Contraindicated Drug-drug Interactions Research Articles

Enhancing Primary Care for Nursing Home Patients with an Artificial Intelligence-Aided Rational Drug Use Web Assistant.

Polypharmacy can result in drug-drug interactions, severe side-effects, drug-disease interactions, inappropriate medication use in the elderly, and escalating costs. This study aims to evaluate nursing home residents' medication regimens using a rational drug use web assistant developed by researchers to mitigate unnecessary medication usage. This analytical, cross-sectional study included data from nursing home residents recently recorded in a training family health center. Sociodemographic information, medical conditions, and prescribed medications of all patients in the nursing home (n = 99) were documented. Medications were assessed using an artificial intelligence-aided rational drug use web assistant. Instances of inappropriate drug use and calculations of contraindicated drug costs were also recorded. The study revealed that 88.9% (n = 88) of patients experienced polypharmacy, with a mean value of 6.96 ± 2.94 drugs per patient. Potential risky drug-drug interactions were present in 89.9% (n = 89) of patients, contraindicated drug-drug interactions in 20.2% (n = 20), and potentially inappropriate drug use in 86.9% (n = 86). Plans to discontinue 83 medications were estimated to reduce total direct medication costs by 9.1% per month. After the assessment with the rational drug use web assistant, the number of drugs that patients needed to use and polypharmacy decreased significantly. This study concludes that the rational drug use web assistant application, which is more cost-effective than the traditional manual method, assisted by artificial intelligence, and integrated into healthcare services, may offer substantial benefits to family physicians and their geriatric patients.

Real world co-prescribing contraindicated drugs with fluconazole and itraconazole.

This study aimed to investigate co-prescribing of contraindicated drugs with fluconazole and itraconazole using real-world nationwide data. 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. 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®. 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 Potential Pharmacological Interactions in Patients Undergoing Systemic Chemotherapy in a Tertiary Hospital.

Introduction: Pharmacological interactions are frequently observed in patients with chronic diseases, and their occurrence is proportional to the amount of medication used daily. Patients undergoing chemotherapy treatment commonly have comorbidities, which favor a greater prevalence of polypharmacy, increasing the risk of drug interactions. Therefore, the aim of this study was to estimate the prevalence of drug interactions in patients undergoing intravenous chemotherapy treated at a hospital oncology service in southern Brazil. Methods: This was an observational study with a cross-sectional design that was carried out with the analysis of secondary data obtained through the review of medical records. The population assessed consisted of all cancer patients who received intravenous chemotherapy from October to December 2020. Results: Out of the 297 patients included in the study, 231 (77.8%) had at least 1 potential pharmacological interaction. In total, 1044 drug interactions were found that were classified according to severity, resulting in 18 (1.7%) contraindicated drug-drug interactions (DDI), 699 (67%) severe, 281 (26.9%) moderate, and 46 (4.4%) minor interactions. There was an association between polypharmacy and the prevalence of drug interactions. Conclusion: The results demonstrate that a large percentage of patients undergoing chemotherapy are susceptible to drug interactions. Thus, it is necessary that prescribers consider all drugs used by patients and, when possible, prescribe alternative drugs that have less potential for interaction in order to prevent drug interactions adverse effects and provide a better prognosis for patients.

Drug treatments and interactions, disease progression and quality of life in ALS patients

Background: Amyotrophic lateral sclerosis (ALS) is a rare neurodegenerative disease that causes a wide range of symptoms demanding treatment, but the evidence base for their effectiveness is limited. Affected individuals may present several comorbidities. Polypharmacy exposes ALS patients to the adverse effects of drugs and to drug-drug interactions. At present, no data on drug–drug and drug–disease interactions are available in patients with ALS. Methods: Multicenter, case-series, observational study aimed to provide a picture of the therapeutic habits of patients with ALS, and identify drug–drug interactions (DDIs) and their effects on the outcome of the disease (measured by ALSFRS-R) and quality of life (ALSAQ-40). Results: 440 patients were included, 50 of them with follow-up data. The maximum number of DDIs at baseline was 2 for minor, 9 for moderate, 3 for major, and 3 for contraindicated interactions. At least one minor, moderate, major, or contraindicated DDI was present in 18 (4.1%), 127 (28.9%), 46 (10.5%) and 37 (8.4%) patients. Patients with DDIs were older. In those with major/contraindicated DDIs, the scores on the emotional domain of the ALSAQ-40 and the ALSFRS-R total score were worse than the scores of patients without DDIs or with minor/moderate DDIs. At the 48-week visit, patients with DDIs showed lower ALSFRS-R scores, and higher scores on all domains of ALSAQ-40, as compared to patients without DDIs. Conclusions: Symptomatic treatment aims to improve quality of life of patients. The higher the number of drugs, the higher the risk to incurring (relevant) interactions.

Potential or contraindicated drug–drug interactions with antiretroviral therapy in real-world settings in Taiwan

Given the complex metabolic pathway of antiretroviral therapy (ART), polypharmacy may increase the risk of drug-drug interactions (DDIs). Therefore, we investigated the frequency of DDIs during ART exposure to improve medical care for patients with human immunodeficiency virus (HIV). This was a nationwide cross-sectional study using claims data from the National Health Insurance in Taiwan in 2016. Potential or contraindicated DDIs with recommended first-line ART (1L-ART) or protease inhibitors (PIs) were identified from the University of Liverpool drug interaction database. Fisher's exact or chi-square test was used to determine the significance of categorical variables. A total of 25,863 HIV-infected individuals were identified. Regarding 1L-ART users, patients with contraindicated DDIs accounted for 1-4%, whereas those with potential DDIs accounted for 15-50%. The most frequently coprescribed medications related to potential DDIs were diclofenac and polyvalent cation-containing antacids. Among PI users, 8-10% of them had contraindicated DDIs while 44-50% of them had potential DDIs. The medications related to potential DDIs with PIs were zolpidem, betamethasone, polyvalent cation-containing antacids, and loperamide. Our study showed a low prevalence of contraindicated DDIs in the HIV population; however, more attention should be paid to a high proportion of potential DDIs. Strategies to avoid these DDIs should be implemented if possible. Further research that focuses on the long-term clinical impact of potential DDIs is warranted.

Multivalent cations interactions with fluoroquinolones or tetracyclines: A cross-sectional study.

IntroductionOral fluoroquinolones and tetracyclines are known to interact with divalent or trivalent cation-containing compounds (DTCCs) via chelation. The objective of this study is to describe the prevalence of these drug-drug interactions (DDIs) in an inpatient setting.MethodsA cross-sectional study of prospectively collected data were conducted at an academic tertiary care hospital. We included hospitalized adults who were receiving oral fluoroquinolones or tetracyclines with DTCCs in 2019. Our hospital uses electronic health records for medication ordering and handwritten medication administration records (MARs). The primary study outcome was the percentage of simultaneous administration of fluoroquinolones or tetracyclines with DTCCs, and the secondary outcome was the percentage of inappropriate separation time.ResultsAmong patients who received oral fluoroquinolones or tetracyclines, 47 patients (26.6%) were co-administered DTCCs and included in this study. Ciprofloxacin (n = 29; 61.7%) was the most commonly interacting antibiotic, followed by moxifloxacin (n = 12; 25.5%) and doxycycline (n = 6; 12.8%). The interacting DTCCs included iron-containing products and calcium-containing products, and half of the patients (n = 24; 51%) received DTCCs once daily. Most patients (n = 35; 74.5%) were found to receive oral fluoroquinolones or tetracyclines at the same time as DTCCs, while one (2.1%) received inappropriately separated DTCCs.ConclusionsDespite being a very known contraindicated DDI, the prevalence of simultaneous co-administration of oral fluoroquinolones or tetracyclines with polyvalent cations was extremely high in a hospital with handwritten MARs. Antimicrobial stewardship programs should target this DDI, and future studies should evaluate the impact of different practical solutions to this problem in different clinical settings.

Comparison of Clinical Importance of Drug Interactions Identified by Hospital Pharmacists and a Local Clinical Decision Support System.

Drug-drug interactions (DDIs) may cause adverse drug events, potentially leading to hospital admission. Clinical decision support systems (CDSSs) can improve decision-making by clinicians as well as drug safety. However, previous research has suggested that pharmacists are concerned about discrepancies between CDSSs and common clinical practice in terms of severity ratings and recommended actions for DDIs. The primary objective was to characterize the level of agreement in terms of DDI severity ranking and actions recommended between the local CDSS and pharmacists. The secondary objectives were to determine the level of agreement among pharmacists concerning DDI severity, to determine the influence of the CDSS on clinicians' decision-making, and to review the literature supporting the severity rankings of DDIs identified in the study institution's database. This 2-part survey study involved pharmacists and pharmacy residents working at 1 of 4 health organizations within the Lower Mainland Pharmacy Services, British Columbia, who were invited to participate by email. Participants were first asked to rank the severity of 15 drug pairs (representing potential DDIs) on a 5-point Likert scale and to select an action to manage each interaction. Participants were then given the CDSS severity classification for the same 15 pairs and again asked to select an appropriate management action. Of the estimated 500 eligible pharmacists, a total of 73 pharmacists participated, for a response rate of about 15%. For DDIs of moderate severity, most participants chose to monitor. For severe and contraindicated interactions, the severity ranking and action proposed by participants varied, despite the same severity classification by the CDSS. There was poor agreement among respondents about the severity of the various DDIs. Moreover, knowledge of the CDSS severity ranking did not seem to change the actions proposed by most respondents. This study identified a gap between the local CDSS and clinical practice. There were discrepancies in terms of severity rankings and actions proposed to manage DDIs, particularly for severe and contraindicated DDIs. The current CDSS did not appear to have a large impact on clinical decision-making, which suggests that it may not be functioning to its full potential.

Evaluation of Contraindicated Drug-Drug Interactions in Electronic Health Data Insurance at 3 Hospitals in Quang Ninh Province Through Navicat Software

Drug-drug interactions is a frequently encountered issue in clinical practice, reduce effectiveness of treatment and lead to dangerous complications for the patients, possibly even death. The purpose of this study is to analyze contraindicated drug interactions when prescribing in some hospitals in Quang Ninh Province. Subjects and methods: List of concentrated contractor drugs applied Quang Ninh province and within the scope of payment of Health Insurance Fund in 2018. The result: the group of researchers developed the list of 112 pairs of the cotraindicated drug-drug interactions to serve as a reference to determine the appearing frequency of contraindicated drug-drug interactions over 519500 medical records and 1254099 prescriptions at 3 hospitals in Quang Ninh province. Of which, 26 contraindicated drug-drug interactions (0.006%) was found over 297 medical records and 789 prescriptions. 5 pairs with the most appearing frequency are: Amiodarone-Amitriptyline, Amiodarone-Moxifloxacin, Amiodarone-Colchicine, Levodopa -Sulpiride, Amiodarone-Haloperidol. Medical reccords with contraindicated drug-drug interactions were found in 13 different departments or interdisciplinary deparments from all 3 hospitals, of which the highest rates were found in Intensive Care Unit and Emergency Departments (24.2%), Endocrinology Departments (16.2%) and Cardiology Pediatric Departments (10.4%). The average age of patients with contraindicated drug-drug interactions in all 3 hospitals was greater than or equal to 67, most are inpatients. Conclusion: Based on retrospective data on drug prescribing at 3 hospitals in Quang Ninh Province, the study analyzed 26 pairs of common contraindicated drug interactions in clinical practice.
 Keywords
 Contraindicated drug-drug interactions, hospitals in Quang Ninh Province, medication review tools.
 References
 [1] D.C. Malone, J. Abarca, P.D. Hansten, A.J. Grizzle, E.P. Armstrong, R.C.V Bergen, B.S. Duncan-Edgar, S.L. Solomon, R.B. Lipton, Indentification of Serious Drug-Drug Interactions: Results of the Partnership to Prevent Drug-Drug Interactions, J. Am. Pharm. Assoc. 44 (2004) 142-151. https://doi.org/10.1331/154434504773062591.[2] P. Vonbachl, A. Dubied, S. Kra¨henbu¨hl, J.H.Beer, Evaluation of frequently used drug interaction screening programs, Pharm. World. Sci. 30 (2008) 367-374. https://doi.org/10.1007/s11096-008-9191-x.[3] N.T. Hanh, V.T.P. Thao, H.Q. Tuan, N.X. Bach, N. T. Hai, Developing a list of important drug-drug interactions in the clinical practice in the internal department – Kien An Hospital, Hai Phong City, VNU Journal of Science: Medical and Pharmaceutical Sciences, 35(2) (2019) 54-67. https://doi.org/10.25073/2588-1132/vnumps.4179.[4] L.H. Duong, Developing clinically signification drug-drug interactions at Hop Luc General Hospital (2017), Specialist Pharmacist Graduation thesis – 1st Class, Ha Noi University of Pharmacy (in Vietnamese).[5] L.T. Phuong, Managing clinically signification drug-drug interactions at the National Geriatric Hospital (2018), Pharmacist Graduation thesis, Ha Noi University of Pharmacy (in Vietnamese).[6] T.T. Hoai, Examining drug-drug interactions in the inpatient medical records at the Thai Nguyen Lung Hospital (2017), Specialist Pharmacist Graduation thesis – 1st Class, Ha Noi University of Pharmacy (in Vietnamese).[7] Ministry of Health, Vietnam Regulatory Affairs Society, Medical Publishing House one member Company Limited, Vietnam, 2015 (in Vietnamese).[8] A.P. Heverton, R.L.P Leonardo, M.V Carlos, F.F.C. Maria, Patient’s lack of understanding producing insulin drug-interactions in Southeast Brazilian primary care clinics, Diabetes & Metabolic Syndrome: Clinical Research & Reviews. 3(2) (2019) 1131-1136. https://doi.org/10.1016/j.dsx.2019.01.032
 

Identification and management of contraindicated drug–drug interactions through pharmaceutical care programs: Experience in direct-acting antivirals therapy

To investigate the impact of pharmaceutical care programs for the management of contraindicated drug-drug interactions (DDIs) in direct-acting antivirals (DAAs) therapy. A prospective observational study was performed at Dalin Tzu Chi Hospital between January 2018 and December 2019. Pharmacists screened DDIs for all hepatitis C patients before DAA therapy. The study outcome included the frequency of contraindicated DDIs, acceptance rate, and cost avoidance of the pharmaceutical care program. A total of 1053 patients were enrolled in the study, with a mean age of 67.1±11.9 years. Most patients received therapy with sofosbuvir/ledipasvir (37.1%; n=391), elbasvir/grazoprevir (23.8%; n=251), or glecaprevir/pibrentasvir (21.1%; n=222). A total of 796 (75.6%) patients received at least one co-medication, with the average number of co-medications being 5.2 per patient (SD: 4.4/patient). In total, 1356 DDIs were identified, with the average DDIs per patient of 1.3 (SD: 1.7). For patients with contraindicated DDIs (2%, n=102), statins and amiodarone were the most common co-medications. Physicians often accepted pharmacists' recommendations (acceptance rate of 72.5%) or withheld co-medication to avoid severe adverse drug events (ADEs). The estimated cost avoidance of preventable ADEs was USD 14,033 for contraindicated DDIs with pharmaceutical care programs. The implementation of pharmaceutical care programs in DAA therapy provides a favorable outcome and substantial cost avoidance.

Drug prevalence and comparison interaction between numbers of patients admitted at two teaching hospitals; Quetta, Pakistan

Aim and objective: Drug-drug interaction (DDI) is of major concern in patients with complex therapeutic regimens. The study aims to assess the prevalence of DDIs in the prescriptions of patients during the hospitalization to evaluate the DDIs of drugs regarding its severity, pharmacodynamic and pharmacokinetic. Methodology: The prescribed drugs in the admission chart of 183 patients were screened for Drug-drug interaction using Mediscape interaction screening program. Drug-drug interactions have been classified on the basis of severity (contraindicated, significant, minor, major, moderate and serious). Frequency and the percentage of pairs of drugs with DDIs, number of prescribed drugs, demographics and DDIs on the basis of and Pharmacodynamics, Pharmacokinetics have been analysed. Results: The overall result shows the frequency and percentage of drug-drug interaction present in hospitalized patients, out of 183 was 125(68.3%). Majority of interactions was found on the basis pharmacokinetic DDI 108(59.0%). On the basis of severity, minor Drug-drug interaction was found dominant 91(49.7%) followed by contraindicated 19(10.4%). Conclusion: Drug-drug interaction is common among hospitalized patient’s medications. My study evaluated a positive relation between prevalence of DDIs and number of drugs prescribed. Survey concluded that percentage of minor and contraindicated DDIs at admission were produced by changes of medication during hospitalization.

Potential drug-drug interactions in acute ischemic stroke patients at the Neurological Intensive Care Unit

BackgroundClinically relevant potential drug-drug interactions are considered preventable adverse drug reactions.ObjectiveThe aim of this study was to ascertain the frequency of potential drug-drug interactions in acute ischemic stroke patients and to explore factors associated with occurrence of potentially contraindicated drug-drug interactions.MethodsThis observational retrospective cohort and nested case-control study was carried out among patients treated for acute ischemic stroke at the Neurological Intensive Care Unit in the Clinical Centre Kragujevac, Serbia. The potentially drug-drug interactions for each day of hospitalization were identifi ed using Micromedex® soft ware. Based on the existence or absence of potentially contraindicated drug-drug interactions, the participants were divided into a group of cases (n=111) and the control group (n=444).ResultsA total of 696 patients were analysed. All patients had a minimum of one potential drug-drug interaction during hospitalization. The most common drugs involved in potential drug-drug interactions were aspirin (8.02%), diclofenac (7.49%) and warfarin (7.14%). The number of medications prescribed for simultaneous use during hospitalisation and the use of antipsychotics in therapy signifi cantly increased the likelihood of potentially contraindicated drug-drug interactions aft er adjustment by means of logistic regression for 1.2 and 3 times, respectively.ConclusionsThis study suggests that patients with acute ischemic stroke are frequently exposed to potential drug-drug interactions. It is essential to identify potentially drug-drug interactions in these patients as early as possible in order to prevent adverse drug reactions and ensure safe recovery. Besides, full attention should be paid when adding each new medication in therapy, particularly when a neurologist decides to prescribe antipsychotics, such as risperidone.

Short Communication: Relationship Between Contraindicated Drug-Drug Interactions and Subsequent Hospitalizations Among Patients Living with HIV Initiating Combination Antiretroviral Therapy.

Persons living with HIV (PLWH) are at an increased risk of contraindicated drug-drug interactions (XDDIs), which may result in deleterious outcomes. Study objectives were to (1) compare the frequency of hospitalizations between patients with and without XDDIs and (2) determine if XDDIs are independently associated with hospitalizations in PLWH. A retrospective cohort study was performed among PLWH receiving care at the Upstate New York Veterans' Healthcare Administration or University of New Mexico Truman Health Services from 2000 to 2013. Hospitalization was defined as an admission to an inpatient hospital facility for ≥24 h. Of the 1329 patients evaluated, 149 (11.2%) patients were hospitalized within 1 year of antiretroviral therapy initiation. A significantly higher proportion of patients with XDDIs were hospitalized compared with those who did not have XDDIs (20.3% vs. 10.2%, risk ratio: 1.98, 95% confidence interval [CI]: 1.35-2.91, p = .001). In the multivariate Cox proportional hazards regression analyses, XDDIs were independently associated with hospitalizations (hazard ratio [HR]: 1.58; 95% CI: 1.00-2.48; p = .05), after adjustment for CD4 < 242 cells/mm3 (HR: 2.38; 95% CI: 1.72-3.33; p < .001), protease inhibitor (PI)-based regimen (HR: 1.35; 95% CI: 0.97-1.89; p = .08), recreational drug use (HR: 2.58, 95% CI: 1.85-3.58, p < .001), and non-HIV medications ≥10 (HR: 1.62; 95% CI: 0.97-2.69; p = .07). In this study an increased risk of hospitalization was observed among PLWH with XDDIs compared with those without XDDIs. This relationship persisted after adjustment for CD4 count, use of a PI-based regimen, recreational drug use, and number of non-HIV medications.

Comparative study of antiretroviral drug regimens and drug-drug interactions between younger and older HIV-infected patients at a tertiary care teaching hospital in South Korea.

BackgroundWith the advent of combination antiretroviral therapy (ART), people living with HIV have lived to older age. So they have experienced age-related illnesses and have taken non-antiretroviral (ARV) medications to manage these illnesses. The aims of this study were to investigate the use patterns of ARV agents in HIV-positive patients by age and to evaluate potential or contraindicated drug–drug interactions (DDIs) between ARV and non-ARV.MethodsThis study was retrospectively conducted with HIV-infected patients receiving ART medications between October 2011 and September 2017 at Chonbuk National University Hospital in South Korea. Data were collected by reviewing patients’ electronic medical charts.ResultsAmong 207 patients diagnosed with HIV infection, 183 (86.9% males; 104 aged <50 years and 79 aged ≥50 years) were selected based on inclusion criteria. In 2017, the most frequently prescribed ART regimen was nucleoside reverse transcriptase inhibitors (NRTIs)/integrase strand transfer inhibitors (INSTIs; total, 66.3%; <50 years, 36.3%; ≥50 years, 30.0%) followed by NRTIs/protease inhibitors (PIs; total, 23.8%; <50 years, 15.0%; ≥50 years, 8.8%). In 2017, the most frequently prescribed NRTI combination was abacavir/lamivudine (total, 34.4%; <50 years, 20.6%; ≥50 years, 13.8%) followed by tenofovir alafenamide/emtricitabine (FTC; total, 31.3%; <50 years, 16.3%; ≥50 years, 15.0%) and tenofovir disoproxil fumarate/FTC (total, 28.1%; <50 years, 16.9%; ≥50 years, 11.3%). In 2017, elvitegravir (EVG)/cobicistat (COBI; total, 57.1%; <50 years, 30.4%; ≥50 years, 26.8%) was most frequently prescribed followed by dolutegravir (total, 32.1%; <50 years, 19.6%; ≥50 years, 12.5%). Potential or contraindicated DDIs between boosted PIs with ritonavir or EVG/COBI and coprescribed drugs occurred most frequently.ConclusionCurrently, NRTIs/INSTIs is the most frequently prescribed ARV combination. Abacavir/lamivudine, tenofovir alafenamide/FTC, and tenofovir disoproxil fumarate/FTC are the most used NRTIs, and EVG/COBI followed by dolutegravir is the most prescribed INSTIs. Potential or contraindicated DDIs occur mainly between boosted PIs or EVG/COBI and non-ARV medications.

Prevalence and nature of statin drug-drug interactions in a university hospital by electronic health record mining.

Our aim was to describe prevalence, nature, and level of severity of potential statin drug-drug interactions in a university hospital. In a cross-sectional study, statin drug-drug interactions were screened from medical record of 10,506 in-patients treated stored in the clinical data warehouse "eHOP." We screened drug-drug interactions using Theriaque and Micromedex drug databases. A total of 22.5% of patients were exposed to at least one statin drug-drug interaction. Given their lipophilicity and CYP3A4 metabolic pathway, atorvastatin and simvastatin presented a higher prevalence of drug-drug interactions while fluvastatin presented the lowest prevalence. Up to 1% of the patients was exposed to a contraindicated drug-drug interaction, the most frequent drug-drug interaction involving influx-transporter (i.e., OATP1B1) interactions between simvastatin or rosuvastatin with cyclosporin. The second most frequent contraindicated drug-drug interaction involved CYP3A4 interaction between atorvastatin or simvastatin with either posaconazole or erythromycin. Furthermore, our analysis showed some discrepancies between Theriaque and Micromedex in the prevalence and the nature of drug-drug interactions. Different drug-drug interaction profiles were observed between statins with a higher prevalence of CYP3A4-based interactions for lipophilic statins. Analyzing the three most frequent DDIs, the more significant DDIs (level 1: contraindication) were reported for transporter-based DDI involving OATP1B1 influx transporter. These points are of concern to improve prescriptions of statins.

Recognition of possible risk factors for clinically significant drug-drug interactions among Indian people living with HIV receiving highly active antiretroviral therapy and concomitant medications.

Greatest challenges for clinician is to recognize risk factors for clinically significant drug interactions (CSDIs). There is a lack of awareness about CSDIs among healthcare professionals in India. To recognize all possible risk factors for drug-drug interactions (DDIs) and to identify clinically significant drug interactions (CSDIs), the prevalence, pattern of occurrence of DDIs in People Living with HIV (PLW-HIV) receiving highly active antiretroviral therapy (HAART) and concomitant medications. A retrospective medical record review was carried out by clinical pharmacist with ethics committee approval. Case files of HIV patients receiving HAART with concomitant medications were analyzed for CSDIs using University of Liverpool drug interaction database and CSDIs were classified based on red flag indication (RFI) or contraindicated drug-drug interaction (XDDIs) and orange flag indication (OFI) or DDIs that needs close monitoring. Patients with DDIs (cases) and patients without DDIs (controls) were compared with Chi-square tests. P value <0.05 was considered as statistically significant. A total of 240 HIV patients' cases were screened. Out of which 267 DDIs were reported in 107 patients. Prevalence of DDIs was higher in male 71 (66.4%) compared to female 36 (33.6%). On zero-inflated poisson regression analysis, factors of polypharmacy, opportunistic infections, comorbid condition like Ischemic heart disease, respiratory tract infections, and psychiatric disorder were found to be predictors of high risk factors for DDIs to HAART. Fourteen XDDIs with RFI and two hundred fifty three DDIs with OFI were reported. XDDIs were atazanavir with fluconazole 4(28.6%), ritonavir with fluconazole 4(28.6%), nevirapine with rifampicin 2(14.4%), ritonavir with quetiapine, atazanavir with pantoprazole. Pharmacokinetic DDIs were highest 238(89.1%). Sixteen DDIs were reported in a single patient. The majority 97(90.6%) patients had developed ≤5 DDIs, 8(7.5%) developed six to eleven DDIs. The highest DDIs were reported with efavirenz 49(18.4%) and zidovudine 44(16.5%) based HAART regimen. In India, with the increasing access to HAART usage, Clinician must focus to pay attention to recognize possible risk factors for CSDIs associated with HAART regimen and strictly to avoid "Red Flag Indication combinations" while prescribing so as to prevent CSDIs.

Comparing the Frequencies of Contraindicated Drug-Drug Interactions Between Differing Antiretroviral Regimens in HIV-Infected Patients.

HIV-infected patients receiving antiretroviral therapy (ART) are at risk for contraindicated drug-drug interactions (XDDIs). This study compared the frequency of XDDIs between different types of ART regimens. A retrospective cohort study was performed among adult HIV-infected patients receiving care at either the Upstate New York Veterans' Healthcare Administration or the University of New Mexico Truman Health Services between 2000 and 2013. The cohort consisted of patients receiving traditional ART regimens composed of 2 nucleoside reverse transcriptase inhibitors plus either a nonnucleoside reverse transcriptase inhibitor (NNRTI), a protease inhibitor (PI), or an integrase strand transfer inhibitor (INSTI). The primary outcome was the presence of XDDIs. Lexi-Interact was used to define XDDIs. Of the 1329 patients who met inclusion criteria, 45.7%, 34.2%, and 20.1% were receiving an NNRTI-, PI-, or INSTI- based ART regimen, respectively. Among the 128 (9.6%) patients with an XDDI, more than half (53.9%) had an interaction involving ART. The presence of XDDIs was highest for PI-based regimens (16.3%) compared with INSTI- (7.9%) and NNRTI-based (5.4%) regimens; P < 0.001. The variables independently associated with XDDIs were ART regimen type (prevalence ratio [PR] = 1.91; 95% CI = 1.51-2.40, P < 0.001), use of ≥6 non-HIV medications (PR = 5.84; 95% CI = 3.92-8.71, P < 0.001), and age ≥40 years (PR = 1.62; 95% CI = 0.92-2.86, P = 0.10). The probability of XDDIs varies as a function of ART regimen type, advanced age, and use of multiple non-HIV medications.

A Cross-Sectional Study Comparing the Frequency of Drug Interactions After Adding Simeprevir- or Sofosbuvir-Containing Therapy to Medication Profiles of Hepatitis C Monoinfected Patients.

IntroductionThis study compares the expected occurrence of contraindicated drug–drug interactions (XDDIs) when simeprevir (SIM)- or sofosbuvir (SOF)-containing therapy is added to medication profiles of patients with hepatitis C (HCV) monoinfection to quantify, in relative terms, the population-based risk of XDDIs. Second, this study identified the predictors of XDDIs when HCV therapies are added to medication profiles.MethodsA cross-sectional study was performed among Veterans’ Affairs patients. Inclusion criteria were: (1) age ≥18 years, (2) HCV infection, and (3) availability of a medication list. Patients with human immunodeficiency virus were excluded. Demographics, comorbidities, year of HCV diagnosis, and most recent medication list were collected from medical records. The primary outcome was the presence of XDDIs involving HCV therapy and the medications in the patient’s home medication list after the addition of either SIM- or SOF-containing regimens. To define XDDIs, Lexi-Interact drug interaction software was used.Results4,251 patients were included. The prevalence of XDDIs involving SIM- or SOF-containing therapy were 12.6% and 4.7% (p < 0.001), respectively. In multivariable analyses examining the predictors of XDDIs involving SIM-containing therapy, the only medication-related predictor was use of ≥6 home medications (odds ratio OR 4.58, 95% confidence interval CI 3.54–5.20, p < 0.001). Similarly, use of ≥6 home medications was also the only variable associated with an increased probability of XDDI involving SOF-containing therapy (OR 3.83, 95% CI 2.57–5.70, p < 0.001).ConclusionsSofosbuvir-containing therapy had a lower frequency of XDDIs than SIM-containing therapy. Polypharmacy with various classes of home medications predicted XDDIs involving SIM- or SOF-containing therapy.Electronic supplementary materialThe online version of this article (doi:10.1007/s40121-015-0058-x) contains supplementary material, which is available to authorized users.

Prevalence of drug-drug interactions upon addition of simeprevir- or sofosbuvir-containing treatment to medication profiles of patients with HIV and hepatitis C coinfection.

The objectives were to (1) compare the frequency of contraindicated drug-drug interactions (XDDI) when simeprevir (SIM)- and sofosbuvir (SOF)-containing regimens are theoretically added to a patient's medication profile; (2) identify which hepatitis C (HCV) regimen is associated with the lowest frequency of XDDIs within different types of antiretroviral treatment (ART) regimens; and (3) determine the risk factors for XDDIs with each regimen. A cross-sectional study was performed among adult HIV/HCV-coinfected patients. Demographics, comorbidities, and medication lists were collected from medical records. Medication lists were entered into Lexi-Interact drug interaction software and XDDI before/after the addition of SIM- and SOF-containing therapy was documented. Classification and regression tree (CART) analyses identified breakpoints in continuous variables. Before the addition of any HCV therapy, XDDIs were present in 20% of the 335 included patients. After the addition of SIM-containing therapy, the frequency of XDDIs significantly increased to 88.4% (p<0.001). After adding SOF-containing therapy, the prevalence of XDDIs increased to 24.5% (p<0.001). The prevalence of XDDIs was significantly lower for SOF-containing HCV therapy within various types of ART regimens. Use of ≥7 non-HIV medications (CART breakpoint) was the only variable to predict XDDIs before the addition of any HCV therapy. Similarly, this was the only variable to predict XDDIs after the addition of SOF-containing therapy (PR: 4.80; 95% CI: 2.57-8.96, p<0.001). Variables independently associated with XDDIs after the addition of SIM-containing therapy were NNRTI regimen (prevalence ratio, PR: 1.62; 95% confidence interval, CI: 1.38-1.91, p<0.001), PI regimen (PR: 1.64; 95% CI: 1.40-1.93, p<0.001), and ≥7 non-HIV medications (PR: 1.06; 95% CI: 1.00-1.14, p=0.09). The addition of SOF-containing therapy was associated with a lower prevalence of XDDI than SIM-containing therapy.

Use of pharmacist integration in oncology clinics to identify and resolve moderate to major drug-drug interactions.

71 Background: Medication reconciliation (MR) in outpatient clinics has been under-evaluated. We postulated that cancer patients would benefit from MR done by a pharmacist as these patients have many care providers, many medications, and are at high risk of drug-drug interactions (DDIs). Hence, we conducted a quality initiative evaluating the role of a pharmacist in the ambulatory clinics. Methods: One pharmacist prospectively rotated amongst four oncology clinics four days a week from June 3 to September 18, 2008. The pharmacist performed MR, and as a consultant developed therapeutic plans related to drug therapeutic problems (DTPs) including adverse reactions and DDIs. Patient medication lists were retrospectively analyzed using Micromedex and DDIs were categorized by frequency, severity and evidence level. A monthly survey (Likert scale) evaluating pharmacist contributions to each clinic team was completed by physicians and nurses. Results: A total of 158 patients were seen in 227 patient visits. The pharmacist identified 141 DTPs in 60 patients across 74 visits. The most frequently observed were no drug for a medical problem (51.1%), dose too low (12.8%), wrong drug (9.9%), and adverse drug reactions (9.2%). In response, 174 therapeutic plans were made. The most frequently recommended actions were drug added (40.8%), dose changed (13.2%), drug discontinued (9.2%), and interval/duration changed (7.5%). A total of 414 DDIs were identified in 110 patients, across 149 patient visits. On average, 2.62 DDIs were reported per patient, and 1.82 DDIs per visit. By severity, 139 (33.6%) major, 258 (62.3%) moderate, 16 (3.9%) minor and 1 (0.2%) contraindicated DDIs were documented. By level of evidence, 46 (11.1%) DDIs were excellent, and 236 (57%) were good. Survey results showed that doctors and nurses agreed/strongly agreed that pharmacist presence was valuable. The most useful contributions identified were consultation regarding DDIs, adverse drug effects, and medication efficacy decisions. Conclusions: DDI rates are high and most are moderate or major in severity. There is a clear benefit from the integration of a pharmacist to the clinics with an improvement in patient safety and quality of care.

Comparing Drug-Drug Interaction Severity for Clinician Opinion to Proprietary Databases

Purpose: Commercial clinical decision support software (CDSS) may overestimate the severity of drug-drug interactions (DDI) because of their broad application; whereas, clinicians with knowledge of the patient should be able to better assess DDI severity. The purpose of this project was to compare DDI severity for clinician opinion in the context of the patient’s clinical status to the severity of proprietary databases. Methods: This was a single-center, prospective evaluation of DDIs at a large, tertiary care academic medical center between October 11, 2010 and November 5, 2010 in a 10-bed cardiac intensive care unit (CCU). A pharmacist identified DDIs using two proprietary databases. The physicians (fellow and attending) and pharmacists (rounding and distribution) caring for the patients evaluated the DDIs for severity while incorporating their clinical knowledge of the patient. Severity was ranked on a scale ranging from A to D and X. Results: A total of 61 patients were included in the evaluation and experienced 769 DDIs. The most common DDIs included: aspirin/clopidogrel (n=21, 2.7%), aspirin/insulin (n=21, 2.7%) and aspirin/furosemide (n=19, 2.5%). Pharmacists ranked the DDIs identically 73.8% of the time, compared to the physicians who agreed 42.2% of the time. Pharmacists agreed with the more severe proprietary database scores for 14.8% of DDIs versus physicians at 7.3%. Among the five contraindicated DDIs, two were rated as category B (minor severity/no action needed) and three as category C (moderate severity/monitor therapy) by the majority of the reviewers. Overall, clinicians agreed with the proprietary database 20.6% of the time while clinicians ranked the DDIs lower than the database 77.3% of the time. Conclusions: Proprietary DDI databases generally label DDIs with a higher severity rating as compared to clinicians who are caring for patients. Developing a DDI knowledgebase for CDSS requires careful consideration of the source of the severity information and should include clinician input in order to create clinically meaningful alerts.