Medical students as adverse drug event managers, learning about side effects while improving their reporting in clinical practice.
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ABSTRACT:
SUBMITTER: Reumerman M
PROVIDER: S-EPMC8233281 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Problem:Rapid growth in the use of medical devices has drawn attention to gaps in the systematic monitoring of medical device-associated adverse events in India. Approach:Implementation of national regulations on medical devices started in January 2018. Supported by a nationwide network of monitoring centres, the Indian Pharmacopoeia Commission coordinates adverse event reports from manufacturers, legal representatives and patients or users. The commission follows-up and reviews reports with subject expert groups and sends recommendations on necessary action to the national regulatory authority. Local setting:Before 2015, no systematic structure was in place to collate adverse events associated with medical devices. Several reports of deaths and hospitalization due to faulty hip implants, cardiac stents and poor-quality devices prompted the health ministry to launch the materiovigilance programme. Relevant changes:From July 2015 to October 2019, the commission received 1931 adverse event reports, mostly from marketing authorization holders; 1277 were serious events. Reporting increased markedly after 2017. Cardiac stents were the most reported device (926 events; 47.95%). To encourage a culture of reporting, the commission has raised awareness about the programme among stakeholders, developed user-friendly reporting tools and guidelines, and conducted training for hospital personnel on medical device adverse event reporting. Lessons learnt:Regular training to stakeholders develops a sense of responsibility towards reporting medical device adverse events and ensures quality data reporting. Reporters must be assured that reporting adverse events does not have any legal implications for them and given acknowledgement of their role in high-quality device associated adverse event reporting.
| S-EPMC7047028 | biostudies-literature
Project description:INTRODUCTION:Adverse event (AE) under-reporting has been a recurrent issue raised during health authorities Good Clinical Practices (GCP) inspections and audits. Moreover, safety under-reporting poses a risk to patient safety and data integrity. The current clinical Quality Assurance (QA) practices used to detect AE under-reporting rely heavily on investigator site and study audits. Yet several sponsors and institutions have had repeated findings related to safety reporting, and this has led to delays in regulatory submissions. Recent developments in data management and IT systems allow data scientists to apply techniques such as machine learning to detect AE under-reporting in an automated fashion. OBJECTIVE:In this project, we developed a predictive model that enables Roche/Genentech Quality Program Leads oversight of AE reporting at the program, study, site, and patient level. This project was part of a broader effort at Roche/Genentech Product Development Quality to apply advanced analytics to augment and complement traditional clinical QA approaches. METHOD:We used a curated data set from 104 completed Roche/Genentech sponsored clinical studies to train a machine learning model to predict the expected number of AEs. Our final model used 54 features built on patient (e.g., demographics, vitals) and study attributes (e.g., molecule class, disease area). RESULTS:In order to evaluate model performance, we tested how well it would detect simulated test cases based on data not used for model training. For relevant simulation scenarios of 25%, 50%, and 75% under-reporting on the site level, our model scored an area under the curve (AUC) of the receiver operating characteristic (ROC) curve of 0.62, 0.79, and 0.92, respectively. CONCLUSION:The model has been deployed to evaluate safety reporting performance in a set of ongoing studies in the form of a QA/dashboard cockpit available to Roche Quality Program Leads. Applicability and production performance will be assessed over the next 12-24 months in which we will develop a validation strategy to fully integrate our model into Roche QA processes.
| S-EPMC6689279 | biostudies-other
Project description:ObjectiveThe US Vaccine Adverse Event Reporting System (VAERS) collects spontaneous reports of adverse events following vaccination. Medical officers review the reports and often apply standardized case definitions, such as those developed by the Brighton Collaboration. Our objective was to demonstrate a multi-level text mining approach for automated text classification of VAERS reports that could potentially reduce human workload.DesignWe selected 6034 VAERS reports for H1N1 vaccine that were classified by medical officers as potentially positive (N(pos)=237) or negative for anaphylaxis. We created a categorized corpus of text files that included the class label and the symptom text field of each report. A validation set of 1100 labeled text files was also used. Text mining techniques were applied to extract three feature sets for important keywords, low- and high-level patterns. A rule-based classifier processed the high-level feature representation, while several machine learning classifiers were trained for the remaining two feature representations.MeasurementsClassifiers' performance was evaluated by macro-averaging recall, precision, and F-measure, and Friedman's test; misclassification error rate analysis was also performed.ResultsRule-based classifier, boosted trees, and weighted support vector machines performed well in terms of macro-recall, however at the expense of a higher mean misclassification error rate. The rule-based classifier performed very well in terms of average sensitivity and specificity (79.05% and 94.80%, respectively).ConclusionOur validated results showed the possibility of developing effective medical text classifiers for VAERS reports by combining text mining with informative feature selection; this strategy has the potential to reduce reviewer workload considerably.
| S-EPMC3168300 | biostudies-literature
Project description:BackgroundRandomised controlled trials (RCTs) provide valuable information for developing harm profiles but current analysis practices to detect between-group differences are suboptimal. Drug trials routinely screen continuous clinical and biological data to monitor participant harm. These outcomes are regularly dichotomised into abnormal/normal values for analysis. Despite the simplicity gained for clinical interpretation, it is well established that dichotomising outcomes results in a considerable reduction in information and thus statistical power. We propose an automated procedure for the routine implementation of the distributional method for the dichotomisation of continuous outcomes proposed by Peacock and Sauzet, which retains the precision of the comparison of means.MethodsWe explored the use of a distributional approach to compare differences in proportions based on the comparison of means which retains the power of the latter. We applied this approach to the screening of clinical and biological data as a means to detect 'signals' for potential adverse drug reactions (ADRs). Signals can then be followed-up in further confirmatory studies. Three distributional methods suitable for different types of distributions are described. We propose the use of an automated approach using the observed data to select the most appropriate distribution as an analysis strategy in a RCT setting for multiple continuous outcomes. We illustrate this approach using data from three RCTs assessing the efficacy of mepolizumab in asthma or COPD. Published reference ranges were used to define the proportions of participants with abnormal values for a subset of 10 blood tests. The between-group distributional and empirical differences in proportions were estimated for each blood test and compared.ResultsWithin trials, the distributions varied across the 10 outcomes demonstrating value in a practical approach to selecting the distributional method in the context of multiple adverse event outcomes. Across trials, there were three outcomes where the method chosen by the automated procedure varied for the same outcome. The distributional approach identified three signals (eosinophils, haematocrit, and haemoglobin) compared to only one when using the Fisher's exact test (eosinophils) and two identified by use of the 95% confidence interval for the difference in proportions (eosinophils and potassium).ConclusionWhen dichotomisation of continuous adverse event outcomes aids clinical interpretation, we advocate use of a distributional approach to retain statistical power. Methods are now easy to implement. Retaining information is especially valuable in the context of the analysis of adverse events in RCTs. The routine implementation of this automated approach requires further evaluation.
| S-EPMC8243742 | biostudies-literature
Project description:AimAdverse drug events (ADEs) are harmful and unintended consequences of medications. Their reporting is essential for drug safety monitoring and research, but it has not been standardized internationally. Our aim was to synthesize information about the type and variety of data collected within ADE reporting systems.MethodsWe developed a systematic search strategy, applied it to four electronic databases, and completed an electronic grey literature search. Two authors reviewed titles and abstracts, and all eligible full-texts. We extracted data using a standardized form, and discussed disagreements until reaching consensus. We synthesized data by collapsing data elements, eliminating duplicate fields and identifying relationships between reporting concepts and data fields using visual analysis software.ResultsWe identified 108 ADE reporting systems containing 1782 unique data fields. We mapped them to 33 reporting concepts describing patient information, the ADE, concomitant and suspect drugs, and the reporter. While reporting concepts were fairly consistent, we found variability in data fields and corresponding response options. Few systems clarified the terminology used, and many used multiple drug and disease dictionaries such as the Medical Dictionary for Regulatory Activities (MedDRA).ConclusionWe found substantial variability in the data fields used to report ADEs, limiting the comparability of ADE data collected using different reporting systems, and undermining efforts to aggregate data across cohorts. The development of a common standardized data set that can be evaluated with regard to data quality, comparability and reporting rates is likely to optimize ADE data and drug safety surveillance.
| S-EPMC4917803 | biostudies-literature
Project description:BackgroundMedical students receive limited exposure to the field of plastic surgery because most students will not rotate in plastic surgery, especially those at schools without dedicated plastic surgery residency programs. This study aimed to create and validate a plastic surgery learning module for medical students to dispel media-propagated myths and misrepresentation of the breadth of plastic surgery and equip students with referral-making capabilities.MethodsThe plastic surgery learning module was created using Articulate Storyline 360 (New York, N.Y.). Student participants were recruited from a single medical school across all four classes. Pre- and postmodule surveys were administered via Qualtrics (Provo, Utah). Scores were computed for the general surgical knowledge section and for each specialty referral question.ResultsTwelve students completed usability testing and edits were subsequently made to optimize the module. The module took on average 66 minutes to complete. Sixty-five students (19 MS1, 16 MS2, 15 MS3, 15 MS4) completed efficacy testing. In the premodule survey, students were nearly 100% accurate in identifying breast-related referrals, unlike pediatric/craniofacial (avg: 68%), reconstruction/microsurgery (avg: 64%), and hand/upper extremity (avg: 30%) referrals. Students of all classes exhibited significant improvement in all testing categories except for the breast category, with the most improvement in the hand referrals category. Prior exposure to plastic surgery (57%) correlated with higher premodule hand (P = 0.003) and breast/cosmetic (P = 0.01) referral scores.ConclusionThe plastic surgery learning module shows promise to be a comprehensive yet affordable and time-efficient tool for medical students to learn about basic surgical principles and the scope of plastic surgery.
| S-EPMC8769105 | biostudies-literature
Project description:The US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) is an important source for detecting adverse drug event (ADE) signals. In this article, we propose a three-component mixture model (3CMM) for FAERS signal detection. In 3CMM, a drug-ADE pair is assumed to have either a zero relative risk (RR), or a background RR (mean RR?=?1), or an increased RR (mean RR >1). By clearly defining the second component (mean RR?=?1) as the null distribution, 3CMM estimates local false discovery rates (FDRs) for ADE signals under the empirical Bayes framework. Compared with existing approaches, the local FDR's top signals have noninferior or better sensitivities to detect true signals in both FAERS analysis and simulation studies. Additionally, we identify that the top signals of different approaches have different patterns, and they are complementary to each other.
| S-EPMC6118321 | biostudies-literature
Project description:It is possible to focus medical genetics education by using a model that integrates the skills of end-user searching of the medical literature into the traditional course content. Since 1988, 313 first-year medical students were studied as they accessed MEDLINE to retrieve information about biochemical genetic disorders. Their search behavior was studied by analyzing data from the National Library of Medicine's traffic files. The skills that they initially learned were reinforced as they searched clinical genetics problem cases in the second-year pathology course, and these skills were consolidated in the third year when the students addressed specific patient-care questions in pediatrics. The students' perception of the value of this model was studied by analyzing questionnaires completed during the exercise. It was demonstrated that when students were taught the skills of accessing MEDLINE by computer, they could formulate a question, retrieve current information, critically review relevant articles, communicate effectively, and use these skills to contribute to patient care.
| S-EPMC1682177 | biostudies-other
Project description:PurposeThe purpose of this study was to investigate the adverse events (AEs) related to the use of off-label drugs.Materials and methodsA cross-sectional study was carried out using available data pertaining to off-label drug were sourced from U.S. FDA spontaneous adverse drug reaction reporting database (FAERS) and Korea Adverse Event Reporting System database (KIDS-KD) for the years 2014 to 2018. The number and frequencies of AE cases were calculated. Disproportionality was analyzed using the proportional reporting ratio (PRR), the reporting odds ratio (ROR), the information component (IC), and the empirical Bayes geometric mean (EBGM) methods.ResultsThe reported AEs associated with off-label drug use were more common among older patients compared with younger patients. Gastric nonspecific symptoms and therapeutic procedure (4.16-4.57%) and haemorrage term (4.16-5.29%) were the most common AE symptoms and antithrombotic agents and immunosuppressants were the drugs most commonly reported to cause AEs in FAERS. Secondary term events (43.45-48.62%) including inappropriate schedule of drug administration and medication error were the most common AEs, and immunosuppressants and antipsychotics were the most common AE-related drugs from KIDS-KD. The numbers of reported AEs in new drug categories such as other antineoplastic agents trended to increase from 2014 to 2018 in both datasets.ConclusionThe numbers of reported AEs with off-label drug increased annually. AEs associated with off-label drugs may have a significant impact on older patients. Healthcare experts should be concerned about prescriptions of off-label drugs, especially anticoagulants and newly developed drugs such as immunosuppressants and antineoplastic agents.
| S-EPMC8387311 | biostudies-literature
Project description:BackgroundTaxanes are an essential class of antineoplastic agents used to treat various cancers and are a fundamental cause of hypersensitivity reactions. In addition, other adverse events, such as bone marrow toxicity and peripheral neuropathy, can lead to chemotherapy discontinuation. This study aimed to evaluate the safety of taxanes in the real world.MethodsTaxane-associated adverse events were identified by the Medical Dictionary for Regulatory Activities Preferred Terms and analyzed and compared by mining the US Food and Drug Administration Adverse Event Reporting System pharmacovigilance database from January 2004 to December 2019. Reported adverse events, such as hypersensitivity reaction, bone marrow toxicity, and peripheral neuropathy, were analyzed with the following signal detection algorithms: reporting odds ratio (ROR), proportional reporting ratio (PRR), multi-item gamma Poisson shrinker (MGPS), Bayesian confidence propagation neural network (BCPNN), and logistic regression methods. Adverse outcome events and death outcome rates were compared between different taxane groups using Pearson's χ2 test, whereas significance was determined at P < 0.05 with a 95% confidence interval (CI).ResultsA total of 966 reports of hypersensitivity reactions, 1109 reports of bone marrow toxicity, and 1374 reports of peripheral neuropathy were analyzed. Compared with paclitaxel and docetaxel, bone marrow toxicity following the use of nab-paclitaxel had the highest ROR of 6.45 (95% two-sided CI, 6.05-6.88), PRR of 5.66, (χ2 = 4342.98), information component of 2.50 (95% one-sided CI = 2.34), and empirical Bayes geometric mean of 5.64 (95% one-sided CI = 5.34). Peripheral neuropathy following the use of nab-paclitaxel showed a higher ROR of 12.78 (95% two-sided CI, 11.55-14.14), PRR of 12.16 (χ2 = 4060.88), information component of 3.59 (95% one-sided CI = 3.25), and empirical Bayes geometric mean of 12.07 (95% one-sided CI = 11.09).ConclusionsThe results showed that bone marrow toxicity and peripheral neuropathy were the major adverse events induced by taxanes. Nab-paclitaxel exhibited the highest potential for taxane-associated adverse events. Further research in the future is warranted to explain taxane-associated adverse effects in real-world circumstances.
| S-EPMC8213312 | biostudies-literature