Preventing Adverse Drug-Drug Interactions: A Need for Improved Data and Logistics

Seminerio, Michael J.; Ratain, Mark J.

doi:10.1016/j.mayocp.2012.12.005

Cited by 4 publications

(4 citation statements)

References 17 publications

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“…Therapies containing SIM or SOF are associated with a high cost and it is imperative that clinicians take appropriate measures to ensure that patients have the highest likelihood of completing therapy, achieving a sustained virologic response and minimizing adverse events [ 13 ]. In some situations, adverse events that are a function of XDDIs can be prevented by proactively identifying patients at risk for XDDIs prior to prescribing SIM or SOF-containing therapy and choosing the type of therapy that is associated with a lower probability or using them in patients who do not have risk factors for XDDIs [ 14 ]. This intervention, coupled with optimal drug adherence, can contribute to the probability of a good treatment outcome.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2015

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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.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2015

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“…30 Drug databases can also help to identify drug-drug interactions. 31 Computerized physician order entry can decrease medication errors by more than onehalf, although not all of these errors would have resulted in an adverse event. 32 In other research, a computerized physician order entry system helped reduce nonemissed-dose medication errors in outpatients from 142 per 1000 patient-days at baseline to 26.6 per 1000 patient-days after the intervention.…”

Section: Avoiding Medication Errorsmentioning

confidence: 99%

Medication Errors: An Overview for Clinicians

Wittich

Burkle

Lanier

2014

Mayo Clinic Proceedings

187

119

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Medication error is an important cause of patient morbidity and mortality, yet it can be a confusing and underappreciated concept. This article provides a review for practicing physicians that focuses on medication error (1) terminology and definitions, (2) incidence, (3) risk factors, (4) avoidance strategies, and (5) disclosure and legal consequences. A medication error is any error that occurs at any point in the medication use process. It has been estimated by the Institute of Medicine that medication errors cause 1 of 131 outpatient and 1 of 854 inpatient deaths. Medication factors (eg, similar sounding names, low therapeutic index), patient factors (eg, poor renal or hepatic function, impaired cognition, polypharmacy), and health care professional factors (eg, use of abbreviations in prescriptions and other communications, cognitive biases) can precipitate medication errors. Consequences faced by physicians after medication errors can include loss of patient trust, civil actions, criminal charges, and medical board discipline. Methods to prevent medication errors from occurring (eg, use of information technology, better drug labeling, and medication reconciliation) have been used with varying success. When an error is discovered, patients expect disclosure that is timely, given in person, and accompanied with an apology and communication of efforts to prevent future errors. Learning more about medication errors may enhance health care professionals' ability to provide safe care to their patients.

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“…Few databases and software programs are capable of checking for all potential interactions among multiple medications. More importantly, the absence of reported interactions does not guarantee a lack of interaction 39 .…”

Section: Resultsmentioning

confidence: 99%

Metabolism‐related pharmacokinetic drug−drug interactions with tyrosine kinase inhibitors: current understanding, challenges and recommendations

Teo

Chan

2015

Brit J Clinical Pharma

132

104

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Drug−drug interactions (DDIs) occur when a patient's response to the drug is modified by administration or co-exposure to another drug. The main cytochrome P450 (CYP) enzyme, CYP3A4, is implicated in the metabolism of almost all of the tyrosine kinase inhibitors (TKIs). Therefore, there is a substantial potential for interaction between TKIs and other drugs that modulate the activity of this metabolic pathway. Cancer patients are susceptible to DDIs as they receive many medications, either for supportive care or for treatment of toxicity. Differences in DDI outcomes are generally negligible because of the wide therapeutic window of common drugs. However for anticancer agents, serious clinical consequences may occur from small changes in drug metabolism and pharmacokinetics. Therefore, the objective of this review is to highlight the current understanding of DDIs among TKIs, with a focus on metabolism, as well as to identify challenges in the prediction of DDIs and provide recommendations.

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Preventing Adverse Drug-Drug Interactions: A Need for Improved Data and Logistics

Cited by 4 publications

References 17 publications

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

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

Medication Errors: An Overview for Clinicians

Metabolism‐related pharmacokinetic drug−drug interactions with tyrosine kinase inhibitors: current understanding, challenges and recommendations

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