Co-prescription trends in a large cohort of subjects predict substantial drug-drug interactions

Fall Outcomes in Older Adults Following Benzodiazepine/Z-Drug Discontinuation: A Retrospective Cohort Study in an Academic Health System

BACKGROUND

Benzodiazepines and z-drugs increase the fall risk in older adults. There is a lack of real-world data examining the association between falls and deprescribing medications.

OBJECTIVE

In a retrospective cohort study of older adults with chronic benzodiazepine or z-drug use receiving care at an academic health system from January 2017 to December 2020, we explored the association between medication discontinuation and falls.

METHODS

Chronic use was defined as ≥ 3 medication dispensings and cumulative days' supply ≥ 45 days within 100 days in 2018. Discontinuation was defined as a dispensing gap of ≥ 180 days within 1 year of chronic use eligibility, with a secondary definition requiring a gap of ≥ 90 days. Non-discontinuers (n = 524) were matched 4:1 to discontinuers (n = 131) if they had a fill in the same month as the matched discontinuation index date. The association between discontinuation and a fall during 2.25-year follow-up was assessed using Cox proportional hazards models. The analysis was repeated using a gap of ≥ 90 days (n = 279 discontinuers; 1116 matched non-discontinuers).

RESULTS

The cumulative incidence of falls-related acute visits was 6.9% for discontinuers and 9.7% for non-discontinuers [adjusted hazard ratio (HR) 0.65, 95% confidence interval (CI) 0.31-1.31]. Using the 90-day-gap definition, the cumulative incidence was 9.3% for discontinuers and 8.5% for non-discontinuers (HR 1.12, 95% CI 0.70-1.77).

CONCLUSION

Benzodiazepine/z-drug discontinuation was not associated with reduced risk of falls. However, the relationship between discontinuation and falls varies depending on the definitions used. It is essential to examine different discontinuation definitions in larger studies while considering other relevant clinical outcomes.

Liposomal Copermeation Assay Reveals Unexpected Membrane Interactions of Commonly Prescribed Drugs

The permeation of small molecules across biological membranes is a crucial process that lies at the heart of life. Permeation is involved not only in the maintenance of homeostasis at the cell level but also in the absorption and biodistribution of pharmacologically active substances throughout the human body. Membranes are formed by phospholipid bilayers that represent an energy barrier for permeating molecules. Crossing this energy barrier is assumed to be a singular event, and permeation has traditionally been described as a first-order kinetic process, proportional only to the concentration gradient of the permeating substance. For a given membrane composition, permeability was believed to be a unique property dependent only on the permeating molecule itself. We provide experimental evidence that this long-held view might not be entirely correct. Liposomes were used in copermeation experiments with a fluorescent probe, where simultaneous permeation of two substances occurred over a single phospholipid bilayer. Using an assay of six commonly prescribed drugs, we have found that the presence of a copermeant can either enhance or suppress the permeation rate of the probe molecule, often more than 2-fold in each direction. This can have significant consequences for the pharmacokinetics and bioavailability of commonly prescribed drugs when used in combination and provide new insight into so-far unexplained drug-drug interactions as well as changing the perspective on how new drug candidates are evaluated and tested.

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

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

Frequencies of Combined Dysfunction of Cytochromes P450 2C9, 2C19, and 2D6 in an Italian Cohort: Suggestions for a More Appropriate Medication Prescribing Process

Improper drug prescription is a main cause of both drug-related harms (inefficacy and toxicity) and ineffective spending and waste of the healthcare system's resources. Nowadays, strategies to support an improved, informed prescription process may benefit from the adequate use of pharmacogenomic testing. Using next-generation sequencing, we analyzed the genomic profile for three major cytochromes P450 (CYP2C9, CYP2C19, CYP2D6) and studied the frequencies of dysfunctional isozymes (e.g., poor, intermediate, or rapid/ultra-rapid metabolizers) in a cohort of 298 Italian subjects. We found just 14.8% of subjects with a fully normal set of cytochromes, whereas 26.5% of subjects had combined cytochrome dysfunction (more than one isozyme involved). As improper drug prescription is more frequent, and more burdening, in polytreated patients, since drug-drug interactions also cause patient harm, we discuss the potential benefits of a more comprehensive PGX testing approach to support informed drug selection in such patients.

Potential drug-drug interactions in adults receiving oral anticoagulant and antiaggregant therapy

BACKGROUND

Anticoagulant and antiaggregant drugs are drug groups with high mortality and the most common cause of malpractice.

RESEARCH DESIGN AND METHODS

18 and 65 years were scheduled for pharmacotherapy in the Family Health Center. 122 patients during their anticoagulant and/or antiaggregant treatment were evaluated in terms of drug-drug interactions.

RESULTS

Drug-drug interactions were detected in 89.7% of the patients included in the study. A total of 212 drug-drug interactions were found in 122 patients. Of these, 12 (5.6%) were identified as A, 16 (7.5%) B, 146 (68.6%) C, 32 (15.2%) D and 6 (2.8%) X risk category. The number of DDI was found to be significantly higher in patients aged between 56 and 65 years. The most drug interactions are significantly higher in the C and D categories, respectively. The most predicted clinical outcomes of DDI's were increased in the therapeutic effect and adverse/toxic reactions.

CONCLUSIONS

Contrary to expectations, it is seen that although polypharmacy is relatively less in patients aged 18-65 years compared to patients over 65 years of age, it is very important to detect drug interactions in this age group in terms of safety, efficacy and treatment benefit in terms of drug-drug interaction.

Development and Validation of an Explainable Machine Learning-Based Prediction Model for Drug-Food Interactions from Chemical Structures

Possible drug-food constituent interactions (DFIs) could change the intended efficiency of particular therapeutics in medical practice. The increasing number of multiple-drug prescriptions leads to the rise of drug-drug interactions (DDIs) and DFIs. These adverse interactions lead to other implications, e.g., the decline in medicament's effect, the withdrawals of various medications, and harmful impacts on the patients' health. However, the importance of DFIs remains underestimated, as the number of studies on these topics is constrained. Recently, scientists have applied artificial intelligence-based models to study DFIs. However, there were still some limitations in data mining, input, and detailed annotations. This study proposed a novel prediction model to address the limitations of previous studies. In detail, we extracted 70,477 food compounds from the FooDB database and 13,580 drugs from the DrugBank database. We extracted 3780 features from each drug-food compound pair. The optimal model was eXtreme Gradient Boosting (XGBoost). We also validated the performance of our model on one external test set from a previous study which contained 1922 DFIs. Finally, we applied our model to recommend whether a drug should or should not be taken with some food compounds based on their interactions. The model can provide highly accurate and clinically relevant recommendations, especially for DFIs that may cause severe adverse events and even death. Our proposed model can contribute to developing more robust predictive models to help patients, under the supervision and consultants of physicians, avoid DFI adverse effects in combining drugs and foods for therapy.

Effect of Comorbidity Burden and Polypharmacy on Poor Functional Outcome in Acute Ischemic Stroke

PURPOSE

Comorbidities and polypharmacy are risk factors for worse outcome in stroke. However, comorbidities and polypharmacy are mostly studied separately with various approaches to assess them. We aimed to analyze the impact of comorbidity burden and polypharmacy on functional outcome in acute ischemic stroke (AIS) patients undergoing mechanical thrombectomy (MT).

METHODS

Acute ischemic stroke patients with large vessel occlusion (LVO) treated with MT from a prospective observational study were analyzed. Relevant comorbidity burden was defined as a Charlson Comorbidity Index (CCI) score ≥ 2, polypharmacy as the intake of ≥ 5 medications at time of stroke onset. Favorable outcome was a score of 0-2 on the modified Rankin scale at 90 days after stroke. The effect of comorbidity burden and polypharmacy on favorable outcome was studied via multivariable regression analysis.

RESULTS

Of 903 patients enrolled, 703 AIS patients (mean age 73.4 years, 54.9% female) with anterior circulation LVO were included. A CCI ≥ 2 was present in 226 (32.1%) patients, polypharmacy in 315 (44.8%) patients. Favorable outcome was less frequently achieved in patients with a CCI ≥ 2 (47, 20.8% vs. 172, 36.1%, p < 0.001), and in patients with polypharmacy (69, 21.9% vs. 150, 38.7%, p < 0.001). In multivariable regression analysis including clinical covariates, a CCI ≥ 2 was associated with lower odds of favorable outcome (odds ratio, OR 0.52, 95% confidence interval, 95% CI 0.33-0.82, p = 0.005), while polypharmacy was not (OR 0.81, 95% CI 0.52-1.27, p = 0.362).

CONCLUSION

Relevant comorbidity burden and polypharmacy are common in AIS patients with LVO, with comorbidity burden being a risk factor for poor outcome.

A one-year follow-up study of treatment-compliant suicide attempt survivors: relationship of CYP2D6-CYP2C19 and polypharmacy with suicide reattempts

This study of a cohort of 1-year treatment-compliant survivors of a suicide attempt examined for the first time whether a high CYP2D6-CYP2C19 metabolic capacity (pharmacogenes related to psychopathology, suicide, and attempt severity) and/or polypharmacy treatments predicted repeat suicide attempts, adjusting for sociodemographic and clinical factors as confounders. Of the 461 (63% women) consecutively hospitalized patients who attempted suicide and were evaluated and treated after an index attempt, 191 (67.5% women) attended their 6- and 12-month follow-up sessions. Clinicians were blinded to the activity scores (AS) of their genotypes, which were calculated as the sum of the values assigned to each allele (CYP2C19 *2, *17; CYP2D6 *3, *4, *4xN, *5, *6, *10, wtxN). No differences were found in polypharmacy prescription patterns and the variability of CYP2D6 and CYP2C19 genotypes between adherents and dropouts, but the formers were older, with a higher frequency of anxiety and bipolar disorders and fewer alcohol and substance use disorders. The risk of reattempts was higher for CYP2D6 ultrarapid (AS > 2) metabolizers (β = 0.561, p = 0.005) and violent suicide survivors (β = -0.219, p = 0.042) if the attempt occurred during the first 6-month period, individuals with an increased number of MINI DSM-IV Axis I mental disorders (β = 0.092, p = 0.032) during the second 6-month period and individuals with a combined high CYP2D6-CYP2C19 metabolic capacity (AS > 4) (β = 0.345, p = 0.024) and an increased use of drugs other than antidepressants, anxiolytics-depressants and antipsychotics-lithium (β = 0.088, p = 0.005) in multiple repeaters during both periods. CYP2D6 and CYP2C19 rapid metabolism and polypharmacy treatment for somatic comorbidities must be considered to prevent the severe side effects of short-term multiple suicide reattempts after a previous attempt.

Mapping Drug Prescription, Polypharmacy, and Pharmaceutical Spending in Older Adults in Iran: A Multilevel Analysis Based on Claims Data

Background: To date, comprehensive data on drug utilization in Iranian people are lacking. The purpose of this study was to graphically describe drug prescription, polypharmacy, and pharmaceutical spending in > 3 million Iranian elderly people. Methods: In this multilevel cross-sectional study, using administrative claims data provided by the Iran Health Insurance Organization (IHIO), we assessed drug claims and drug costs in 2018 in >3 million individuals living in Iran and who have been insured with health insurance (Bimeh Salamat). In particular, we analyzed the prevalence of polypharmacy and pharmaceutical spending use according to the annual Report of Iranian Health Insurance Organization. Multilevel ordinal logistic and multilevel beta regression models were used to analyze the data. Significance level was set as P ≤ .05 and CI at 95%. Results: Nationally, the mean number of drug prescriptions per patient was 1.46 (SD, 0.81). The mean number of prescribed drugs per patient was 4.32 (SD, = 3.04). The drug cost for each elderly patient was $6.86 (interquartile range (IQR), 12.26), with $4.96 and $1.76 for the insurance and the insured shares, respectively. For elderly women, the odds of polypharmacy (excessive and nonexcessive vs no polypharmacy) were 1.164 (95% CI, 1.142 to 1.186) times that of elderly men. In addition, in the spring season, the odds of polypharmacy were 1.274 (95% CI, 1.241 to 1.309) times that of the winter. Similarly, polypharmacy was strongly higher among patients who had noncommunicable diseases (OR, 2.174; 95% CI, 2.069 to 2.275 (P < 0.001)). Conclusion: The high prevalence of hyper prescription in Iran elderly people may indicate a need for interventions aiming at deprescribing drugs with an unfavorable benefit-risk profile. The best practice guidelines should be developed for improved medical practice in the prescription of medications for such a vulnerable population.

Prevalence of potentially harmful multidrug interactions on medication lists of elderly ambulatory patients

Background

It has been hypothesized that polypharmacy may increase the frequency of multidrug interactions (MDIs) where one drug interacts with two or more other drugs, amplifying the risk of associated adverse drug events (ADEs). The main objective of this study was to determine the prevalence of MDIs in medication lists of elderly ambulatory patients and to identify the medications most commonly involved in MDIs that amplify the risk of ADEs.

Methods

Medication lists stored in the electronic health record (EHR) of 6,545 outpatients ≥60 years old were extracted from the enterprise data warehouse. Network analysis identified patients with three or more interacting medications from their medication lists. Potentially harmful interactions were identified from the enterprise drug-drug interaction alerting system. MDIs were considered to amplify the risk if interactions could increase the probability of ADEs.

Results

MDIs were identified in 1.3 % of the medication lists, the majority of which involved three interacting drugs (75.6 %) while the remainder involved four (15.6 %) or five or more (8.9 %) interacting drugs. The average number of medications on the lists was 3.1 ± 2.3 in patients with no drug interactions and 8.6 ± 3.4 in patients with MDIs. The prevalence of MDIs on medication lists was greater than 10 % in patients prescribed bupropion, tramadol, trazodone, cyclobenzaprine, fluoxetine, ondansetron, or quetiapine and greater than 20 % in patients prescribed amiodarone or methotrexate. All MDIs were potentially risk-amplifying due to pharmacodynamic interactions, where three or more medications were associated with the same ADE, or pharmacokinetic, where two or more drugs reduced the metabolism of a third drug. The most common drugs involved in MDIs were psychotropic, comprising 35.1 % of all drugs involved. The most common serious potential ADEs associated with the interactions were serotonin syndrome, seizures, prolonged QT interval and bleeding.

Conclusions

An identifiable number of medications, the majority of which are psychotropic, may be involved in MDIs in elderly ambulatory patients which may amplify the risk of serious ADEs. To mitigate the risk, providers will need to pay special attention to the overlapping drug-drug interactions which result in MDIs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-021-02594-z.

Association between Adherence with an Atypical Antipsychotic and with Other Psychiatric Drugs in Patients with Bipolar Disorder

BACKGROUND

Using U.S. pharmacy and medical claims, medication adherence patterns of patients with serious mental illness suggest that adherence to atypical antipsychotics may be related to adherence to other prescription drugs. This study investigated whether adherence to an atypical antipsychotic was related to adherence to other prescribed psychiatric drugs using self-reported data from patients with bipolar disorder.

METHODS

Daily self-reported medication data were available from 123 patients with a diagnosis of bipolar disorder receiving treatment as usual who took at least 1 atypical antipsychotic over a 12-week period. Patients took a mean of 4.0±1.7 psychiatric drugs including the antipsychotic. The adherence rate for the atypical antipsychotic was compared to that for other psychiatric drugs to determine if the adherence rate for the atypical antipsychotic differed from that of the other psychiatric drug by at least ±10%.

RESULTS

Of the 123 patients, 58 (47.2%) had an adherence rate for the atypical antipsychotic that differed from the adherence rate for at least 1 other psychiatric drug by at least±10%, and 65 (52.8%) patients had no difference in adherence rates. The patients with a difference took a larger total number of psychiatric drugs (p<0.001), had a larger daily pill burden (p=0.020) and a lower adherence rate with the atypical antipsychotic (p=0.007), and were more likely to take an antianxiety drug (p<0.001).

CONCLUSION

Adherence with an atypical antipsychotic was not useful for estimating adherence to other psychiatric drugs in about half of the patients with bipolar disorder.

Stressors and Resources Related to Medication Management: Associations With Spousal Caregivers' Role Overload

BACKGROUND AND OBJECTIVES

Managing medications can be stressful for spousal caregivers, but little is known about particular aspects of medication management that are most consequential for caregiving outcomes. We examined care stressors and resources related to medication management, their associations with role overload among spousal caregivers, and whether these links vary by care recipients' number of chronic health conditions and dementia status.

RESEARCH DESIGN AND METHODS

This cross-sectional study included 377 spousal caregivers of adults aged 65 and older from the 2011 National Health and Aging Trends Study and National Study of Caregiving. Linear regressions were estimated to evaluate how medication-related stressors (ordering medication, keeping track of medications, giving injections) and resources (medication reminder systems, shared medication management within the spousal dyad) are associated with caregivers' role overload. Care recipients' number of chronic health conditions and dementia status were considered as moderators. Models controlled for caregivers' sociodemographic characteristics, chronic health conditions, and other care tasks.

RESULTS

Caregivers who administered injections reported more role overload, whereas those who worked with care recipients to jointly manage medications reported less role overload. Keeping track of medications was linked to caregivers' greater role overload when care recipients had 5 or more chronic health conditions. Finally, care recipients' use of medication reminder systems was linked to less role overload for caregivers of a partner with dementia.

DISCUSSION AND IMPLICATIONS

Devising strategies to assist spousal caregivers in the more onerous components of medication management and promote resources that mitigate medication-related stress may improve caregiver well-being.

Pharmacogenetic testing in psychiatric inpatients with polypharmacy is associated with decreased medication side effects but not via medication changes

In psychiatric patients, medication adverse effects are regularly attributed to psychosomatic causes. However, many psychotropic medications are metabolized by cytochrome P450 (CYP450) enzymes. In the setting of polypharmacy, the activity of these enzymes may produce unfavorable drug-drug interactions (DDI) and drug-genotype interactions (DGI) that contribute to morbidity and mortality. This study sought to estimate the risk of adverse DDI and DGI in psychiatric inpatients with polypharmacy. We assessed whether medication changes made after pharmacogenetics (PGx) testing correlated with changes in side effects and overall improvement. Adult psychiatry inpatients with polypharmacy, defined as 5 or more scheduled prescription medications, completed the 24-item Antidepressant Side Effect Checklist (ASEC) questionnaire on enrollment and underwent PGx testing. Analysis of PGx results focused on whether the CYP2D6 and CYP2C19 phenotypes were "extreme," defined as poor, poor to intermediate, or ultrarapid. Approximately 30 days after PGx results were sent to outpatient providers, patients were contacted to obtain their current medication list and ASEC and Clinical Global Impression Improvement (CGI-I) scores. A total of 80 patients were enrolled, and 52 (65%) completed follow-up. ASEC scores improved from 11.5 (±8.1) to 7.2 (±6.0) (p = 0.0009). Mean CGI-I score was 2.7 (±1.4), between "minimal" to "much improved." However, linear regression revealed that these improvements were not correlated with whether medications were changed. We concluded that the impact of drug-genotype interactions in this small sample of inpatients with polypharmacy was low, and that patient improvement was related not to PGx-guided medication changes but to other treatments during hospitalization.

Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare

Precision Medicine implies a deep understanding of inter-individual differences in health and disease that are due to genetic and environmental factors. To acquire such understanding there is a need for the implementation of different types of technologies based on artificial intelligence (AI) that enable the identification of biomedically relevant patterns, facilitating progress towards individually tailored preventative and therapeutic interventions. Despite the significant scientific advances achieved so far, most of the currently used biomedical AI technologies do not account for bias detection. Furthermore, the design of the majority of algorithms ignore the sex and gender dimension and its contribution to health and disease differences among individuals. Failure in accounting for these differences will generate sub-optimal results and produce mistakes as well as discriminatory outcomes. In this review we examine the current sex and gender gaps in a subset of biomedical technologies used in relation to Precision Medicine. In addition, we provide recommendations to optimize their utilization to improve the global health and disease landscape and decrease inequalities.

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.

Prescription Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) and Depression among Adults with Inflammatory Chronic Conditions in the United States

The association of prescription NSAIDs to presence of depression among adults with inflammatory chronic conditions in adults with and without depression and seeking care in routine clinical practice remains unknown. We examined the association of prescription NSAIDs to depression among adults with inflammatory chronic conditions in a nationally representative sample of the US non-institutionalized civilian population. We used a retrospective cross-sectional design. Data on 10,713 adults with inflammatory chronic conditions were derived from 2015 Medical Expenditure Panel Survey (MEPS). The dependent variable was the presence or absence of depression and the key independent variable was prescription NSAIDs use. Logistic regression models were used to examine the adjusted associations of prescription NSAIDs to depression. In these regressions, other independent variables (biological, sociocultural, socio-economic, access to healthcare services, medical conditions and treatment factors, behavioural, and environmental factors) that may affect the relationship of prescription NSAIDs to depression were also included. All analyses accounted for the complex survey design of MEPS. Overall, 18.2% reported depression. Almost 21% used prescription NSAIDs. In the unadjusted model, prescription NSAIDs use had higher odds of depression (OR = 1.59;95%CI = [1.40, 1.82]) as compared to those without NSAIDs. In the fully-adjusted logistic regression model, with controls for other independent variables, adults using prescription NSAIDs had no significant association with depression (AOR = 0.97;95%CI = [0.84, 1.13]) compared to those without NSAIDs. In this first real-world study of all adults (with and without depression) in the US, we did not observe a statistically significant association of prescription NSAIDs to the presence of depression.

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.

Multi-drug use among patients with multiple sclerosis: A cross-sectional study of associations to clinicodemographic factors

Multiple sclerosis (MS) is the most prevalent immune-mediated disease affecting the central nervous system. A treatment strategy with multiple therapies is a frequent clinical scenario. Unmonitored multi-drug use can lead to adverse outcomes, higher health care costs and medication non-adherence. The primary aim of this study was to evaluate the frequency of polypharmacy and related clinicodemographic factors in a single-center MS patient cohort. Furthermore, medication aspects of therapy management were examined. After the patients agreed to participate in the study, data were collected through patient interviews, patient records and clinical investigations. Subsequently, a statistical data analysis regarding various medication subgroups and polypharmacy (use of at least five drugs) was performed. Polypharmacy was observed in 56.5% of the patients (N = 306). High degrees of disability (odds ratio [OR] = 1.385), comorbidities (OR = 4.879) and inpatient treatment (OR = 5.146) were associated with a significantly higher risk of polypharmacy (p ≤ 0.001). Among patients with polypharmacy, disease-modifying drugs, antihypertensives, gastrointestinal drugs, thrombosis prophylactics, osteoporosis medications and sedatives were frequently used. In summary, polypharmacy plays a large role in MS patients, especially in those with higher degrees of disability, those with comorbidities and those treated in an inpatient setting.

Characteristics of doctor-shoppers: a systematic literature review

Objective: Doctor-shopping has significant consequences for patients and payers and can indicate misuse of drugs, polypharmacy, less continuity of care, and increased medical expenses. This study reviewed the literature describing doctor-shoppers in the adult population. Methods: A systematic literature review was performed in PubMed and supplemented by a Google search of grey literature. Overall, 2885 records were identified; 43 papers served as a source of definition of a doctor-shopper, disease, treatment, patient characteristics, patient special needs, country. Results: Definitions of doctor-shopping were heterogeneous. Overall, 40% of studies examined the use of opioids, antidepressants, or psychoactive drugs, while the others focused on chronic or frequent diseases. Most studies were conducted in countries with easy access to healthcare resources (USA, France, Taiwan, Hong Kong). The prevalence of doctor-shopping ranged from 0.5% among opioid users in the USA to 25% of patients registered at general practices in Japan. Comorbidities, active substance abuse, greater distance from healthcare facility, younger age, longer disease and poor patient satisfaction increased doctor-shopping. Conclusions: Knowing the characteristics of doctor-shoppers may help identify such patients and reduce the associated waste of medical resources, but concerns about the misuse of drugs or healthcare resources should not prevent proper disease management.

Preoperative Management of Medications

Increasingly complex medication regimens for many comorbidities in patients for planned surgical and procedural interventions necessitate detailed preoperative evaluation of the pharmacologic therapy, including the indications, the specific drugs, and dosing amount and interval. The implications of continuing or withholding these agents in the perioperative period need to be elucidated, as well as the risks of interactions and side effects. A comprehensive plan of the management of the therapeutic agents should be devised during the preoperative visit, with input from all relevant specialists, and clearly communicated to the patients in a format that ensures their comprehension and consistent compliance.

Assessment of Patient Medication Adherence, Medical Record Accuracy, and Medication Blood Concentrations for Prescription and Over-the-Counter Medications

IMPORTANCE

Inaccurate medication records and poor medication adherence result in incomplete knowledge of therapy for patients.

OBJECTIVE

To study accuracy of medical records and patient adherence by measuring blood concentrations of medications.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study validated a serum-based liquid chromatography-tandem mass spectrometry assay to simultaneously quantify 263 medications used for acute and chronic conditions. The assay panel was applied to 3 clinical patient cohorts: residual serum from 1000 randomly selected samples sent for routine clinical chemistry testing between April 8 and October 6, 2015 (residuals cohort), 50 prospectively enrolled patients in a gastroenterology clinic between March 1 and March 15, 2016, who were prescribed more than 5 medications (gastroenterology care cohort), and a convenience cohort of 296 patients with hypertension who sought care in an emergency department (ED care cohort) between July 1, 2012, and April 25, 2013. Integrated data analysis of the cohorts was performed from August 22 to November 29, 2017.

MAIN OUTCOMES AND MEASURES

Medication serum concentrations, electronic health record medication lists, and predicted drug interactions.

RESULTS

Of the 1346 total samples, 1000 came from the residuals cohort (640 women and 360 men; median age, 60 years [interquartile range (IQR), 44-71 years]), 50 from the gastroenterology care cohort (30 women and 20 men; median age, 66 years [IQR, 62-70 years]), and 296 from the ED care cohort (160 women and 136 men; median age, 59 years [IQR, 52-66 years]). Median medication adherence, defined as the subset of detected medications from the prescription record, was 83% (IQR, 50%-100%) in the residuals cohort, 100% (IQR, 84%-100%) in the gastroenterology care cohort, and 78% (IQR, 57%-100%) in the ED care cohort. Patients adherent to 1 medication were more often adherent to other medications. Among patients prescribed 3 medications or more, there were no significant associations between medication adherence and sex or number of prescribed medications, and there was a modest association between adherence and age. By comparing detected vs prescribed medications, we detected a median of 0 (IQR, 0-2) medications per patient that were not listed in the electronic health record in the residuals cohort, 1 (IQR, 0-2) medication per patient that was not listed in the electronic health record in the gastroenterology care cohort, and 1 (IQR, 0-2) medication per patient that was not listed in the electronic health record in the ED care cohort. A total of 435 patients (43.5%) in the residuals cohort had no discrepancy between the electronic health record and detected medication lists, 22 patients (44.0%) in the gastroenterology care cohort had no discrepancy between the electronic health record and detected medication lists, and 41 patients (13.9%) in the ED care cohort had no discrepancy between the electronic health record and detected medication lists. Half of adverse drug reaction alerts occurred among medications detected without prescription.

CONCLUSIONS AND RELEVANCE

Comprehensive medication monitoring offers promise to improve adherence, the accuracy of medical records, and the safety for patients with polypharmacy.

New approaches to pharmacosurveillance for monitoring prescription frequency, diversity, and co-prescription in a large sentinel network of companion animal veterinary practices in the United Kingdom, 2014-2016

Pharmaceutical agents (PAs) are commonly prescribed in companion animal practice in the United Kingdom. However, little is known about PA prescription on a population-level, particularly with respect to PAs authorised for human use alone prescribed via the veterinary cascade; this raises important questions regarding the efficacy and safety of PAs prescribed to companion animals. This study explored new approaches for describing PA prescription, diversity and co-prescription in dogs, cats and rabbits utilising electronic health records (EHRs) from a sentinel network of 457 companion animal-treating veterinary sites throughout the UK over a 2-year period (2014-2016). A novel text mining-based identification and classification methodology was utilised to semi-automatically map practitioner-defined product descriptions recorded in 918,333 EHRs from 413,870 dogs encompassing 1,242,270 prescriptions; 352,730 EHRs from 200,541 cats encompassing 491,554 prescriptions, and 22,526 EHRS from 13,398 rabbits encompassing 18,490 prescriptions respectively. PA prescription as a percentage of booked consultations was 65.4% (95% confidence interval, CI, 64.6-66.3) in dogs; in cats it was 69.1% (95% CI, 67.9-70.2) and in rabbits, 56.3% (95% CI, 54.7-57.8). Vaccines were the most commonly prescribed PAs in all three species, with antibiotics, antimycotics, and parasiticides also commonly prescribed. PA prescription utilising products authorised for human use only (hence, 'human-authorised') comprised 5.1% (95% CI, 4.7-5.5) of total canine prescription events; in cats it was 2.8% (95% CI, 2.6-3.0), and in rabbits, 7.8% (95% CI, 6.5-9.0). The most commonly prescribed human-authorised PA in dogs was metronidazole (antibiotic); in cats and rabbits it was ranitidine (H2 histamine receptor antagonist). Using a new approach utilising the Simpson's Diversity Index (an ecological measure of relative animal, plant etc. species abundance), we identified differences in prescription based on presenting complaint and species, with rabbits generally exposed to a less diverse range of PAs than dogs or cats, potentially reflecting the paucity of authorised PAs for use in rabbits. Finally, through a novel application of network analysis, we demonstrated the existence of three major co-prescription groups (preventive health; treatment of disease, and euthanasia); a trend commonly observed in practice. This study represents the first time PA prescription has been described across all pharmaceutical families in a large population of companion animals, encompassing PAs authorised for both veterinary and human-only use. These data form a baseline against which future studies could be compared, and provides some useful tools for understanding PA comparative efficacy and risks when prescribed in the varied setting of clinical practice.

A data-driven method to detect adverse drug events from prescription data

Drug safety issues such as Adverse Drug Events (ADEs) can cause serious consequences for the public. The clinical trials that are undertaken to assess medicine efficacy and safety prior to marketing, generally, may provide sufficient samples for discovering common ADEs. However, more samples are needed to detect infrequent and rare events. Additionally, clinical trials may not include all subgroups of patients. For these reasons, post-marketing surveillance of medicines is necessary for identifying drug safety issues. Most regulatory agencies use the Spontaneous Reporting Systems to identify associations between medicines and suspected ADEs. Data mining with effective analytical frameworks and large-scale medical data is potentially an alternative method to discover and monitor ADEs. In the present paper, we aim to detect potential ADEs from prescription data by discovering ADE associated prescription sequences. In an ADE associated prescription sequence 〈D_p→D_s〉, the prior medicine D_p leads to an ADE for which the succeeding medicine D_s is dispensed to treat. We propose a data-driven method which integrates (1) a constrained sequential pattern mining to uncover prescription sequences as potential signals of ADEs, (2) domain constraints to eliminate interference signals and (3) an adapted Self-Controlled Case Series model to evaluate the potential signals of ADEs. Despite ample prior works using Electronic Health Records (EHRs), our method utilises pure prescription data which does not contain additional information, e.g. symptoms or diagnoses as included in EHRs. To assess the performance of the proposed method, we apply it to a real-world dataset from the Pharmaceutical Benefits Scheme of Australia. The dataset contains over 50 million records covering approximately 2 million patients. The results demonstrate the effectiveness of our method in identifying both known ADEs and unknown yet suspicious ADEs with limited detection of false positive signals. Comparing to a recognised gold standard, our method successfully detects 67.4% of the positive adverse events while only 8.78% false positives exist.

Compatibility of medication with PRISCUS criteria and identification of drug interactions in a large cohort of patients with COPD

Patients with COPD are often of advanced age and have a high number of medications due to their lung disease and comorbidities. Thus they are at risk for unwanted effects from drugs, either due to age or due to interactions between drugs. These issues are not clarified. We therefore assessed the number of medications and potential adverse effects in a large cohort of patients with COPD. The analysis was performed in 2741 patients of the German COPD cohort COSYCONET, using baseline data (visit 1) and follow-up data after about 1.5 years (visit 3). Spirometric GOLD grades 1-4 were found in 8/35/32/9% of patients and GOLD groups ABCD in 7/25/4/48% of patients, while the remaining patients (n = 450, 16.4%) could not be classified according to GOLD criteria. The compatibility of medication with age was evaluated via the PRISCUS list, drug interactions via the AiD clinic system, whereby only drug combinations occurring in at least 10 patients were considered (nine unwanted interactions, one wanted interaction). The median numbers of medications were 5 or more in all patient categories, among them 3 or more non-respiratory medications. In the total population there were 153 patients (10.2%) aged ≥65 years who had any medication of the PRISCUS list with intermediate or low risk. Serious adverse combinations of drugs according to AiD occurred in 114 patients (4.2%), while the number of unwanted but only potentially clinically relevant combinations was 175 (6.4%). The number of wanted combinations was 219 (8.0%). These numbers did not markedly change when restricting the analysis to patients of GOLD grades 1-4. Moreover, the results were similar for visit 1 and visit 3. We conclude that in a large cohort of COPD patients about 10% of patients aged at least 65 years had medications that could interfere with their age and that the proportions of patients with either unwanted or wanted drug interactions were both in the range of 8-10%. These results suggest that problems arising from the high number of medications were not very frequent in the COPD cohort analysed.

A dataset quantifying polypharmacy in the United States

Polypharmacy is increasingly common in the United States, and contributes to the substantial burden of drug-related morbidity. Yet real-world polypharmacy patterns remain poorly characterized. We have counted the incidence of multi-drug combinations observed in four billion patient-months of outpatient prescription drug claims from 2007-2014 in the Truven Health MarketScan® Databases. Prescriptions are grouped into discrete windows of concomitant drug exposure, which are used to count exposure incidences for combinations of up to five drug ingredients or ATC drug classes. Among patients taking any prescription drug, half are exposed to two or more drugs, and 5% are exposed to 8 or more. The most common multi-drug combinations treat manifestations of metabolic syndrome. Patients are exposed to unique drug combinations in 10% of all exposure windows. Our analysis of multi-drug exposure incidences provides a detailed summary of polypharmacy in a large US cohort, which can prioritize common drug combinations for future safety and efficacy studies.

Medication adherence, medical record accuracy, and medication exposure in real-world patients using comprehensive medication monitoring

Background

Poor adherence to medication regimens and medical record inconsistencies result in incomplete knowledge of medication therapy in polypharmacy patients. By quantitatively identifying medications in the blood of patients and reconciling detected medications with the medical record, we have defined the severity of this knowledge gap and created a path toward optimizing medication therapy.

Methods and findings

We validated a liquid chromatography-tandem mass spectrometry assay to detect and/or quantify 38 medications across a broad range of chronic diseases to obtain a comprehensive survey of patient adherence, medical record accuracy, and exposure variability in two patient populations. In a retrospectively tested 821-patient cohort representing U.S. adults, we found that 46% of medications assessed were detected in patients as prescribed in the medical record. Of the remaining medications, 23% were detected, but not listed in the medical record while 30% were prescribed to patients, but not detected in blood. To determine how often each detected medication fell within literature-derived reference ranges when taken as prescribed, we prospectively enrolled a cohort of 151 treatment-regimen adherent patients. In this cohort, we found that 53% of medications that were taken as prescribed, as determined using patient self-reporting, were not within the blood reference range. Of the medications not in range, 83% were below and 17% above the lower and upper range limits, respectively. Only 32% of out-of-range medications could be attributed to short oral half-lives, leaving extensive exposure variability to result from patient behavior, undefined drug interactions, genetics, and other characteristics that can affect medication exposure.

Conclusions

This is the first study to assess compliance, medical record accuracy, and exposure as determinants of real-world treatment and response. Variation in medication detection and exposure is greater than previously demonstrated, illustrating the scope of current therapy issues and opening avenues that warrant further investigation to optimize medication therapy.

Systolic Blood Pressure and Biochemical Assessment of Adherence: A Cross-Sectional Analysis in the Emergency Department

Elevated blood pressure (BP) is common in the emergency department (ED), but the relationship between antihypertensive medication adherence and BP in the ED is unclear. This cross-sectional study tested the hypothesis that higher antihypertensive adherence is associated with lower systolic BP (SBP) in the ED among adults with hypertension who sought ED care at an academic hospital from July 2012 to April 2013. Biochemical assessment of antihypertensive adherence was performed using a mass spectrometry blood assay, and the primary outcome was average ED SBP. Analyses were stratified by number of prescribed antihypertensives (<3, ≥3) and adjusted for age, sex, race, insurance, literacy, numeracy, education, body mass index, and comorbidities. Among 85 patients prescribed ≥3 antihypertensives, mean SBP for adherent patients was 134.4 mm Hg (±26.1 mm Hg), and in adjusted analysis was -20.8 mm Hg (95% confidence interval, -34.2 to -7.4 mm Hg; P=0.003) different from nonadherent patients. Among 176 patients prescribed <3 antihypertensives, mean SBP was 135.5 mm Hg (±20.6 mm Hg) for adherent patients, with no difference by adherence in adjusted analysis (+2.9 mm Hg; 95% confidence interval, -4.7 to 10.5 mm Hg; P=0.45). Antihypertensive nonadherence identified by biochemical assessment was common and associated with higher SBP in the ED among patients who had a primary care provider and health insurance and who were prescribed ≥3 antihypertensives. Biochemical assessment of antihypertensives could help distinguish medication nonadherence from other contributors to elevated BP and identify target populations for intervention.

Potential drug-drug interactions among prescriptionsfor elderly patients in primary health care

BACKGROUND/AIM

Elderly patients are at high risk from drug-drug interactions (DDIs). This study evaluates the potential DDIs in Turkish elderly patients at a primary health care outpatient clinic.

MATERIALS AND METHODS

Online database systems were used to examine DDIs on the prescriptions of patients (n = 1206). The clinical severity of DDIs was classified by the Lexi-Interact Online database.

RESULTS

Of the 5059 prescriptions, 33% were found to have DDIs. We detected 29 (0.9%) A, 380 (11.8%) B, 2494 (77.7%) C, 289 (9%) D, and 18 (0.6%) X risk rating category DDIs among the prescriptions. Prescriptions of female patients and patients aged between 65 and 72 years showed significantly higher number of DDIs. The frequency of DDIs increased both with the number of drugs and combined preparations per prescription. Acetylsalicylic acid and salbutamol were the most frequently prescribed drugs contributing to clinically important DDIs. Additionally, acetylsalicylic acid and escitalopram, which interact with each other, were found on the list of Beers criteria. The most predicted clinical outcomes of DDIs were increase in therapeutic efficacy and adverse/toxic reactions. Conclusions: Prediction of DDIs in elderly patients will provide better prescribing and drug safety. Use of nonsteroidal anti-inflammatory agents, selective serotonin reuptake inhibitors, and beta-2 adrenergic receptor agonists should be closely monitored.

A Case of Transient Global Amnesia: A Review and How It May Shed Further Insight into the Neurobiology of Delusions

Transient global amnesia is a clinical syndrome characterized by the sudden onset of anterograde amnesia, accompanied by repetitive questioning, sometimes with a retrograde component, lasting up to 24 hours, without compromise of other neurologic function. Neuroimaging after an acutetransient global amnesia event often shows transient perturbation of specific hippocampal circuits that are involved in memory processing. Critical clinical distinctions, such as between transient global amnesia and other forms of transient amnesic episodes, as well as important clues to the underlying pathophysiologies are herein reviewed. Finally, we discuss the role of hippocampal insufficiency in the neurobiology of delusions.

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

AIMS

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Metformin Does Not Suppress Serum Thyrotropin by Increasing Levothyroxine Absorption

BACKGROUND

Levothyroxine (LT4) absorption is affected by concomitant ingestion of certain minerals, medications, and foods. It has been hypothesized that metformin may suppress serum thyrotropin (TSH) concentrations by enhancing LT4 absorption or by directly affecting the hypothalamic-pituitary axis. This study examined the effect of metformin ingestion on LT4 absorption, as assessed by serum total thyroxine (TT4) concentrations.

METHODS

A modified Food and Drug Administration LT4 bioequivalence protocol was applied to healthy, metformin-naïve, euthyroid adult volunteers. Following an overnight fast, 600 μg LT4 was administered orally. Serum TT4 concentrations were measured at baseline and at 0.5, 1, 1.5, 2, 4, and 6 h following LT4 administration. Measurements were performed before and after one week of metformin ingestion (850 mg three times daily). Peak serum TT4 concentrations, time to peak TT4 concentrations, and area under the concentration-time curve (AUC) were calculated.

RESULTS

Twenty-six subjects (54% men, 27% white, age 33 ± 10 years) were studied. There were no significant differences in peak serum TT4 concentrations (p = 0.13) and time to peak TT4 concentrations (p = 0.19) before and after one week of metformin use. A trend toward reduced TT4 AUC was observed after metformin ingestion (pre-metformin 3893 ± 568 μg/dL-min, post-metformin 3765 ± 588 μg/dL-min, p = 0.09).

CONCLUSIONS

LT4 absorption is unchanged by concomitant metformin ingestion. Mechanisms other than increased LT4 absorption may be responsible for the suppressed TSH concentrations observed in patients ingesting both drugs.