Pharmacist recognition of potential drug interactions

Drug use survey to identify significant drug-drug interactions and assess clinical importance in the outpatient setting of a tertiary care hospital

OBJECTIVES

Drug-drug interactions (DDIs) are a common problem in pharmacotherapy, particularly in situations where multiple disorders must be treated at the same time. We conducted a drug use survey in the general medicine outpatient department of a tertiary care hospital with the objective of assessing the potential for DDI in individual prescriptions for adult patients.

MATERIALS AND METHODS

Drugs prescribed in the current physician-patient encounter were considered in conjunction with medicines already being received by the patient as well as those discontinued in the past 1 month. Free online DDI checkers (available at https://www.drugs.com/drug_interactions.html and https://reference.medscape.com/) were used to identify potential DDI and categorize them into mild, moderate, and severe categories. We did not consider food, alcohol, or smoking-related interactions.

RESULTS

A total of 153 prescriptions, having two or more drugs, were collected, and they accounted for 1052 prescribed drugs. Among them, 613 (58.27%) were prescribed in index visits, and the rest 438 (41.63%) were preexisting medication. The number of drugs prescribed in index visits ranged from 1 to 9 (mean ± standard deviation [SD] 4.0 ± 1.86; median 4). Potential DDIs were identified in 103 (67.32%) instances. The total number of interactions identified was 412. Of these, 19.66% had minor, 77.67% moderate, and 7.19% major clinical implications. Potential DDI count in each prescription was found from 0 to 13 in number (mean ± SD 2.7 ± 3.12; median 2.0). This number correlated strongly with the number of drugs being received by individual subjects (Rho 0.744; P < 0.001).

CONCLUSIONS

Potential DDIs are a reality in day to day prescribing practice. Substantial proportion of these DDIs may have significant clinical implications. Prescribers need to be sensitized to this issue. Combining human expertise with technological solutions such as automated drug interaction alerts can help rectify the situation. Similar surveys are needed on a periodic basis to improve medication safety for patients.

Knowledge and information sources of potential drug-drug interactions of healthcare professionals among Buraydah Hospitals

BACKGROUND AND OBJECTIVE

Drug-drug interactions (DDI) are known to increase the risk of morbidity and mortality, and adversely affect the patient's quality of life. The study was to assess healthcare professional's (HCP) knowledge of DDIs in general hospitals of Buraydah.

METHODS

A cross-sectional survey using convenience sampling methods was conducted among 135 healthcare professionals in general hospitals of Buraydah between November and December 2016. The study was carried out after approval and permission from the Regional Research Ethics Committee (November 2016). Respondents were asked to classify 15 drug pairs as 'contraindicated', 'could be used with monitoring', or 'no interaction'. A response option of 'not sure' was also provided. Data were collected using a self-administered questionnaire. The descriptive analysis was done using frequency distribution and percentage for demographic data and other responses to questions. Data were collected, tabulated, and analyzed using Statistical Package for Social Sciences (SPSS) software (version 23). Logistic regression analysis was used to assess the independent variables that affect the HCP knowledge, the significant levels were set at p-value < 0.05.

RESULTS

A total of 135 healthcare professionals were included in the study. The percentage of HCPs who correctly classified the drug pairs ranged from 15 (11.1%) for "Allopurinol + Pyrazinamide" to 90 (66.7%) for "acetaminophen with codeine + amoxicillin". The average number of correctly categorized drug pairs was 5. About one-half of the respondents 73 (54.1%) answered correctly. The level of education was found to be an independent predictor of DDI knowledge. The results from the multivariate analysis indicated that a higher potential DDI knowledge level was associated with pharmacists. Pharmacists had 8.27 times higher DDI knowledge tests than nurses, P value = 0.001. Pharmacists 43(31.9%) were the most cited information source.

CONCLUSIONS

The present study revealed that health care professional's DDI knowledge was inadequate. Level of education was significantly associated with healthcare professionals' DDI knowledge. Pharmacists were the most cited DDI information source. Healthcare professionals should update their DDI knowledge through continuing education and should improve their familiarity with DDI information sources. These updated educations help to provide the appropriate therapeutic outcomes.

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

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

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

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

Reviewing Data Integrated for PBPK Model Development to Predict Metabolic Drug-Drug Interactions: Shifting Perspectives and Emerging Trends

Physiologically-based pharmacokinetics (PBPK) modeling is a robust tool that supports drug development and the pharmaceutical industry and regulatory authorities. Implementation of predictive systems in the clinics is more than ever a reality, resulting in a surge of interest for PBPK models by clinicians. We aimed to establish a repository of available PBPK models developed to date to predict drug-drug interactions (DDIs) in the different therapeutic areas by integrating intrinsic and extrinsic factors such as genetic polymorphisms of the cytochromes or environmental clues. This work includes peer-reviewed publications and models developed in the literature from October 2017 to January 2021. Information about the software, type of model, size, and population model was extracted for each article. In general, modeling was mainly done for DDI prediction via Simcyp^® software and Full PBPK. Overall, the necessary physiological and physio-pathological parameters, such as weight, BMI, liver or kidney function, relative to the drug absorption, distribution, metabolism, and elimination and to the population studied for model construction was publicly available. Of the 46 articles, 32 sensibly predicted DDI potentials, but only 23% integrated the genetic aspect to the developed models. Marked differences in concentration time profiles and maximum plasma concentration could be explained by the significant precision of the input parameters such as Tissue: plasma partition coefficients, protein abundance, or Ki values. In conclusion, the models show a good correlation between the predicted and observed plasma concentration values. These correlations are all the more pronounced as the model is rich in data representative of the population and the molecule in question. PBPK for DDI prediction is a promising approach in clinical, and harmonization of clearance prediction may be helped by a consensus on selecting the best data to use for PBPK model development.

Designing and evaluating contextualized drug-drug interaction algorithms

Objective

Alert fatigue is a common issue with off-the-shelf clinical decision support. Most warnings for drug-drug interactions (DDIs) are overridden or ignored, likely because they lack relevance to the patient's clinical situation. Existing alerting systems for DDIs are often simplistic in nature or do not take the specific patient context into consideration, leading to overly sensitive alerts. The objective of this study is to develop, validate, and test DDI alert algorithms that take advantage of patient context available in electronic health records (EHRs) data.

Methods

Data on the rate at which DDI alerts were triggered but for which no action was taken over a 3-month period (override rates) from a single tertiary care facility were used to identify DDIs that were considered a high-priority for contextualized alerting. A panel of DDI experts developed algorithms that incorporate drug and patient characteristics that affect the relevance of such warnings. The algorithms were then implemented as computable artifacts, validated using a synthetic health records data, and tested over retrospective data from a single urban hospital.

Results

Algorithms and computable knowledge artifacts were developed and validated for a total of 8 high priority DDIs. Testing on retrospective real-world data showed the potential for the algorithms to reduce alerts that interrupt clinician workflow by more than 50%. Two algorithms (citalopram/QT interval prolonging agents, and fluconazole/opioid) showed potential to filter nearly all interruptive alerts for these combinations.

Conclusion

The 8 DDI algorithms are a step toward addressing a critical need for DDI alerts that are more specific to patient context than current commercial alerting systems. Data commonly available in EHRs can improve DDI alert specificity.

Assessment of Physician's Knowledge of Potential Drug-Drug Interactions: An Online Survey in China

Background: Drug interactions are the most common preventable cause of adverse drug reaction, which may result in drug toxicity or undesired therapeutic effect with harmful outcomes to patients. Given the rising use of combination therapies, the main objectives of this study were to estimate the degree to which physicians can identify potential drug-drug interactions (PDDIs) correctly and to describe the common source of information used by physicians when they need to check PDDIs. Methods: A cross-sectional survey utilizing a self-administered online questionnaire was conducted among physicians in China. Participants were asked to classify 20 drug pairs as "no interaction," "may be used together with monitoring," "contraindication," and "not sure." We also collected data on the physician's source of information and altitude toward the PDDIs. An ordinary least square regression model was performed to investigate the potential predictors of PDDI knowledge. Results: Eligible questionnaires were obtained from 618 physicians. The respondents classified correctly 6.7 out of 20 drug pairs, or 33.4% of the drug interactions investigated. The number of drug pairs recognized by respondents was ranged from 0 to 16. The percentage of physicians who recognized specific drug pairs ranged from 8.3% for no interactions between conjugated estrogens and raloxifene, to 64.0% for the interaction between dopamine and phenytoin. When the respondents want to check PDDI information, the most commonly used source of information was package inserts (n = 572, 92.6%), followed by the Internet or mobile Apps (n = 424, 68.6%), consultation with clinical pharmacists (n = 384, 62.1%), medical textbooks (n = 374, 60.5%), knowledge base in Chinese (n = 283, 45.8%), and other physicians (n = 366, 59.2%). In the multiple regression analysis, the significant predictors of a higher number of recognized drug pairs were years of practice and altitudes toward PDDIs. Conclusion: In this online survey accessing physician's ability to detect PDDIs, less than half of the drug pairs were recognized, indicating unsatisfactory level of knowledge about the clinically significant drug interactions. Continuing education and accessible electronic database can help physicians detecting PDDIs and improve drug safety.

Effectiveness of an Advanced Clinical Decision Support System on Clinical Decision-Making Skills in a Call Center Medication Therapy Management Pharmacy Setting: A Pilot Study

(1) Background: There is limited evidence related to the efficacy of advanced clinical decision support systems (CDSS) on the quantity of high-quality clinical recommendations in a pharmacy-related medication therapy management (MTM) setting. The study aimed to assess the effect of an advanced CDSS on the quantity of relevant clinical pharmacist recommendations in a call center MTM setting. (2) Methods: This pre-test/post-test with comparator group study compared clinical skills assessment scores between certified MTM pharmacists in March 2020. A Wilcoxon Signed Rank test assessed the difference between pre- and post-test scores in both groups. (3) Results: Of 20 participants, the majority were less than 40 years old (85%) with a Doctor of Pharmacy degree (90%). Nine were female. Intervention group participants had less than three years of experience as a pharmacist. The control group had less than three years (40%) or seven to ten years (40%) of experience. There was a significant increase in intervention group scores between pre- (median = 3.0, IQR = 3.0) and post-test segments (median = 6.5, IQR = 4.0, p = 0.02). There was no significant change between control group pre- and post-test segments (p = 0.48). (4) Conclusion: Pharmacist exposure to an advanced CDSS was associated with significantly increased quantity of relevant clinical recommendations in an MTM pharmacy setting.

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

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

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

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

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

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

Opioids, Polypharmacy, and Drug Interactions: A Technological Paradigm Shift Is Needed to Ameliorate the Ongoing Opioid Epidemic

Polypharmacy is a common phenomenon among adults using opioids, which may influence the frequency, severity, and complexity of drug–drug interactions (DDIs) experienced. Clinicians must be able to easily identify and resolve DDIs since opioid-related DDIs are common and can be life-threatening. Given that clinicians often rely on technological aids—such as clinical decision support systems (CDSS) and drug interaction software—to identify and resolve DDIs in patients with complex drug regimens, this narrative review provides an appraisal of the performance of existing technologies. Opioid-specific CDSS have several system- and content-related limitations that need to be overcome. Specifically, we found that these CDSS often analyze DDIs in a pairwise manner, do not account for relevant pharmacogenomic results, and do not integrate well with electronic health records. In the context of polypharmacy, existing systems may encourage inadvertent serious alert dismissal due to the generation of multiple incoherent alerts. Future technological systems should minimize alert fatigue, limit manual input, allow for simultaneous multidrug interaction assessments, incorporate pharmacogenomic data, conduct iterative risk simulations, and integrate seamlessly with normal workflow.

Use of the French healthcare insurance database to estimate the prevalence of exposure to potential drug-drug interactions

PURPOSE

Drug-drug interactions (DDIs) require monitoring in an aging population with increasing polypharmacy exposure. We aimed to estimate the prevalence of exposure to potential DDIs using the French healthcare insurance system database, for six DDIs with various clinical relevance: angiotensin II receptor blockers or angiotensin-converting enzyme inhibitors and nonsteroidal anti-inflammatory drugs (ARBs-ACEIs + NSAIDs), antiplatelet agents and NSAIDs (AAP + NSAIDs), serotonergic drugs and tramadol (SD + T), statins and macrolides (S + M), oral anticoagulant and NSAIDs (OAC + NSAIDs), and colchicine and macrolides (C + M).

METHODS

We used exhaustive healthcare data from a 1/97th random sample of the population covered by the French health insurance system (EGB) between 2006 and 2016. Exposure to a DDI was defined as overlapping exposure to two interacting drugs. The prevalence of exposure was estimated by year.

RESULTS

Prevalence of exposure in 2016 was estimated at 3.7% for ARBs-ACEIs + NSAIDs, 1.5% for AAP + NSAIDs, 0.76% for SD + T, 0.36% for S + M, 0.24% for AOC + NSAIDs, and 0.02% for C + M. In 26% to 58% of episodes of exposure, the two interacting drugs were prescribed by the same physician and dispensed by the same pharmacy the same day. Between 2006 and 2016, the yearly prevalence was increasing for SD + T and for DDIs involving NSAIDs, and it was decreasing for those involving macrolides.

CONCLUSION

Exposures to potential DDIs in France are not uncommon with a high proportion resulting from a co-prescription by the same physician. Monitoring the prevalence of exposure to DDIs is needed to implement prevention measures. Administrative data enable this surveillance in large and representative cohorts.

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 impact of pharmacists' input to reduce serotonin syndrome drug interactions in an Australian hospital

BACKGROUND

Drug interactions contribute significantly to adverse-related events and hospital admissions. Example of common drug interactions includes combinations of medications that induces serotonin syndrome. Pharmacists are well placed in the multidisciplinary team to alert prescribers of these drug interactions and offer an alternative management.

OBJECTIVE

The objective is to evaluate the effectiveness of pharmacists' input in preventing patients being discharged on clinically relevant drug interactions that have the potential to cause serotonin syndrome in an Australian hospital.

METHOD

A retrospective cross-sectional audit of patients' case notes who were prescribed a combination of drugs likely to induce serotonin syndrome on admission were examined over a 3-month period. A predefined list of serotonin syndrome-inducing drugs of severity 1 and 2 was used to search for patients on these drug combinations on admission. The severities of the drug combinations were classified as per the Monthly Index of Medical Specialties drug interactions guide. Subsequent pharmacists' interventions were recorded on discharge to observe any change in prescribing practice. Descriptive statistics were used to analyze the data. P values were obtained using the Student's t-test and Fisher's exact tests.

RESULTS

A total of 144 patients over 3 months were identified to have been prescribed a combination of drugs with a potential to cause serotonin syndrome during admission. Of these patients, 79 and 21% were prescribed combination of serotonergic drugs that were classified as severity 1 and 2, respectively, according to Monthly Index of Medical Specialties. A total of 56% (n = 81) of the audited patients were discharged with no serotonin syndrome-inducing drug combinations and 44% (n = 63) were discharged on serotonin syndrome-inducing drug combinations of severity 1 or 2. Pharmacist input has led to a significant reduction (relative risk reduction 44%; P < 0.0001) in the total number of patients who were discharged on severity 1 and 2 serotonin syndrome-inducing drug combinations. There were 87 patients (60%) who had a pharmacist input during admission. In this subset of the cohort, 36% (n = 31) of patients were discharged on serotonin syndrome-inducing drug combinations (combined both severity 1 and 2) compared with 56% (n = 32) in those who did not get a pharmacist input, P = 0.017. In addition, 64% (n = 56) of patients in this group were discharged on no serotonin syndrome-inducing drug combinations compared with 44% (n =  25) in the nonpharmacist group, P = 0.017.

CONCLUSION

The audit highlights the pharmacists' role in significantly reducing clinically relevant drug interaction in patients prescribed serotonin syndrome-inducing drug combination in a single-center Australian hospital on discharge.

Evaluation of an Electronic Critical Drug Interaction Program Coupled with Active Pharmacist Intervention

Background:

Failure to detect significant drug interactions may result in adverse outcomes. While proper screening and management of drug interactions can prevent the majority of adverse events, studies indicate that current practice is suboptimal. In the last quarter of 2001, physicians and pharmacists in Kaiser Permanente Colorado, a group model health maintenance organization, developed an electronic critical drug interaction alert program (CDIX). Electronic screening was coupled with active intervention to prevent dispensing of critically interacting drug combinations.

Objective:

To assess the impact of CDIX on the co-dispensing ol critically interacting drug combinations.

Methods:

A physician and team of outpatient pharmacists and clinical pharmacy staff developed a condensed list of critical drug interactions (8 drug combinations) to be included in the evaluation of CDIX. Monthly electronic outpatient pharmacy data were collected 20 months before and 37 months after CDIX implementation, with no lag period following implementation. Univariate analyses were completed to compare baseline subject characteristics of the pre- and post-CDIX groups using χ2 and Wilcoxon Rank Sum tests. Interrupted time series analysis was used to estimate changes in the rates of critical drug interactions.

Results:

Three hundred sixty-seven instances of co-dispensing were observed in 348 subjects during the pre-CDIX period and 256 instances of co-dispensing were observed in 248 subjects during the post-CDIX period. Following CDIX implementation, the overall rate of co-dispensing dropped abruptly from 21.3 to 14.7 per 10,000 prescriptions, representing a relative decrease in co-dispensing of 31% from the month before CDIX implementation (p = 0.0125). Significant reductions in co-dispensing were noted for 7 of the 8 drug class combinations.

Conclusions:

Employing an intervention system that limits electronic alerts regarding drug interactions to those deemed critical but that also requires pharmacist intervention and collaboration with the prescriber decreases the number of critical drug interactions dispensed.

Mentoring and supervising clinical pharmacist students at patients' bedside: which benefits?

RATIONALE, AIMS AND OBJECTIVES

 Hospital clinical pharmacists are involved in teaching students during professional internship. Organization between the unit care and the pharmacy place is complicated. This study evaluated the effectiveness of two pharmaceutical teams: an experienced pharmacist in the pharmacy place, reachable by phone (team 1) or an experienced pharmacist in the ward, near patients and students (team 2).

METHODS

Pharmaceutical interventions were collected during two successive time periods, each of 6 months in a 15-bed unit (neurology). During the first time period, prescriptions were analyzed by the student (resident) in the ward and experienced pharmacist in the pharmacy place. During the second time period, prescriptions were analyzed by both experienced pharmacist and the resident in the ward. We compared the number, the type, the approval of pharmaceutical interventions and the medication reconciliation activities. Proportions were compared by a chisquared test (or Fisher exact test) as well as the quantitative value was calculated by a Student test.

RESULTS

'Mentoring and supervising' students in the ward increased significantly the number of pharmaceutical interventions (PI; 104 interventions for 1408 analyzed prescriptions (7.4%) by the students in the ward and 317 interventions for 1391 (22.8%) by both the experienced pharmacist and the students in the ward (P = 0.002). Furthermore, specific interventions from medication reconciliation were significantly increased by the presence of experienced pharmacist in the ward (0.96% vs. 8.83% P = 0.018).

CONCLUSION

Effectiveness of clinical pharmacists can be improved by the presence of experienced pharmacist at patients' bedside, near students.

A comparison of five common drug-drug interaction software programs regarding accuracy and comprehensiveness

Objective:

Drug–drug interactions (DDIs) can cause failure in treatment and adverse events. DDIs screening software is an important tool to aid clinicians in the detection and management of DDIs. However, clinicians should be aware of the advantages and limitations of these programs. We compared the ability of five common DDI programs to detect clinically important DDIs.

Methods:

Lexi-Interact, Micromedex Drug Interactions, iFacts, Medscape, and Epocrates were evaluated. The programs' sensitivity, specificity, and positive and negative predictive values were determined to assess their accuracy in detecting DDIs. The accuracy of each program was identified using 360 unknown pair interactions, taken randomly from prescriptions, and forty pairs of clinically important ones. The major reference was a clinical pharmacist alongside the Stockley's Drug Interaction and databases including PubMed, Scopus, and Google Scholar. Comprehensiveness of each program was determined by the number of components in the drug interaction monograph. The aggregate score for accuracy and comprehensiveness was calculated.

Findings:

Scoring 250 out of possible 400 points, Lexi-Interact and Epocrates, provided the most accurate software programs. Micromedex, Medscape, and iFacts ranked third, fourth, and fifth, scoring 236, 202, and 191, respectively. In comprehensiveness test, iFacts showed the highest score, 134 out of possible 134 points, whereas Lexi-Interact rated second, with a score of 120. Scoring 370 and 330 out of possible 534 points, Lexi-Interact and Micromedex, respectively, provided the most competent, complete, and user-friendly applications.

Conclusion:

Lexi-Interact and Micromedex showed the best performances. An increase in sensitivity is possible by the combination of more than one programs and expert pharmacist intervention.

Identifying effective computerized strategies to prevent drug-drug interactions in hospital: A user-centered approach

BACKGROUND

Drug-drug interactions (DDIs) are an important and preventable cause of medication errors in hospitals. Recent developments in technology have seen new strategies emerge for preventing DDIs but these computerized strategies are rarely evaluated and are typically implemented with little input from the individuals using them.

AIM

To determine the opinions of both experts and users (prescribers) on computerized strategies available to assist in the identification and prevention of DDIs in hospitals.

METHOD

Eight drug safety experts and 18 prescribers took part in semi-structured interviews. Participants were asked about their confidence in identifying DDIs and their views on potential computerized strategies to prevent DDIs.

RESULTS

No prescribers reported complete confidence in identifying dangerous DDIs, with junior prescribers appearing less confident than senior prescribers. Most prescribers believed that computerized alerts would be the most effective strategy for preventing DDIs, while experts were more critical of alerts.

CONCLUSION

The lack of confidence displayed by prescribers in their ability to identify DDIs suggests that an appropriate strategy would be one that does not rely on individuals seeking out the information themselves. While a large number of problems related to DDI alert implementation have been reported in the literature (e.g. alert overload), prescribers appeared to be receptive to the idea of being alerted. By ensuring users are aware of the limitations of the system and involving them in DDI strategy design we expect greater use and satisfaction with the adopted strategy.

Statins in the elderly: a patient-focused approach

Individuals age >65 years represent the fastest-growing subpopulation in the United States. Although these individuals with the highest cardiovascular risk profile would be anticipated to be the most aggressively treated, paradoxically, treatment and baseline risk are inversely related. Presumably, the elderly population would benefit from high-intensity statin therapy; however, as per the 2013 American College of Cardiology/American Heart Association guidelines, given the scarcity of evidence in patients age >75, there are only sufficient data from randomized controlled trials to support use of moderate-intensity statin therapy for secondary prevention. Despite evidence demonstrating statins are beneficial in the elderly, the decision to initiate and sustain treatment should be a well-informed and collaborative decision. One must balance the benefits (secondary atherosclerotic cardiovascular prevention, stroke reduction, decreased morbidity and mortality) with the potential risks to the elderly (altered metabolism, comorbidities, polypharmacy and drug-drug interactions, side effects, cognitive limitations, and cost).

Investigation of Simulated Pharmacist Decision Making Involving Prescriptions With a High Probability of Causing Patient Harm

Background: Medication errors pose a significant risk to patients, resulting in morbidity, mortality, and unnecessary health care utilization. Pharmacists, using their professional judgment, have an important role as a final check for identifying and resolving these problems. Little is known, however, about pharmacist perspectives and experiences with dispensing or withholding potentially dangerous prescriptions. Objectives: To (a) evaluate the extent to which pharmacists would not dispense a likely harmful prescription which has been confirmed by the prescriber and (b) assess pharmacist attitudes and experiences with dispensing likely harmful prescriptions. Methods: An anonymous, self-administered, 25-item survey was emailed to members of a state pharmacy association and a pharmacy college alumni list. A series of static prescription vignettes (1 reasonable and 4 likely dangerous doses) were presented and asked if they would fill each prescriber-confirmed prescription. Pharmacists also were asked a series of Likert-type, open-ended, multiple choice, and demographic items regarding their professional experiences and role perceptions. Results: There were 497 usable responses. Three of the 4 dangerous prescriptions were withheld by the majority of pharmacists (sumatriptan as the exception). No demographic variable was universally associated with filling dangerous vignette prescriptions; rather, there were vignette-specific differences. The majority of pharmacists reported refusing to fill a potentially harmful prescription during their career. Conclusions: There appears to be meaningful variation in how pharmacists react when presented with likely harmful prescriptions. More research is needed to better understand this role, its determinants, and the potential effects on patient safety.

The role of clinical decision support in pharmacist response to drug-interaction alerts

BACKGROUND

With over 100,000 different types of drug-drug interactions health care professionals rely heavily on automated drug-interaction alerts. Substantial variance in drug-interaction alerts yields opportunities for the use of clinical decision support (CDS) as a potential benefit to pharmacists.

OBJECTIVE

The purpose of this research was to determine whether decision support during dispensing impacts pharmacist response to drug-interaction alerts.

METHODS

A brief survey was administered to pharmacists in the community consisting of three patient cases, each containing three drug-drug interactions of varying severity. For each interaction, pharmacists were asked how they would respond, one group of pharmacists was randomly assigned to receive CDS while the other group did not.

RESULTS

There were no significant differences in pharmacist response to alerts between the two groups. The control group did appear to be more likely to consult a drug reference, but this difference was not significant. While this study did not demonstrate a significant difference, drug-interaction alerts are still an area where improvements could be made. Advancements have the potential to reduce risk to patients and limit unnecessary hospital admissions.

CONCLUSION

This study suggests that this level of clinical decision support has limited impact, but may prove beneficial by reducing the need to consult additional references.

Prevalence of the prescription of potentially interacting drugs

The use of multiple medications is becoming more common, with a correspondingly increased risk of untoward effects and drug-related morbidity and mortality. We aimed at estimating the prevalence of prescription of relevant potentially interacting drugs and at evaluating possible predictors of potentially interacting drug exposure. We retrospectively analyzed data on prescriptions dispensed from January 2004 to August 2005 to individuals of two Italian regions with a population of almost 2.1 million individuals. We identified 27 pairs of potentially interacting drugs by examining clinical relevance, documentation, and volume of use in Italy. Subjects who received at least one prescription of both drugs were selected. Co-prescribing denotes “two prescriptions in the same day”, and concomitant medication “the prescription of two drugs with overlapping coverage”. A logistic regression analysis was conducted to examine the predictors of potential Drug-Drug Interaction (pDDIs). 957,553 subjects (45.3% of study population) were exposed to at least one of the drugs/classes of the 27 pairs. Overall, pDDIs occurred 2,465,819 times. The highest rates of concomitant prescription and of co-prescription were for ACE inhibitors+NSAIDs (6,253 and 4,621/100,000 plan participants). Considering concomitance, the male/female ratio was <1 in 17/27 pairs (from 0.31 for NSAIDs-ASA+SSRI to 0.74 for omeprazole+clopidogrel). The mean age was lowest for methotrexate pairs (+omeprazole, 59.9 years; +NSAIDs-ASA, 59.1 years) and highest for digoxin+verapamil (75.4 years). In 13/27 pairs, the mean ages were ≥70 years. On average, subjects involved in pDDIs received ≥10 drugs. The odds of exposure were more frequently higher for age ≥65 years, males, and those taking a large number of drugs. A substantial number of clinically important pDDIs were observed, particularly among warfarin users. Awareness of the most prevalent pDDIs could help practitioners in preventing concomitant use, resulting in a better quality of drug prescription and potentially avoiding unwanted side effects.

Recommendations for generating, evaluating, and implementing drug-drug interaction evidence

In October 2009, a 2-day, multistakeholder, national conference was held in Rockville, Maryland, to discuss and propose methods to improve the drug-drug interaction (DDI) evidence base and its evaluation and integration into clinical decision support (CDS) systems. The conference featured participants representing consumers, health care providers, those responsible for relevant policies and guidelines, and developers and vendors of DDI compendia, databases, and CDS systems. One desired outcome of the conference was to prepare recommendations on critical issues surrounding DDI evidence. A set of recommendations was developed to improve the generation, evaluation, and translation of DDI evidence into CDS systems based on presentations by experts and the supporting literature. These recommendations were reviewed initially by conference moderators, speakers, and Scientific Steering and Planning Committee members, and subsequently by all attendees. The following recommendations were developed to increase patient safety by improving the relevance and assessment of DDI evidence: conduct well-designed studies to determine the incidence, outcomes, and patient-level risk factors for DDIs; use a systematic and transparent process for evaluating the DDI evidence in order to estimate the severity and risks of DDIs; and improve the integration of DDI evidence into electronic CDS. Opportunities exist to improve the DDI evidence base, develop and promote a systematic approach for evaluating the evidence, and integrate this evidence into meaningful CDS.

Impact of a drug-drug interaction intervention on pharmacy and medical students' knowledge and attitudes: a 1-year follow-up

BACKGROUND

There have been many interventions aimed at improving retention of drug-drug interaction (DDI) knowledge of health care professionals. Much less is known about their retention of such knowledge for extended periods of time after an educational intervention.

OBJECTIVES

To evaluate pharmacy and medical students' knowledge retention and attitudes 1 year after participating in an educational session on DDIs.

METHODS

This study used a pre-post design with an assessment of DDI knowledge and attitude by pharmacy and medical students before and after the final didactic year of their professional education. The intervention was a 1-hour program.

RESULTS

A total of 74 of 193 students (38%) completed the pre, post, and final questionnaire. The median numbers of correctly identified DDIs before the program were 8 and 7 for pharmacy and medical students, respectively, out of a possible score of 15. One year after, the median identification knowledge scores were 12 and 8, respectively, for pharmacy and medical students. The median difference scores of correctly managed DDIs on this evaluation 1 year after the program were -4 and -8 for pharmacy and medical students, respectively (P<.05).

CONCLUSION

This study found that the ability to identify important DDIs is poor among both pharmacy and medical students 1 year after being exposed to the educational session.

Healthcare professional students' knowledge of drug-drug interactions

OBJECTIVES

To evaluate changes in medical, pharmacy, and nurse practitioner students' drug-drug interaction (DDI) knowledge after attending an educational program.

DESIGN

A DDI knowledge assessment containing 15 different drug pairs was administered to participants before and after a 45-minute educational session.

EVALUATION

Pharmacy, medical, and nursing students scored significantly higher on the posttest assessment for DDI recognition (median change 3, 9, and 8, respectively) and management strategy (median change 5, 9, 8, respectively), indicating a significant improvement in DDI knowledge as a result of the educational session. Pharmacy students scored significantly higher on the pretest; however, no difference was observed between the students' posttest scores. Posttest scores for all student groups were significantly greater than their respective pretest scores (p < 0.001).

CONCLUSIONS

Significant improvement in healthcare professional students' DDI knowledge was observed following participation in the educational session.

Pharmacy students' retention of knowledge of drug-drug interactions

OBJECTIVES

To evaluate pharmacy students' drug-drug interaction (DDI) knowledge retention over 1 year and to determine whether presenting DDI vignettes increased knowledge retention.

METHODS

A knowledge assessment tool was distributed to fourth-year pharmacy students before and after completing a DDI educational session. The questionnaire was re-administered after 1 year to assess knowledge retention. During the intervening year, students had the option of presenting DDI case vignettes to preceptors and other health professionals as part of their advanced pharmacy practice experiences (APPEs).

RESULTS

Thirty-four of 78 pharmacy students completed both the post-intervention and 1-year follow-up assessments. Students' knowledge of 4 DDI pairs improved, knowledge of 3 DDI pairs did not change, and knowledge of the remainder of DDI pairs decreased. Average scores of the 18 students who completed all tests and presented at least 1 vignette during their APPEs were higher on the 1-year follow-up assessment than students who did not, suggesting greater DDI knowledge retention (p = 0.04).

CONCLUSION

Although pharmacy students' overall DDI knowledge decreased in the year following an educational session, those who presented vignettes to health professionals retained more DDI knowledge, particularly on those DDIs for which they gave presentations. Other methods to enhance pharmacy students' retention of DDI knowledge of clinically important DDIs are needed.

Medical, nursing, and pharmacy students' ability to recognize potential drug-drug interactions: a comparison of healthcare professional students

PURPOSE

To evaluate and compare the drug-drug interaction (DDI) knowledge of pharmacy, medical, and nurse practitioner (NP) students who are beginning supervised clinical practice.

DATA SOURCES

This study utilized a prospective evaluation of DDI knowledge among healthcare professional students who were currently enrolled in their final didactic year at the University of Arizona Colleges of Medicine, Pharmacy, or Nursing's NP program. Students were asked to assess 15 drug pairs and to select an appropriate management strategy for each pair. The primary outcome measure was the ability to correctly categorize each drug pair into one of the five management responses. The secondary outcome measure was the number of clinically significant DDIs recognized.

CONCLUSIONS

Pharmacy students demonstrated significantly better knowledge than medical and NP students with respect to identifying and selecting management strategies for possible DDIs. However, there is much room for improvement for all groups.

IMPLICATIONS FOR PRACTICE

An increase in curricular content that focuses on DDIs has the potential to better prepare medical, pharmacy, and NP students for practice situations involving DDI alerts, and to increase the quality of patient care.

Occurrence of potential drug-drug interactions in nursing home residents

Objective

It has been suggested that elderly people are at increased risk of drug-related problems such as drug-induced adverse effects, drug-drug interactions and drug-disease interactions. This is particularly the case for nursing home residents because of the often complicated and multiple co-morbidity that occurs in these people. The aim of this study was to develop prescribing indicators to assess systematically the occurrence and nature of potential drug-drug interactions (DDIs) in a cohort of Dutch nursing home residents.

Method

The study was conducted in residents aged 65 years and over in six nursing homes (n=2,355, two-year study period). Computerised medication data for the residents were evaluated with respect to co-prescribing of potentially interacting drugs. All DDIs that were classified as clinically relevant according to the Dutch National Drug Interaction Database were studied. DDIs were classified into three categories according to their pharmacological mechanism: 1 — pharmacokinetic interactions at the level of gastrointestinal (GI) absorption; 2 — pharmacokinetic interactions at the level of metabolism and excretion; and 3 — pharmacodynamic interactions.

Key findings

Thirty-two per cent (n=748) of all residents were exposed to one or more combinations of drugs that could lead to clinically adverse outcomes. The numbers of residents who received drug combinations with a mechanism of interaction from category 1, 2 or 3 were 73 (3 per cent), 164 (7 per cent) and 612 (26 per cent) respectively. The number of medications prescribed was significantly associated with the occurrence of a potential DDI (P&lt;0.05). Drugs most frequently involved were oral anticoagulants, antibiotics and theophylline.

Conclusion

During the two-year study period, about one-third of the residents were exposed to at least one drug interaction considered clinically relevant. Adequate surveillance systems are needed to enable better identification of these interactions with a view to preventing potential clinical problems. Using the prescribing indicators developed in this study, such surveillance could focus on detection and clinical aspects of potential DDIs and possible alternative treatments.

Ability of pharmacy clinical decision-support software to alert users about clinically important drug-drug interactions

OBJECTIVE

Pharmacy clinical decision-support (CDS) software that contains drug-drug interaction (DDI) information may augment pharmacists' ability to detect clinically significant interactions. However, studies indicate these systems may miss some important interactions. The purpose of this study was to assess the performance of pharmacy CDS programs to detect clinically important DDIs.

DESIGN

Researchers made on-site visits to 64 participating Arizona pharmacies between December 2008 and November 2009 to analyze the ability of pharmacy information systems and associated CDS to detect DDIs. Software evaluation was conducted to determine whether DDI alerts arose from prescription orders entered into the pharmacy computer systems for a standardized fictitious patient. The fictitious patient's orders consisted of 18 different medications including 19 drug pairs-13 of which were clinically significant DDIs, and six were non-interacting drug pairs.

MEASUREMENTS

The sensitivity, specificity, positive predictive value, negative predictive value, and percentage of correct responses were measured for each of the pharmacy CDS systems.

RESULTS

Only 18 (28%) of the 64 pharmacies correctly identified eligible interactions and non-interactions. The median percentage of correct DDI responses was 89% (range 47-100%) for participating pharmacies. The median sensitivity to detect well-established interactions was 0.85 (range 0.23-1.0); median specificity was 1.0 (range 0.83-1.0); median positive predictive value was 1.0 (range 0.88-1.0); and median negative predictive value was 0.75 (range 0.38-1.0).

CONCLUSIONS

These study results indicate that many pharmacy clinical decision-support systems perform less than optimally with respect to identifying well-known, clinically relevant interactions. Comprehensive system improvements regarding the manner in which pharmacy information systems identify potential DDIs are warranted.

Evaluation of three brands of drug interaction software for use in intensive care units

OBJECTIVE

To evaluate drug interaction software programs and determine their accuracy in identifying drug-drug interactions that may occur in intensive care units. Setting The study was developed in Brazil.

METHOD

Drug interaction software programs were identified through a bibliographic search in PUBMED and in LILACS (database related to the health sciences published in Latin American and Caribbean countries). The programs' sensitivity, specificity, and positive and negative predictive values were determined to assess their accuracy in detecting drug-drug interactions. The accuracy of the software programs identified was determined using 100 clinically important interactions and 100 clinically unimportant ones. Stockley's Drug Interactions 8th edition was employed as the gold standard in the identification of drug-drug interaction.

MAIN OUTCOME

Sensitivity, specificity, positive and negative predictive values.

RESULTS

The programs studied were: Drug Interaction Checker (DIC), Drug-Reax (DR), and Lexi-Interact (LI). DR displayed the highest sensitivity (0.88) and DIC showed the lowest (0.69). A close similarity was observed among the programs regarding specificity (0.88-0.92) and positive predictive values (0.88-0.89). The DIC had the lowest negative predictive value (0.75) and DR the highest (0.91).

CONCLUSION

The DR and LI programs displayed appropriate sensitivity and specificity for identifying drug-drug interactions of interest in intensive care units. Drug interaction software programs help pharmacists and health care teams in the prevention and recognition of drug-drug interactions and optimize safety and quality of care delivered in intensive care units.

Patient Factors That Influence Warfarin Dose Response

Warfarin has long been the mainstay of oral anticoagulation therapy for the treatment and prevention of venous and arterial thrombosis. The narrow therapeutic index of warfarin, and the complex number of factors that influence international normalized ratio (INR) response, makes optimization of warfarin therapy challenging. Determination of the appropriate warfarin dose during initiation and maintenance therapy requires an understanding of patient factors that influence dose response: age, body weight, nutritional status, acute and chronic disease states, and changes in concomitant drug therapy and diet. This review will examine specific clinical factors that can affect the pharmacokinetics and pharmacodynamics of warfarin, as well as the role of pharmacogenetics in optimizing warfarin therapy.

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.

Opinions on Drug Interaction Sources in Anticancer Treatments and Parameters for an Oncology-Specific Database by Pharmacy Practitioners in Asia

Cancer patients undergoing chemotherapy are particularly susceptible to drug-drug interactions (DDIs). Practitioners should keep themselves updated with the most current DDI information, particularly involving new anticancer drugs (ACDs). Databases can be useful to obtain up-to-date DDI information in a timely and efficient manner. Our objective was to investigate the DDI information sources of pharmacy practitioners in Asia and their views on the usefulness of an oncology-specific database for ACD interactions. A qualitative, cross-sectional survey was done to collect information on the respondents' practice characteristics, sources of DDI information and parameters useful in an ACD interaction database. Response rate was 49%. Electronic databases (70%), drug interaction textbooks (69%) and drug compendia (64%) were most commonly used. Majority (93%) indicated that a database catering towards ACD interactions was useful. Essential parameters that should be included in the database were the mechanism and severity of the detected interaction, and the presence of a management plan (98% each). This study has improved our understanding on the usefulness of various DDI information sources for ACD interactions among pharmacy practitioners in Asia. An oncology-specific DDI database targeting ACD interactions is definitely attractive for clinical practice.

Potential drug-drug interactions in prescriptions dispensed in community pharmacies in Greece

OBJECTIVES

To evaluate the nature, type and prevalence of potential drug-drug interactions (DDIs) in prescriptions dispensed in community pharmacies in Thessaloniki, Greece. Secondary objectives included the classification of DDIs as per pharmacotherapeutic class of the medications and the investigation of the relationship between medical specialties and the frequency of potential DDIs, as well as the relationship between DDIs and prescription size. Setting DDIs are a common cause of adverse drug reactions (ADRs) among patients using multiple drug therapy. In Greece a reliable computerized surveillance system for monitoring potential DDIs is not yet fully established. As a result, the prevalence of such DDIs in prescriptions dispensed by community pharmacies in Greece is unknown.

METHODS

We conducted a prospective, descriptive study. Over a 3-month period (November 2007-January 2008), a total of 1,553 handwritten prescriptions were collected from three community pharmacies in Thessaloniki, Greece. The prescriptions were processed using the Drug Interactions Checker within the www.drugs.com database. The identified potential DDIs were categorized into two classes, major and moderate, according to their level of clinical significance.

MAIN OUTCOME MEASURES

Overall 213 prescriptions had one or more potential DDIs and a total of 287 major and moderate DDIs were identified. Potential DDIs were identified in 18.5% of all prescriptions. Major DDIs were identified in 1.9% of all prescriptions and represented 10.5% of all DDIs detected, whereas moderate DDIs were identified in 16.6% of all prescriptions and represented 89.5% of all DDIs detected. The rate of DDIs increased with prescription size. The most common drug involved in major DDIs was amiodarone which interacts with potassium-wasting diuretics, digoxin, simvastatin and acenocoumarol.

CONCLUSIONS

Our results indicate that patients in Greece are at risk of ADRs caused by medications due to potential DDIs. An appropriate surveillance system for monitoring such interactions should be implemented and physicians should be more aware of potentially harmful DDIs. Pharmacists can contribute to the detection and prevention of drug-related injuries, especially of clinically meaningful DDIs that pose a potential risk to patient safety.

An evidence-based assessment of the clinical significance of drug-drug interactions between disease-modifying antirheumatic drugs and non-antirheumatic drugs according to rheumatologists and pharmacists

BACKGROUND

Clinically relevant drug-drug interactions (DDIs) must be recognized in a timely manner and managed appropriately to prevent adverse drug reactions or therapeutic failure. Because the evidence for most DDIs is based on case reports or poorly documented clinical information, there is a need for better assessment of their clinical relevance.

OBJECTIVE

This study evaluates the interdisciplinary agreement between rheumatologists and clinical (hospital) pharmacists in assessing the clinical relevance of DDIs with disease-modifying antirheumatic drugs (DMARDs) and non-DMARD medications.

METHODS

Potential DDIs were identified from the medical literature using MEDLINE and EMBASE for the years 1968-2009. The following search terms were used for the key word, title, and abstract sections of the publications: interaction(s), DMARD, disease-modifying antirheumatic drug(s), antirheumatic, rheumatology, rheumatoid arthritis, and the names of the individual DMARDs of interest (abatacept, adalimumab, anakinra, auranofin, aurothioglucose, aurothiomalate, d-penicillamine, etanercept, gold, [hydroxy]-chloroquine, interleukin-1 receptor antagonist, IL1-RA, infliximab, leflunomide, methotrexate, rituximab, and sulfasalazine/sulphasalazine). Reference lists of the retrieved publications were searched for further information on potential DDIs. All pharmacodynamic or pharmacokinetic DDIs between a DMARD and a non-DMARD identified were included in the study, with the exception of evidence regarding DMARD doses higher than used in the treatment of rheumatoid arthritis and interactions with phytotherapeutic or homeopathic preparations. Using a standard information set for each DDI (eg, from product labeling, textbooks, and the medical literature), a group of rheumatologists and a group of clinical pharmacists independently assessed whether the individual drug-DMARD combinations interacted and whether they required immediate intervention. Both groups consisted of 3 members (2 men and 1 woman), aged 40 to 60 years, who had >5 years of clinical experience and were currently involved in clinical practice in large, nonacademic teaching hospitals in the Netherlands.

RESULTS

Forty potential DDIs with DMARDs were retrieved and assessed by the 2 groups. For 30 (75%) of these, rheumatologists and clinical pharmacists agreed about the requirement for immediate intervention. Specifically, 17 drug combinations (43%) were judged to interact and to require immediate intervention, and 13 combinations (33%) were judged either not to interact or to interact but not to require immediate intervention. For 10 combinations (25%), rheumatologists and clinical pharmacists were not in agreement. Overall, agreement between the groups was good (kappa = 0.80) for judging whether the drug combinations were interactions, and agreement was fair (kappa = 0.39) for judging whether immediate intervention was required. Prospective analysis of the data showed that rheumatologists tended to recommend immediate intervention more often when the adverse reaction to the DDI involved an increased risk of toxicity of the DMARD. In contrast, clinical pharmacists more often advocated immediate intervention when the adverse reaction involved decreased effectiveness of the DMARD.

CONCLUSION

For a subset of DMARD-drug combinations, rheumatologists and clinical pharmacists differed in their assessments of clinical relevance.

Pharmacy students' ability to identify potential drug-drug interactions

OBJECTIVE

To evaluate the ability of third- and fourth-year pharmacy students to identify clinically significant drug-drug interactions (DDIs)

METHODS

A questionnaire designed to measure DDI knowledge was disseminated to fourth-year pharmacy students in a school of pharmacy. A second questionnaire was distributed to third-year pharmacy students in 2 schools of pharmacy (schools A and B) and re-administered to students in 1 of the schools 1 year later.

RESULTS

Class of 2005 fourth-year pharmacy students correctly categorized an average of 52% +/- 13% DDI pairs on the first questionnaire. Third-year pharmacy students at schools A and B correctly categorized an average of 61% +/- 18% and 66% +/- 15% of DDI pairs, respectively. The average percentage of correct responses for fourth-year students from the class of 2007 was 65% (+/- 17%).

CONCLUSION

Pharmacy students' ability to identify important DDIs is far from optimal, even after completing experiential requirements.

Prescribers' knowledge of and sources of information for potential drug-drug interactions: a postal survey of US prescribers

BACKGROUND

Given the high prevalence of medication use in the US, the risk of drug-drug interactions (DDIs) and potential for patient harm is of concern. Despite the rise in technologies to identify potential DDIs, the ability of physicians and other prescribers to recognize potential DDIs is essential to reduce their occurrence. The objectives of this study were to assess prescribers' ability to recognize potential clinically significant DDIs and to examine the sources of information they use to identify potential DDIs and prescribers' opinions on the usefulness of various DDI information sources.

METHODS

A postal questionnaire was developed to assess prescriber knowledge of medications that may interact and prescribers' usual sources of DDI information. Recipients were asked to classify 14 drug pairs as 'contraindicated', 'may be used together but with monitoring' or 'no interaction'. A response option of 'not sure' was also provided. The questionnaires were sent to a national sample of 12 500 prescribers based on past history of prescribing drugs associated with known potential for DDI, who were identified using data from a pharmacy benefit manager covering over 50 million individuals.

RESULTS

Usable questionnaires were obtained from 950 prescribers. The percentage of prescribers who correctly classified specific drug pairs ranged from 18.2% for warfarin and cimetidine to 81.2% for paracetamol (acetaminophen) with codeine and amoxicillin, with 42.7% of all combinations classified correctly. The number of drug pairs correctly classified by the prescribers ranged from 0 to 13. For half of the drug pairs over one-third of the respondents answered 'not sure'; among those drug pairs, two were contraindicated. When asked what source was used to learn more about a potential DDI, a quarter of the prescribers reported using personal digital assistants and another quarter used printed material. The majority of the prescribers (68.4%) reported that they were usually informed by pharmacists about their patients' potential exposure to DDIs. Compared with the prescribers who used other sources, those who used computerized DDI alerts as their usual source of DDI information consistently gave a lower rating score to the five statements that assessed the usefulness of the information.

CONCLUSION

This study suggests that prescribers' knowledge of potential clinically significant DDIs is generally poor. These findings are supported by other research and emphasize the need to develop systems that alert prescribers about potential interactions that are clinically relevant. Physicians most commonly reported learning about potential DDIs from pharmacists, suggesting further work is needed to improve the drug-prescribing process to identify potential safety issues earlier in the medication use process.

Potential determinants of prescribers' drug-drug interaction knowledge

BACKGROUND

Health care professionals' ability to recognize potential drug-drug interactions (potential DDIs) is important in reducing the risk of potential DDIs and their adverse consequences. Until now, little is known about the determinants of prescribers' potential DDI knowledge.

OBJECTIVES

This study was conducted to develop interval scales to evaluate prescribers' potential DDI knowledge and perceived usefulness of various potential DDI information sources, and to examine demographic and practice factors that may be related to prescribers' knowledge of potential DDIs.

METHODS

Data were obtained from a national mail survey of prescribers who were determined from pharmacy claims obtained from a pharmacy benefits manager to have prescribed at least 1 drug involved in a potential DDI, and a control group who had prescribed either 1 of the medications in the drug combinations of interest. The survey instrument included 14 drug-drug pairs that evaluated prescribers' ability to recognize clinically important potential DDIs and 5-point Likert scale-type questions that assessed prescribers' perceived usefulness of potential DDI information provided by various sources. The knowledge and usefulness questions were examined via Rasch dichotomous and rating scale models, respectively.

RESULTS

A total of 950 completed surveys were included in the analysis (overall adjusted response rate: 7.9%). Rasch analysis of knowledge and usefulness items revealed satisfactory model-data-fit (infit mean square [MNSQ]< or =1.5 and outfit MNSQ< or =2.0) and moderate person reliability of 0.72 and 0.61, respectively. Multiple regression analysis revealed that compared with generalists, specialists had lower potential DDI knowledge test scores. In addition, poorer potential DDI knowledge was associated with a lack of clinical experience witnessing harm caused by a potential DDI. Also, the prescribers whose drug selections were affected by the risk of potential DDIs "very much" scored higher than those who reported that their prescribing was affected by the risk "a little" or "not at all."

CONCLUSIONS

This study found that specialists were less likely to correctly identify interactions compared with generalists. Other important predictors of potential DDI knowledge included the experience of seeing a potential DDI-caused harm and the extent to which the risk of potential DDIs affected prescribers' drug selection.

'Noisy patients'--can signal detection theory help?

Signal detection theory tests an observer's ability to discriminate between signal and noise. Deciding whether or not a patient's symptoms warrant further investigation or treatment is an example of this task in the clinical setting. Noise can exist within the observer--for example, in the brain of a tired or inexperienced doctor--or can arise from an external source such as the patient. Patients can produce external noise by giving numerous unrelated presenting complaints, providing overly detailed accounts of their symptoms, or simply talking too quickly. The more noise that is present, the harder the signal (such as a new disease or a notable change in an old condition) is to detect. Patients in the neurology clinic seem to be 'noisier' than average, perhaps owing to the long duration of their condition in many cases and the relatively high proportion of patients with medically unexplained symptoms. The ability to interpret such 'noisy' histories often underpins the neurological diagnosis. This Review aims to promote the relevance of signal detection theory to the overworked neurologist on the ward or in the clinic and explores strategies to reduce the noise generated both within the brain of the doctor and by patients.

Prevalence and predictors of potential drug-drug interactions in Regione Emilia-Romagna, Italy

BACKGROUND AND OBJECTIVE

Drug-drug interactions (DDIs) are preventable medication errors associated with potentially serious adverse events and death. Several studies have examined the prevalence of potential DDIs among ambulatory patients in various countries. Limited recent data on the prevalence of potential DDIs in Italy are available in the medical literature. The objective of this study was to estimate the prevalence of clinically important potential DDIs among the approximately 4 million residents of Regione Emilia-Romagna (RER), Italy, and to examine possible predictors of potential DDI exposure.

METHODS

A retrospective follow-up study of 2004 outpatient prescription data from RER was conducted. A previously published list of clinically important potential DDIs was refined to include only pairs of drugs in which both drugs were reimbursed by the 2004 Italian National Formulary. A potential DDI was defined as the presence of a minimum 5-day overlap in days supply for drugs in an interacting pair. The 1-year period prevalence of each potential DDI was quantified. A logistic regression analysis was conducted to examine patient characteristics as predictors of potential DDIs.

RESULTS AND DISCUSSION

The list of clinically important potential DDIs included 12 drug pairs that could be captured using the RER database. These 12 potential DDIs occurred 8894 times in the RER population in 2004. The most commonly identified potentially interacting medication pairs were warfarin and non-steroidal anti-inflammatory drugs (6824 cases), theophylline/aminophylline and ciprofloxacin/fluvoxamine (930), and warfarin and barbiturates (567). Odds of exposure were highest among those aged 65 years or older, males, and those with more chronic conditions. Odds of exposure increased 1.39 times with each addition of a prescription medication.

CONCLUSION

A substantial number of clinically important potential DDIs were identified, particularly among warfarin users. Awareness of the most prevalent potential DDIs can help practitioners prevent concomitant use of these dangerous medication combinations.

[Prospective assessment of drug interactions in hospitalized patients using a computer programme]

OBJECTIVE

To describe the implementation, functioning and results of a prospective automated system monitoring clinically relevant interactions in hospitalised patients in a 400-bed hospital for the period between 1 January 2005 and 31 March 2006.

METHOD

We created a computer programme in Access(R) 97 that checks, twice daily, the drug treatments of all of the patients admitted to the hospital in order to search for the 198 pairs of drugs previously selected from: a validated tertiary source (Hansten PD, Horn JR. Hansten and Horn's Drug Interactions Analysis and Management. St. Louis, MO: Facts and Comparisons; 2001 and updates), most relevant primary sources, expert opinions and alerts from the Spanish Agency of Medicines and Health Products. The clinical pharmacist will assess the drug-drug interaction (DDI) taking into account the timeline sequence, dose, administration route, management opportunities, patient diagnosis, clinical relevance, etc. If necessary, the doctor is contacted by phone and/or letter to inform him/her of the type of interaction, the mechanism and possible management. The programme files the following variables every day: interaction, sex, age, service, number of drugs, pharmaceutical intervention and doctor response.

RESULTS

Clinically relevant drug interactions were detected in 3% of patients during their stay in hospital. These patients were an average of 10 years older and received an average of four drugs more than other patients. A total of 538 interactions were detected in 15 months. Forty-three of 198 possible DDls appeared at some time. The pharmacist intervened on 126 (23%) occasions. The doctor accepted the recommendation on at least 66 (52%) occasions. Fourteen drugs were responsible for 91% of the interactions reported.

CONCLUSIONS

The patients with interactions are older and receive more drugs. The prior intervention of the pharmacist eliminated 77% of unnecessary alerts.

Potential drug-drug and drug-disease interactions in prescriptions for ambulatory patients over 50 years of age in family medicine clinics in Mexico City

BACKGROUND

In Mexico, inappropriate prescription of drugs with potential interactions causing serious risks to patient health has been little studied. Work in this area has focused mainly on hospitalized patients, with only specific drug combinations analyzed; moreover, the studies have not produced conclusive results. In the present study, we determined the frequency of potential drug-drug and drug-disease interactions in prescriptions for ambulatory patients over 50 years of age, who used Mexican Institute of Social Security (IMSS) family medicine clinics. In addition, we aimed to identify the associated factors for these interactions.

METHODS

We collected information on general patient characteristics, medical histories, and medication (complete data). The study included 624 ambulatory patients over 50 years of age, with non-malignant pain syndrome, who made ambulatory visits to two IMSS family medicine clinics in Mexico City. The patients received 7-day prescriptions for non-opioid analgesics. The potential interactions were identified by using the Thompson Micromedex program. Data were analyzed using descriptive, bivariate and multiple logistic regression analyses.

RESULTS

The average number of prescribed drugs was 5.9 +/- 2.5. About 80.0% of patients had prescriptions implying one or more potential drug-drug interactions and 3.8% of patients were prescribed drug combinations with interactions that should be avoided. Also, 64.0% of patients had prescriptions implying one or more potential drug disease interactions. The factors significantly associated with having one or more potential interactions included: taking 5 or more medicines (adjusted Odds Ratio (OR): 4.34, 95%CI: 2.76-6.83), patient age 60 years or older (adjusted OR: 1.66, 95% CI: 1.01-2.74) and suffering from cardiovascular diseases (adjusted OR: 7.26, 95% CI: 4.61-11.44).

CONCLUSION

The high frequency of prescription of drugs with potential drug interactions showed in this study suggests that it is common practice in primary care level. To lower the frequency of potential interactions it could be necessary to make a careful selection of therapeutic alternatives, and in cases without other options, patients should be continuously monitored to identify adverse events.

Pharmacist workload and pharmacy characteristics associated with the dispensing of potentially clinically important drug-drug interactions

BACKGROUND

Drug-drug interactions (DDIs) are preventable medical errors, yet exposure to DDIs continues despite systems that are designed to prevent such exposures. The purpose of this study was to examine pharmacy characteristics that may be associated with dispensed potential DDIs.

METHODS

This study combined survey data from community pharmacies in 18 metropolitan statistical areas with pharmacy claims submitted to 4 pharmacy benefit managers (PBMs) over a 3-month period from January 1, 2003 to March 31, 2003. Pharmacy characteristics of interest included prescription volume, the number of full-time equivalent pharmacists and pharmacy staff, computer software programs, and the ability to modify those programs with respect to DDI alerts, the use of technologies to assist in receiving, filling and dispensing medication orders, and prescription volume. The dependent variable in this study was the rate of dispensed medications that may interact.

RESULTS

A total of 672 pharmacies were included in the analysis. On average (+/-SD), the respondents filled 1375 +/- 691 prescriptions per week, submitted 17,948 +/- 23,889 pharmacy claims to the participating PBMs, had 1.2 +/- 0.3 full-time equivalent pharmacists per hour open, and 545 (81%) were affiliated with a chain drug store organization. Factors significantly related to an increased risk of dispensing a potential DDI included pharmacist workload (odds ratio [OR] 1.03; 95% confidence interval [CI] 1.028-1.048), pharmacy staffing (OR 1.10; 95% CI: 1.09-1.11), and various technologies (eg, sophisticated telephone systems, internet receipt of orders, and refill requests) that assist with order processing, and the ability to modify DDI alert-screening sensitivity and detailed pharmacological information about DDIs.

CONCLUSIONS

This study found that there was an increase in the risk of dispensing a potential DDI with higher pharmacist and pharmacy workload, use of specific automation, and dispensing software programs providing alerts and clinical information.

Clinical relevance of drug-drug interactions : a structured assessment procedure

INTRODUCTION

Computerised drug interaction surveillance systems (CIS) may be helpful in detecting clinically significant drug interactions. Experience with CIS reveals that they often yield alerts with questionable clinical significance, fail to provide relevant information on risk factors for the adverse reaction of the interaction and fail to detect all significant drug interactions. These problems highlight the importance of transparency and selectivity in choosing the drug interactions to be included in CIS. In The Netherlands, the Working Group on Pharmacotherapy and Drug Information is responsible for maintenance of the CIS of the Royal Dutch Association for the Advancement of Pharmacy (KNMP).

METHODS

The Working Group developed an evidence-based procedure for structured assessment of drug-drug interactions and revised all drug interactions in the CIS accordingly.

RESULTS

For every drug interaction four core parameters were assessed: (i) evidence on the interaction; (ii) clinical relevance of the potential adverse reaction resulting from the interaction; (iii) risk factors identifying patient, medication or disease characteristics for which the interaction is of special importance; and (iv) the incidence of the adverse reaction. On the basis of this assessment the drug-drug interactions for inclusion in the CIS were selected. After revision of the drug combinations in the KNMP-CIS, the Working Group judged 22% of the combinations to be not interacting and another 12% to be interacting but not requiring action. On the basis of this assessment the subset of drug combinations for which interaction alerts are generated and the information on management of a drug interaction alert for users of the CIS were adapted. When an alert is generated by the CIS, the user of the system is supplied with comprehensive information on the four core parameters, the mechanism of the interaction and critical information for management of the interaction for the individual patient.

DISCUSSION

This structured procedure offers the possibility for transparent and reproducible assessment of the clinical relevance of drug interactions.

CONCLUSION

A CIS selectively generating interaction alerts based on this assessment may help in realising the goal of good clinical practice and may offer a methodology to further increase drug safety.

Clinical risk management of herb-drug interactions

The concomitant use of conventional and herbal medicines can lead to clinically relevant herb-drug interactions. Clinical risk management offers a systematic approach to minimize the untoward consequences of these interactions by paying attention to: (i) risk identification and assessment; (ii) development and execution of risk reduction strategies; and (iii) evaluation of risk reduction strategies. This paper reviews which steps should be explored or taken in these domains to improve the clinical risk management of adverse herb-drug interactions.