Grading the severity of drug-drug interactions in the intensive care unit: a comparison between clinician assessment and proprietary database severity rankings

Drug-Drug interactions prediction calculations between cardiovascular drugs and antidepressants for discovering the potential co-medication risks

Predicting Drug-Drug Interactions (DDIs) enables cost reduction and time savings in the drug discovery process, while effectively screening and optimizing drugs. The intensification of societal aging and the increase in life stress have led to a growing number of patients suffering from both heart disease and depression. These patients often need to use cardiovascular drugs and antidepressants for polypharmacy, but potential DDIs may compromise treatment effectiveness and patient safety. To predict interactions between drugs used to treat these two diseases, we propose a method named Multi-Drug Features Learning with Drug Relation Regularization (MDFLDRR). First, we map feature vectors representing drugs in different feature spaces to the same. Second, we propose drug relation regularization to determine drug pair relationships in the interaction space. Experimental results demonstrate that MDFLDRR can be effectively applied to two DDI prediction goals: predicting unobserved interactions among drugs within the drug network and predicting interactions between drugs inside and outside the network. Publicly available evidence confirms that MDFLDRR can accurately identify DDIs between cardiovascular drugs and antidepressants. Lastly, by utilizing drug structure calculations, we ascertained the severity of newly discovered DDIs to mine the potential co-medication risks and aid in the smart management of pharmaceuticals.

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.

Modulators Influencing Medication Alert Acceptance: An Explorative Review

OBJECTIVES

Clinical decision support systems (CDSSs) use alerts to enhance medication safety and reduce medication error rates. A major challenge of medication alerts is their low acceptance rate, limiting their potential benefit. A structured overview about modulators influencing alert acceptance is lacking. Therefore, we aimed to review and compile qualitative and quantitative modulators of alert acceptance and organize them in a comprehensive model.

METHODS

In accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guideline, a literature search in PubMed was started in February 2018 and continued until October 2021. From all included articles, qualitative and quantitative parameters and their impact on alert acceptance were extracted. Related parameters were then grouped into factors, allocated to superordinate determinants, and subsequently further allocated into five categories that were already known to influence alert acceptance.

RESULTS

Out of 539 articles, 60 were included. A total of 391 single parameters were extracted (e.g., patients' comorbidity) and grouped into 75 factors (e.g., comorbidity), and 25 determinants (e.g., complexity) were consequently assigned to the predefined five categories, i.e., CDSS, care provider, patient, setting, and involved drug. More than half of all factors were qualitatively assessed (n = 21) or quantitatively inconclusive (n = 19). Furthermore, 33 quantitative factors clearly influenced alert acceptance (positive correlation: e.g., alert type, patients' comorbidity; negative correlation: e.g., number of alerts per care provider, moment of alert display in the workflow). Two factors (alert frequency, laboratory value) showed contradictory effects, meaning that acceptance was significantly influenced both positively and negatively by these factors, depending on the study. Interventional studies have been performed for only 12 factors while all other factors were evaluated descriptively.

CONCLUSION

This review compiles modulators of alert acceptance distinguished by being studied quantitatively or qualitatively and indicates their effect magnitude whenever possible. Additionally, it describes how further research should be designed to comprehensively quantify the effect of alert modulators.

Heterogeneity in the identification of potential drug-drug interactions in the intensive care unit: A systematic review, critical appraisal, and reporting recommendations

Patients admitted to the intensive care unit (ICU) are frequently exposed to potential drug‐drug interactions (pDDIs). However, reported frequencies of pDDIs in the ICU vary widely between studies. This can be partly explained by significant variation in their methodological approach. Insight into methodological choices affecting pDDI frequency would allow for improved comparison and synthesis of reported pDDI frequencies. This study aimed to evaluate the association between methodological choices and pDDI frequency and formulate reporting recommendations for pDDI frequency studies in the ICU. The MEDLINE database was searched to identify papers reporting pDDI frequency in ICU patients. For each paper, the pDDI frequency and methodological choices such as pDDI definition and pDDI knowledge base were extracted, and the risk of bias was assessed. Each paper was categorized as reporting a low, medium, or high pDDI frequency. We sought associations between methodological choices and pDDI frequency group. Based on this comparison, reporting recommendations were formulated. Analysis of methodological choices showed significant heterogeneity between studies, and 65% of the studies had a medium to high risk of bias. High risk of bias, small sample size, and use of drug prescriptions instead of administrations were related to a higher pDDI frequency. The findings of this review may support researchers in designing a reliable methodology assessing pDDI frequency in ICU patients. The reporting recommendations may contribute to standardization, comparison, and synthesis of pDDI frequency studies, ultimately improving knowledge about pDDIs in and outside the ICU setting.

Prevalence of Clinically Significant Drug-Drug Interactions Across US Children's Hospitals

BACKGROUND

Little is known about the prescribing of medications with potential drug-drug interactions (DDIs) in the pediatric population. The objective of this study was to determine the prevalence and variation of prescribing medications with clinically significant DDIs across children's hospitals in the United States.

METHODS

We performed a retrospective cohort study of patients <26 years of age who were discharged from 1 of 52 US children's hospitals between January 2016 and December 2018. Fifty-three drug pairings with clinically significant DDIs in children were evaluated. We identified patient-level risk factors associated with DDI using multivariable logistic regression. Adjusted hospital-level rates of DDI exposure were derived by using a generalized linear mixed-effects model, and DDI exposure variations were examined across individual hospitals.

RESULTS

Across 52 children's hospitals, 47 414 (2.0%) hospitalizations included exposure to a DDI pairing (34.9 per 1000 patient-days) during the study period. One-quarter of pairings were considered contraindicated (risk grade X). After adjusting for hospital and clinical factors, there was wide variation in the percentage of DDI prescribing across hospitals, ranging from 1.05% to 4.92%. There was also substantial hospital-level variation of exposures to individual drug pairings. Increasing age, number of complex chronic conditions, length of stay, and surgical encounters were independently associated with an increased odds of DDI exposure.

CONCLUSIONS

Patients hospitalized at US children's hospitals are frequently exposed to medications with clinically significant DDIs. Exposure risk varied substantially across hospitals. Further study is needed to determine the rate of adverse events due to DDI exposures and factors amenable for interventions promoting safer medication use.

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Prediction of Drug-Drug Interactions Related to Inhibition or Induction of Drug-Metabolizing Enzymes

Drug-drug interaction (DDI) is the phenomenon of alteration of the pharmacological activity of a drug(s) when another drug(s) is co-administered in cases of so-called polypharmacy. There are three types of DDIs: pharmacokinetic (PK), pharmacodynamic, and pharmaceutical. PK is the most frequent type of DDI, which often appears as a result of the inhibition or induction of drug-metabolising enzymes (DME). In this review, we summarise in silico methods that may be applied for the prediction of the inhibition or induction of DMEs and describe appropriate computational methods for DDI prediction, showing the current situation and perspectives of these approaches in medicinal and pharmaceutical chemistry. We review sources of information on DDI, which can be used in pharmaceutical investigations and medicinal practice and/or for the creation of computational models. The problem of the inaccuracy and redundancy of these data are discussed. We provide information on the state-of-the-art physiologically- based pharmacokinetic modelling (PBPK) approaches and DME-based in silico methods. In the section on ligand-based methods, we describe pharmacophore models, molecular field analysis, quantitative structure-activity relationships (QSAR), and similarity analysis applied to the prediction of DDI related to the inhibition or induction of DME. In conclusion, we discuss the problems of DDI severity assessment, mention factors that influence severity, and highlight the issues, perspectives and practical using of in silico methods.

Drug interactions in maternal intensive care: prevalence, risk factors, and potential risk medications

Objective:

To characterize severe potential drug interactions in maternal intensive care, and to determine their frequency, risk factors and potential risk medications.

Methods:

An observational and longitudinal study conducted between December 2014 and December 2015 in a maternal intensive care unit. Clinical data were collected and severe potential drug interactions were identified on pregnant inpatients. The drug interactions were classified by type, prevalence and exposure rate. A multivariate logistic regression model was used to identify the severe potential drug interactions and the related drugs (p<0.05).

Results:

A total of 95.1% of patients were exposed to, at least, one potential drug interaction; in that, 91.7% 33.9% were related to, respectively, moderate and severe potential drug interactions. The patients were exposed, on average, on 69.2% of days they were in the intensive care unit. The main drugs involved in more severe drug interactions were magnesium sulfate, metoclopramide, propranolol and diazepam.

Conclusion:

The severe potential drug interactions were observed in almost all patients of the study, and, approximately one third of those interactions were related to greater severity and resulted in exposure during long hospital stay. The higher number of prescribed drugs and its previous use of medications at home increase the occurrence of severe potential drug interactions.

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

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSION

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

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.

Clinically relevant potential drug-drug interactions among outpatients: A nationwide database study

BACKGROUND

Adverse drug events due to drug-drug interactions (DDIs) represent a considerable public health burden, also in Slovenia. A better understanding of the most frequently occurring potential DDIs may enable safer pharmacotherapy and minimize drug-related problems.

OBJECTIVES

The aim of this study was to evaluate the prevalence and predictors of potential DDIs among outpatients in Slovenia.

METHODS

An analysis of potential DDIs was performed using health claims data on prescription drugs from a nationwide database. The Lexi-Interact Module was used as the reference source of interactions. The influence of patient-specific predictors on the risk of potential clinically relevant DDIs was evaluated using logistic regression model.

RESULTS

The study population included 1,179,803 outpatients who received 15,811,979 prescriptions. The total number of potential DDI cases identified was 3,974,994, of which 15.6% were potentially clinically relevant. Altogether, 9.3% (N = 191,213) of the total population in Slovenia is exposed to clinically relevant potential DDIs, and the proportion is higher among women and the elderly. After adjustment for cofactors, higher number of medications and older age are associated with higher odds of clinically relevant potential DDIs. The burden of DDIs is highest with drug combinations that increase risk of bleeding, enhance CNS depression or anticholinergic effects or cause cardiovascular complications.

CONCLUSION

The current study revealed that 1 in 10 individuals in the total Slovenian population is exposed to clinically relevant potential DDIs yearly. Taking into account the literature based conservative estimate that approximately 1% of potential DDIs result in negative health outcomes, roughly 1800 individuals in Slovenia experience an adverse health outcome each year as a result of clinically relevant potential interactions alone.

Comparative analysis of three drug-drug interaction screening systems against probable clinically relevant drug-drug interactions: a prospective cohort study

PURPOSE

Drug-drug interaction (DDI) screening systems report potential DDIs. This study aimed to find the prevalence of probable DDI-related adverse drug reactions (ADRs) and compare the clinical usefulness of different DDI screening systems to prevent or warn against these ADRs.

METHODS

A prospective cohort study was conducted in patients urgently admitted to medical departments. Potential DDIs were checked using Complete Drug Interaction®, Lexicomp® Online™, and Drug Interaction Checker®. The study team identified the patients with probable clinically relevant DDI-related ADRs on admission, the causality of which was assessed using the Drug Interaction Probability Scale (DIPS). Sensitivity, specificity, and positive and negative predictive values of screening systems to prevent or warn against probable DDI-related ADRs were evaluated.

RESULTS

Overall, 50 probable clinically relevant DDI-related ADRs were found in 37 out of 795 included patients taking at least two drugs, most common of them were bleeding, hyperkalemia, digitalis toxicity, and hypotension. Complete Drug Interaction showed the best sensitivity (0.76) for actual DDI-related ADRs, followed by Lexicomp Online (0.50), and Drug Interaction Checker (0.40). Complete Drug Interaction and Drug Interaction Checker had positive predictive values of 0.07; Lexicomp Online had 0.04. We found no difference in specificity and negative predictive values among these systems.

CONCLUSION

DDI screening systems differ significantly in their ability to detect probable clinically relevant DDI-related ADRs in terms of sensitivity and positive predictive value.

National Rules for Drug-Drug Interactions: Are They Appropriate for Tertiary Hospitals?

The application of appropriate rules for drug-drug interactions (DDIs) could substantially reduce the number of adverse drug events. However, current implementations of such rules in tertiary hospitals are problematic as physicians are receiving too many alerts, causing high override rates and alert fatigue. We investigated the potential impact of Korean national DDI rules in a drug utilization review program in terms of their severity coverage and the clinical efficiency of how physicians respond to them. Using lists of high-priority DDIs developed with the support of the U.S. government, we evaluated 706 contraindicated DDI pairs released in May 2015. We evaluated clinical log data from one tertiary hospital and prescription data from two other tertiary hospitals. The measured parameters were national DDI rule coverage for high-priority DDIs, alert override rate, and number of prescription pairs. The coverage rates of national DDI rules were 80% and 3.0% at the class and drug levels, respectively. The analysis of the system log data showed an overall override rate of 79.6%. Only 0.3% of all of the alerts (n = 66) were high-priority DDI rules. These showed a lower override rate of 51.5%, which was much lower than for the overall DDI rules. We also found 342 and 80 unmatched high-priority DDI pairs which were absent in national rules in inpatient orders from the other two hospitals. The national DDI rules are not complete in terms of their coverage of severe DDIs. They also lack clinical efficiency in tertiary settings, suggesting improved systematic approaches are needed.

Drug-drug interactions in the intensive care unit: Do they really matter?

PURPOSE

To describe prevalence and patterns of potential drug-drug interactions (pDDIs) in the intensive care unit (ICU), occurrence of adverse drug events (ADEs), and agreement between different compendia and intensivists' perceptions.

METHODS

A cross-sectional study. Drug profiles of all adult patients from 2 academic ICUs were screened on day 3 upon admission. We identified pDDIs using 3 compendia (Stockley's, Micromedex, and Epocrates) and documented their mechanism of action, clinical consequences, severity, level of evidence, and management. Medical records were searched to identify ADEs potentially related to major pDDIs. Agreement between information sources (compendia, intensivists) was evaluated.

RESULTS

We identified 1120 pDDIs among 275 patients. Median number of pDDIs per patient was 3.0 (interquartile range, 1-6), with 79% of patients presenting with at least 1 pDDI. Major pDDIs were detected in 18% of patients, with potentially related to ADEs in 4% of patients. Only 13% of all pDDIs were documented simultaneously in all 3 compendia. Different information sources (compendia, clinicians) showed "no" to "fair" agreement.

CONCLUSIONS

Potential drug-drug interactions occurred in most ICU patients, contrasting with low rates of potentially related ADEs, which may have been underestimated. Sources of information are inconsistent, challenging the identification of pDDIs.

Potential drug-drug interactions in paediatric outpatient prescriptions in Nigeria and implications for the future

BACKGROUND

Information regarding the incidence of drug-drug interactions (DDIs) and adverse drug events (ADEs) among paediatric patients in Nigeria is limited.

METHODS

Prospective clinical audit among paediatric outpatients in four general hospitals in Nigeria over a 3-month period. Details of ADEs documented in case files was extracted.

RESULTS

Among 1233 eligible patients, 208 (16.9%) received prescriptions with at least one potential DDI. Seven drug classes were implicated with antimalarial combination therapies predominating. Exposure mostly to a single potential DDI, commonly involved promethazine, artemether/lumefantrine, ciprofloxacin and artemether/lumefantrine. Exposure mostly to major and serious, and moderate and clinically significant, potential DDIs. Overall exposure similar across all age groups and across genders. A significant association was seen between severity of potential DDIs and age. Only 48 (23.1%) of these patients presented at follow-up clinics with only 15 reporting ADEs.

CONCLUSION

There was exposure to potential DDIs in this population. However, potential DDIs were associated with only a few reported ADEs.

Clinical-pharmacist intervention reduces clinically relevant drug-drug interactions in patients with heart failure: A randomized, double-blind, controlled trial

BACKGROUND

Incidence of drug-drug interactions (DDIs) increases with complexity of treatment and comorbidities, as in heart failure (HF). This randomized, double-blind study evaluated the intervention of the pharmacist on prevalence of clinically relevant DDIs (NCT01855165).

METHODS

Patients admitted with HF were screened for clinically relevant DDIs, and randomized to control or intervention. All attending physicians received standard advice about pharmacological therapy; those in the intervention group also received alerts about clinically relevant DDIs. Primary endpoint was DDI at discharge and secondary were re-hospitalization or death during follow-up.

RESULTS

Of 213 patients, 51 (mean age, 79 ± 6 years; male, 47%) showed 66 clinically relevant DDIs and were randomized. For intervention (n=26) versus control (n=25), the number of patients with and the number of DDIs were significantly lower at discharge: 8 vs. 18 and 10 vs. 31; p=0.003 and 0.0049, respectively. Over a 6 month follow-up period, 11 control and 9 intervention patients were re-hospitalized or died (p>0.2 for all). No significant differences were seen between control and intervention for patients with eGFR <60 mL/min/1.73 m(2) (78%) for re-hospitalization or death (10 vs. 7; p=0.74).

CONCLUSIONS

Pharmacist intervention significantly reduces the number of patients with clinically relevant DDIs, but not clinical endpoints 6 months from discharge.

Optimization of drug-drug interaction alert rules in a pediatric hospital's electronic health record system using a visual analytics dashboard

OBJECTIVE

To develop and evaluate an electronic dashboard of hospital-wide electronic health record medication alerts for an alert fatigue reduction quality improvement project.

METHODS

We used visual analytics software to develop the dashboard. We collaborated with the hospital-wide Clinical Decision Support committee to perform three interventions successively deactivating clinically irrelevant drug-drug interaction (DDI) alert rules. We analyzed the impact of the interventions on care providers' and pharmacists' alert and override rates using an interrupted time series framework with piecewise regression.

RESULTS

We evaluated 2 391 880 medication alerts between January 31, 2011 and January 26, 2014. For pharmacists, the median alert rate prior to the first DDI deactivation was 58.74 alerts/100 orders (IQR 54.98-60.48) and 25.11 alerts/100 orders (IQR 23.45-26.57) following the three interventions (p<0.001). For providers, baseline median alert rate prior to the first round of DDI deactivation was 19.73 alerts/100 orders (IQR 18.66-20.24) and 15.11 alerts/100 orders (IQR 14.44-15.49) following the three interventions (p<0.001). In a subgroup analysis, we observed a decrease in pharmacists' override rates for DDI alerts that were not modified in the system from a median of 93.06 overrides/100 alerts (IQR 91.96-94.33) to 85.68 overrides/100 alerts (IQR 84.29-87.15, p<0.001). The medication serious safety event rate decreased during the study period, and there were no serious safety events reported in association with the deactivated alert rules.

CONCLUSIONS

An alert dashboard facilitated safe rapid-cycle reductions in alert burden that were temporally associated with lower pharmacist override rates in a subgroup of DDIs not directly affected by the interventions; meanwhile, the pharmacists' frequency of selecting the 'cancel' option increased. We hypothesize that reducing the alert burden enabled pharmacists to devote more attention to clinically relevant alerts.

Co-Prescribing of Potentially Interacting Drugs during Warfarin Therapy - A Population-Based Register Study

We analysed the occurrence of co-prescribing of potentially interacting drugs during warfarin therapy in the community-dwelling population of Finland. We identified drugs having interaction potential with warfarin using the Swedish Finnish INteraction X-referencing drug-drug interaction database (SFINX) and obtained data on drug purchases from the nationwide Prescription Register. We defined warfarin users as persons purchasing warfarin in 2010 (n = 148,536) and followed them from their first prescription in 2010 until the end of the calendar year. Co-prescribing was defined as at least 1-day overlap between warfarin and interacting drug episodes. In addition, we identified persons who initiated warfarin therapy between 1 January 2007 and 30 September 2010 (n = 110,299) and followed these incident users for a 3-month period since warfarin initiation. Overall, 74.4% of warfarin users were co-prescribed interacting drugs. Co-prescribing covered 46.4% of the total person-years of warfarin exposure. Interacting drugs that should be avoided with warfarin were co-prescribed for 13.4% of warfarin users. The majority of the co-prescriptions were for drugs that are not contraindicated during warfarin therapy but require special consideration. Among incident users, 57.1% purchased potentially interacting drugs during the 3-month period after initiation, while 9.0% purchased interacting drugs that should be avoided with warfarin. To conclude, the occurrence of co-prescribing of potentially interacting drugs was high during warfarin therapy. Our findings highlight the importance of close monitoring of warfarin therapy and the need for further studies on the clinical consequences of co-prescribing of interacting drugs with warfarin.

Drug-drug interaction software in clinical practice: a systematic review

PURPOSE

Several electronic databases which report the prevalence of drug-drug interactions (DDIs) are used as a tool for evaluation of potentially harmful DDIs. The aim of our review was to evaluate the usability and appropriateness of commercially available electronic databases which assess the prevalence of potential DDIs.

METHODS

The systematic electronic literature search was conducted with the following search terms: "database" AND "software," and "drug-drug interactions" AND "database," and the inclusion and exclusion criteria were applied in order to identify the publications of interest.

RESULTS

A total of 3766 papers were identified by systematic search. After applying inclusion and exclusion criteria, 38 publications were included in the analysis. The most commonly used software in the included studies was Micromedex® Drug-Reax, for which some authors argue to be the most reliable due to highest sensitivity. It gives information about clinical consequences of DDIs, classifies underlying mechanism and onset of the adverse outcome (either rapid, or delayed) as well as severity (such as minor, moderate, or major), and provides the level of evidence which supports this information. This data is also provided by Drug Interaction Facts®, Lexi-Interact®, and Pharmavista®. A small number of studies which compared assessment of DDIs with electronic database and the clinician's assessment showed large discrepancy in number and relevance of detected DDIs. The overlap was in some cases as low as 11 %.

CONCLUSION

The deficiency of clinical relevance of detected DDIs should be addressed in the upcoming research as it would provide more relevant information to the prescribers' in clinical practice.

Clinical relevancy and risks of potential drug-drug interactions in intensive therapy

Purpose

Evaluate the potential Drug–Drug Interactions (pDDI) found in prescription orders of adult Intensive Care Unit (ICU) of a Brazilian public health system hospital; quantify and qualify the pDDI regarding their severity and risks to the critical patient, using the database from Micromedex®.

Methods

Prospective study (January–December of 2011) collecting and evaluating 369 prescription orders (convenient sampling), one per patient.

Results

During the study 1844 pDDIs were identified and distributed in 405 pairs (medication A × medication B combination). There was an average of 5.00 ± 5.06 pDDIs per prescription order, the most prevalent being moderate and important interactions, present in 74% and 67% of prescription orders, respectively. In total, there were 9 contraindicated, 129 important and 204 moderate pDDIs. Among them 52 had as management recommendation to “avoid concomitant use” or “suspension of medication”, while 306 had as recommendation “continuous and adequate monitoring”.

Conclusion

The high number of pDDIs found in the study combined with the evaluation of the clinical relevancy of the most frequent pDDIs in the ICU shows that moderate and important interactions are highly incident. As the majority of them demand monitoring and adequate management, being aware of these interactions is major information for the safe and individualized risk management.

Differences of Reasons for Alert Overrides on Contraindicated Co-prescriptions by Admitting Department

OBJECTIVES

To reveal differences in drug-drug interaction (DDI) alerts and the reasons for alert overrides between admitting departments.

METHODS

A retrospective observational study was performed using longitudinal Electronic Health Record (EHR) data and information from an alert and logging system. Adult patients hospitalized in the emergency department (ED) and general ward (GW) during a 46-month period were included. For qualitative analyses, we manually reviewed all reasons for alert overrides, which were recorded as free text in the EHRs.

RESULTS

Among 14,780,519 prescriptions, 51,864 had alerts for DDIs (0.35%; 1.32% in the ED and 0.23% in the GW). The alert override rate was higher in the ED (94.0%) than in the GW (57.0%) (p < 0.001). In an analysis of the study population, including ED and GW patients, 'clinically irrelevant alert' (52.0%) was the most common reason for override, followed by 'benefit assessed to be greater than the risk' (31.1%) and 'others' (17.3%). The frequency of alert overrides was highest for anti-inflammatory and anti-rheumatic drugs (89%). In a sub-analysis of the population, 'clinically irrelevant alert' was the most common reason for alert overrides in the ED (69.3%), and 'benefit assessed to be greater than the risk' was the most common reason in the GW (61.4%).

CONCLUSIONS

We confirmed that the DDI alerts and the reasons for alert overrides differed by admitting department. Different strategies may be efficient for each admitting department.

Potential drug-drug interactions in hospitalized patients with chronic heart failure and chronic obstructive pulmonary disease

INTRODUCTION

Polypharmacy is common in patients with chronic heart failure (HF) and/or chronic obstructive pulmonary disease (COPD), but little is known about the prevalence and significance of drug-drug interactions (DDIs). This study evaluates DDIs in hospitalized patients.

MATERIAL AND METHODS

We retrospectively screened medical charts over a 6-month period for diagnosis of chronic HF and/or COPD. Potential DDIs were evaluated using Lexi-Interact software.

RESULTS

Seven hundred and seventy-eight patients were included in the study (median age 75 years, 61% men). The median number of drugs on admission and discharge was 6 (interquartile range (IQR) 4-9) and 7 (IQR 5-), respectively (p = 0.10). We recorded 6.5 ±5.7 potential DDIs per patient on admission and 7.2 ±5.6 on discharge (p = 0.2). From admission to discharge, type-C and type-X potential DDIs increased (p < 0.05 for both). Type X interactions were rare (< 1%), with the combination of a β-blocker and a β2 agonist being the most common (64%). There were significantly more type-C and type-D potential DDIs in patients with chronic HF as compared to patients with COPD (p < 0.001). Patients with concomitant chronic HF and COPD had more type-C and type-X potential DDIs when compared to those with individual disease (p < 0.005). An aldosterone antagonist and ACE inhibitor/ARB were prescribed to 3% of chronic HF patients with estimated glomerular filtration rate < 30 ml/(min × 1.73 m(2)).

CONCLUSIONS

The DDIs are common in patients with chronic HF and/or COPD, but only a few appear to be of clinical significance. The increase in potential DDIs from admission to discharge may reflect better guideline implementation rather than poor clinical practice.

Core drug-drug interaction alerts for inclusion in pediatric electronic health records with computerized prescriber order entry

OBJECTIVE

The study aims to develop a core set of pediatric drug-drug interaction (DDI) pairs for which electronic alerts should be presented to prescribers during the ordering process.

METHODS

A clinical decision support working group composed of Children's Hospital Association (CHA) members was developed. CHA Pharmacists and Chief Medical Information Officers participated.

RESULTS

Consensus was reached on a core set of 19 DDI pairs that should be presented to pediatric prescribers during the order process.

CONCLUSIONS

We have provided a core list of 19 high value drug pairs for electronic drug-drug interaction alerts to be recommended for inclusion as high value alerts in prescriber order entry software used with a pediatric patient population. We believe this list represents the most important pediatric drug interactions for practical implementation within computerized prescriber order entry systems.

Detection of potential drug-drug interactions for outpatients across hospitals

The National Health Insurance Administration (NHIA) has adopted smart cards (or NHI-IC cards) as health cards to carry patients' medication histories across hospitals in Taiwan. The aims of this study are to enhance a computerized physician order entry system to support drug-drug interaction (DDI) checking based on a patient's medication history stored in his/her NHI-IC card. For performance evaluation, we developed a transaction tracking log to keep track of every operation on NHI-IC cards. Based on analysis of the transaction tracking log from 1 August to 31 October 2007, physicians read patients' NHI-IC cards in 71.01% (8,246) of patient visits; 33.02% (2,723) of the card reads showed at least one medicine currently being taken by the patient, 82.94% of which were prescribed during the last visit. Among 10,036 issued prescriptions, seven prescriptions (0.09%) contained at least one drug item that might interact with the currently-taken medicines stored in NHI-IC cards and triggered pop-up alerts. This study showed that the capacity of an NHI-IC card is adequate to support DDI checking across hospitals. Thus, the enhanced computerized physician order entry (CPOE) system can support better DDI checking when physicians are making prescriptions and provide safer medication care, particularly for patients who receive medication care from different hospitals.

Comparing Drug-Drug Interaction Severity Ratings between Bedside Clinicians and Proprietary Databases

Purpose. The purpose of this project was to compare DDI severity for clinician opinion in the context of the patient’s clinical status to the severity of proprietary databases. Methods. This was a single-center, prospective evaluation of DDIs at a large, tertiary care academic medical center in a 10-bed cardiac intensive care unit (CCU). A pharmacist identified DDIs using two proprietary databases. The physicians and pharmacists caring for the patients evaluated the DDIs for severity while incorporating their clinical knowledge of the patient. Results. A total of 61 patients were included in the evaluation and experienced 769 DDIs. The most common DDIs included: aspirin/clopidogrel, aspirin/insulin, and aspirin/furosemide. Pharmacists ranked the DDIs identically 73.8% of the time, compared to the physicians who agreed 42.2% of the time. Pharmacists agreed with the more severe proprietary database scores for 14.8% of DDIs versus physicians at 7.3%. Overall, clinicians agreed with the proprietary database 20.6% of the time while clinicians ranked the DDIs lower than the database 77.3% of the time. Conclusions. Proprietary DDI databases generally label DDIs with a higher severity rating than bedside clinicians. Developing a DDI knowledgebase for CDSS requires consideration of the severity information source and should include the clinician.

Use of an on-demand drug-drug interaction checker by prescribers and consultants: a retrospective analysis in a Swiss teaching hospital

BACKGROUND

Offering a drug-drug interaction (DDI) checker on-demand instead of computer-triggered alerts is a strategy to avoid alert fatigue.

OBJECTIVE

The purpose was to determine the use of such an on-demand tool, implemented in the clinical information system for inpatients.

METHODS

The study was conducted at the University Hospital Zurich, an 850-bed teaching hospital. The hospital-wide use of the on-demand DDI checker was measured for prescribers and consulting pharmacologists. The number of DDIs identified on-demand was compared to the number that would have resulted by computer-triggering and this was compared to patient-specific recommendations by a consulting pharmacist.

RESULTS

The on-demand use was analyzed during treatment of 64,259 inpatients with 1,316,884 prescriptions. The DDI checker was popular with nine consulting pharmacologists (648 checks/consultant). A total of 644 prescribing physicians used it infrequently (eight checks/prescriber). Among prescribers, internists used the tool most frequently and obtained higher numbers of DDIs per check (1.7) compared to surgeons (0.4). A total of 16,553 DDIs were identified on-demand, i.e., <10 % of the number the computer would have triggered (169,192). A pharmacist visiting 922 patients on a medical ward recommended 128 adjustments to prevent DDIs (0.14 recommendations/patient), and 76 % of them were applied by prescribers. In contrast, computer-triggering the DDI checker would have resulted in 45 times more alerts on this ward (6.3 alerts/patient).

CONCLUSIONS

The on-demand DDI checker was popular with the consultants only. However, prescribers accepted 76 % of patient-specific recommendations by a pharmacist. The prescribers' limited on-demand use indicates the necessity for developing improved safety concepts, tailored to suit these consumers. Thus, different approaches have to satisfy different target groups.

Appropriateness of commercially available and partially customized medication dosing alerts among pediatric patients

OBJECTIVES

To evaluate dosing alert appropriateness, categorize orders with alerts, and compare the appropriateness of alerts due to customized and non-customized dose ranges at a pediatric hospital.

METHODS

This was a retrospective analysis of medication orders causing dosing alerts. Orders for outpatient prescriptions, patients ≥18 years of age, and research protocols were excluded. Patient medical records were reviewed and ordered doses compared with a widely used pediatric reference (Lexi-Comp) and institutional recommendations. The alerted orders were categorized and the occurrence of appropriate alerts was compared.

RESULTS

There were 47 181 inpatient orders during the studied period; 1935 orders caused 3774 dosing alerts for 369 medications in 573 patients (median age 6.1 years). All alerted orders had an alert overridden by the prescriber. The majority (86.2%) of alerted orders inappropriately caused alerts; 58.0% were justifiable doses and 28.2% were within Lexi-Comp. However, 13.8% of alerted orders appropriately caused alerts; 8.0% were incorrect doses and 5.8% had no dosing recommendations available. Appropriately alerted orders occurred in 19.7% of alerted orders due to customized ranges compared to 12.8% due to non-customized ranges (p=0.002). Preterm and term neonates, infants, and children (2-5 years) had higher proportions of inappropriate alerts compared to appropriate alerts (all p<0.01).

CONCLUSIONS

The vast majority of dosing alerts were presented to practitioners inappropriately, potentially contributing to alert fatigue. Appropriate alerts occurred more often when alerts were due to customized ranges. Advances in dosing alerts should aim to provide accurate and clinically relevant alerts that minimize excessive inappropriate alerting. Medications requiring dosing adjustments based on clinical parameters must be taken into account when designing and evaluating dosing alerts.

Drug-drug interactions contributing to QT prolongation in cardiac intensive care units

PURPOSE

To determine the most common drug-drug interaction (DDI) pairs contributing to QTc prolongation in cardiac intensive care units (ICUs).

MATERIALS AND METHODS

This retrospective evaluation included patients who were admitted to the cardiac ICUs between January 2009 and July 2009 aged ≥ 18 years with electrocardiographic evidence of a QTc ≥ 500 ms. Patients receiving at least two concomitant drugs known to prolong the QT interval were considered to experience a pharmacodynamic DDI. Drugs causing CYP450 inhibition of the metabolism of QT prolonging medications were considered to cause pharmacokinetic DDIs. The causality between drug and QTc prolongation was evaluated with an objective scale.

RESULTS

One hundred eighty-seven patients experienced QT prolongation out of a total of 501 patients (37%) admitted during the study period. Forty-three percent and 47% of patients experienced 133 and 179 temporally-related pharmacodynamic and pharmacokinetic interactions, respectively. The most common medications related to these DDIs were ondansetron, amiodarone, metronidazole, and haloperidol.

CONCLUSION

DDIs may be a significant cause of QT prolongation in cardiac ICUs. These data can be used to educate clinicians on safe medication use. Computerized clinical decision support could be applied to aid in the detection of these events.

Computerized clinical decision support for medication prescribing and utilization in pediatrics

BACKGROUND AND OBJECTIVE

Accurate and informed prescribing is essential to ensure the safe and effective use of medications in pediatric patients. Computerized clinical decision support (CCDS) functionalities have been embedded into computerized physician order entry systems with the aim of ensuring accurate and informed medication prescribing. Owing to a lack of comprehensive analysis of the existing literature, this review was undertaken to analyze the effect of CCDS implementation on medication prescribing and use in pediatrics.

MATERIALS AND METHODS

A literature search was performed using keywords in PubMed to identify research studies with outcomes related to the implementation of medication-related CCDS functionalities.

RESULTS AND DISCUSSION

Various CCDS functionalities have been implemented in pediatric patients leading to different results. Medication dosing calculators have decreased calculation errors. Alert-based CCDS functionalities, such as duplicate therapy and medication allergy checking, may generate excessive alerts. Medication interaction CCDS has been minimally studied in pediatrics. Medication dosing support has decreased adverse drug events, but has also been associated with high override rates. Use of medication order sets have improved guideline adherence. Guideline-based treatment recommendations generated by CCDS functionalities have had variable influence on appropriate medication use, with few studies available demonstrating improved patient outcomes due to CCDS use.

CONCLUSION

Although certain medication-related CCDS functionalities have shown benefit in medication prescribing for pediatric patients, others have resulted in high override rates and inconsistent or unknown impact on patient care. Further studies analyzing the effect of individual CCDS functionalities on safe and effective prescribing and medication use are required.

Modeling chemical interaction profiles: II. Molecular docking, spectral data-activity relationship, and structure-activity relationship models for potent and weak inhibitors of cytochrome P450 CYP3A4 isozyme

Polypharmacy increasingly has become a topic of public health concern, particularly as the U.S. population ages. Drug labels often contain insufficient information to enable the clinician to safely use multiple drugs. Because many of the drugs are bio-transformed by cytochrome P450 (CYP) enzymes, inhibition of CYP activity has long been associated with potentially adverse health effects. In an attempt to reduce the uncertainty pertaining to CYP-mediated drug-drug/chemical interactions, an interagency collaborative group developed a consensus approach to prioritizing information concerning CYP inhibition. The consensus involved computational molecular docking, spectral data-activity relationship (SDAR), and structure-activity relationship (SAR) models that addressed the clinical potency of CYP inhibition. The models were built upon chemicals that were categorized as either potent or weak inhibitors of the CYP3A4 isozyme. The categorization was carried out using information from clinical trials because currently available in vitro high-throughput screening data were not fully representative of the in vivo potency of inhibition. During categorization it was found that compounds, which break the Lipinski rule of five by molecular weight, were about twice more likely to be inhibitors of CYP3A4 compared to those, which obey the rule. Similarly, among inhibitors that break the rule, potent inhibitors were 2–3 times more frequent. The molecular docking classification relied on logistic regression, by which the docking scores from different docking algorithms, CYP3A4 three-dimensional structures, and binding sites on them were combined in a unified probabilistic model. The SDAR models employed a multiple linear regression approach applied to binned 1D ¹³C-NMR and 1D ¹⁵N-NMR spectral descriptors. Structure-based and physical-chemical descriptors were used as the basis for developing SAR models by the decision forest method. Thirty-three potent inhibitors and 88 weak inhibitors of CYP3A4 were used to train the models. Using these models, a synthetic majority rules consensus classifier was implemented, while the confidence of estimation was assigned following the percent agreement strategy. The classifier was applied to a testing set of 120 inhibitors not included in the development of the models. Five compounds of the test set, including known strong inhibitors dalfopristin and tioconazole, were classified as probable potent inhibitors of CYP3A4. Other known strong inhibitors, such as lopinavir, oltipraz, quercetin, raloxifene, and troglitazone, were among 18 compounds classified as plausible potent inhibitors of CYP3A4. The consensus estimation of inhibition potency is expected to aid in the nomination of pharmaceuticals, dietary supplements, environmental pollutants, and occupational and other chemicals for in-depth evaluation of the CYP3A4 inhibitory activity. It may serve also as an estimate of chemical interactions via CYP3A4 metabolic pharmacokinetic pathways occurring through polypharmacy and nutritional and environmental exposures to chemical mixtures.

Existing and Developing Approaches for QSAR Analysis of Mixtures

This review is devoted to the critical analysis of advantages and disadvantages of existing mixture descriptors and their usage in various QSAR/QSPR tasks. We describe good practices for the QSAR modeling of mixtures, data sources for mixtures, a discussion of various mixture descriptors and their application, recommendations about proper external validation specific for mixture QSAR modeling, and future perspectives of this field. The biggest problem in QSAR of mixtures is the lack of reliable data about the mixtures' properties. Various mixture descriptors are used for the modeling of different endpoints. However, these descriptors have certain disadvantages, such as applicability only to 1 : 1 binary mixtures, and additive nature. The field of QSAR of mixtures is still under development, and existing efforts could be considered as a foundation for future approaches and studies. The usage of non-additive mixture descriptors, which are sensitive to interaction effects, in combination with best practices of QSAR model development (e.g., thorough data collection and curation, rigorous external validation, etc.) will significantly improve the quality of QSAR studies of mixtures.

Comparative evaluation of three clinical decision support systems: prospective screening for medication errors in 100 medical inpatients

PURPOSE

Clinical decision support systems (CDSS) are promoted as powerful screening tools to improve pharmacotherapy. The aim of our study was to evaluate the potential contribution of CDSS to patient management in clinical practice.

METHODS

We prospectively analyzed the pharmacotherapy of 100 medical inpatients through the parallel use of three CDSS, namely, Pharmavista, DrugReax, and TheraOpt. After expert discussion that also considered all patient-specific clinical information, we selected apparently relevant alerts, issued suitable recommendations to physicians, and recorded subsequent prescription changes.

RESULTS

For 100 patients with a median of eight concomitant drugs, Pharmavista, DrugReax, and TheraOpt generated a total of 53, 362, and 328 interaction alerts, respectively. Among those we identified and forwarded 33 clinically relevant alerts to the attending physician, resulting in 19 prescription changes. Four adverse drug events were associated with interactions. The proportion of clinically relevant alerts among all alerts (positive predictive value) was 5.7, 8.0, and 7.6%, and the sensitivity to detect all 33 relevant alerts was 9.1, 87.9, and 75.8% for Pharmavista, DrugReax and TheraOpt, respectively. TheraOpt recommended 31 dose adjustments, of which we considered 11 to be relevant; three of these were followed by dose reductions.

CONCLUSIONS

CDSS are valuable screening tools for medication errors, but only a small fraction of their alerts appear relevant in individual patients. In order to avoid overalerting CDSS should use patient-specific information and management-oriented classifications. Comprehensive information should be displayed on-demand, whereas a limited number of computer-triggered alerts that have management implications in the majority of affected patients should be based on locally customized and supported algorithms.

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.

Physicians' response to computerised alerts for psychotropic drugs in older persons: a multilevel analysis of the associated alert, patient and physician characteristics

OBJECTIVE

Computerised drug alerts are expected to reduce patients' risk of adverse drug events. However, physicians over-ride most drug alerts, because they believe that the benefit exceeds the risk. The purpose of this study was to determine the drug alert, patient and physician characteristics associated with the: (1) occurrence of psychotropic drug alerts for elderly patients and the (2) response to these alerts by their primary care physicians.

SETTING

Primary care, Quebec, Canada.

DESIGN

Prospective cohort study.

PARTICIPANTS

Sixty-one physicians using an electronic prescribing and drug alert decision-support system in their practice, and 3413 elderly patients using psychotropic drugs. PRIMARY AND SECONDARY MEASURES: Psychotropic drug class, alert severity, patient risk for fall injuries and physician experience, practice volume and computer use were evaluated in relationship to the likelihood of having: (1) a psychotropic drug alert, (2) the prescription revised in response to an alert. Cluster-adjusted alternating logistic regression was used to assess multilevel predictors of alert occurrence and response.

RESULTS

In total 13 080 psychotropic drug alerts were generated in 8931 visits. Alerts were more likely to be generated for male patients at higher risk of fall-related injury and for physicians who established the highest alert threshold. In 9.9% of alerts seen, the prescription was revised. The highest revision rate was for antipsychotic alerts (22.6%). Physicians were more likely to revise prescriptions for severe alerts (OR 2.03; 95%CI 1.39 to 2.98), if patients had cognitive impairment (OR 1.95; 95%CI 1.13 to 3.36), and if they made more visits to their physician (OR 1.05 per 5 visits; 95%CI 1 to 1.09).

CONCLUSIONS

Physicians view and respond to a small proportion of alerts, mainly for higher-risk patients. To reduce the risk of psychotropic drug-related fall injuries, a new generation of evidence-based drug alerts should be developed.

Evaluation of drug interactions in a large sample of psychiatric inpatients: a data interface for mass analysis with clinical decision support software

In order to improve medication safety, more epidemiological data on the prevalence and clinical relevance of drug interactions are required. We developed an interface for mass analysis using the Clinical Decision Support Software (CDSS) MediQ and a multidimensional classification (Zurich Interaction System (ZHIAS)) incorporating the Operational Classification of Drug Interactions (ORCA). These were applied to 359,207 cross-sectional prescriptions from 84,607 psychiatric inpatients collected through the international AMSP program. MediQ issued 2,308 "high" and 71,112 "average" danger interaction alerts. Among these, after ORCA reclassification, there were 151 contraindicated and 4,099 provisionally contraindicated prescriptions. The ZHIAS provided further detailed categorical information on recommended management and specific increased risks (QTc prolongation being the most frequent one) associated with interactions. We developed a highly efficient solution for the identification and classification of drug interactions in large prescription data sets; this solution may help to reduce the frequency of overalerting and improve acceptance of the efficacy of CDSS in reducing the occurrence of potentially harmful drug interactions.

Evaluation of drug interactions and dosing in 484 neurological inpatients using clinical decision support software and an extended operational interaction classification system (Zurich Interaction System)

PURPOSE

The current study aimed at identifying and quantifying critical drug interactions in neurological inpatients using clinical decision support software (CDSS). Reclassification of interactions with a focus on clinical management aimed to support the development of CDSS with higher efficacy to reduce overalerting and improve medication safety in clinical practice.

METHODS

We conducted a cross-sectional study in consecutive patients admitted to the neurology ward of a tertiary care hospital. We developed a customized interface for mass analysis with the CDSS MediQ, which we used for automated retrospective identification of drug interactions during the first day of hospitalization. Interactions were reclassified according to the Zurich Interaction System (ZHIAS), which incorporates the Operational Classification of Drug Interactions (ORCA). Dose adjustments for renal impairment were also evaluated.

RESULTS

In 484 patients with 2812 prescriptions, MediQ generated 8 "high danger," 518 "average danger," and 1233 "low danger" interaction alerts. According to ZHIAS, 6 alerts involved contraindicated and 33 alerts involved provisionally contraindicated combinations, and 327 alerts involved a conditional and 1393 alerts involved a minimal risk of adverse outcomes. Thirty-five patients (6.2%) had at least one combination that was at least provisionally contraindicated. ZHIAS also provides categorical information on expected adverse outcomes and management recommendations, which are presented in detail. We identified 13 prescriptions without recommended dose adjustment for impaired renal function.

CONCLUSIONS

MediQ detected a large number of drug interactions with variable clinical relevance in neurological inpatients. ZHIAS supports the selection of those interactions that require active management, and the effects of its implementation into CDSS on medication safety should be evaluated in future prospective studies.