT-prolonging interactions occurred most frequently with antidepressants (n = 102) and antipsychotics (n = 100). The precautions and follow-up provided by the different institutions when combining QT-prolonging drugs were very diverse.

CONCLUSION

Drug combinations that are associated with QT-prolongation are frequently used in the chronic psychiatric setting. Persistent efforts should be undertaken to provide caregivers with clear guidelines on how to use these drugs in a responsible and safe way.

What, if all alerts were specific - estimating the potential impact on drug interaction alert burden

PURPOSE

Clinical decision support systems (CDSS) may potentially improve prescribing quality, but are subject to poor user acceptance. Reasons for alert overriding have been identified and counterstrategies have been suggested; however, poor alert specificity, a prominent reason of alert overriding, has not been well addressed. This paper aims at structuring modulators that determine alert specificity and estimating their quantitative impact on alert burden.

METHODS

We developed and summarized optimizing strategies to guarantee the specificity of alerts and applied them to a set of 100 critical and frequent drug interaction (DDI) alerts. Hence, DDI alerts were classified as dynamic, i.e. potentially sensitive to prescription-, co-medication-, or patient-related factors that would change alert severity or render the alert inappropriate compared to static, i.e. always applicable alerts not modulated by cofactors.

RESULTS

Within the subset of 100 critical DDI alerts, only 10 alerts were considered as static and for 7 alerts, relevant factors are not generally available in today's patient charts or their consideration would not impact alert severity. The vast majority, i.e. 83 alerts, might require a decrease in alert severity due to factors related to the prescription (N=13), the co-medication (N=11), individual patient data (N=36), or combinations of them (N=23). Patient-related factors consisted mainly of three lab values, i.e. renal function, potassium, and therapeutic drug monitoring results.

CONCLUSION

This paper outlines how promising the refinement of knowledge bases is in order to increase specificity and decrease alert burden and suggests how to structure knowledge bases to refine DDI alerting.

Checklist for standardized reporting of drug-drug interaction management guidelines

PURPOSE

Inconsistencies and omissions in drug-drug interaction (DDI) management guidelines may lead to harm and suboptimal therapy. The purpose of this study was to define a checklist for DDI management guidelines to help developers produce high-quality guidelines that will support healthcare providers in clinical practice.

METHODS

We carried out a two-round Delphi process with an international panel of healthcare providers, most of whom are pharmacists involved in providing DDI information, in order to select those items that should be addressed in DDI management guidelines (including grading systems that could be used).

RESULTS

Twenty-three panellists reached consensus on 19 items in two main domains. These were consolidated into a checklist of 15 elements for standardized reporting in management guidelines. For each element a description is provided to specify what information should be documented in that specific element.

CONCLUSIONS

It was possible to reach a broad consensus on which relevant items should be included in a checklist for the development of DDI management guidelines.

Potentially inappropriate prescribing and drug-drug interactions among elderly Chinese nursing home residents in Macao

BACKGROUND

The ageing of the population has become a concern all over the world, including Macao. In general, older people are more prone to adverse drug events which can result from potentially inappropriate medication (PIM) use and drug-drug interactions (DDIs).

OBJECTIVE

This study was designed to evaluate the prevalence of PIM use and DDIs among elderly nursing home residents in Macao, and to find out the factors associated with these drug-related problems.

SETTING

This study was conducted in the largest nursing home in Macao, with a bed capacity of 168.

METHOD

All data of this cross-sectional study were collected from medical charts and medication administration records. PIM use was determined by the screening tool of older person's prescription (STOPP) criteria and potential DDIs were detected using the preset criteria of two compendia, Drug-Reax and Lexi-Interact. Multivariate logistic regression analysis was performed to identify the independent factors associated with each drug-related problem.

MAIN OUTCOME MEASURES

The proportions of elderly nursing home residents who regularly used PIMs and who were exposed to DDIs.

RESULTS

A total of 114 elderly residents were eligible for PIM analysis. They consumed an average of 6.9 ± 3.1 different medications. About 46.5 % of them regularly used one or more PIMs. The prevalence of DDIs was 37.8 % among the 111 elderly residents who consumed at least two different medications. An increased number of drugs used was identified as the independent factor associated with PIM use and DDIs (p < 0.05). However, the use of STOPP-related PIMs did not appear to raise the likelihood of DDIs among the study population.

CONCLUSION

Both PIM use and DDIs are common among elderly nursing home residents in Macao. Further studies should be conducted to evaluate the clinical outcomes of pharmacist-led interventions for elderly residents in the local nursing home setting.

Criteria for assessing high-priority drug-drug interactions for clinical decision support in electronic health records

BACKGROUND

High override rates for drug-drug interaction (DDI) alerts in electronic health records (EHRs) result in the potentially dangerous consequence of providers ignoring clinically significant alerts. Lack of uniformity of criteria for determining the severity or validity of these interactions often results in discrepancies in how these are evaluated. The purpose of this study was to identify a set of criteria for assessing DDIs that should be used for the generation of clinical decision support (CDS) alerts in EHRs.

METHODS

We conducted a 20-year systematic literature review of MEDLINE and EMBASE to identify characteristics of high-priority DDIs. These criteria were validated by an expert panel consisting of medication knowledge base vendors, EHR vendors, in-house knowledge base developers from academic medical centers, and both federal and private agencies involved in the regulation of medication use.

RESULTS

Forty-four articles met the inclusion criteria for assessing characteristics of high-priority DDIs. The panel considered five criteria to be most important when assessing an interaction- Severity, Probability, Clinical Implications of the interaction, Patient characteristics, and the Evidence supporting the interaction. In addition, the panel identified barriers and considerations for being able to utilize these criteria in medication knowledge bases used by EHRs.

CONCLUSIONS

A multi-dimensional approach is needed to understanding the importance of an interaction for inclusion in medication knowledge bases for the purpose of CDS alerting. The criteria identified in this study can serve as a first step towards a uniform approach in assessing which interactions are critical and warrant interruption of a provider's workflow.

Clinically significant interactions between antiretroviral and co-prescribed drugs for HIV-infected children: profiling and comparison of two drug databases

BACKGROUND

Drug-drug interactions are an important therapeutic challenge among human immunodeficiency virus-infected patients. Early recognition of drug-drug interactions is important, but conflicts do exist among drug compendia on drug interaction information. We aimed to evaluate the consistencies of two drug information resources with regards to the severity rating and categorization of the potential interactions between antiretroviral and co-prescribed drugs.

METHODS

We reviewed the case files of human immunodeficiency virus-infected children who were receiving treatment at the human immunodeficiency virus (HIV) clinic of the Lagos University Teaching Hospital, Idi Araba, between January 2005 and December 2010. All of the co-prescribed and antiretroviral drug pairs were screened for potential interactions using the Medscape Drug Interaction Checker and the Monthly Index of Medical Specialties Interaction Checker. Drug-drug interaction (DDI) severity and categorization were rated on a scale of A (no known interaction); B (minor/no action needed); C (moderate/monitor therapy); D (major/therapy modification); and X (contraindicated/avoid combination).

RESULTS

A total of 280 patients were at risk of 596 potential DDIs. The databases showed discrepancies, with Medscape database identifying 504 (84.6%) and USA MIMS database identifying 302 (50.7%) potential DDIs. Simultaneous identification of DDIs by both databases occurred for only 275 (46.1%) listed interactions. Both databases have a weak correlation on the severity rating (rs = 0.45; P < 0.001). The most common DDIs identified by the databases were nevirapine and artemisinin-based combination therapy (170; 28.5%), nevirapine and fluconazole (58; 9.7%), and zidovudine and fluconazole (55; 9.2%). There were 272 (45.6%) interaction severity agreements between the databases.

CONCLUSION

Discrepancies occurred in DDI listings between Medscape and USA MIMS databases. Health care professionals may need to consult more than one DDI information database to ensure safe concomitant prescribing for HIV patients.

Worldwide analysis of factors associated with medicines compendia publishing

BACKGROUND

Medicines compendia, also called formularies, are the most commonly used drug information source among health care professionals.

OBJECTIVE

The aim was to identify the countries publishing medicines compendia and the socio-demographic factors associated to this fact. Additionally, we sought to determine the use of foreign compendia in countries lacking their own.

SETTING

Global web-based survey.

METHOD

Healthcare practitioners and researchers from 193 countries worldwide were invited to complete a web-based survey. The questionnaire investigated the existence of a national compendium, or the use of foreign compendia in the absence of one. Demographic and socioeconomic variables were used to predict compendia publishing through a multivariate analysis.

MAIN OUTCOME MEASURE

Existence of national medicines compendia and foreign compendia used.

RESULTS

Professionals from 132 countries completed the survey (response rate at a country level 68.4%, comprising 90.9% global population). Eighty-four countries (63.6%) reported publishing a medicines compendium. In the multivariate analysis, only two covariates had significant association with compendia publishing. Being a member of the Organisation for the Economic Cooperation and Development was the only variable positively associated with compendia publishing (OR = 37.5; 95% CI = 2.3:599.8). In contrast, the countries that listed French as an official language were less likely to publish a compendium (OR = 0.07; 95% CI = 0.007:0.585). Countries without national compendia reported using the British National Formulary most commonly, followed by the Dictionnaire Vidal.

CONCLUSION

Publication of medicines compendia is associated with socio-economic development. Countries lacking a national compendium, use foreign compendia from higher-income countries. Creating an international medicines compendium under the leadership of the World Health Organisation, rather than merely a 'model', would reduce the risks of using information sources not-adapted to the necessities of developing countries.

Inhibitory metabolic drug interactions with newer psychotropic drugs: inclusion in package inserts and influences of concurrence in drug interaction screening software

BACKGROUND

Food and Drug Administration (FDA) regulations mandate that package inserts (PIs) include observed or predicted clinically significant drug-drug interactions (DDIs), as well as the results of pharmacokinetic studies that establish the absence of effect.

OBJECTIVE

To quantify how frequently observed metabolic inhibition DDIs affecting US-marketed psychotropics are present in FDA-approved PIs and what influence the source of DDI information has on agreement between 3 DDI screening programs.

METHODS

The scientific literature and PIs were reviewed to determine all drug pairs for which there was rigorous evidence of a metabolic inhibition interaction or noninteraction. The DDIs were tabulated noting the source of evidence and the strength of agreement over chance. Descriptive statistics were used to examine the influence of source of DDI information on agreement among 3 DDI screening tools. Logistic regression was used to assess the influence of drug class, indication, generic status, regulatory approval date, and magnitude of effect on agreement between the literature and PI as well as agreement among the DDI screening tools.

RESULTS

Thirty percent (13/44) of the metabolic inhibition DDIs affecting newer psychotropics were not mentioned in PIs. Drug class, indication, regulatory approval date, generic status, or magnitude of effect did not appear to be associated with more complete DDI information in PIs. DDIs found exclusively in PIs were 3.25 times more likely to be agreed upon by all 3 DDI screening tools than were those found exclusively in the literature. Generic status was inversely associated with agreement among the DDI screening tools (odds ratio 0.11; 95% CI 0.01 to 0.89).

CONCLUSIONS

The presence in PIs of DDI information for newer psychotropics appears to have a strong influence on agreement among DDI screening tools. Users of DDI screening software should consult more than 1 source when considering interactions involving generic psychotropics.

High-priority drug-drug interactions for use in electronic health records

OBJECTIVE

To develop a set of high-severity, clinically significant drug-drug interactions (DDIs) for use in electronic health records (EHRs).

METHODS

A panel of experts was convened with the goal of identifying critical DDIs that should be used for generating medication-related decision support alerts in all EHRs. Panelists included medication knowledge base vendors, EHR vendors, in-house knowledge base developers from academic medical centers, and both federal and private agencies involved in the regulation of medication use. Candidate DDIs were assessed by the panel based on the consequence of the interaction, severity levels assigned to them across various medication knowledge bases, availability of therapeutic alternatives, monitoring/management options, predisposing factors, and the probability of the interaction based on the strength of evidence available in the literature.

RESULTS

Of 31 DDIs considered to be high risk, the panel approved a final list of 15 interactions. Panelists agreed that this list represented drugs that are contraindicated for concurrent use, though it does not necessarily represent a complete list of all such interacting drug pairs. For other drug interactions, severity may depend on additional factors, such as patient conditions or timing of co-administration.

DISCUSSION

The panel provided recommendations on the creation, maintenance, and implementation of a central repository of high severity interactions.

CONCLUSIONS

A set of highly clinically significant drug-drug interactions was identified, for which warnings should be generated in all EHRs. The panel highlighted the complexity of issues surrounding development and implementation of such a list.

Novel oral anticancer drugs: a review of adverse drug reactions, interactions and patient adherence

Each aspect of oncological care is widely affected by the spread of oral anticancer agents, which raises several questions in terms of safe medication use and patient adherence. Over the past decade targeted therapies have appeared in clinical practice and revolutionized the pharmacological treatment of malignancies. Regular patient – doctor visits and proper patient education is crucial in order to comply with the therapy previously agreed upon with the oncologist, to increase patient adherence, to detect and to treat adverse effects in early stages. Since the information on the new medicines in Hungarian language is sparse it is the intention of the authors to give an overview of the basic knowledge, patient safety issues, adverse effects and interactions. Official drug information summaries and data on pharmacokinetics, interactions and adverse effects from the literature are reviewed as the basis for this overview. Orv. Hetil., 2012, 153, 66–78.

Polypharmacy in elderly patients with cancer: clinical implications and management

More and more elderly people with cancer are treated in oncology clinics worldwide every year, many of whom have comorbid disorders treated with one or more drugs. Moreover, these patients might also take self-prescribed over-the-counter drugs or complementary and alternative medicines, which they might not tell their doctor about. Initiation of chemotherapy with one or more cytotoxic or targeted agents and drugs for treatment of cancer symptoms or toxic effects related to treatment can result in polypharmacy. We examine the clinical implications of polypharmacy. Challenges for the medical teams who treat elderly patients with cancer include identification of what drugs are actually being taken by the patient, avoidance or management of any adverse effects or drug interactions, and reassessing the patient's overall treatment. We address these issues and propose practical recommendations for management of treatment for elderly patients with cancer.

Evaluation of warfarin drug interaction listings in US product information for warfarin and interacting drugs

BACKGROUND

Because interactions with warfarin represent a serious risk to patients, drug information sources used by clinicians should contain accurate, timely, and practical drug interaction information.

OBJECTIVE

The aim of this study was to assess the information regarding warfarin interactions that is included in the official labeling of prescription products that interact with warfarin.

METHODS

We examined the official labeling information approved by the US Food and Drug Administration for the 50 drugs, biologics, and drug classes that were commonly identified by 3 drug information compendia--Clinical Pharmacology, ePocrates(®), and Micromedex(®)--and the warfarin US prescribing information (PI) as having an interaction with warfarin. The PI of each product was assessed for possible mention of an interaction with warfarin. The data were collected and tabulated by 1 investigator. A clinical investigator evaluated the data for accuracy and consistency. Unresolved issues were discussed with a third investigator and decided by consensus. The interaction listings were compared to determine similarities, differences, and inconsistencies and analyzed by 5 investigators.

RESULTS

Of the labeling for 73 products evaluated, 62 (85%) included mention of an interaction with warfarin. Those failing to mention the warfarin interaction were for older generic drugs or influenza vaccine. Among the labels listing an interaction with warfarin, the location of the information, the terminology used, and the inclusion of evidence for the interaction was inconsistent . When considering the PI for all 73 products, Fleiss' kappa coefficient (κ = 0.467) suggested moderate concordance according to the method of Landis and Koch.

CONCLUSION

This assessment of official US product labeling for 50 drugs, biologics, and drug classes known to interact with warfarin, comprising 73 distinct agents, found that 15% failed to mention the interaction, even though the interaction was mentioned in the warfarin labeling.

Warfarin drug interactions: a comparative evaluation of the lists provided by five information sources

PURPOSE

Detecting potential drug interactions can lead to early interventions that protect patients from serious drug-related problems. The aim of this study was to evaluate the agreement among the lists of warfarin interactions provided by five information sources.

METHODS

The lists of warfarin interactions and the corresponding severity ratings and documentation levels presented by the three compendia and by the World Health Organization (WHO) Model Formulary were all compared, and each list was compared to that provided on the package insert of Marevan, a brand of warfarin. The compendia used were: Drug Interaction Facts, Drug Interactions: Analysis and Management and DRUG-REAX. A kappa coefficient was used to calculate the agreement among the sources.

RESULTS

A total of 537 interactions were listed. Only 13 (2.4%) were common to the five sources. The global Fleiss' kappa coefficient was -0.0080, which indicated poor agreement. Eleven warfarin interactions appeared only in the Marevan package insert. Importantly, 243 interactions (45.3% of the total) were deemed significant in at least one compendium. Only two warfarin interactions were reported as critical by all three compendia and by WHO. The most critical interactions cited by the compendia were missing from the package insert.

CONCLUSIONS

Poor agreement was found among five sources listing warfarin interactions. Potentially severe clinical consequences might occur due to these discrepant recommendations. Finally, the lack of standard terminology and clinical guidance, as well as the possible inaccuracy of severity ratings and documentation might contribute to heterogeneous procedures in clinical practice.

Dosing modifications of targeted cancer therapies in patients with special needs: evidence and controversies

Targeted therapies have revolutionized the treatment of malignancies over the past decade. These agents are generally regarded to posses fewer systemic side effects than traditional cytotoxic chemotherapies. However, patients manifesting organ dysfunction or drug interactions with concurrent medications may require dosing modifications of their targeted therapies in order to reduce the risk of systemic toxicities or reduction of drug efficacies. Studies have shown that wide variations and controversies exist with regard to dosing modifications of drugs, due to the lack of well conducted studies and consensus. Hence, this review was conducted to review the literature on the dosing modification strategies, for 30 commercially available targeted cancer drugs, and to evaluate the current mainstay recommendations and controversies.

Towards meaningful medication-related clinical decision support: recommendations for an initial implementation

SUMMARY

Clinical decision support (CDS) can improve safety, quality, and cost-effectiveness of patient care, especially when implemented in computerized provider order entry (CPOE) applications. Medication-related decision support logic forms a large component of the CDS logic in any CPOE system. However, organizations wishing to implement CDS must either purchase the computable clinical content or develop it themselves. Content provided by vendors does not always meet local expectations. Most organizations lack the resources to customize the clinical content and the expertise to implement it effectively. In this paper, we describe the recommendations of a national expert panel on two basic medication-related CDS areas, specifically, drug-drug interaction (DDI) checking and duplicate therapy checking. The goals of this study were to define a starter set of medication-related alerts that healthcare organizations can implement in their clinical information systems. We also draw on the experiences of diverse institutions to highlight the realities of implementing medication decision support. These findings represent the experiences of institutions with a long history in the domain of medication decision support, and the hope is that this guidance may improve the feasibility and efficiency CDS adoption across healthcare settings.

Safe handling of oral chemotherapeutic agents in clinical practice: recommendations from an international pharmacy panel

Although there has been a significant increase in the availability and use of oral chemotherapeutic agents, the guidelines around their safe handling are still evolving. Although oral chemotherapy is associated with ease of administration, it has the same exposure risks to health care practitioners, patients, and their caregivers as intravenous formulations, and because it is administered in the home, to the families of patients. However, the general misconception appears to be that exposure risk is low and therefore oral chemotherapeutic agents present little risk and are safer to handle. In a series of three roundtable meetings, a team of international pharmacists from North America and Europe reviewed existing guidelines and identified gaps in recommendations that we believe are important for safe handling. The present article is a compilation of these gaps, especially applicable to manufacturers and distributors, storage and handling, and patient education regarding safe handling. These recommendations, on the basis of our experience and of best practices, provide an international perspective and can be adapted by institutions and practices for development of standardized procedures specific to their needs for the safe handling of oral chemotherapeutic agents.

Discovering drug-drug interactions: a text-mining and reasoning approach based on properties of drug metabolism

MOTIVATION

Identifying drug-drug interactions (DDIs) is a critical process in drug administration and drug development. Clinical support tools often provide comprehensive lists of DDIs, but they usually lack the supporting scientific evidences and different tools can return inconsistent results. In this article, we propose a novel approach that integrates text mining and automated reasoning to derive DDIs. Through the extraction of various facts of drug metabolism, not only the DDIs that are explicitly mentioned in text can be extracted but also the potential interactions that can be inferred by reasoning.

RESULTS

Our approach was able to find several potential DDIs that are not present in DrugBank. We manually evaluated these interactions based on their supporting evidences, and our analysis revealed that 81.3% of these interactions are determined to be correct. This suggests that our approach can uncover potential DDIs with scientific evidences explaining the mechanism of the interactions.

A drug-drug interaction knowledge assessment instrument for health professional students: a Rasch analysis of validity evidence

BACKGROUND

It is essential that current and future health professionals be able to evaluate for possible clinically significant drug-drug interactions (DDIs) and when detected, determine appropriate management strategies to prevent patient harm.

OBJECTIVE

Assess the validity of a DDI knowledge assessment instrument in a health professional student population.

METHODS

This study recruited health professional students (medical, nurse practitioner, and pharmacy) beginning experiential training at the University of Arizona. Students were given a knowledge assessment instrument that included 15 medication pairings selected on the basis of clinical importance and were asked to select the most appropriate DDI management strategy for each pair by selecting "avoid combination," "usually avoid combination," "take precautions," or "no special precautions." Data were analyzed in 2 ways because of the subjective nature of classifying DDIs into specific management categories. In the first analysis, respondents were given credit for a correct item only if they selected the management strategy deemed appropriate (management strategy analysis). In another analysis, students were given credit for an item only if they correctly identified specific DDIs (DDI recognition analysis). Rasch analysis was used to assess the validity of the knowledge instrument.

RESULTS

A total of 165 of the 226 eligible health professional students completed the DDI knowledge assessment (73% response rate). The mean score for management strategy analysis was 3.82 out of 15, whereas DDI recognition analysis produced a higher average (mean=6.55). Good reliability was demonstrated in both strategies, and no ceiling or floor effects were observed. Some construct underrepresentation occurred with both scoring strategies, and some mistargeting was identified when analyzing the management strategy.

CONCLUSIONS

Although improvements in construct representation may be beneficial, the instrument used demonstrated good reliability and validity and could be used by educators to assess and improve DDI knowledge. The ability of the participants to identify DDIs and select an appropriate management strategy was low. These results support the need for additional DDI education in this institution's health curricula.

Clinically significant drug-drug interactions between oral anticancer agents and nonanticancer agents: a Delphi survey of oncology pharmacists

BACKGROUND

Drug-drug interactions (DDIs) can lead to adverse clinical outcomes, particularly in oncology, because of the narrow therapeutic index of chemotherapeutic agents and because patients with cancer are at a high risk due to polypharmacy and age-related organ dysfunction. In a previously published study, drug profiles were developed based on primary and tertiary literature reviews for a list of clinically significant DDIs involving 28 oral anticancer agents (OAAs).

OBJECTIVE

This study was based on a Delphi survey of oncology pharmacists; the survey results were used to develop a consensus list of clinically significant DDIs involving OAAs and nonanticancer agents.

METHODS

In this study, the DDI profiles previously developed were updated, and the DDI pairs that were listed both in the 2009 Drug Interaction Facts (DIF) and the Thomson Micromedex DrugDex System compendia and that also met the predetermined criteria for clinical significance were selected for further evaluation. In a 2-round, electronically administered Delphi survey of oncology pharmacists, a 5-point Likert scale (1-5, where 1 = strongly agree and 5 = strongly disagree) was used to evaluate the DDI pairs based on 8 clinical aspects (clinical importance; irreversible morbidity and mortality; quality of data; quantity of data; patient's organ functions; comorbid conditions; awareness of interaction; and management burden). International pharmacists who specialized in oncology pharmacy practice and had > or =5 years of practice experience were eligible to participate.

RESULTS

Nine of the 23 surveyed pharmacists responded, giving a response rate of 39.1%. A total of 37 DDI pairs were selected from DIF and DrugDex and evaluated by the survey respondents, resulting in the identification, via consensus, of 12 clinically significant DDI pairs. The clinical aspects with the most DDIs that reached consensus of agreement were clinical importance (82.9%) and awareness of interaction (73.2%).

CONCLUSION

An expert panel identified 12 clinically significant DDIs involving OAAs.