Severity Of Drug-drug Interactions Research Articles

Triazole antifungal drug interactions-practical considerations for excellent prescribing.

Systemic antifungal therapy is critical for reducing the mortality from many invasive and chronic fungal infections. Triazole antifungals are the most frequently prescribed antifungals but require attention to dosing and drug interactions. Nearly 600 severe drug-drug interactions and over 1100 moderate interactions requiring dose modifications are described or anticipated with systemic antifungal agents (see https://www.aspergillus.org.uk/antifungal-drug-interactions/). In this article, we address the common and less common, but serious, drug interactions observed in clinical practice with triazole antifungals, including a group of drugs that cannot be prescribed with all or most triazole antifungals (ivabradine, ranolazine, eplerenone, fentanyl, apomorphine, quetiapine, bedaquiline, rifampicin, rifabutin, sirolimus, phenytoin and carbamazepine). We highlight interactions with drugs used in children and new agents introduced for the treatment of haematological malignancies or graft versus host disease (midostaurin, ibrutinib, ruxolitinib and venetoclax). We also summarize the multiple interactions between oral and inhaled corticosteroids and triazole antifungals, and the strategies needed to optimize the therapeutic benefits of triazole antifungal therapy while minimizing potential harm to patients.

POTENTIAL DRUG-DRUG INTERACTIONS OF CARDIOVASCULAR DRUGS BASED ON LITERATURE IN GERIATRIC PATIENTS WITH CONGESTIVE HEART FAILURE AT Dr. M. DJAMIL PADANG HOSPITAL

Objective: Congestive Heart Failure (CHF) is a notable cardiovascular disease impacting global morbidity and mortality. Geriatric patients with CHF typically require multiple medications that can potentially cause drug-drug interactions and affect patient therapy outcomes. This study aims to determine the potential drug-drug interactions, the relationship between the average number of cardiovascular drugs per day and the potential drug-drug interactions, and the relationship between the severity of drug-drug interactions and the clinical symptoms and signs of the patients.
 Methods: The research method used was analytical observational with retrospective data collection through the medical records of inpatients in 2021. A total of 63 patients were included using the total sampling method.
 Results: Results revealed that furosemide was the most commonly prescribed cardiovascular medication (15.27%). Among the participants, 93.65% exhibited potential drug-drug interactions (332 occurrences), with the most frequent involving furosemide and bisoprolol (32 cases). Pharmacodynamic interactions were the dominant mechanism (85.24%), with moderate severity (65.06%) being common. A significant relationship existed between the average number of cardiovascular drugs per day and the potential drug-drug interactions (p<0.05). Nonetheless, there was no notable correlation discovered between the severity of the interaction and the presence of symptoms and clinical signs (p>0.05).
 Conclusion: When considering the high incidence of potential drug-drug interactions, it is expected that clinical pharmacists have the competence to analyze potential drug interactions to prevent harmful effects on patients.

Polypharmacy in acute and chronic kidney diseases

The prevalence for chronic kidney disease (CKD) has steadily increased over the past decades. It is agradually progressive disease that is associated with several comorbidities including cardiovascular diseases, hypertension, anemia, disorders of bone and mineral metabolism, electrolyte imbalance and acid-base abnormalities. All these comorbidities require adequate medication. Therefore, patients with CKD have a high risk for polypharmacy, which is defined as five or more medications daily. Polypharmacy causes agreatly increased risk for adverse drug effects and severe drug-drug interactions, which if not closely controlled and the individual doses adapted to the decreased renal function during the progression of the CKD, can result in increased morbidity and mortality. Therefore, several aspects of the medication need to be considered and constantly addressed. This article summarizes the problems arising from inadequate polypharmacy in CKD patients, including undesired adverse drug effects, drug interactions, the complexity of medication plans, treatment burden and nonadherence to the treatment. Furthermore, the most important steps to identify patients with inadequate polypharmacy are discussed, whereby complications can also be avoided and the benefits of the medication can be increased. Finally, the polypharmacy in acute kidney injury is dealt with.

Pharmacologic Stewardship in a Rural Community Pharmacy.

Pharmacotherapy is an essential part of patient care. In order to achieve optimal health outcomes, safe and effective prescribing and administering of medications is crucial, especially since the process of pharmacotherapy can cause serious problems, mainly adverse events and/or interactions, that often pass undetected. To investigate the feasibility of using community pharmacies as checkpoints to detect errors and failures in prescribing, as well as patients' compliance with pharmacotherapy. To this end, analysis and recording of the prescribing process was carried out and error-prone points were identified. Patients and caregivers filling prescriptions during the first 4 weeks of November 2017 and February 2018 answered questions in order to evaluate their attendance of regular checkups and their compliance with prescribing instructions. All prescriptions filled at the pharmacy were examined for detection of prescription errors and drug-drug interactions. Statistical analyses, including calculations of the correlation coefficient phi (φ), chi-square, and confidence intervals, were carried out. Detected errors and failures were evaluated by application of the Health Failure Mode Effect Analysis (HFMEA) quality tool. A significant number of patients (16.7%) failed to regularly attend checkups regarding known health problems (95% CI: 10.6-22.7%), a corresponding percentage (16%, 95% CI: 10.1-21.9%) did not comply with prescribed pharmacotherapy, and a significant proportion of patients self-medicated regularly (32%, 95% CI: 24.5-39.5%). A total of 8.6% of prescriptions included medication combinations with a potential for severe drug-drug interactions (95% CI: 7.1-10.2%) while 58.7% of the prescriptions included combinations that could lead to moderate ones (95% CI: 56.1-61.4). The HFMEA indicated that all problems recorded required immediate interventions, except for prescribing errors. Community pharmacies can be potential checkpoints for the detection and evaluation of prescribing errors and pharmacotherapy failures.

An Improved Graph Isomorphism Network for Accurate Prediction of Drug–Drug Interactions

Drug–drug interaction (DDI) prediction is one of the essential tasks in drug development to ensure public health and patient safety. Drug combinations with potentially severe DDIs have been verified to threaten the safety of patients critically, and it is therefore of great significance to develop effective computational algorithms for identifying potential DDIs in clinical trials. By modeling DDIs with a graph structure, recent attempts have been made to solve the prediction problem of DDIs by using advanced graph representation learning techniques. Still, their representational capacity is limited by isomorphic structures that are frequently observed in DDI networks. To address this problem, we propose a novel algorithm called DDIGIN to predict DDIs by incorporating a graph isomorphism network (GIN) such that more discriminative representations of drugs can thus be learned for improved performance. Given a DDI network, DDIGIN first initializes the representations of drugs with Node2Vec according to the topological structure and then optimizes these representations by propagating and aggregating the first-order neighboring information in an injective way. By doing so, more powerful representations can thus be learned for drugs with isomorphic structures. Last, DDIGIN estimates the interaction probability for pairwise drugs by multiplying their representations in an end-to-end manner. Experimental results demonstrate that DDIGIN outperforms several state-of-the-art algorithms on the ogbl-ddi (Acc = 0.8518, AUC = 0.8594, and AUPR = 0.9402) and DDInter datasets (Acc = 0.9763, AUC = 0.9772, and AUPR = 0.9868). In addition, our case study indicates that incorporating GIN enhances the expressive power of drug representations for improved performance of DDI prediction.

Potential drug-drug interactions among U.S. adults treated with nirmatrelvir/ritonavir: A cross-sectional study of the National Covid Cohort Collaborative (N3C).

To estimate the prevalence of potential moderate to severe drug-drug interactions (DDIs) involving nirmatrelvir/ritonavir, identify interacting medications, and evaluate risk factors associated with potential DDIs. Cross-sectional study. Electronic health records from the National COVID Cohort Collaborative Enclave, one of the largest COVID-19 data resources in the United States. Outpatients aged ≥18 years and started nirmatrelvir/ritonavir between December 23, 2021 and March 31, 2022. Nirmatrelvir/ritonavir. The outcome is potential moderate to severe DDIs, defined as starting interacting medications reported by National Institutes of Health 30 days before or 10 days after starting nirmatrelvir/ritonavir. Of 3214 outpatients who started nirmatrelvir/ritonavir, the mean age was 56.8 ± 17.1 years, 39.5% were male, and 65.8% were non-Hispanic white. Overall, 521 (16.2%) were potentially exposed to at least one moderate to severe DDI, most commonly to atorvastatin (19.2% of all DDIs), hydrocodone (14.0%), or oxycodone (14.0%). After adjustment for covariates, potential DDIs were more likely among individuals who were older (odds ratio [OR] 1.16 per 10-year increase, 95% confidence interval [CI] 1.08-1.25), male (OR 1.36, CI 1.09-1.71), smokers (OR 1.38, CI 1.10-1.73), on more co-medications (OR 1.35, CI 1.31-1.39), and with a history of solid organ transplant (OR 3.63, CI 2.05-6.45). One in six of individuals receiving nirmatrelvir/ritonavir were at risk of a potential moderate or severe DDI, underscoring the importance of clinical and pharmacy systems to mitigate such risks.

Potential drug-drug interactions with phentermine among long-term phentermine consumers: A retrospective analysis.

Phentermine is an internationally recognised amphetamine derivative with significant appetite-suppressing properties. The drug is indicated for the short-term management of obesity, as the long-term (LT) use of phentermine may potentially be associated with severe cardiovascular side-effects, abuse and dependence. The LT use hereinafter describes periods exceeding 12consecutive weeks. This use may also be associated with potential drug-drug interactions (PDDIs), which may result in adverse drug reactions (ADRs). The literature reports that phentermine is often prescribed LT and for several other off-label indications, increasing the risk for individuals to experience adverse drug events (ADEs) and drug-drug interactions (DDIs). There are, to our knowledge, no South African (SA) studies investigating the prevalence of co-prescribing LT phentermine with drugs that may potentially cause DDIs. To determine the prevalence of mild, moderate and severe DDIs with phentermine use when the duration of therapy in private healthcare exceeded 12consecutive weeks. A cross-sectional drug utilisation review (DUR) was done by using data obtained from a SA pharmacy benefit management (PBM) company's database. Retrospective data of medicine claims for phentermine, from 1 January 2015 to 31 December 2019, were extracted for analysis. The number of days phentermine was supplied was used to identify the study population, in other words, those patients who received the drug LT. A drug interaction checker (Drugs.com) was used to identify potential mild, moderate and severe DDIs when using phentermine and co-prescribed drugs concurrently. A total of 889 patients received phentermine LT. The top 20 drugs identified as being frequently co-prescribed in this study population demonstrated no mild PDDI, 15 (75%) moderate PDDIs and 5 (25%) severe PDDIs. The most common co-prescribed drug in the moderate group was dextromethorphan (n=282, 31.72%) and the least co-prescribed was formoterol (n=52, 5.85%). Among the drug group 'severe PDDIs', tramadol (n=416, 46.79%) was most frequently prescribed, whereas phenylpropanolamine (n=69, 7.76%) was the least prescribed to patients in this group. There are patients who receive LT phentermine therapy despite the potential severe consequences that may result. These patients may receive concomitant therapy with phentermine and other pharmaceutical constituents, which may potentially cause DDIs, more specifically, moderate and severe DDIs. As such, these patients are not only confronted with the consequences of DDIs but are also at risk to experience ADRs as the residual effect of PDDIs.

Determination of drug-drug interaction in breast cancer patients receiving doxorubicin and cyclophosphamide regimen.

Drug interactions constitute a significant issue in cancer treatment. Therefore, it is important to assess and analyze all cancer patients' therapies prior to the initiation of chemotherapy. A retrospective observational study was conducted at the Near East University Hospital Oncology Department located in North Cyprus between June 2019 and December 2021. The aim of the study was to determine the nature, type, and frequency of potential drug-drug interactions in breast cancer patients receiving a doxorubicin and cyclophosphamide regimen using Drugs.com, Lexicomp, and Micromedex databases while comparing the three electronic databases, according to the frequency, mechanism, and severity of drug-drug interactions. The study included 40 patients (44.4%) out of 90 patients diagnosed with breast cancer, as 50 patients (55.6%) who did not match the criteria were excluded. According to the Lexicomp database, 12 patients (30%) with breast cancer had 14 potential drug-drug interactions, according to the Drugs.com database, 15 patients (37.5%) with breast cancer had 22 potential drug-drug interactions, and according to the Micromedex database, 13 patients (32.5%) with breast cancer had 15 potential drug-drug interactions. Pearson correlation indicated a weak association (Lexicomp: r = 0.475, p = 0.002 and Micromedex: r = 0.491, p = 0.001) and a moderate association (Drugs.com: r = 0.500, p = 0.001) between potential drug-drug interactions and five or more medications. This study showed that the Drugs.com database detected more potential drug interactions between chemotherapy and nonchemotherapy than the Lexicomp and Micromedex databases. Comprehensive drug review, use of electronic health record systems, and collaboration between healthcare providers such as pharmacists and physicians may be necessary strategies to minimize potential drug-drug interactions and optimize cancer treatment in patients with breast cancer.

Absence of Clinically Meaningful Drug-Drug Interactions with Rezafungin: Outcome of Investigations.

Rezafungin is a novel once-weekly echinocandin for intravenous injection currently in development for the treatment of Candida infections and the prevention of Candida, Aspergillus, and Pneumocystis infections in allogeneic blood and marrow transplant recipients. While in vitro data indicated that rezafungin exposure was unlikely to be affected by commonly prescribed medicines, interactions resulting in the altered systemic exposure of some drugs coadministered with rezafungin could not be excluded. Two phase 1 open label crossover studies, conducted in healthy subjects, examined drug interactions between rezafungin and multiple drug probe cytochrome P450 (CYP) substrates and/or transporter proteins, immunosuppressants, and cancer therapies. Statistical analysis compared the outcomes for drugs coadministered with rezafungin to those for the drugs administered alone. The geometric mean ratio was reported, and a default 90% confidence interval (CI) no-effect equivalence range of 80 to 125% was used for the maximal plasma concentration (Cmax), the area under the curve from time zero to the final sampling time point (AUC0-t), and the AUC from time zero to infinity (AUC0-∞). Most probes and concomitant drugs were within the equivalence range. For tacrolimus, ibrutinib, mycophenolic acid, and venetoclax, the AUC or Cmax was reduced (10 to 19%), with lower bounds of the 90% CI values falling outside the no-effect range. The rosuvastatin AUC and Cmax and the repaglinide AUC0-∞ were increased (12 to 16%), with the 90% CI being marginally above the upper bound. Overall, the in vitro and in vivo data demonstrated a low drug interaction potential with rezafungin via CYP substrate/transporter pathways and commonly prescribed comedications, suggesting that coadministration was unlikely to result in clinically significant effects. Treatment-emergent adverse events were typically mild, and rezafungin was generally well tolerated. IMPORTANCE Antifungal agents used to treat life-threatening infections are often associated with severe drug-drug interactions (DDIs) that may limit their usefulness. Rezafungin, a newly approved once-weekly echinocandin, has been shown to be free of DDIs based on extensive nonclinical and clinical testing described in this study.

Evaluation of Inpatients Medication Prescribing Practice at Ibrahim Malik Teaching Hospital: A Descriptive Cross-sectional Study

Introduction: Prescribing errors account for almost 50% of medication errors and represent the eighth leading cause of death in the US. The current study aimed to evaluate inpatient prescribing practice by assessing adherence to prescription-writing guidelines and identifying the frequency and severity of drug-drug interactions (DDIs) and drug-disease interactions (DDSIs). Methods: An observational-descriptive cross-sectional study was conducted among inpatient wards in Ibrahim Malik teaching hospital, Khartoum, Sudan. Total coverage sampling was performed and the sample size consisted of 132 prescriptions containing a total of 700 prescribed drugs. The data were collected from patients’ files, and the completeness of prescriptions was assessed according to national and international guidelines. Drug interactions were checked by the interaction checker of www.drug.com. Results: The overall prescription completeness was 0%, and the completeness of both prescriber data and medication data were 1.5%, (0.4% for Intravenous [IV] and 0.6% for non-IV drugs), respectively. The most missing elements of the prescription were duration and qualification. Moreover, only 25% of the prescribed drugs were written in the Prescription and Medication Administration Record or drug chart, but the rest were written elsewhere within the patient’s file. The prescriptions containing DDIs and DDSIs were 49.2% and 46.2%, respectively, ranging from major (9.1%), moderate, (40.9%), and minor (28%) for DDIs, major (30.3%), moderate (33.3%), and minor (0.8%) for DDSIs. The highest percentage of drug interactions was found in the internal medicine ward. Diabetes was significantly associated with both DDIs (P = 0.003) and DDSIs (P = 0.031). Conclusion: The findings of this study reflect a real problem in prescribing practice, as no prescription was considered to be complete, and nearly half of the patients were exposed to DDIs and/or DDSIs.

Effectiveness of an Antifungal Stewardship Program in a Hematology Ward at a Madrid Teaching Hospital: The Time Is Now

BACKGROUND: Invasive fungal infections (IFIs) are a leading infectious cause of morbidity, mortality, and Health Care Resource Utilization (HCRU) in patients with hematological malignancies. Patients with at higher risk of acquiring IFIs are those with acute leukemia (AL), myelodysplastic syndrome (MDS) or undergoing hematopoietic stem cell transplant (HSCT). Needed to optimize antifungal use, Guidelines rarely include this group of patients, and more effort is needed to optimize the use of antifungal. AIMS: The aim of this study was to demonstrate the potential benefits of the implementation of an antifungal stewardship (AFS) program in a Spanish Hematology-Oncology Department, in a tertiary care center. To test the long-lasting effects of a 2 years antifungal stewardship (AFS) pilot project, we analyzed and compared antifungal prescriptions regarding efficacy, IFI incidence, adaptation of prescriptions to current protocols, antifungal consumption and costs saving before and during the project METHODS AND PATIENTS: This single-center comparative study included 156 inpatients ≥ 18 years, receiving systemic antifungals for prophylaxis or therapy of IFI for over four years: 85 in a phase 1 (December 2017- December 2019) and 71 in a phase 2 (January 2020-June 2022). In January 2020, we have designed a protocol for the use of antifungals in hospitalized patients based on the ECIL guidelines and according to local susceptibility patterns and, approved by the antimicrobial therapy team. First line antifungal prophylaxis for high-risk patients on our unit is nebulized Liposomal Amphotericin B 20 mg 3/weekly combined with fluconazole 400 mg po/iv qd (salvage therapy: Posaconazole 300 mg tablet qd). Patients undergoing an autologous-HSCT and other medium risk patients receive fluconazole monotherapy 400 mg po/iv qd as primary prophylaxis during the neutropenic period. Front-line therapy for possible (empirical antifungal therapy) and proven/probable IFI (targeted therapy) is Voriconazole 2x6 mg/kg on day 1 then 2x4 mg/kg (salvage therapy: Liposomal Amphotericin B 3 mg/kg/day). A comprehensive mould-directed diagnostic approach was established combining microbiological work-up (cytology, culture & Galactomannan screening) and CT imaging. From that date on, AFS included medical training (short sessions), a poster with main recommendations regarding antifungal treatment, and daily pharmaceutical counselling onwards. RESULTS: In total, 156/1670 (9.4%) patients received antifungals. In phase 1, 182 antifungals were prescribed (76% for prophylaxis, 21.2% for empirical and 2.8% for targeted therapy) and 150 in phase 2 (72% for prophylaxis, 24.7% for empirical and 3.3% for targeted therapy). The number of prescriptions classified as inappropriate decreased from 47.2% to 10.7% during implementation of the AFS-period Antifungal prophylaxis main reasons for the inappropriate prescriptions were (phase 1/phase 2): lack of indication (44.5%/1.3%), wrong dose (4.4%/1.3%), and potential severe drug-drug interaction (12.1%/4%). Regarding antifungal therapy: drug choice (7%/0%), dosage (5%/1%) and potential severe drug-drug interactions with concomitant therapy (7%/2.7%) were the main reason for inadequate prescriptions (details are showed in table 1). Associated costs for antifungals accounted in phase 1 for 92.664€ (65.192€ for prophylaxis and 27.472€ for therapy) while in phase 2, from January 2020 to January 2022, were 58.274€ (33.883€ for prophylaxis and 24.391€ for therapy). CONCLUSIONS: Our results demonstrate that the implementation of a regulated protocol would lead to a decrease of inappropriate prescriptions of antifungals with a drastic reduction of prescriptions associated to potential severe drug-drug interactions and medication errors or lack of indication. In addition, we could show economic savings, a total of 34.390€ in two years, without a significant increase in the diagnosis of IFI in critically ill patients (0.5% in phase 1 versus 6% in phase 2). Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Application of physiologically-based pharmacokinetic/pharmacodynamic models to evaluate the interaction between nifedipine and apatinib.

Aim: It has been found that the co-administration of nifedipine with apatinib could cause exposure changes of nifedipine in vivo. But, whether this pharmacokinetic drug-drug interaction (DDI) between nifedipine and apatinib could enhance the antihypertensive effect of nifedipine, causing sever changes of blood pressure was unknown. Therefore, the aim of the present study was to conduct the pharmacokinetic/pharmacodynamic (PK/PD) modelling to evaluate the effect of pharmacokinetic changes on the antihypertensive effect of nifedipine. Thus, the results could guide the co-administration of these two drugs in clinic. Methods: A physiologically-based pharmacokinetic (PBPK) model was first developed for nifedipine. The pharmacokinetic DDI between nifedipine and apatinib was evaluated. Then the verified PBPK models were linked to a PD model for investigating whether the exposure changes of nifedipine could cause severe changes in blood pressure. Furthermore, the changes in blood pressure caused by combination with apatinib were also assessed in patients with hepatic impairment via the PBPK/PD models. Results: The predicted area under plasma concentration-time profile (AUC), maximum concentration (Cmax), area under effect-time profile (AUE), and maximum reduction in systolic blood pressure (Rmax) are all within 0.5–2.0-fold of the observed data, indicating that the PBPK/PD models for nifedipine are successfully established. The increases of predicted AUC and Cmax of nifedipine in the presence of apatinib are 1.73 and 1.41-fold, respectively. Co-administration of nifedipine with apatinib could cause exposure changes of nifedipine in vivo. However, the predicted AUE and Rmax changes of nifedipine in the presence to the absence of apatinib in cancer patients as well as in patients with hepatic impairment are all within 1.25-fold. The results indicate that the exposure changes of nifedipine caused by combination of apatinib has little effect on the changes of systolic blood pressure both in cancer patients and patients with hepatic impairment. Conclusion: The pharmacokinetic changes of nifedipine caused by co-administration with apatinib has little impact on the antihypertensive effect of nifedipine. Apatinib is unlikely to cause severe pharmacodynamic DDI via inhibition of CYP3A4. It is suggested that nifedipine could be used in combination with apatinib without dose adjustment in clinic.

Screening for severe drug-drug interactions in patients with multiple sclerosis: A comparison of three drug interaction databases.

Background: Patients with multiple sclerosis (MS) often undergo complex treatment regimens, resulting in an increased risk of polypharmacy and potential drug-drug interactions (pDDIs). Drug interaction databases are useful for identifying pDDIs to support safer medication use. Objective: To compare three different screening tools regarding the detection and classification of pDDIs in a cohort of MS patients. Furthermore, we aimed at ascertaining sociodemographic and clinical factors that are associated with the occurrence of severe pDDIs. Methods: The databases Stockley’s, Drugs.com and MediQ were used to identify pDDIs by screening the medication schedules of 627 patients. We determined the overlap of the identified pDDIs and the level of agreement in pDDI severity ratings between the three databases. Logistic regression analyses were conducted to determine patient risk factors of having a severe pDDI. Results: The most different pDDIs were identified using MediQ (n = 1,161), followed by Drugs.com (n = 923) and Stockley’s (n = 706). The proportion of pDDIs classified as severe was much higher for Stockley’s (37.4%) than for Drugs.com (14.4%) and MediQ (0.9%). Overall, 1,684 different pDDIs were identified by at least one database, of which 318 pDDIs (18.9%) were detected with all three databases. Only 55 pDDIs (3.3%) have been reported with the same severity level across all databases. A total of 336 pDDIs were classified as severe (271 pDDIs by one database, 59 by two databases and 6 by three databases). Stockley’s and Drugs.com revealed 47 and 23 severe pDDIs, respectively, that were not included in the other databases. At least one severe pDDI was found for 35.2% of the patients. The most common severe pDDI was the combination of acetylsalicylic acid with enoxaparin, and citalopram was the drug most frequently involved in different severe pDDIs. The strongest predictors of having a severe pDDI were a greater number of drugs taken, an older age, living alone, a higher number of comorbidities and a lower educational level. Conclusions: The information on pDDIs are heterogeneous between the databases examined. More than one resource should be used in clinical practice to evaluate pDDIs. Regular medication reviews and exchange of information between treating physicians can help avoid severe pDDIs.

Prevalence of Pharmacologic Treatments for Chronic Obstructive Pulmonary Disease in Relation of Diagnosis of Dementia Among Nursing Homes Residents.

Chronic obstructive pulmonary disease (COPD) is a chronic disease affecting millions of older adults. We investigated the prevalence of nursing home (NH) residents with COPD and their pharmacologic treatment in relation to the diagnosis of dementia and the prevalence of potentially severe drug-drug interactions (DDIs) of COPD medications with drugs prescribed to treat comorbidities. Retrospective cross-sectional multicentre study. Individuals living in long-term care NHs. This cross-sectional cohort study was conducted in a sample of Italian long-term care NHs located throughout the country. Information on drug prescriptions, diseases, and sociodemographic characteristics was collected using medical records between 2018 and 2020. Potentially severe DDIs were analyzed using INTERCheck, developed by the Istituto di Ricerche Farmacologiche Mario Negri IRCCS. Among the 2604 residents living in 27 NHs (mean age 86.4 ± 8.5years; women 1995, 76.6%; number of drugs 7.3 ± 3.6), 306 had a diagnosis of COPD (11.8%) and 84 (27.5%) of them were treated with at least 1 medication for COPD (27.5%). Those without dementia had a higher prevalence of drugs for COPD than those with dementia (48% vs 20%, P < .0001). The most used classes for COPD were adrenergics in combination with corticosteroids (16.7%) and inhalant anticholinergics (14.7%). Among those receiving medications for COPD, 45 had at least 1 potentially severe DDI with medications for comorbidities (53.6%) and most of them were associated with an increased risk of QTc prolongation, as beta2 agonists with diuretics, antipsychotics or antidepressants. Most NH residents with COPD did not receive any medication for this disease despite the potential benefits. Residents with dementia received fewer COPD medications probably because of their compromised physical and cognitive status. One-half of the NH residents taking medication for COPD were exposed to an increased risk of QTc prolongation and torsades de pointe because of pharmacodynamic DDIs with medication for comorbidities. Close monitoring of electrocardiograms for NH residents with COPD is recommended.

Overall performance of a drug\u2013drug interaction clinical decision support system: quantitative evaluation and end-user survey

BackgroundClinical decision support systems are implemented in many hospitals to prevent medication errors and associated harm. They are however associated with a high burden of false positive alerts and alert fatigue. The aim of this study was to evaluate a drug–drug interaction (DDI) clinical decision support system in terms of its performance, uptake and user satisfaction and to identify barriers and opportunities for improvement.MethodsA quantitative evaluation and end-user survey were performed in a large teaching hospital. First, very severe DDI alerts generated between 2019 and 2021 were evaluated retrospectively. Data collection comprised alert burden, override rates, the number of alert overrides reviewed by pharmacists and the resulting pharmacist recommendations as well as their acceptance rate. Second, an e-survey was carried out among prescribers to assess satisfaction, usefulness and relevance of DDI alerts as well as reasons for overriding.ResultsA total of 38,409 very severe DDI alerts were generated, of which 88.2% were overridden by the prescriber. In 3.2% of reviewed overrides, a recommendation by the pharmacist was provided, of which 79.2% was accepted. False positive alerts were caused by a too broad screening interval and lack of incorporation of patient-specific characteristics, such as QTc values. Co-prescribing of a non-vitamin K oral anticoagulant and a low molecular weight heparin accounted for 49.8% of alerts, of which 92.2% were overridden. In 88 (1.1%) of these overridden alerts, concurrent therapy was still present. Despite the high override rate, the e-survey revealed that the DDI clinical decision support system was found useful by prescribers.ConclusionsIdentified barriers were the lack of DDI-specific screening intervals and inclusion of patient-specific characteristics, both leading to a high number of false positive alerts and risk for alert fatigue. Despite these barriers, the added value of the DDI clinical decision support system was recognized by prescribers. Hence, integration of DDI-specific screening intervals and patient-specific characteristics is warranted to improve the performance of the DDI software.

An introduction to network analysis for studies of medication use.

Network Analysis (NA) is a method that has been used in various disciplines such as Social sciences and Ecology for decades. So far, NA has not been used extensively in studies of medication use. Only a handful of papers have used NA in Drug Prescription Networks (DPN). We provide an introduction to NA terminology alongside a guide to creating and extracting results from the medication networks. To introduce the readers to NA as a tool to study medication use by demonstrating how to apply different NA measures on 3 generated medication networks. We used the Norwegian Prescription Database (NorPD) to create a network that describes the co-medication in elderly persons in Norway on January 1, 2013. We used the Norwegian Electronic Prescription Support System (FEST) to create another network of severe drug-drug interactions (DDIs). Lastly, we created a network combining the two networks to show the actual use of drugs with severe DDIs. We used these networks to elucidate how to apply and interpret different network measures in medication networks. Interactive network graphs are made available online, Stata and R syntaxes are provided. Various useful network measures for medication networks were applied such as network topological features, modularity analysis and centrality measures. Edge lists data used to generate the networks are openly available for readers in an open data repository to explore and use. We believe that NA can be a useful tool in medication use studies. We have provided information and hopefully inspiration for other researchers to use NA in their own projects. While network analyses are useful for exploring and discovering structures in medication use studies, it also has limitations. It can be challenging to interpret and it is not suitable for hypothesis testing.

Association of body composition with toxicity to first-line chemotherapy and three-year survival in women with ovarian adenocarcinoma

Background This study aimed to evaluate the association of body composition with toxicity to first-line chemotherapy and three-year survival in women with ovarian adenocarcinoma. Methods We enrolled, in a retrospective cohort, 239 women treated with carboplatin and paclitaxel between 2008 and 2017. Pretreatment computed tomography scans were used to quantify skeletal muscle index (SMI), skeletal muscle radiodensity (SMD), and subcutaneous adipose tissue index (SATI). Chemotherapy doses, related toxicities, potential drug-drug interactions (DDI), and clinical variables were collected from medical records. Outcomes were the number of adverse events ≥ grade 3 toxicity, toxicity-induced modification of treatment (TIMT), and three-year survival. Results Average age was 56.3 years and 35.1% had myopenia. Almost 33% had TIMT and 51.3% presented any grade 3 toxicity. Potential severe DDI occurred in 48.1% of the patients and 65.1% died three years after the first treatment. The SMD and SATI below the median were independent predictors for the number of adverse events ≥ grade 3 and TIMT. Also, SMD was the only body composition parameter able to predict reduced three-year survival. The SMI was not associated with any of the outcomes. Conclusion Fewer amounts of SATI and low SMD were associated with the occurrence of toxicity to chemotherapy, and the low SMD increased the risk of death in the three years after oncologic treatment.

Potential severe drug-drug interactions in elderly patients

Potential severe drug-drug interactions in elderly patients

Potential clinically significant drug-drug interactions of hydroxychloroquine used in the treatment of COVID-19.

AimsHydroxychloroquine (HCQ) is using as a repurposed drug in considerable proportion of COVID‐19 patients. However, being a substrate of cytochrome P450 (CYP) enzymes of CYP3A4/5, CYP2C8 and CYP2D6, the safety and efficacy of this drug may be affected by the coadministration of respective CYP inhibitors, substrates or inducer drugs. It was aimed to identify potential clinically significant drug‐drug interaction (DDI) pairs of HCQ.MethodsInhibitors, substrates and inducer drugs lists of CYP enzymes of interest from international well‐recognised evidence‐based drug interaction resources were used to identify potential clinically significant pharmacokinetic DDI pairs of HCQ.ResultsAmong 329 identified interacting drugs that predicted to cause clinically significant DDIs of HCQ, 45 (13.7%), 43 (13.1%) and 123 (37.4%) unique DDI pairs were identified from the FDA, Stockley's and Flockhart lists, respectively. Of interest, 55 (16.7%) DDI pairs were recognised by all three resources. At least, 29 (8.8%) severe DDI pairs were identified predicted to cause severe toxicity of HCQ in patients with COVID‐19. When comparing these interactions with Liverpool DDI lists, it was found that out of 423 total interactions, 238 (56.3%) and 94 (22.2%) unique DDI pairs were identified from all three resources and Liverpool DDI lists, respectively. Of interest, only three (0.7%) DDI pairs were recognised by both the three international resources and Liverpool DDI lists of HCQ.ConclusionUsing HCQ has clinical debate whether it should or should not continue in COVID‐19 patients, however, potential clinically significant DDIs identified in this study may optimise safety or efficacy of HCQ in considerable proportion of patients.

Safety and interaction of direct oral anticoagulants with antiarrhythmic drugs

The use of direct oral anticoagulants minimized the risks associated with vitamin K antagonist (warfarin) therapy. Currently, direct oral anticoagulants have priority over warfarin for the prevention of thromboembolic events in patients with atrial fibrillation and a number of other conditions requiring anticoagulant therapy. Direct oral anticoagulants along with antiarrhythmic therapy are the accepted strategy for atrial fibrillation treatment. At the same time, the effect of drug-drug interactions (DDI) between direct oral anticoagulants and antiarrhythmic drugs, which have common points of metabolic application, has not been fully elucidated. In order to provide effective and safe anticoagulant and antiarrhythmic therapy in patients with AF, it is important to understand the mechanisms and severity of DDI of direct oral anticoagulants and antiarrhythmic agents. This review discusses the issues of DDI of direct oral anticoagulants and antiarrhythmic drugs used to treat atrial fibrillation.