scholarly journals Improved Detection Criteria for Detecting Drug-Drug Interaction Signals Using the Proportional Reporting Ratio

2020 ◽  
Vol 14 (1) ◽  
pp. 4
Author(s):  
Yoshihiro Noguchi ◽  
Keisuke Aoyama ◽  
Satoaki Kubo ◽  
Tomoya Tachi ◽  
Hitomi Teramachi
Keyword(s):  
Drug Interaction ◽  
Confidence Interval ◽  
Drug Interactions ◽  
Signal Detection ◽  
Proportional Reporting Ratio ◽  
Detection Accuracy ◽  
Detection Algorithms ◽  
Proposed Model ◽  
Signal Score ◽  
Drug Drug Interaction

There is a current demand for “safety signal” screening, not only for single drugs but also for drug-drug interactions. The detection of drug-drug interaction signals using the proportional reporting ratio (PRR) has been reported, such as through using the combination risk ratio (CRR). However, the CRR does not consider the overlap between the lower limit of the 95% confidence interval of the PRR of concomitant-use drugs and the upper limit of the 95% confidence interval of the PRR of single drugs. In this study, we proposed the concomitant signal score (CSS), with the improved detection criteria, to overcome the issues associated with the CRR. “Hypothetical” true data were generated through a combination of signals detected using three detection algorithms. The signal detection accuracy of the analytical model under investigation was verified using machine learning indicators. The CSS presented improved signal detection when the number of reports was ≥3, with respect to the following metrics: accuracy (CRR: 0.752 → CSS: 0.817), Youden’s index (CRR: 0.555 → CSS: 0.661), and F-measure (CRR: 0.780 → CSS: 0.820). The proposed model significantly improved the accuracy of signal detection for drug-drug interactions using the PRR.

scholarly journals Subset Analysis for Screening Drug–Drug Interaction Signal Using Pharmacovigilance Database

Pharmaceutics ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 762 ◽  
Author(s):  
Yoshihiro Noguchi ◽  
Tomoya Tachi ◽  
Hitomi Teramachi
Keyword(s):  
Drug Interaction ◽  
Drug Interactions ◽  
Signal Detection ◽  
Predictive Value ◽  
Drug Event ◽  
Detection Accuracy ◽  
Analysis Model ◽  
Subset Analysis ◽  
Pharmacovigilance Database ◽  
Drug Drug Interaction

Many patients require multi-drug combinations, and adverse event profiles reflect not only the effects of individual drugs but also drug–drug interactions. Although there are several algorithms for detecting drug–drug interaction signals, a simple analysis model is required for early detection of adverse events. Recently, there have been reports of detecting signals of drug–drug interactions using subset analysis, but appropriate detection criterion may not have been used. In this study, we presented and verified an appropriate criterion. The data source used was the Japanese Adverse Drug Event Report (JADER) database; “hypothetical” true data were generated through a combination of signals detected by three detection algorithms. The accuracy of the signal detection of the analytic model under investigation was verified using indicators used in machine learning. The newly proposed subset analysis confirmed that the signal detection was improved, compared with signal detection in the previous subset analysis, on the basis of the indicators of Accuracy (0.584 to 0.809), Precision (= Positive predictive value; PPV) (0.302 to 0.596), Specificity (0.583 to 0.878), Youden’s index (0.170 to 0.465), F-measure (0.399 to 0.592), and Negative predictive value (NPV) (0.821 to 0.874). The previous subset analysis detected many false drug–drug interaction signals. Although the newly proposed subset analysis provides slightly lower detection accuracy for drug–drug interaction signals compared to signals compared to the Ω shrinkage measure model, the criteria used in the newly subset analysis significantly reduced the amount of falsely detected signals found in the previous subset analysis.

scholarly journals Drug-drug Interactions between Hypoglycemic and Non-hypoglycemic Medication in Diabetic Patients with Comorbidities in a Tertiary Care Center: A Descriptive Cross-sectional Study

2021 ◽  
Vol 59 (243) ◽  
pp. 1125-1130
Author(s):  
Lujaw Ratna Tuladhar ◽  
Shirish Lal Shrestha ◽  
Sneha Bimali ◽  
Srijana Bhusal ◽  
Pingala Khadka
Keyword(s):  
Drug Interaction ◽  
Confidence Interval ◽  
Drug Interactions ◽  
Tertiary Care ◽  
Cross Sectional Study ◽  
Outpatient Department ◽  
Diabetic Patients ◽  
Sectional Study ◽  
Cross Sectional ◽  
Drug Drug Interaction

Introduction: Drug-drug interaction is one of the causes of adverse drug reactions. Generally, drug-drug interaction is common in multidrug therapy. Diabetic patients, particularly due to associated comorbidities tend to have various drug-drug interactions due to the effect of multiple drugs. The objective of this study was to find out the prevalence of drug-drug interactions in diabetic patients. Methods: It was a descriptive cross-sectional study that was conducted among previously diagnosed diabetic patients visiting the outpatient department of medicine at a tertiary care hospital between March 2021 and August 2021. Ethical approval was taken from the institutional review committee (Ref no: 030-076/077). Data was collected from diabetic patients presenting to the outpatient department of medicine using a preformed self-constructed questionnaire. Convenient sampling was done. Statistical Package for Social Sciences version 21 and Microsoft Excel were used for data analysis. Point estimate at 95% confidence interval was calculated along with frequency and proportion for binary data. Results: The prevalence of drug-drug interaction between hypoglycemic and non-hypoglycemic medication was 56 (44.1%) (35.5-52.7 at 95% Confidence Interval) of the patients out of which at least one drug-drug interaction was seen in 48 (37.8%) of the patients. Conclusions: Our study showed the prevalence of drug-drug interactions in diabetic patients to be higher than other studies done in similar settings. Based on the severity, we observed two types of drug-drug interactions; close monitoring drug-drug interactions and minor drug-drug interactions.

scholarly journals Potential drug–drug interactions and associated factors among hospitalized cardiac patients at Jimma University Medical Center, Southwest Ethiopia

2019 ◽  
Vol 7 ◽  
pp. 205031211985735 ◽  
Author(s):  
Netsanet Diksis ◽  
Tsegaye Melaku ◽  
Desta Assefa ◽  
Andualem Tesfaye
Keyword(s):  
Drug Interaction ◽  
Confidence Interval ◽  
Drug Interactions ◽  
Hospital Stay ◽  
Prevalence Rate ◽  
Odds Ratio ◽  
Adjusted Odds Ratio ◽  
Cardiac Patients ◽  
Potential Drug ◽  
Drug Drug Interaction

Background: Concomitant use of several drugs for a patient is often imposing increased risk of drug–drug interactions. Drug–drug interactions are a major cause for concern in patients with cardiovascular disorders due to multiple co-existing conditions and the wide class of drugs they receive. This study is aimed to assess the prevalence of potential drug–drug interactions and associated factors among hospitalized cardiac patients at medical wards of Jimma University Medical Center, Southwest Ethiopia. Methods: A hospital-based prospective observational study was conducted among hospitalized cardiac adult patients based on the inclusion criteria. Patient-specific data were collected using structured data collection tool. Potential drug–drug interaction was analyzed using Micromedex 3.0 DRUG-REAX® System. Data were analyzed using statistical software package, version 20.0. To identify the independent predictors of potential drug–drug interaction, multiple stepwise backward logistic regression analysis was done. Statistical significance was considered at a p-value < 0.05. Written informed consent from patients was obtained and the patients were informed about confidentiality of the information obtained. Results: Of the total 200 patients, majority were male (52.50%) and with a mean(±standard deviation) age of 42.54(±7.89) years. Out of 673 patients’ prescriptions analyzed, 521 prescriptions comprised potential drug interactions and it was found that 967 drug interactions were present. The prevalence rate of potential drug–drug interactions among the study unit was 4.83 per patient and 1.44 per prescription regardless of the severity during their hospital stay. Overall the prevalence rate of potential drug interactions was 74.41%. Older age (adjusted odds ratio (95% confidence interval): 1.067 (2.33–27.12), p = 0.049), long hospital stay (⩾7 days) (adjusted odds ratio (95% confidence interval): 2.80 (1.71–4.61), p = 0.024), and polypharmacy (adjusted odds ratio (95% confidence interval): 1.64 (0.66–4.11), p = 0.041) were independent predictors for the occurrence of potential drug–drug interactions. Conclusion: This study demonstrated a high prevalence of potential DIs among hospitalized cardiac patients in medical wards due to the complexity of pharmacotherapy. The prevalence rate is directly related to age, number of prescribed drugs, and length of hospital stay. Pharmacodynamic drug–drug interaction was the common mechanism of drug–drug interactions. Therefore, close monitoring of hospitalized patients is highly recommended.

Pharmacokinetic Drug-drug Interaction of Antibiotics Used in Sepsis Care in China

2020 ◽  
Vol 21 ◽  
Author(s):  
Xuan Yu ◽  
Zixuan Chu ◽  
Jian Li ◽  
Rongrong He ◽  
Yaya Wang ◽  
...  
Keyword(s):  
Drug Interaction ◽  
Drug Interactions ◽  
Penicillin G ◽  
Literature Mining ◽  
Human Pharmacokinetics ◽  
P450 Enzyme ◽  
Drug Drug Interaction ◽  
Pharmacokinetic Drug ◽  
Sepsis Care

Background: Many antibiotics have a high potential for having an interaction with drugs, as perpetrator and/or victim, in critically ill patients, and particularly in sepsis patients. Methods: The aim of this review is to summarize the pharmacokinetic drug-drug interaction (DDI) of 45 antibiotics commonly used in sepsis care in China. Literature mining was conducted to obtain human pharmacokinetics/dispositions of the antibiotics, their interactions with drug metabolizing enzymes or transporters, and their associated clinical drug interactions. Potential DDI is indicated by a DDI index > 0.1 for inhibition or a treated-cell/untreated-cell ratio of enzyme activity being > 2 for induction. Results: The literature-mined information on human pharmacokinetics of the identified antibiotics and their potential drug interactions is summarized. Conclusion: Antibiotic-perpetrated drug interactions, involving P450 enzyme inhibition, have been reported for four lipophilic antibacterials (ciprofloxacin, erythromycin, trimethoprim, and trimethoprim-sulfamethoxazole) and three lipophilic antifungals (fluconazole, itraconazole, and voriconazole). In addition, seven hydrophilic antibacterials (ceftriaxone, cefamandole, piperacillin, penicillin G, amikacin, metronidazole, and linezolid) inhibit drug transporters in vitro. Despite no reported clinical PK drug interactions with the transporters, caution is advised in the use of these antibacterials. Eight hydrophilic antibacterials (all β-lactams; meropenem, cefotaxime, cefazolin, piperacillin, ticarcillin, penicillin G, ampicillin, and flucloxacillin), are potential victims of drug interactions due to transporter inhibition. Rifampin is reported to perpetrate drug interactions by inducing CYP3A or inhibiting OATP1B; it is also reported to be a victim of drug interactions, due to the dual inhibition of CYP3A4 and OATP1B by indinavir. In addition, three antifungals (caspofungin, itraconazole, and voriconazole) are reported to be victims of drug interactions because of P450 enzyme induction. Reports for other antibiotics acting as victims in drug interactions are scarce.

Drug-Drug Interaction Signal Detection from Drug Safety Reports

2018 ◽  
Author(s):  
Brian Zylich ◽  
Brian McCarthy ◽  
Andrew Schade ◽  
Huy Tran ◽  
Xiao Qin ◽  
...  
Keyword(s):  
Drug Interaction ◽  
Signal Detection ◽  
Drug Safety ◽  
Drug Drug Interaction

scholarly journals Detection of Micro-Defects on Irregular Reflective Surfaces Based on Improved Faster R-CNN

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5000 ◽  
Author(s):  
Zhuangzhuang Zhou ◽  
Qinghua Lu ◽  
Zhifeng Wang ◽  
Haojie Huang
Keyword(s):  
Specular Reflection ◽  
Receptive Fields ◽  
Ground Truth ◽  
Detection Performance ◽  
Support Vector ◽  
Detection Accuracy ◽  
Irregular Surfaces ◽  
Detection Algorithms ◽  
Proposed Model ◽  
Improved Model

The detection of defects on irregular surfaces with specular reflection characteristics is an important part of the production process of sanitary equipment. Currently, defect detection algorithms for most irregular surfaces rely on the handcrafted extraction of shallow features, and the ability to recognize these defects is limited. To improve the detection accuracy of micro-defects on irregular surfaces in an industrial environment, we propose an improved Faster R-CNN model. Considering the variety of defect shapes and sizes, we selected the K-Means algorithm to generate the aspect ratio of the anchor box according to the size of the ground truth, and the feature matrices are fused with different receptive fields to improve the detection performance of the model. The experimental results show that the recognition accuracy of the improved model is 94.6% on a collected ceramic dataset. Compared with SVM (Support Vector Machine) and other deep learning-based models, the proposed model has better detection performance and robustness to illumination, which proves the practicability and effectiveness of the proposed method.

Comparison of eight screening tools to detect interactions between herbal supplements and oncology agents

2020 ◽  
Vol 26 (8) ◽  
pp. 1843-1849
Author(s):  
Faisal Shakeel ◽  
Fang Fang ◽  
Kelley M Kidwell ◽  
Lauren A Marcath ◽  
Daniel L Hertz
Keyword(s):  
Drug Interaction ◽  
Positive Predictive Value ◽  
Drug Interactions ◽  
Predictive Value ◽  
Screening Tools ◽  
Herbal Supplements ◽  
Interaction Detection ◽  
Natural Medicine ◽  
Interaction Screening ◽  
Drug Drug Interaction

Introduction Patients with cancer are increasingly using herbal supplements, unaware that supplements can interact with oncology treatment. Herb–drug interaction management is critical to ensure optimal treatment outcomes. Several screening tools exist to detect drug–drug interactions, but their performance to detect herb–drug interactions is not known. This study compared the performance of eight drug–drug interaction screening tools to detect herb–drug interaction with anti-cancer agents. Methods The herb–drug interaction detection performance of four subscription (Micromedex, Lexicomp, PEPID, Facts & Comparisons) and free (Drugs.com, Medscape, WebMD, RxList) drug–drug interaction tools was assessed. Clinical relevance of each herb–drug interaction was determined using Natural Medicine and each drug–drug interaction tool. Descriptive statistics were used to calculate sensitivity, specificity, positive predictive value, and negative predictive value. Linear regression was used to compare performance between subscription and free tools. Results All tools had poor sensitivity (<0.20) for detecting herb–drug interaction. Lexicomp had the highest positive predictive value (0.98) and best overall performance score (0.54), while Medscape was the best performing free tool (0.52). The worst subscription tools were as good as or better than the best free tools, and as a group subscription tools outperformed free tools on all metrics. Using an average subscription tool would detect one additional herb–drug interaction for every 10 herb–drug interactions screened by a free tool. Conclusion Lexicomp is the best available tool for screening herb–drug interaction, and Medscape is the best free alternative; however, the sensitivity and performance for detecting herb–drug interaction was far lower than for drug–drug interactions, and overall quite poor. Further research is needed to improve herb–drug interaction screening performance.

Potential Drug Interactions— Fact or Fallacy?

1975 ◽  
Vol 9 (11) ◽  
pp. 586-590 ◽  
Author(s):  
Curtis D. Black ◽  
Nicholas G. Popovich
Keyword(s):  
Drug Interaction ◽  
Drug Interactions ◽  
Patient Care ◽  
Clinical Studies ◽  
Literature Search ◽  
Potential Drug ◽  
Careful Evaluation ◽  
Current Drug ◽  
Drug Drug Interaction

At present, the pharmacist is faced with a perplexing number of potential drug interactions as they relate to patient care. The purpose of the investigation was to evaluate current drug-drug interaction literature, specifically gastrointestinal drug interactions. Literature search and review evaluated the authoritative basis on which conclusions were made. From this, a review was written to illustrate fallacies and misconceptions that could be derived from the literature with the intent it would serve as a guide in interpreting and evaluating drug-drug interactions. The overall study illustrates the vast need for careful evaluation of drug interaction literature before erroneous recommendations are made on conceivably inconclusive clinical studies.

scholarly journals DRUG-DRUG INTERACTION;

2017 ◽  
Vol 24 (03) ◽  
pp. 357-365
Author(s):  
Hina Hasnain ◽  
Huma Ali ◽  
Farya Zafar ◽  
Ali Akbar Sial ◽  
Kamran Hameed ◽  
...  
Keyword(s):  
Patient Outcome ◽  
Adverse Event ◽  
Drug Interaction ◽  
Drug Interactions ◽  
Multidisciplinary Approach ◽  
Drug Information ◽  
Drug Effects ◽  
Information Provision ◽  
Drug Drug Interaction ◽  
Drug Order

Drug-drug interaction (DDI) is a specific type of adverse event, which developsdue to multiple regimen therapy, and that may lead to significant hospitalization and death.Clinical and economic impact of drug interactions are increasingly accredited as a chiefconcern in critical care. Potentiating effects of DDIs in intensive care units are far more criticaldue to complex medications regimen, high risk severely ill population and associated metabolicand physiological disturbances which can impede drug effects. Pharmacist contribution isclassified as clarification of drug order, appropriate drug information provision, and advice forsubstitute treatment. A multidisciplinary approach is very necessary in developing a pharmacotherapeuticregimen designed to optimize patient outcome and minimize any potential dugdrug interactions. This review encompasses the prevalence, categorization, significance interm of patient safety and prescription efficacy, clinical and economic burdens, national andinternational data comparisons related to drug-drug interactions.

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