High-throughput framework for genetic analyses of adverse drug reactions using electronic health records

Non-IgE-Mediated Immediate Drug-Induced Hypersensitivity Reactions

Immediate drug-induced hypersensitivity reactions (IDHSRs) have conventionally been attributed to an immunoglobulin E (IgE)-mediated mechanism. Nevertheless, it has now been acknowledged that IDHSRs can also occur independently of IgE involvement. Non-IgE-mediated IDHSRs encompass the activation of effector cells, both mast cell-dependent and -independent and the initiation of inflammatory pathways through immunogenic and nonimmunogenic mechanisms. The IDHSRs involve inflammatory mediators beyond histamine, including the platelet-activating factor, which activates multiple cell types, including smooth muscle, endothelium, and MC, and evidence supports its importance in IgE-mediated reactions in humans. Clinically, distinguishing IgE from non-IgE mechanisms is crucial for future treatment strategies, including drug(s) restriction, readministration approaches, and pretreatment considerations. However, this presents significant challenges because certain drugs can trigger both mechanisms, and their presentations can appear similarly, ranging from mild to life-threatening symptoms. Thus, history alone is often inadequate for differentiation, and skin tests lack a standardized approach. Moreover, drug-specific IgE immunoassays have favorable specificity but low sensitivity, and the usefulness of the basophil activation test remains debatable. Lastly, no biomarker reliably differentiates between both mechanisms. Whereas non-IgE-mediated mechanisms likely predominate in IDHSRs, reclassifying most drug-related IDHSRs as non-IgE-mediated, with suggested prevention through dose administration adjustments, is premature and risky. Therefore, continued research and validated diagnostic tests are crucial to improving our capacity to distinguish between these mechanisms, ultimately enhancing patient care.

Evaluation of the portability of computable phenotypes with natural language processing in the eMERGE network

The electronic Medical Records and Genomics (eMERGE) Network assessed the feasibility of deploying portable phenotype rule-based algorithms with natural language processing (NLP) components added to improve performance of existing algorithms using electronic health records (EHRs). Based on scientific merit and predicted difficulty, eMERGE selected six existing phenotypes to enhance with NLP. We assessed performance, portability, and ease of use. We summarized lessons learned by: (1) challenges; (2) best practices to address challenges based on existing evidence and/or eMERGE experience; and (3) opportunities for future research. Adding NLP resulted in improved, or the same, precision and/or recall for all but one algorithm. Portability, phenotyping workflow/process, and technology were major themes. With NLP, development and validation took longer. Besides portability of NLP technology and algorithm replicability, factors to ensure success include privacy protection, technical infrastructure setup, intellectual property agreement, and efficient communication. Workflow improvements can improve communication and reduce implementation time. NLP performance varied mainly due to clinical document heterogeneity; therefore, we suggest using semi-structured notes, comprehensive documentation, and customization options. NLP portability is possible with improved phenotype algorithm performance, but careful planning and architecture of the algorithms is essential to support local customizations.

Biobanking as a Tool for Genomic Research: From Allele Frequencies to Cross-Ancestry Association Studies

In recent years, great advances have been made in the field of collection, storage, and analysis of biological samples. Large collections of samples, biobanks, have been established in many countries. Biobanks typically collect large amounts of biological samples and associated clinical information; the largest collections include over a million samples. In this review, we summarize the main directions in which biobanks aid medical genetics and genomic research, from providing reference allele frequency information to allowing large-scale cross-ancestry meta-analyses. The largest biobanks greatly vary in the size of the collection, and the amount of available phenotype and genotype data. Nevertheless, all of them are extensively used in genomics, providing a rich resource for genome-wide association analysis, genetic epidemiology, and statistical research into the structure, function, and evolution of the human genome. Recently, multiple research efforts were based on trans-biobank data integration, which increases sample size and allows for the identification of robust genetic associations. We provide prominent examples of such data integration and discuss important caveats which have to be taken into account in trans-biobank research.

Novel Analysis Methods to Mine Immune-Mediated Phenotypes and Find Genetic Variation Within the Electronic Health Record (Roadmap for Phenotype to Genotype: Immunogenomics)

The field of immunogenomics has the opportunity for accelerated genetic discovery aided by the maturation of electronic health records (EHRs) linked to DNA biobanks. Novel analysis methods in deep phenotyping of EHR data allow the full realization of the paired and increasingly dense genetic/phenotypic information available. This enables researchers to uncover genetic risk factors for the prevention and optimal treatment of immune-mediated diseases and immune-mediated adverse drug reactions. This article reviews the background of EHRs linked to DNA biobanks, potential applications to immunogenomic discovery, and current and emerging techniques in EHR-based deep phenotyping.

Future of Perioperative Precision Medicine: Integration of Molecular Science, Dynamic Health Care Informatics, and Implementation of Predictive Pathways in Real Time

Conceptually, precision medicine is a deep dive to discover disease origin at the molecular or genetic level, thus providing insights that allow clinicians to design corresponding individualized patient therapies. We know that a disease state is created by not only certain molecular derangements but also a biologic milieu promoting the expression of such derangements. These factors together lead to manifested symptoms. At the level of molecular definition, every average, "similar" individual stands to be "dissimilar." Hence, there is the need for customized therapy, moving away from therapy based on aggregate statistics. The perioperative state is a mix of several, simultaneously active molecular mechanisms, surgical insult, drugs, severe inflammatory response, and the body's continuous adaptation to maintain a state of homeostasis. Postoperative outcomes are a net result of several of those rapid genetic and molecular transformations that do or do not ensue. With the advent and advances of artificial intelligence, the translation from identifying these intricate mechanisms to implementing them in clinical practice has made a huge leap. Precision medicine is gaining ground with the help of personalized health recorders and personal devices that identify disease mechanics, patient-reported outcomes, adverse drug reactions, and drug-drug interaction at the individual level in a closed-loop feedback system. This phenomenon is especially true given increasing surgeries in older adults, many of whom are on multiple medications and varyingly frail. In this era of precision medicine, to provide a comprehensive remedy, the perioperative surgical home must expand, incorporating not only clinicians but also basic science experts and data scientists.

The Use of Electronic Health Records to Study Drug-Induced Hypersensitivity Reactions from 2000 to 2021

Electronic health records (EHRs) have revolutionized the field of drug hypersensitivity reaction (DHR) research. In this systematic review, we assessed 140 articles from 2000-2021, classifying them under six themes: observational studies (n=61), clinical documentation (n=27), case management (n=22), clinical decision support (CDS) (n=18), case identification (n=9), and genetic studies (n=3). EHRs provide convenient access to millions of medical records, facilitating epidemiological studies of DHRs. Though the goal of CDS is to promote safe drug prescribing, allergy alerts must be designed and used in a way that supports this effort. Ultimately, accurate allergy documentation is essential for DHR prevention.