Hypersensitivity Adverse Event Reporting in Clinical Cancer Trials: Barriers and Potential Solutions to Studying Severe Events on a Population Level

ABSTRACT HYPERSENSITIVITY ADVERSE EVENT REPORTING IN CLINICAL CANCER TRIALS: BARRIERS AND POTENTIAL SOLUTIONS TO STUDYING ALLERGIC EVENTS ON A POPULATION LEVEL by Christina Eldredge The University of Wisconsin-Milwaukee, 2020 Under the Supervision of Professor Timothy Patrick Clinical cancer trial interventions are associated with hypersensitivity events (HEs) which are recorded in the national clinical trial registry, ClinicalTrials.gov and publicly available. This data could potentially be leveraged to study predictors for HEs to identify at risk patients who may benefit from desensitization therapies to prevent these potentially life-threatening reactions. However, variation in investigator reporting methods is a barrier to leveraging this data for aggregation and analysis. The National Cancer Institute has developed the CTCAE classification system to address this barrier. This study analyzes the comprehensiveness of CTCAE to describe severe HEs in clinical cancer trials in comparison to other systems or terminologies. An XML parser was used to extract readable text from adverse event tables. Queries of the parsed data elements were performed to identify immune disorder events associated with biological and chemotherapy interventions. A data subset of severe anaphylactic and anaphylactoid events was created and analyzed. 1,331 clinical trials with 13088 immune disorder events occurred from September 20, 1999 to March 2018. 2409 (18.4%) of these were recorded as “serious” events. In the severe subset, MedDRA terminology, CTCAE or CTC classification systems were used to describe HEs, however, a large number of studies did not specify the system. The CTCAE term “anaphylaxis” was miscoded as “other (not including serious)” in 76.2% of events. The CTCAE classification system severity grades levels were not used to describe any of the severe events and the majority of terms did not include the allergen and therefore, in dual or multi- drug therapies, the etiologic agent was not identifiable. Furthermore, collection methods were not specified in 76% of events. Therefore, CTCAE was not found to improve the ability to capture event etiology or severity in anaphylaxis and anaphylactoid events in cancer clinical trials. Potential solutions to improving CTCAE HE description include adapting terms with a low percentage of HE severity miscoding (e.g. anaphylactic reaction) and terms which include drugs, biological agents and/or drug classes to improve study of anaphylaxis etiology and incidence in multi-drug cancer therapy, therefore, making a significant impact on patient safety

Developing Metadata Categories as a Strategy to Mobilize Computable Biomedical Knowledge

A work by a group of volunteer members drawn from the Mobilizing Computable Biomedical Knowledge community's Standards Workgroup. See mobilizecbk.org for more information about this community and workgroup.Computable biomedical knowledge artifacts (CBKs) are digital objects or entities representing biomedical knowledge as machine-independent data structures that can be parsed and processed by different information systems. The breadth of content represented in CBKs spans all biomedical knowledge related to human health and so it includes knowledge about molecules, cells, organs, individual people, human populations, and the environment. CBKs vary in their scope, purpose, and audience. Some CBKs support biomedical research. Other CBKs help improve health outcomes by enabling clinical decision support, health education, health promotion, and population health analytics. In some instances, CBKs have multiple uses that span research, education, clinical care, or population health. As the number of CBKs grows large, producers must describe them with structured, searchable metadata so that consumers can find, deploy, and use them properly. This report delineates categories of metadata for describing CBKs sufficiently to enable CBKs to be mobilized for various purposes.https://deepblue.lib.umich.edu/bitstream/2027.42/155655/1/MCBK.Metadata.Paper.June2020.f.pdfDescription of MCBK.Metadata.Paper.June2020.f.pdf : MCBK 2020 Virtual Meeting version of Standards Workgroup's Working Paper on CBK Metadat

Cheek Tooth Morphology and Ancient Mitochondrial DNA of Late Pleistocene Horses from the Western Interior of North America: Implications for the Taxonomy of North American Late Pleistocene Equus

Horses were a dominant component of North American Pleistocene land mammal communities and their remains are well represented in the fossil record. Despite the abundant material available for study, there is still considerable disagreement over the number of species of Equus that inhabited the different regions of the continent and on their taxonomic nomenclature. In this study, we investigated cheek tooth morphology and ancient mtDNA of late Pleistocene Equus specimens from the Western Interior of North America, with the objective of clarifying the species that lived in this region prior to the end-Pleistocene extinction. Based on the morphological and molecular data analyzed, a caballine (Equus ferus) and a non-caballine (E. conversidens) species were identified from different localities across most of the Western Interior. A second non-caballine species (E. cedralensis) was recognized from southern localities based exclusively on the morphological analyses of the cheek teeth. Notably the separation into caballine and non-caballine species was observed in the Bayesian phylogenetic analysis of ancient mtDNA as well as in the geometric morphometric analyses of the upper and lower premolars. Teeth morphologically identified as E. conversidens that yielded ancient mtDNA fall within the New World stilt-legged clade recognized in previous studies and this is the name we apply to this group. Geographic variation in morphology in the caballine species is indicated by statistically different occlusal enamel patterns in the specimens from Bluefish Caves, Yukon Territory, relative to the specimens from the other geographic regions. Whether this represents ecomorphological variation and/or a certain degree of geographic and genetic isolation of these Arctic populations requires further study

Accurate Determination of Imaging Modality Using an Ensemble of Text- and Image-based Classifiers

Imaging modality can aid retrieval of medical images for clinical practice, research, and education. We evaluated whether an ensemble classifier could outperform its constituent individual classifiers in determining the modality of figures from radiology journals. Seventeen automated classifiers analyzed 77,495 images from two radiology journals. Each classifier assigned one of eight imaging modalities--computed tomography, graphic, magnetic resonance imaging, nuclear medicine, positron emission tomography, photograph, ultrasound, or radiograph-to each image based on visual and/or textual information. Three physicians determined the modality of 5,000 randomly selected images as a reference standard. A Simple Vote ensemble classifier assigned each image to the modality that received the greatest number of individual classifiers\u27 votes. A Weighted Vote classifier weighted each individual classifier\u27s vote based on performance over a training set. For each image, this classifier\u27s output was the imaging modality that received the greatest weighted vote score. We measured precision, recall, and F score (the harmonic mean of precision and recall) for each classifier. Individual classifiers\u27 F scores ranged from 0.184 to 0.892. The simple vote and weighted vote classifiers correctly assigned 4,565 images (F score, 0.913; 95% confidence interval, 0.905-0.921) and 4,672 images (F score, 0.934; 95% confidence interval, 0.927-0.941), respectively. The weighted vote classifier performed significantly better than all individual classifiers. An ensemble classifier correctly determined the imaging modality of 93% of figures in our sample. The imaging modality of figures published in radiology journals can be determined with high accuracy, which will improve systems for image retrieval

A Systematic Approach for Developing a Corpus of Patient Reported Adverse Drug Events: A Case Study for SSRI and SNRI Medications

Psychiatric Treatment Adverse Reactions (PsyTAR) corpus is an annotated corpus that has been developed using patients narrative data for psychiatric medications, particularly SSRIs (Selective Serotonin Reuptake Inhibitor) and SNRIs (Serotonin Norepinephrine Reuptake Inhibitor) medications. This corpus consists of three main components: sentence classification, entity identification, and entity normalization. We split the review posts into sentences and labeled them for presence of adverse drug reactions (ADRs) (2168 sentences), withdrawal symptoms (WDs) (438 sentences), sign/symptoms/illness (SSIs) (789 sentences), drug indications (517), drug effectiveness (EF) (1087 sentences), and drug infectiveness (INF) (337 sentences). In the entity identification phase, we identified and extracted ADRs (4813 mentions), WDs (590 mentions), SSIs (1219 mentions), and DIs (792). In the entity normalization phase, we mapped the identified entities to the corresponding concepts in both UMLS (918 unique concepts) and SNOMED CT (755 unique concepts). Four annotators double coded the sentences and the span of identified entities by strictly following guidelines rules developed for this study. We used the PsyTAR sentence classification component to automatically train a range of supervised machine learning classifiers to identifying text segments with the mentions of ADRs, WDs, DIs, SSIs, EF, and INF. SVMs classifiers had the highest performance with F-Score 0.90. We also measured performance of the cTAKES (clinical Text Analysis and Knowledge Extraction System) in identifying patients\u27 expressions of ADRs and WDs with and without adding PsyTAR dictionary to the core dictionary of cTAKES. Augmenting cTAKES dictionary with PsyTAR improved the F-score cTAKES by 25%. The findings imply that PsyTAR has significant implications for text mining algorithms aimed to identify information about adverse drug events and drug effectiveness from patients\u27 narratives data, by linking the patients\u27 expressions of adverse drug events to medical standard vocabularies. The corpus is publicly available at Zolnoori et al. [30]

Categorizing metadata to help mobilize computable biomedical knowledge

IntroductionComputable biomedical knowledge artifacts (CBKs) are digital objects conveying biomedical knowledge in machine‐interpretable structures. As more CBKs are produced and their complexity increases, the value obtained from sharing CBKs grows. Mobilizing CBKs and sharing them widely can only be achieved if the CBKs are findable, accessible, interoperable, reusable, and trustable (FAIR+T). To help mobilize CBKs, we describe our efforts to outline metadata categories to make CBKs FAIR+T.MethodsWe examined the literature regarding metadata with the potential to make digital artifacts FAIR+T. We also examined metadata available online today for actual CBKs of 12 different types. With iterative refinement, we came to a consensus on key categories of metadata that, when taken together, can make CBKs FAIR+T. We use subject‐predicate‐object triples to more clearly differentiate metadata categories.ResultsWe defined 13 categories of CBK metadata most relevant to making CBKs FAIR+T. Eleven of these categories (type, domain, purpose, identification, location, CBK‐to‐CBK relationships, technical, authorization and rights management, provenance, evidential basis, and evidence from use metadata) are evident today where CBKs are stored online. Two additional categories (preservation and integrity metadata) were not evident in our examples. We provide a research agenda to guide further study and development of these and other metadata categories.ConclusionA wide variety of metadata elements in various categories is needed to make CBKs FAIR+T. More work is needed to develop a common framework for CBK metadata that can make CBKs FAIR+T for all stakeholders.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/171602/1/lrh210271.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/171602/2/lrh210271_am.pd

Life-Threatening Allergies: Using a Patient-Engaged Approach

BACKGROUND: Adolescents at risk for anaphylaxis are a growing concern. Novel training methods are needed to better prepare individuals to manage anaphylaxis in the community. INTRODUCTION: Didactic training as the sole method of anaphylaxis education has been shown to be ineffective. We developed a smartphone-based interactive teaching tool with decision support and epinephrine auto-injector (EAI) training to provide education accessible beyond the clinic. METHODS: This study consisted of two parts: (1) Use of food allergy scenarios to assess the decision support\u27s ability to improve allergic reaction management knowledge. (2) An assessment of our EAI training module on participant\u27s ability to correctly demonstrate the use of an EAI by comparing it to label instructions. RESULTS: Twenty-two adolescents were recruited. The median (range) baseline number of correct answers on the scenarios before the intervention was 9 (3-11). All subjects improved with decision support, increasing to 11 (9-12) (p \u3c .001). The median (range) demonstration score was 6 (5-6) for the video training module group and 4.5 (3-6) for the label group (p \u3c 0.001). DISCUSSION: Results suggest that the use of this novel m-health application can improve anaphylaxis symptom recognition and increase the likelihood of choosing the appropriate treatment. In addition, performing EAI steps in conjunction with the video training resulted in more accurate medication delivery with fewer missed steps compared to the use of written instructions alone. CONCLUSION: The results suggest that mobile health decision support technology for anaphylaxis emergency preparedness may support traditional methods of training by providing improved access to anaphylaxis training in the community setting

AMIA Board White Paper: AMIA 2017 Core Competencies for Applied Health Informatics Education at the Master\u27s Degree Level

This White Paper presents the foundational domains with examples of key aspects of competencies (knowledge, skills, and attitudes) that are intended for curriculum development and accreditation quality assessment for graduate (master\u27s level) education in applied health informatics. Through a deliberative process, the AMIA Accreditation Committee refined the work of a task force of the Health Informatics Accreditation Council, establishing 10 foundational domains with accompanying example statements of knowledge, skills, and attitudes that are components of competencies by which graduates from applied health informatics programs can be assessed for competence at the time of graduation. The AMIA Accreditation Committee developed the domains for application across all the subdisciplines represented by AMIA, ranging from translational bioinformatics to clinical and public health informatics, spanning the spectrum from molecular to population levels of health and biomedicine. This document will be periodically updated, as part of the responsibility of the AMIA Accreditation Committee, through continued study, education, and surveys of market trends

Targeting autophagy in cancer

Autophagy is a mechanism by which cellular material is delivered to lysosomes for degradation, leading to the basal turnover of cell components and providing energy and macromolecular precursors. Autophagy has opposing, context-dependent roles in cancer, and interventions to both stimulate and inhibit autophagy have been proposed as cancer therapies. This has led to the therapeutic targeting of autophagy in cancer to be sometimes viewed as controversial. In this Review, we suggest a way forwards for the effective targeting of autophagy by understanding the context-dependent roles of autophagy and by capitalizing on modern approaches to clinical trial design