Consistency of psychotropic drug–drug interactions listed in drug monographs

Potential Drug-Drug Interactions among Patients with Schizophrenia Spectrum Disorders: Prevalence, Association with Risk Factors, and Replicate Analysis in 2021

Background and Objectives: Patients with schizophrenia are often exposed to polypharmacotherapy, which may lead to drug-drug interactions. The aim of the study was to investigate the prevalence of potential drug-drug interactions (pDDIs) in hospitalized patients with schizophrenia spectrum disorders and to identify factors associated with pDDIs and manifested symptoms and signs. Materials and Methods: This cross-sectional observational study included 311 inpatients admitted to a psychiatric hospital. The LexiComp drug interaction program was used to identify pDDIs in 2014. Factors associated with the prevalence of pDDIs and factors related to clinically observed symptoms and signs were assessed using multivariable regression. In addition, replicate analysis of pDDI was performed using 2021 program updates. Results: The prevalence of pDDIs was 88.7%. Our study showed that more than half of the patients received at least one drug combination that should be avoided. The most common pDDIs involved combinations of two antipsychotics or combinations of antipsychotics and benzodiazepines, which can lead to cardio-respiratory depression, sedation, arrhythmias, anticholinergic effects, and neuroleptic malignant syndrome. The number of prescribed drugs was a risk factor for pDDIs (OR 2.85; 95% CI 1.84-5.73). All groups of clinically observed symptoms and signs were associated with the number of drugs. In addition, symptoms and signs characteristic of the nervous system and psychiatric disorders were associated with antipsychotic dosage (IRR 1.33; 95% CI 1.12-1.58), which could contribute to the development of extrapyramidal syndrome, insomnia, anxiety, agitation, and bipolar mania. The 2021 version of the drug interaction program showed a shift in drug interactions toward a lower risk rating, implying less severe patient management and possibly less alert fatigue. Conclusions: Patients with schizophrenia spectrum disorders are at high risk of developing drug-drug interactions. Optimization of drug therapy, patient monitoring, and use of drug interaction programs could help to prevent pDDIs and subsequent adverse drug events.

Potential drug-drug interactions in outpatients with depression of a psychiatry department

Objective

This study aims to explore the prevalence and associated risk factors for potential drug-drug interactions (pDDIs) in prescriptions among outpatients with depression, and report the widespread relevant drug interactions.

Methods

The cross-sectional retrospective study was conducted on outpatients in a psychiatric hospital. We included prescriptions of outpatients with a principal diagnosis of depression from April 1st to June 30th in 2021. The patients were ≥ 18 years old and treated with two or more drugs including at least one psychotropic drug. pDDIs were detected and identified mainly using Medscape's drug interactions checker. Gender, the number of concomitant drugs, age and diagnosis were analysed as potential risk factors for the occurrence of pDDIs by logistic regression.

Results

A total of 13,617 prescriptions were included in the present analysis, and 4222 prescriptions (31.0%) were at risk of 8557 pDDIs. The risk of pDDIs in patients who were prescribed 4-6 drugs (OR: 3.49, 95% CI: 3.11-3.91, p < 0.001) or 7 or more drugs simultaneously (OR: 7.86, 95% CI: 1.58-39.04, p < 0.05) increased compared with patients prescribed 2-3 drugs. Patients with recurrent depressive disorders (OR: 1.18, 95% CI: 1.02-1.36, p < 0.05) had an increased risk of pDDIs compared with patients with depressive episodes. In terms of severity of pDDIs identified by Medscape's drug interactions checker, 0.7%, 16.4%, 77.5% and 5.4% of pDDIs were classified as contraindicated, serious, monitor closely and minor, respectively. The most common pDDI was escitalopram + quetiapine (374 prescriptions), which was classified as serious and monitor closely due to different mechanisms of interaction. Increased central nervous system (CNS)-depressant effect was the most frequent potential clinical adverse outcome of the identified pDDIs.

Conclusions

pDDIs in outpatients with depression were prevalent in this retrospective study. The number of concomitant drugs and severity of the disease were important risk factors for pDDIs. The pDDIs of the category monitor closely were the most common, and the CNS-depressant effect was the most frequent potential clinical adverse outcome.

Core Concepts in Pharmacoepidemiology: Measures of drug utilization based on individual-level drug dispensing data

Background

Drug utilization studies are essential to facilitate rational drug use in the society.

Aim

In this review, we provide an overview of drug utilization measures that can be used with individual‐level drug dispensing data, referencing additional reading on the individual analysis. This is intended to serve as a primer for those new to drug utilization research and a shortlist from which researchers can identify useful analytical approaches when designing their drug utilization study.

Results and Discussion

We provide an overview of: (1) basic measures of drug utilization which are used to describe changes in drug use over time or compare drug use in different populations; (2) treatment adherence measures with specific focus on persistence and implementation; (3) how to measure drug combinations which is useful when assessing drug–drug interactions, concomitant treatment, and polypharmacy; (4) prescribing quality indicators and measures to assess variations in drug use which are useful tools to assess appropriate use of drugs; (5) proxies of prescription drug misuse and skewness in drug use; and (6) considerations when describing the characteristics of drug users or prescribers.

Comparison of potential psychiatric drug interactions in six drug interaction database programs: A replication study after 2 years of updates

OBJECTIVE

Drug interaction database programs are a fundamental clinical tool. In 2018, we compared the category of potential drug-drug interaction (DDI) provided by six drug interaction database programs for 100 drug interaction pairs including psychiatric drugs, and found the category often differed. This study replicated the comparison in 2020 after 2 years of updates to all six drug interaction database programs.

METHODS

The 100 drug pairs included 94 different drugs: 67 pairs with a psychiatric and non-psychiatric drug, and 33 pairs with two psychiatric drugs. The assigned category of potential DDI for the drug pairs was compared using percent agreement and Fleiss kappa statistic of interrater reliability.

RESULTS

Despite 67 updates involving 46 of the 100 drug pairs, differences remained. The overall percent agreement among the six drug interaction database programs for the category of potential DDI was 67%. The interrater agreement results did not change. The Fleiss kappa overall interrater agreement was fair. The kappa agreement for a drug pair with any severe category rating was substantial, and the kappa agreement for a drug pair with any major category rating was fair.

CONCLUSIONS

Physicians should be aware of the inconsistency among drug interaction database programs in the category of potential DDI for drug pairs including psychiatric drugs. Additionally, the category of potential DDI for a drug pair may change over time. This study highlights the importance of ongoing international efforts to standardize methods used to define and classify potential DDI.

Major Cardiac-Psychiatric Drug-Drug Interactions: a Systematic Review of the Consistency of Drug Databases

PURPOSE

Major depressive disorder (MDD) and anxiety disorders (AD) are both highly prevalent among individuals with arrhythmia, ischemic heart disease, heart failure, hypertension, and dyslipidemia. There should be increased support for MDD and AD diagnosis and treatment in individuals with cardiac diseases, because treatment rates have been low. However, cardiac-psychiatric drug interaction can make pharmacologic treatment challenging.

METHODS

The objective of the present systematic review was to investigate cardiac-psychiatric drug interactions in three different widely used pharmacological databases (Micromedex, Up to Date, and ClinicalKey).

RESULTS

Among 4914 cardiac-psychiatric drug combinations, 293 significant interactions were found (6.0%). When a problematic interaction is detected, it may be easier to find an alternative cardiac medication (32.6% presented some interaction) than a psychiatric one (76.9%). Antiarrhythmics are the major class of concern. The most common problems produced by these interactions are related to cardiotoxicity (QT prolongation, torsades de pointes, cardiac arrest), increased exposure of cytochrome P450 2D6 (CYP2D6) substrates, or reduced renal clearance of organic cation transporter 2 (OCT2) substrates and include hypertensive crisis, increased risk of bleeding, myopathy, and/or rhabdomyolysis.

CONCLUSION

Unfortunately, there is considerable inconsistency among the databases searched, such that a clinician's discretion and clinical experience remain invaluable tools for the management of patients with comorbidities present in psychiatric and cardiac disorders. The possibility of an interaction should be considered. With a multidisciplinary approach, particularly involving a pharmacist, the prescriber should be alerted to the possibility of an interaction. MDD and AD pharmacologic treatment in cardiac patients could be implemented safely both by cardiologists and psychiatrists.

TRIAL REGISTRATION

PROSPERO Systematic Review Registration Number: CRD42018100424.

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

Background

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

Methodology

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

Results

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

Conclusions

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

Artificial intelligence-based conversational agent to support medication prescribing

Objective

This article describes the system architecture, training, initial use, and performance of Watson Assistant (WA), an artificial intelligence-based conversational agent, accessible within Micromedex^®.

Materials and methods

The number and frequency of intents (target of a user’s query) triggered in WA during its initial use were examined; intents triggered over 9 months were compared to the frequency of topics accessed via keyword search of Micromedex. Accuracy of WA intents assigned to 400 queries was compared to assignments by 2 independent subject matter experts (SMEs), with inter-rater reliability measured by Cohen’s kappa.

Results

In over 126 000 conversations with WA, intents most frequently triggered involved dosing (N = 30 239, 23.9%) and administration (N = 14 520, 11.5%). SMEs with substantial inter-rater agreement (kappa = 0.71) agreed with intent mapping in 247 of 400 queries (62%), including 16 queries related to content that WA and SMEs agreed was unavailable in WA. SMEs found 57 (14%) of 400 queries incorrectly mapped by WA; 112 (28%) queries unanswerable by WA included queries that were either ambiguous, contained unrecognized typographical errors, or addressed topics unavailable to WA. Of the queries answerable by WA (288), SMEs determined 231 (80%) were correctly linked to an intent.

Discussion

A conversational agent successfully linked most queries to intents in Micromedex. Ongoing system training seeks to widen the scope of WA and improve matching capabilities.

Conclusion

WA enabled Micromedex users to obtain answers to many medication-related questions using natural language, with the conversational agent facilitating mapping to a broader distribution of topics than standard keyword searches.

Potential Drug interactions with Drugs used for Bipolar Disorder: A Comparison of 6 Drug Interaction Database Programs

Background Patients with bipolar disorder frequently experience polypharmacy, putting them at risk for clinically significant drug-drug interactions (DDI). Online drug interaction database programs are used to alert physicians, but there are no internationally recognized standards to define DDI. This study compared the category of potential DDI returned by 6 commercial drug interaction database programs for drug interaction pairs involving drugs commonly prescribed for bipolar disorder.

Methods The category of potential DDI provided by 6 drug interaction database programs (3 subscription, 3 open access) was obtained for 125 drug interaction pairs. The pairs involved 103 drugs (38 psychiatric, 65 nonpsychiatric); 88 pairs included a psychiatric and nonpsychiatric drug; 37 pairs included 2 psychiatric drugs. Every pair contained at least 1 mood stabilizer or antidepressant. The category provided by 6 drug interaction database programs was compared using percent agreement and Fleiss kappa statistic of interrater reliability.

Results For the 125 drug pairs, the overall percent agreement among the 6 drug interaction database programs was 60%; the Fleiss kappa agreement was slight. For drug interaction pairs with any category rating of severe (contraindicated), the kappa agreement was moderate. For drug interaction pairs with any category rating of major, the kappa agreement was slight.

Conclusion There is poor agreement among drug interaction database programs for the category of potential DDI involving psychiatric drugs. Drug interaction database programs provide valuable information, but the lack of consistency should be recognized as a limitation. When assistance is needed, physicians should check more than 1 drug interaction database program.

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

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

Automation to optimise physician treatment of individual patients: examples in psychiatry

There is widespread agreement by health-care providers, medical associations, industry, and governments that automation using digital technology could improve the delivery and quality of care in psychiatry, and reduce costs. Many benefits from technology have already been realised, along with the identification of many challenges. In this Review, we discuss some of the challenges to developing effective automation for psychiatry to optimise physician treatment of individual patients. Using the perspective of automation experts in other industries, three examples of automation in the delivery of routine care are reviewed: (1) effects of electronic medical records on the patient interview; (2) effects of complex systems integration on e-prescribing; and (3) use of clinical decision support to assist with clinical decision making. An increased understanding of the experience of automation from other sectors might allow for more effective deployment of technology in psychiatry.

Prescription Medication Use Among Children and Adolescents in the United States

BACKGROUND AND OBJECTIVES

Information on the use of prescription medications among children and adolescents in the United States is lacking. We estimate the prevalence of prescription medication use, concurrent use, and potential major drug-drug interactions (DDIs) in this population.

METHODS

We conducted descriptive analyses using nationally representative data for people ≤19 years old from NHANES. Data were derived from a medication log administered by direct observation during in-home interviews. Acute medications were used for ≤30 days. Concurrent use was defined as use of ≥2 prescription medications. Micromedex was used to identify potentially major DDIs.

RESULTS

During 2013-2014, 19.8% of children and adolescents used at least 1 prescription medication, and 7.1% used acute medications. Concurrent use of prescription medications was 7.5% overall and was highest among boys 6 to 12 years old (12%) and among boys and girls ages 13 to 19 years old (10% for both). Using pooled 2009-2014 data, we found that 8.2% of concurrent users of prescription medications were at risk for a potentially major DDI. The vast majority of interacting regimens involved antidepressants and were more common among adolescent girls than boys (18.1% vs 6.6%; P < .05), driven largely by greater rates of use of acute medications.

CONCLUSIONS

Many US children and adolescents use prescription medications with nearly 1 in 12 concurrent users of prescription medications potentially at risk for a major DDI. Efforts to prevent adverse drug events in children and adolescents should consider the role of interacting drug combinations, especially among adolescent girls.