Mind the developmental gap: Identifying adverse drug effects across childhood to evaluate biological mechanisms from growth and development

Adverse drug reactions are a leading cause of morbidity and mortality that costs billions of dollars for the healthcare system. In children, there is increased risk for adverse drug reactions with potentially lasting adverse effects into adulthood. The current pediatric drug safety landscape, including clinical trials, is limited as it rarely includes children and relies on extrapolation from adults. Children are not small adults but go through an evolutionarily conserved and physiologically dynamic process of growth and maturation. We hypothesize that adverse drug reactions manifest from the interaction between drug exposure and dynamic biological processes during child growth and development. While pediatric pharmacologists have studied and recognized this interaction, the evidence from these studies have focused on a few, well-known drug toxicities largely within animal models that have limited translation to children and their clinical care. Moreover, preclinical studies during drug development do not consider growth and maturation of children, which severely limits our knowledge of drug safety in this population. Post-marketing pediatric drug safety studies, on the other hand, leverage large amounts of observations to identify and characterize adverse drug events in the pediatric population after drugs enter the market. However, these observational studies have been limited to event surveillance and have not focused on evaluating why adverse drug events may manifest in children. We hypothesize that by developing statistical methodologies with prior knowledge of dynamic, shared information during development, we can improve the detection of adverse drug events in children. We further hypothesize that detecting adverse drug events in this way also improves the evaluation of dynamic biological and physiological processes during child growth and development. In chapter 1, we described the pediatric drug safety landscape, dynamic processes from pediatric developmental biology, and motivation for a large-scale and data-driven approach to study the interaction between drug treatment and child development. In chapter 2, using drug event reports collected by the Food and Drug Administration (FDA), we evaluated statistical models for identifying temporal trends of adverse effects across childhood. We found the generalized additive model (GAM), as compared to a popular disproportionality method, show improved detection performance especially of rare pediatric adverse drug events. In chapter 3, we applied covariate-adjusted drug-event GAMs in a systematic way to develop a resource of nearly half a million adverse drug event (ADE) risk estimates across child development stages. We showed that not only do significant ADEs through childhood recapitulate dynamic organ and system maturation, but we also provide granular, development-specific risk for known pediatric drug effects that were previously unknown. Importantly, this approach facilitated the evaluation of dynamic biological processes, such as drug-metabolizer gene expression levels across childhood, that we observed coincided with dynamic risk of adverse drug effects. In chapter 4, we performed several case studies showing population-level evidence for well-known pediatric adverse drug reactions using our generated resource. In addition, we developed an accessible web portal, the Pediatric Drug Safety portal (PDSportal), to retrieve from our resource the population-level evidence of user-specified adverse drug events in the pediatric population across child development stages. In conclusion, we summarize three key research directions in data-driven pediatric drug safety research: quantifying child vs. adult drug safety profiles, predicting pre-clinical drug toxicity across childhood, and detecting genetic susceptibility of pediatric adverse drug events. Our results demonstrate that developing pediatric drug safety methods directly for children using data-driven approaches improves both identification and evaluation of adverse drug events during the period of child growth and development

Evaluating risk detection methods to uncover ontogenic-mediated adverse drug effect mechanisms in children

Background Identifying adverse drugs effects (ADEs) in children, overall and within pediatric age groups, is essential for preventing disability and death from marketed drugs. At the same time, however, detection is challenging due to dynamic biological processes during growth and maturation, called ontogeny, that alter pharmacokinetics and pharmacodynamics. As a result, methodologies in pediatric drug safety have been limited to event surveillance and have not focused on investigating adverse event mechanisms. There is an opportunity to identify drug event patterns within observational databases for evaluating ontogenic-mediated adverse event mechanisms. The first step of which is to establish statistical models that can identify temporal trends of adverse effects across childhood. Results Using simulation, we evaluated a population stratification method (the proportional reporting ratio or PRR) and a population modeling method (the generalized additive model or GAM) to identify and quantify ADE risk at varying reporting rates and dynamics. We found that GAMs showed improved performance over the PRR in detecting dynamic drug event reporting across child development stages. Moreover, GAMs exhibited normally distributed and robust ADE risk estimation at all development stages by sharing information across child development stages. Conclusions Our study underscores the opportunity for using population modeling techniques, which leverage drug event reporting across development stages, as biologically-inspired detection methods for evaluating ontogenic mechanisms

ROMOP: a light-weight R package for interfacing with OMOP-formatted electronic health record data.

Objectives:Electronic health record (EHR) data are increasingly used for biomedical discoveries. The nature of the data, however, requires expertise in both data science and EHR structure. The Observational Medical Out-comes Partnership (OMOP) common data model (CDM) standardizes the language and structure of EHR data to promote interoperability of EHR data for research. While the OMOP CDM is valuable and more attuned to research purposes, it still requires extensive domain knowledge to utilize effectively, potentially limiting more widespread adoption of EHR data for research and quality improvement. Materials and methods:We have created ROMOP: an R package for direct interfacing with EHR data in the OMOP CDM format. Results:ROMOP streamlines typical EHR-related data processes. Its functions include exploration of data types, extraction and summarization of patient clinical and demographic data, and patient searches using any CDM vocabulary concept. Conclusion:ROMOP is freely available under the Massachusetts Institute of Technology (MIT) license and can be obtained from GitHub (http://github.com/BenGlicksberg/ROMOP). We detail instructions for setup and use in the Supplementary Materials. Additionally, we provide a public sandbox server containing synthesized clinical data for users to explore OMOP data and ROMOP (http://romop.ucsf.edu)

PatientExploreR: an extensible application for dynamic visualization of patient clinical history from electronic health records in the OMOP common data model.

MotivationElectronic health records (EHRs) are quickly becoming omnipresent in healthcare, but interoperability issues and technical demands limit their use for biomedical and clinical research. Interactive and flexible software that interfaces directly with EHR data structured around a common data model (CDM) could accelerate more EHR-based research by making the data more accessible to researchers who lack computational expertise and/or domain knowledge.ResultsWe present PatientExploreR, an extensible application built on the R/Shiny framework that interfaces with a relational database of EHR data in the Observational Medical Outcomes Partnership CDM format. PatientExploreR produces patient-level interactive and dynamic reports and facilitates visualization of clinical data without any programming required. It allows researchers to easily construct and export patient cohorts from the EHR for analysis with other software. This application could enable easier exploration of patient-level data for physicians and researchers. PatientExploreR can incorporate EHR data from any institution that employs the CDM for users with approved access. The software code is free and open source under the MIT license, enabling institutions to install and users to expand and modify the application for their own purposes.Availability and implementationPatientExploreR can be freely obtained from GitHub: https://github.com/BenGlicksberg/PatientExploreR. We provide instructions for how researchers with approved access to their institutional EHR can use this package. We also release an open sandbox server of synthesized patient data for users without EHR access to explore: http://patientexplorer.ucsf.edu.Supplementary informationSupplementary data are available at Bioinformatics online

Adequate Sleep and Its Effect on Study Group Performance

poster, CAS 352/SG, fall 201

ROMOP: a light-weight R package for interfacing with OMOP-formatted electronic health record data.

ObjectivesElectronic health record (EHR) data are increasingly used for biomedical discoveries. The nature of the data, however, requires expertise in both data science and EHR structure. The Observational Medical Out-comes Partnership (OMOP) common data model (CDM) standardizes the language and structure of EHR data to promote interoperability of EHR data for research. While the OMOP CDM is valuable and more attuned to research purposes, it still requires extensive domain knowledge to utilize effectively, potentially limiting more widespread adoption of EHR data for research and quality improvement.Materials and methodsWe have created ROMOP: an R package for direct interfacing with EHR data in the OMOP CDM format.ResultsROMOP streamlines typical EHR-related data processes. Its functions include exploration of data types, extraction and summarization of patient clinical and demographic data, and patient searches using any CDM vocabulary concept.ConclusionROMOP is freely available under the Massachusetts Institute of Technology (MIT) license and can be obtained from GitHub (http://github.com/BenGlicksberg/ROMOP). We detail instructions for setup and use in the Supplementary Materials. Additionally, we provide a public sandbox server containing synthesized clinical data for users to explore OMOP data and ROMOP (http://romop.ucsf.edu)

Promoting Self-Determination for Better Health and Wellbeing for Adolescents who have an Intellectual Disability

The focus of this paper is on an Australian research project that evaluated the effectiveness of a resource called the Ask Health Diary, which is used in the school curriculum to promote self-determination for better health and well being for adolescents who have an intellectual disability. Education and health researchers used questionnaires and interviews to gather data from adolescents attending special schools and special education units located in secondary schools in south-east Queensland, their teachers and their parents/carers. This paper reports on two research questions: First, 'How did the teachers use the Ask Health Diary to promote self-determination in health?', and second, 'How did teachers, parents/carers and students perceive the benefits and value of the Ask Health Diary?' The findings indicate that the Ask Health Diary provides a sound curriculum framework for teachers, adolescents and parents/carers to work together to promote self-determination and better health outcomes for young people who have an intellectual disability