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

Sedative load and anticholinergic burden among older adults in Slovenia over a decade: Potential for optimization of pharmacotherapy

This study investigates the 10-year trend in the sedative and anticholinergic burden among older adults in Slovenia, with the aim of identifying opportunities to optimize pharmacotherapy in this population. A retrospective drug utilization analysis was conducted based on a national anonymized database of dispensed prescriptions from 2009 to 2019. The study employed the sedative load model and the anticholinergic cognitive burden scale to assess the sedative and anti cholinergic burden, respectively. The findings indicate that in 2019, 45.6 % and 40.8 % of older adults (≥ 65 years) used sedative and anticholinergic medications, respectively. A high sedative load and a clinically significant anticholinergic burden were observed in a considerable proportion of older adults (13.2 % and 11.2 %, respectively, in 2019). The age-standardized prevalence of sedative load and anti-cholinergic burden significantly decreased over the 10-year study period by 5.6 % and 1.7 %, respectively (absolute difference), while the prevalence of clinically significant anticholinergic burden remained stable. Notably, the age groups 85-89 years and above 90 years had an increase in the proportion of individuals with a clinically significant anticholinergic burden over the years. These results emphasize the need for targeted interventions, particularly in the oldest age groups, to promote safe and effective medication use among older adults.

Informational continuity of medication management in transitions of care: Qualitative interviews with stakeholders from the HYPERION-TransCare study

BACKGROUND

The transition of patients between inpatient and outpatient care can lead to adverse events and medication-related problems due to medication and communication errors, such as medication discontinuation, the frequency of (re-)hospitalizations, and increased morbidity and mortality. Older patients with multimorbidity and polypharmacy are particularly at high risk during transitions of care. Previous research highlighted the need for interventions to improve transitions of care in order to support information continuity, coordination, and communication. The HYPERION-TransCare project aims to improve the continuity of medication management for older patients during transitions of care.

METHODS AND FINDINGS

Using a qualitative design, 32 expert interviews were conducted to explore the perspectives of key stakeholders, which included healthcare professionals, patients and one informal caregiver, on transitions of care. Interviews were conducted between October 2020 and January 2021, transcribed verbatim and analyzed using content analysis. We narratively summarized four main topics (stakeholders' tasks, challenges, ideas for solutions and best practice examples, and patient-related factors) and mapped them in a patient journey map. Lacking or incomplete information on patients' medication and health conditions, inappropriate communication and collaboration between healthcare providers within and across settings, and insufficient digital support limit the continuity of medication management.

CONCLUSIONS

The study confirms that medication management during transitions of care is a complex process that can be compromised by a variety of factors. Legal requirements and standardized processes are urgently needed to ensure adequate exchange of information and organization of medication management before, during and after hospital admissions. Despite the numerous barriers identified, the findings indicate that involved healthcare professionals from both the inpatient and outpatient care settings have a common understanding.

The Prevalence and Severity of Potential Drug-Drug Interactions in Internal Medicine Ward at Soba Teaching Hospital

Background

Multiple drug therapies are commonly used to achieve a desired therapeutic goal, especially in hospitalized patients. However, drug-drug interactions might occur and threaten the patients' safety.

Objective

This study aims to assess the prevalence and severity of potential drug-drug interactions (PDDIs) in the internal medicine ward at Soba Teaching Hospital.

Methods

A retrospective cross-sectional hospital-based study was carried out in the internal medicine ward at Soba Teaching Hospital from June 2021 to December 2021. The data was collected from patients' medical records. PDDIs were identified using Lexicomp® drug interaction software.

Results

A total of 377 patients were included in this study, and overall prevalence of PDDIs was 62.9%. We have identified 989 potential DDIs and 345 pairs of interacting drugs, the mean of the PDDIs per patient was 4.17 ± 4.079. Among 345 PDDIs most were of moderate interactions 70.1% (n=242) followed by Minor interactions 19.1% (n=66). The most common type of interaction was of category C representing 63.5% (n=219). A significant association was observed between the occurrence of PDDIs with patients' age, presence of chronic diseases, length of hospital stay, and number of medications received by the patients.

Conclusion

Drug-drug interactions were highly prevalent in the internal medicine ward. Therefore, certain attempts are required to increase the awareness of the physicians about these interactions and minimize their occurrence.

Potential drug-drug interactions in patients with non-small cell lung cancer at a university hospital in Turkey

BACKGROUND

The aim of this study was to determine the drug profile of patients with non-small cell lung cancer (NSCLC) and to identify potential drug-drug interactions (PDDIs) during hospitalization. In particular, PDDIs in categories X and D were determined.

METHODS

This retrospective cross-sectional study was conducted in the oncology services of a university hospital between 2018 and 2021. PDDIs were evaluated using Lexicomp Drug Interactions® software included in UpToDate®.

RESULTS

A total of 199 patients were included in the study. Polypharmacy was present in 92.5% of the patients and the median (min-max) number of drugs used was 8 (2-16). 32% of the patients had D and X PDDIs. A total of 16 PDDIs at risk grade X were found in 15 (7.5%) patients. A total of 81 PDDIs of risk grade D were found in 54 (27.1%) patients and a total of 276 PDDIs of risk grade C were identified in 97 (48.7%) patients. Anticancer drugs (p = 0.008), opioids (p = 0.046), steroids (p = 0.003), 5-HT3 receptor antagonists (p = 0.012), aprepitant (p = 0.025) and antihistamines (p < 0.001) were statistically more frequent among patients with PDDIs than among those without.

CONCLUSION

The results of our study indicated that polypharmacy and PDDIs are common in hospitalized patients with NSCLC cancer. The monitoring of medications is critical for maximizing therapeutic effects and minimizing side effects related to PDDIs. As a part of multidisciplinary team, clinical pharmacists can contribute significantly to preventing, detecting and managing PDDIs.

Real world co-prescribing contraindicated drugs with fluconazole and itraconazole

PURPOSE

This study aimed to investigate co-prescribing of contraindicated drugs with fluconazole and itraconazole using real-world nationwide data.

METHODS

This retrospective cross-sectional study was performed using claims data collected by the Health Insurance Review and Assessment Service (HIRA) of Korea during 2019-2020. To determine the drugs that should be avoided in patients taking fluconazole or itraconazole, Lexicomp® and Micromedex® were used. The co-prescribed medications, co-prescription rates, and potential clinical consequences of the contraindicated drug-drug interactions (DDIs) were investigated.

RESULTS

Of the 197 118 prescriptions of fluconazole, 2847 co-prescriptions with drugs classified as contraindicated DDI by either Micromedex® or Lexicomp® were identified. Further, of the 74 618 prescriptions of itraconazole, 984 co-prescriptions with contraindicated DDI were identified. Solifenacin (34.9%), clarithromycin (18.1%), alfuzosin (15.1%), and donepezil (10.4%) were frequently found in the co-prescriptions of fluconazole, whereas tamsulosin (40.4%), solifenacin (21.3%), rupatadine (17.8%), and fluconazole (8.8%) were frequently found in the co-prescriptions of itraconazole. In 1105 and 95 co-prescriptions of fluconazole and itraconazole, accounting for 31.3% of all co-prescriptions, potential DDIs were associated with a risk of corrected QT interval (QTc) prolongation. Of the total 3831 co-prescriptions, 2959 (77.2%) and 785 (20.5%) were classified as contraindicated DDI by Micromedex® alone and by Lexicomp® alone, respectively, whereas 87 (2.3%) were classified as contraindicated DDI by both Micromedex® and Lexicomp®.

CONCLUSIONS

Many co-prescriptions were associated with the risk of DDI-related QTc prolongation, warranting the attention of healthcare providers. Narrowing the discrepancy between databases that provide information on DDIs is required for optimized medicine usage and patient safety.

Prevalence of Drug-Drug Interactions in Older Community-Dwelling Individuals: A Systematic Review and Meta-analysis

BACKGROUND

Drug-drug interactions (DDIs) can lead to medication-related harm, and the older population is at greatest risk. We conducted a systematic review and meta-analysis to estimate DDI prevalence and identify common DDIs in older community-dwelling adults.

METHODS

PubMed and EMBASE were searched for observational studies published between 01/01/2010 and 10/05/2021 reporting DDI prevalence in community-dwelling individuals aged ≥ 65 years. Nursing home and inpatient hospital studies were excluded. Study quality was assessed using the Joanna Briggs Institute critical appraisal tool. Meta-analysis was performed using a random-effects model with logit transformation. Heterogeneity was evaluated using Cochran's Q and I². DDI prevalence and 95% confidence intervals (CIs) are presented. All analyses were performed in R (version 4.1.2).

RESULTS

There were 5144 unique articles identified. Thirty-three studies involving 17,011,291 community-dwelling individuals aged ≥ 65 years met inclusion criteria. Thirty-one studies reported DDI prevalence at the study-participant level, estimates ranged from 0.8% to 90.6%. The pooled DDI prevalence was 28.8% (95% CI 19.3-40.7), with significant heterogeneity (p < 0.10; I² = 100%; tau² = 2.13) largely explained by the different DDI identification methods. Therefore, 26 studies were qualitatively synthesised and seven studies were eligible for separate meta-analyses. In a meta-analysis of three studies (N = 1122) using Micromedex^®, pooled DDI prevalence was 57.8% (95% CI 52.2-63.2; I² = 69.6%, p < 0.01). In a meta-analysis of two studies (N = 809,113) using Lexi-Interact^®, pooled DDI prevalence was 30.3% (95% CI 30.2-30.4; I² = 6.8%). In a meta-analysis of two studies (N = 947) using the 2015 American Geriatrics Society Beers criteria^®, pooled DDI prevalence was 16.6% (95% CI 5.6-40.2; I² = 97.5%, p < 0.01). Common DDIs frequently involved cardiovascular drugs, including ACE inhibitor-potassium-sparing diuretic; amiodarone-digoxin; and amiodarone-warfarin.

CONCLUSIONS

DDIs are prevalent among older community-dwelling individuals; however, the methodology used to estimate these events varies considerably. A standardised methodology is needed to allow meaningful measurement and comparison of DDI prevalence.

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.

Assessment of A new Strategy to Prevent Prescribing Errors Involving COVID-19 Patients in Community Pharmacies

Background: Because COVID-19 patients are vulnerable to prescribing errors (PEs) and adverse drug events, designing and implementing a new approach to prevent prescribing errors (PEs) involving COVID-19 patients has become a priority in pharmacotherapy research. Objectives: To investigate whether using WhatsApp to deliver prescribing error (PE)-related clinical scenarios to community pharmacists could enhance their ability to detect PEs and conduct successful pharmaceutical interventions (PIs). Methods: In this study, 110 community pharmacies were recruited from different regions across Jordan and equally allocated to 2 groups. Over the course of 4 weeks, WhatsApp was used to send PEs-related clinical case scenarios to the active group. The second group was controlled with no clinical scenarios. After completion of the 4-week phase, pharmacists from both groups were asked to document PEs in COVID-19 patients and their interventions using a data-collection form. Results: The incidence of PEs in COVID-19 patients documented in the active group (18.54%) was higher than that reported in the control group (3.09%) (P = .001). Of the 6598 and 968 PIs conducted by participants working in the active and control group pharmacies, 6013 (91.13%) and 651 (67.25%) were accepted, respectively. The proportions of wrong drug (contraindication), wrong drug (unnecessary drug prescribed/no proof of its benefits), and omission of a drug between the active and control groups were 15.30% versus 7.21% (P = .001), 11.85% versus 6.29% (P = .03), and 17.78% versus 10.50% (0.01), respectively. Additionally, the proportions of lethal, serious, and significant errors were 0.74% versus 0.35% (P = .04), 10.52% versus 2.57% (0.002), and 47.88% versus 9.57% (0.001), respectively. Addition of drug therapy interventions (AOR = 0.62; 95% CI, 0.21-0.84) and errors with significant clinical seriousness (AOR = 0.32; 95% CI, 0.16-0.64). Conclusions PEs involving COVID-19 patients in community settings are common and clinically significant. The intervention assessed in this study could be promising for designing a feasible and time-efficient interventional tool to encourage pharmacists' involvement in identifying and correcting PEs in light of COVID-19.

The impact of telepharmacy on hypertension management in the United Arab Emirates

Objectives

To assess the effectiveness of telepharmacy services delivered by community pharmacies in hypertension management and examine its influence on pharmacists' ability to identify drug-related problems (DRPs).

Methods

This was a 2-arm, randomised, clinical trial conducted among 16 community pharmacies and 239 patients with uncontrolled HTN in the U.A.E over a period of 12 months. The first arm (n=119) received telepharmacy services and the second arm (n=120) received traditional pharmaceutical services. Both arms were followed up to 12 months. Pharmacists self-reported the study outcomes, which primarily were the changes in SBP and DBP from baseline to 12-month meeting. Blood pressure readings were taken at baseline, 3, 6, 9, and 12 months. Other outcomes were the mean knowledge, medication adherence and DRP incidence and types. The frequency and nature of pharmacist interventions in both groups were also reported.

Results

The mean SBP and DBP differences were statistically significant across the study groups at 3-, 6-, and 9-month follow-up and 3-, 6-, 9-, 12- month follow-up, respectively. In detail, the mean SBP was reduced from 145.9 mm Hg in the intervention group (IG) and 146.7 mm Hg in the control group (CG) to 124.5, 123.2, 123.5, and 124.9 mm Hg at 3-, 6-, 9-, and 12-month follow-up in the IG and 135.9, 133.8, 133.7, and 132.4 mm Hg at 3-, 6-, 9-, and 12-month follow-up in the CG. The mean DBP was reduced from 84.3 mm Hg in IG and 85.1 mm Hg in CG to 77.6, 76.2, 76.1, and 77.8 mm Hg at 3-, 6-, 9-, and 12-month follow-up in the IG and 82.3, 81.5, 81.5, and 81.9 mm Hg at 3-, 6-, 9-, and 12-month follow-up in the CG. Medication adherence and knowledge of participants in the IG towards hypertension were significantly improved. The DRP incidence and DRPs per patient identified by pharmacists in the intervention and control groups were 2.1% versus 1.0% (p=0.002) and 0.6 versus 0.3 (p=0.001), respectively. The total numbers of pharmacist interventions in the IG and CG were 331 and 196, respectively. The proportions of pharmacist interventions related to patient education, cessation of drug therapy, adjustment of drug dose, and addition of drug therapy across the IG and CG were 27.5% versus 20.9%, 15.4% versus 18.9%, 14.5% versus 14.8%, and 13.9% versus 9.7%, respectively (all with p<0.05).

Conclusion

Telepharmacy may have a sustained effect for up to 12 months on blood pressure of patients with hypertension. This intervention also improves pharmacists' ability to identify and prevent drug-related problems in community setting.

Polypharmacy and the occurrence of potential drug-drug interactions among geriatric patients at the outpatient pharmacy department of a regional hospital in Durban, South Africa

Background

Polypharmacy is the administration of an excessive number of medicines and a significant irrational medicine use practice. Little is known about this practice in South Africa. This study aimed to determine the level of polypharmacy and potential drug–drug interactions amongst the geriatric patient population in a facility in South Africa.

Method

A cross-sectional retrospective prescription chart review for 250 geriatric patients was conducted at the outpatient pharmacy department of a regional hospital. Variables extracted included demographic information, diagnosis, type of prescriber contact, and polypharmacy. Potential drug–drug interactions were determined with web-based multi-drug interaction checkers.

Results

The average (SD) number of diagnosed clinical problems was 3.54 ± 1.26, with hypertension, diabetes mellitus, and heart disease occurring most frequently. The level of polypharmacy was high with patients receiving an average (SD) of 12.13 ± 4.25 prescribed medicines from 3032 prescribed medicines. The level of polypharmacy was highest within the age categories, 60–64, and 70–74 years of age, respectively. The level of potential drug–drug interactions was also high with an average (SD) of 10.30 ± 7.48 from 2570 potential drug interactions. The majority of these interactions were moderate (72.5%) and pharmacodynamic (73.2%) by nature of the clinical severity of action and mechanism of action, respectively. Polypharmacy and type of prescriber contact were statistically significant contributors to the occurrence of potential drug–drug interactions, (F (2, 249) = 68.057, p < 0.05). However, in a multivariate analysis of variables to determine the strength of the association, polypharmacy was determined to be the strongest contributor to the occurrence of potential drug–drug interactions (p < 0.05) when compared with the type of prescriber contact (p value = 0.467). Therefore, irrespective of the type of prescriber contact, polypharmacy increases the potential for drug interactions among the sampled patient population.

Conclusion

A comprehensive consideration of disease management guidelines, patient factors, and rational medicine review could be measurable strategies towards improving medicine use. This would also limit the occurrence of significant drug interactions among the geriatric patient population. A national study is required to determine if differences occur across hospitals and regions.

Prevalence and Factors Associated with Potential Drug-Drug Interactions in Older Community-Dwelling Adults: A Prospective Cohort Study

Background

Older patients are at increased risk of drug-drug interactions (DDIs) due to polypharmacy. Cardiovascular and central nervous system (CNS) drugs are commonly implicated in serious DDIs.

Objectives

This study aimed to determine the prevalence and factors associated with potential ‘severe’ cardiovascular and CNS DDIs among older (≥ 70 years) community-dwellers.

Methods

This was a prospective cohort study using linked data from a national pharmacy claims database and waves 1 and 2 of The Irish LongituDinal study on Ageing (TILDA). ‘Severe’ cardiovascular and CNS DDIs were identified using the British National Formulary 77 and Stockley’s Drug Interactions. The prevalence of ‘severe’ DDIs (any DDI vs. none) was calculated. Logistic regression was used to examine the association between sociodemographic, functional ability, and medication-related factors and the risk of DDI exposure between waves 1 and 2.

Results

A total of 1466 patients were included [mean age (standard deviation) = 78 (5.5) years; female n = 795, 54.2%]. In total, 332 community-dwellers aged ≥ 70 years [22.65%, 95% confidence interval (CI) 20.58–24.86] were potentially exposed to at least one ‘severe’ cardiovascular or CNS DDI, with more than half (54.82%) of this cohort dispensed the same DDI for a prolonged time (≥ 3 consecutive claims). Aspirin-warfarin was the most frequently dispensed (co-prescribed) DDI (n = 34, 10.24%, 95% CI 7.39–14.00), followed by atorvastatin-clarithromycin (n = 19, 5.72%, 95% CI 3.64–8.81). Polypharmacy [≥ 10 vs. < 5 drugs, odds ratio (OR) 13.40, 95% CI 8.22–21.85] and depression (depressed vs. not, OR 2.12, 95% CI 1.34–3.34) were significantly associated with these DDIs, after multivariable adjustment.

Conclusion

‘Severe’ cardiovascular and CNS DDIs are prevalent in older community-dwellers in Ireland, and those with polypharmacy and depression are at a significantly increased risk.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40266-021-00898-8.

Investigating Potential Drug-Drug Interactions from Greek e-Prescription Data

BACKGROUND

The prevalence of potential drug-drug interactions (pDDIs) is indicative of the prevalence of actual drug-drug interactions and prescription quality. However, they are significantly understudied in Greece.

OBJECTIVE

To determine the prevalence of pDDIs among outpatients and identify factors associated with their occurrence.

METHODS

Anonymous e-prescription data between 2012 and 2017 were obtained from community pharmacies in Thessaloniki, Greece. Patients taking more than one medication for at least three months were included. pDDIs were identified and categorized depending on their clinical significance using Drug Interactions Checker. Crude and adjusted odds ratios (ORs) with accompanying 95% confidence intervals (CIs) of risk factors of pDDIs occurrence were identified using multivariable logistic regression.

RESULTS

During the study period, 6,000 anonymous e-prescriptions (1,000 per year) satisfying the inclusion criteria, were collected. The overall prevalence of major pDDIs was 17.4% (63.0% for moderate pDDIs). The most common major pDDIs were between amlodipine and simvastatin (22.8% of major interactions) followed by clopidogrel and omeprazole (6.4% of major interactions). Polypharmacy (≥5 concomitantly received medications) was associated with an increased risk of major pDDIs (adjusted OR, 5.72; 95% CI, 4.87-6.72); no associations were observed regarding age, sex and number of prescribing physicians.

CONCLUSION

The prevalence of pDDIs in this study was higher than previously reported in other European countries, with polypharmacy being a potential risk factor. Those results argue for a need for improvement in the area of prescribing in Greece.

Analysis of interaction risks of patients with polypharmacy and the pharmacist interventions performed to solve them-A multicenter descriptive study according to medication reviews in Hungarian community pharmacies

Objective

The study examined the Drug-Related Problems (DRPs) of patients with polypharmacy in 78 Hungarian community pharmacies, especially the interaction risks in terms of their clinical severity. Also, the objective was to analyze pharmacists’ interventions to solve the identified interaction risks.

Methodology

The research was carried out in the framework of the training of specialist pharmacists at Semmelweis University, with the participation of 78 graduated pharmacists with the collaboration of 98 GPs. A total of 755 patients participated in pharmaceutical counseling which meant a medication review process. DRPs were uniformly categorized and the interventions were recorded by pharmacists, while a detailed analysis of interaction risks was performed by authors.

Results

A total of 984 DRPs were registered. The most common category of DRPs was the "non-quantitative safety problems" (62.6%). Interaction risk was the most common cause of DRPs (54.0%). The highest proportion of interaction risks were between two prescription drugs (66.7%). In 30.7% of interaction risks’ cases, there was not known negative outcome. In contrast, it was recommended to modify the therapy in 14.9% of interaction risks. Acetylsalicylic acid (22.8%), acenocoumarol (17.7%), and diclofenac (13.9%) were the most common active substances which caused serious interaction risks.

A total of 599 pharmacist interventions were used to solve the 531 interaction risks. Pharmacists notified the GPs about the problem in 28.4% of cases and they intervened without the GP in 63.1% of cases, most often with patient education (27.4%).

Conclusion

Medication review by community pharmacists is required for the safe medicine using of patients with polypharmacy, as a significant number of DRPs have been recorded. The incidence of interaction risks stood out. It is essential to develop a pharmaceutical guideline to properly classify the clinical relevance of interaction risks (e.g. according to high-risk active substances) and to increase the collaboration with GPs.

Novel deep learning-based transcriptome data analysis for drug-drug interaction prediction with an application in diabetes

Background

Drug-drug interaction (DDI) is a serious public health issue. The L1000 database of the LINCS project has collected millions of genome-wide expressions induced by 20,000 small molecular compounds on 72 cell lines. Whether this unified and comprehensive transcriptome data resource can be used to build a better DDI prediction model is still unclear. Therefore, we developed and validated a novel deep learning model for predicting DDI using 89,970 known DDIs extracted from the DrugBank database (version 5.1.4).

Results

The proposed model consists of a graph convolutional autoencoder network (GCAN) for embedding drug-induced transcriptome data from the L1000 database of the LINCS project; and a long short-term memory (LSTM) for DDI prediction. Comparative evaluation of various machine learning methods demonstrated the superior performance of our proposed model for DDI prediction. Many of our predicted DDIs were revealed in the latest DrugBank database (version 5.1.7). In the case study, we predicted drugs interacting with sulfonylureas to cause hypoglycemia and drugs interacting with metformin to cause lactic acidosis, and showed both to induce effects on the proteins involved in the metabolic mechanism in vivo.

Conclusions

The proposed deep learning model can accelerate the discovery of new DDIs. It can support future clinical research for safer and more effective drug co-prescription.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04241-1.

A novel educational approach for improving medication-related problems in community pharmacies

BACKGROUND

Community pharmacists have the responsibilities of identifying and resolving medication-related problems (MRPs), thereby improving patient safety.

OBJECTIVES

To deliver a series of clinical case scenarios using WhatsApp and assess the impact of this method on the ability of pharmacists to identify MRPs.

METHODS

This study was conducted in 104 community pharmacies in the United Arab Emirates (UAE) over a period of six months. Recruited pharmacies were randomly allocated to either intervention or control groups using a 1:1 allocation ratio. Senior experts in clinical pharmacy created a series of clinical case scenarios based on their clinical practice and based on previous published studies related to MRPs. WhatsApp®, a well-known messenger application, which has been proven to be an efficient platform to improve communication between learners and educators, was used to deliver clinical scenarios-based educational interventions to pharmacists. Then, pharmacists from both groups filled a standardized data reporting form. The clinical importance of pharmacist recommendations was assessed by a multidisciplinary expert panel.

RESULTS

The total number of patients with MRPs across the intervention and control groups was 492 versus 194 (p = 0.01). While the number of MRPs identified, the mean time needed to resolve MRPs for patients with major polypharmacy, and physicians' acceptance of pharmacist recommendations across the intervention and control groups were 492 versus 194, 1589 versus 255, 6.82 (±3.86) versus 10.78 (±6.38), and 1065/1284 (82.94%) versus 125/201 (62.18%), respectively, all with p < 0.05. Efficacy-related problems (27.56%) and safety-related problems (28.44%) were the most commonly identified MRPs by pharmacists in the intervention group. Clinically significance of pharmacist recommendations was a significant predictive factor for physicians' acceptance of pharmacist recommendations.

CONCLUSION

Clinical case scenarios delivered by WhatsApp may be useful for improving the ability of pharmacists to identify MRPs and for shortening the mean time needed to resolve MRPs.

The Prevalence of Selected Potential Drug-Drug Interactions of Analgesic Drugs and Possible Methods of Preventing Them: Lessons Learned From the Analysis of the Real-World National Database of 38 Million Citizens of Poland

Introduction: Drug-drug interactions may lead to poor health outcomes, as well as increased costs and utilization of healthcare services. Unfortunately, real-world data continuously prove high prevalence of potential drug-drug interactions (pDDIs) worldwide. Among identified drivers, ageing, multimorbidity and polypharmacy play a very important role. With these factors being widespread, the need for implementation of strategies minimizing the burden of pDDIs becomes an urgency. This, however, requires a better understanding of the prevalence of pDDIs and the underlying causative factors.

Aim of study: To assess the real-world prevalence of pDDIs and its characteristics in the general population of Poland, using analgesic drugs as a model, and to find out whether pDDIs are caused by prescribing coming from the very same prescribers (co-prescribing).

Methods: A retrospective analysis of the 2018 dispensation data of the National Health Fund (NHF) - the only Polish public healthcare payer organization with nationwide coverage. We searched for selected pDDIs of non-steroidal anti-inflammatory drugs (NSAIDs) with antihypertensives, other NSAIDs (double use), oral glucocorticoids, oral anticoagulants, selective serotonin reuptake inhibitors (SSRIs), serotonin–norepinephrine reuptake inhibitors (SNRIs), and antiplatelet drugs; as well as opioides with SSRIs, SNRIs, gabapentinoids, and benzodiazepines. A pDDI was deemed present if two drugs standing in a possible conflict were dispensed within the same calendar month.

Results: Out of 38.4 million citizens of Poland, 23.3 million were dispensed prescribed drugs reimbursed by NHF in 2018. In this cohort, we have identified 2,485,787 cases of analgesic drug pDDIs, corresponding with 6.47% of the Polish population. Out of these, the most prevalent pDDI was caused by “NSAIDs + antihypertensives” (1,583,575 cases, i.e., 4.12% of the Polish population), followed by “NSAIDs + NSAIDs” (538,640, 1.40%) and “NSAIDs + glucocorticoids” (213,504, 0.56%). The most persistent pDDIs among those studied were caused by “Opioids + Gabapentinoids” (2.19, 95%CI: 2.16–2.22 months). On average, 76.63% of all cases of pDDIs were caused by drugs prescribed by the very same prescribers.

Conclusion: Based on high-quality, nationwide data, we have found a high prevalence of analgesic drugs-related pDDIs in Poland. Over ¾ of the identified pDDIs were caused by co-prescribing, i.e., prescriptions issued by the same prescribers. The significance of the problem, illustrated with our findings on analgesic drugs-related pDDIs in Poland, deserves much more scientific and policymaker attention.

Targeting continuity of care and polypharmacy to reduce drug-drug interaction

Drug–drug interaction (DDI) is common among the elderly, and it can have detrimental effects on patients. However, how DDI can be targeted has been under-researched. This study investigates whether DDI can be reduced by targeting continuity of care (COC) through reducing polypharmacy. Population claims data of Taiwan National Health Insurance were used to conduct a 7-year-long longitudinal study on patients aged ≥ 65 years (n = 2,318,766). Mediation analysis with counterfactual method and a 4-way decomposition of the effect of COC on DDI was conducted. Mediation effect through excessive polypharmacy differed from that through lower-level polypharmacy. Compared with the low COC group, the high COC group demonstrated reduced excess relative risk of DDI by 26% (excess relative risk =  − 0.263; 95% Confidence Interval (CI) =  − 0.263 to − 0.259) to 30% (excess relative risk =  − 0.297; 95% CI =  − 0.300 to − 0.295) with excessive polypharmacy as the mediator. The risk only reduced by 8% (excess relative risk =  − 0.079; 95% CI, − 0.08 to − 0.078) to 10% (excess relative risk =  − 0.096; 95% CI, − 0.097 to − 0.095) when the mediator was changed to lower-level polypharmacy. The effect of COC on DDI was mediated by polypharmacy, and the mediation effect was higher with excessive polypharmacy. Therefore, to reduce DDI in the elderly population, different policy interventions should be designed by considering polypharmacy levels to maximize the positive effect of COC on DDI.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Anticholinergic Burden in Children, Adults and Older Adults in Slovenia: A Nationwide Database Study

Anticholinergic burden has been widely studied in specific patient populations with specific conditions. However, the prevalence in the general population is poorly understood. This retrospective cross-sectional study was a nationwide database analysis of outpatient prescriptions of anticholinergic medications. The study was based on Slovenian health claims data of all outpatient prescriptions in 2018. Anticholinergic burden was evaluated using the Anticholinergic Cognitive Burden scale. Three age groups were analysed: children (≤18 years), adults (19–64 years) and older adults (≥65 years). Anticholinergic medications were prescribed to 29.8% of the participants; 7.6% were exposed to a clinically significant anticholinergic burden. The proportion of patients exposed to anticholinergic burden was highest in older adults (43.2%), followed by adults (25.8%) and children (20.7%). The most frequently prescribed medications with the highest anticholinergic activity were antipsychotics and medications for urinary diseases (42.8% and 40.2%, respectively). Medications with second highest activity were mostly antiepileptics (87.3%). Medications with possible anticholinergic activity included diverse therapeutic groups. Anticholinergic burden is highest in older adults but is also considerable among adults and children. Medications with anticholinergic activity belong to diverse therapeutic groups. Further research is needed on safe use of these medications in all age groups.

Prevalence of potential drug-drug interactions in outpatients of a general hospital in China: a retrospective investigation

Background Potential drug–drug interactions are important factors resulting in adverse drug reactions or therapeutic failure. Therefore, potential drug–drug interactions need to be identified to prevent the related risk and improve drug safety. Objective This study was designed to determine the prevalence of potential drug–drug interactions and investigate the association of potential drug–drug interactions with characteristics in outpatient prescriptions. Setting A large-scale general university hospital in Jinshan District of Shanghai, China. Method The retrospective study was conducted on data obtained from prescriptions containing two or more drugs, written for outpatients older than 18 years. They were screened for potential drug–drug interactions using Lexi-Interact in UpToDate, Stockley’s Drug Interactions and Medicine Specification in the order of priority. Main outcome measure Drug–drug interactions with C, D, X risk rating and clinical parameters recorded at the prescriptions. Results 16,120 prescriptions were screened for the presence of potential drug–drug interactions and 4882 (30.29%) prescriptions containing 6667 potential drug–drug interactions were identified. Among 6667 potential drug–drug interactions, 90.81% (6054/6667), 8.49% (566/6667), 0.70% (47/6667) potential drug–drug interactions belonged to the risk category of C, D and X, respectively. Male, old age and polypharmacy increased the likelihood of potential drug–drug interactions. The most frequently prescribed drugs responsible for potential drug–drug interactions included pioglitazone, dihydrocodeine, thalidomide, sotalol, amiodarone and amlodipine. The predominant potential adverse outcome of potential drug–drug interactions was the increased central nervous system suppression function with the mechanism of reinforced pharmacological effects. Conclusion This study showed that potentially significant drug–drug interactions in outpatients were prevalent in real-world practice. Considering the risk of potential clinical consequences related to potential drug–drug interactions, it is necessary to implement the computerized surveillance and warning systems with drug–drug interactions databases as well as develop the clinical guidelines regarding the widespread potential drug–drug interactions.

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.

The prevalence and severity of potential drug-drug interactions among adult polypharmacy patients at outpatient clinics in Jordan

Objective

To assess the prevalence of potential drug-drug interactions (pDDIs) among polypharmacy patients in Jordan using Lexicomp®. Additionally, this study aims to categorize and rate the identified pDDIs according to interaction risk, severity, and reliability.

Methods

A descriptive cross-sectional study was conducted at six different hospitals representing different public health sectors in Jordan (ministry of health, royal medical services, and university-affiliated hospitals). Polypharmacy patients from outpatient clinics (e.g., cardiology,& and internal medicine) were identified, recruited, and interviewed by clinical pharmacists. pDDIs were assessed using the Lexicomp® mobile application and classified according to interaction risk rating, severity, and reliability rating. Furthermore, the prevalence of pDDIs across chronic medical conditions was assessed. P-value <0.05 was considered as significant.

Results

A total of 801 patients with polypharmacy were identified. The average number of drugs per patient was 6.6 ± 1.96, with an average of 4.2 ± 3.0 pDDIs per patient. Potential drug-drug interactions were detected in 769 patients (96%), with a total of 3359 interactions. Blood pressure lowering agents were involved in 39.9% of the pDDIs. Cardiovascular system drugs contributed to the largest share of pDDIs (46.6%). While diuretics had the major share of interactions among cardiovascular system drugs (16.2%), drugs used in diabetes had the highest share across all groups (17.1%). The majority of pDDIs were of “C” risk rating with a moderate interaction severity, whilst 1.6% of pDDIs could have been avoided in the first place as the concurrent administration of these agents is contraindicated (i.e., risk rating X). Patients with cardiovascular diseases, diabetes, chronic obstructive pulmonary disease, gout, and chronic kidney disease were associated with the highest number of potential drug-drug interactions.

Conclusion

Our study showed that 96% of polypharmacy patients at outpatient clinics have at least one pDDI. Almost half of the detected interactions involved cardiovascular medications. The majority of these pDDIs had moderate severity, with no more than 10% of the interactions requiring therapy modification.

Are patients with a nasally placed feeding tube at risk of potential drug-drug interactions? A multicentre cross-sectional study

Aims

The primary aims were to determine the rate of potential drug-drug interactions (pDDIs) in patients with nasally placed feeding tubes (NPFT) and the factors significantly associated with pDDIs. The secondary aim was to assess the change in pDDIs for patients between admission and discharge.

Material and methods

This multicentre study applied a cross-sectional design and was conducted in six Brazilian hospitals, from October 2016 to July 2018. Data from patients with NPFT were collected through electronic forms. All regular medications prescribed were recorded. Medications were classified according to the World Health Organization (WHO) Anatomical Therapeutic Chemical code. Drug-drug interaction screening software was used to screen patients’ medications for pDDIs. Negative binomial regression was used to account for the over dispersed nature of the pDDI count. Since the number of pDDIs was closely related to the number of prescribed medications, we modelled the rate of pDDIs with the count of pDDIs as the numerator and the number of prescribed medications as the denominator; six variables were considered for inclusion: time (admission or discharge), patient age, patient gender, age-adjusted Charlson Comorbidity Index (CCI) score, type of prescription (electronic or handwritten) and patient care complexity. To account for correlation within the two time points (admission and discharge) for each patient a generalised estimating equations approach was used to adjust the standard error estimates. To test the change in pDDI rate between admission and discharge a full model of six variables was fitted to generate an adjusted estimate.

Results

In this study, 327 patients were included. At least one pDDI was found in more than 91% of patients on admission and discharge and most of these pDDIs were classified as major severity. Three factors were significantly associated with the rate of pDDIs per medication: patient age, patient care complexity and prescription type (handwritten vs electronic). There was no evidence of a difference in pDDI rate between admission and discharge.

Conclusion

Patients with a NPFT are at high risk of pDDIs. Drug interaction screening tools and computerized clinical decision support systems could be effective risk mitigation strategies for this patient group.

Countrywide prevalence of critical drug interactions in Hungarian outpatients: a retrospective analysis of pharmacy dispensing data

Background

Drug-drug interactions (DDIs) present a significant source of adverse drug reactions. Despite being one of the commonly cited risks to patient safety, prevention of DDIs still poses a challenge to healthcare systems. The prevalence of DDIs can be used as a quality indicator for the safety of prescribing. With the analysis of drug utilization databases, real-world data on critical DDIs can be obtained. The aim of this study was to establish a list of critical DDIs and estimate their prevalence in the Hungarian outpatient population.

Methods

Since there is no conclusive and generally accepted repository of high-risk DDIs, a systematic search of the literature for consensus-based lists was performed. Based on these results and their analysis with 5 interaction compendia, we propose a simple methodology to identify critical combinations. Present study focused on DDIs which are (1) of high clinical importance thus being most likely to cause significant harm if not detected, (2) well-supported by available evidence and (3) affect drugs which are routinely dispensed in the community pharmacy setting. A retrospective analysis of prescriptions filled between 2013 and 2016 was performed. The source of drug utilization data was the IQVIA’s national prescription fill database. The number of interacting drug pairs dispensed at the same time to the same patient was established.

Results

After excluding drugs with low dispensing rates, the analysis covered 39 DDIs. The distribution of risk categories of the analysed DDIs was inconsistent among different drug interaction compendia. The total number of prescriptions filled varied between 173924449 and 176368468 per year. The prevalence of the selected potential DDIs ranged from 0.00 to 355.89 per 100000 prescriptions per year. There was significant variation between how the number of cases had changed for each DDI throughout the study period, no general tendency could have been described.

Conclusions

There were 1.8 million cases of co-dispensing each year, where prescribers’ and community pharmacists’ role in recognizing and managing potentially serious interactions was or would have been critical. The method presented to identify high-risk DDIs can serve as a starting point for the much-needed improvement of routine interaction screening.

Electronic supplementary material

The online version of this article (10.1186/s40360-019-0311-0) contains supplementary material, which is available to authorized users.

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.

Potential drug-drug interactions in outpatient department of a tertiary care hospital in Pakistan: a cross-sectional study

BACKGROUND

Potential drug-drug interactions (pDDIs) are one of the preventable drug related problems having the risk of serious adverse events or therapeutic failure. In developing countries like Pakistan, this issue remains poorly addressed. The objective of this study was to explore prevalence of pDDIs in the Outpatient Department (OPD) of a tertiary care hospital in Pakistan. The secondary aim was to describe the levels of reported pDDIs and develop a list of widespread clinically relevant interactions.

METHODS

Prescriptions of 2400 OPD patients were analyzed for pDDIs through Micromedex Drug-Reax®. Prevalence, severity- and documentation-levels and widespread clinically relevant interactions were reported.

RESULTS

Of total 2400 prescriptions, pDDIs were present in 22.3%. Whereas, moderate- and major-pDDIs were found in 377 (15.7%) and 225 (9.4%), respectively. PDDIs were more prevalent in Medicine (9.2%) and Cardiology (2.6%) as compared with other OPD specialties. Total 942 pDDIs were identified, of which, the majority were either moderate- (61.9%) or major-pDDIs (32.1%). Some of the most common interactions were ibuprofen + levofloxacin (n = 50), ciprofloxacin + diclofenac (32), aspirin + atenolol (24), and diclofenac + levofloxacin (19). The potential adverse outcomes of widespread interactions were seizures, bleeding, QT-interval prolongation, arrhythmias, tendon rupture, hypoglycemia/hyperglycemia, serotonin syndrome, drug toxicity, and decreased therapeutic response.

CONCLUSIONS

OPD patients were at risk to pDDIs, particularly to major- and moderate-pDDIs. Screening of prescriptions for pDDIs and monitoring of pharmacotherapy in terms of response and associated adverse drug events will contribute to patient safety.

Analysis of Drug-Drug Interactions in Swiss Claims Data Using Tizanidine and Ciprofloxacin as a Prototypical Contraindicated Combination

BACKGROUND

Potential drug-drug interactions (pDDIs) are described in various case reports, but few studies have evaluated the impact of specific combinations on a population level.

OBJECTIVE

To analyze the type and frequency of multiple contraindicated (X-pDDIs) and major interactions (D-pDDIs) and to subsequently assess the impact of the particular combination of tizanidine and ciprofloxacin on outpatient physician visits and hospitalizations.

METHODS

Anonymized Swiss claims data from 524 797 patients in 2014-2015 were analyzed. First, frequencies of X- and D-pDDIs were calculated. Next, a retrospective cohort study was conducted among patients prescribed tizanidine and ciprofloxacin (exposed, n = 199) or tizanidine and other antibiotics (unexposed, n = 960). Hospitalizations and outpatient physician visits within 7, 14, and 30 days after initiation of antibiotic therapy were evaluated using multiple binary logistic regression and multiple linear regression.

RESULTS

The relative frequencies of X- and D-pDDIs were 0.4% and 6.65%, respectively. In the cohort study, significant associations between exposure to tizanidine and ciprofloxacin and outpatient physician visits were identified for 14 and 30 days (odds ratio [OR] = 1.61 [95% CI = 1.17-2.24], P = 0.004, and OR = 1.59 [95% CI = 1.1-2.34], P = 0.016). A trend for increased risk of hospitalization was found for all evaluated time periods (OR = 1.68 [95% CI = 0.84-3.17], OR = 1.52 [95% CI = 0.63-3.33], and OR = 2.19 [95% CI = 0.88-5.02]). Conclusion and Relevance: The interaction between tizanidine and ciprofloxacin is not only relevant for individual patients, but also at the population level. Further investigation of the impact of other clinically relevant DDIs is necessary to improve patient safety and reduce avoidable health care utilization.