Cohort Identification Using Semantic Web Technologies: Ontologies and Triplestores as Engines for Complex Computable Phenotyping

Electronic health record (EHR)-based computable phenotypes are algorithms used to identify individuals or populations with clinical conditions or events of interest within a clinical data repository. Due to a lack of EHR data standardization, computable phenotypes can be semantically ambiguous and difficult to share across institutions. In this research, I propose a new computable phenotyping methodological framework based on semantic web technologies, specifically ontologies, the Resource Description Framework (RDF) data format, triplestores, and Web Ontology Language (OWL) reasoning. My hypothesis is that storing and analyzing clinical data using these technologies can begin to address the critical issues of semantic ambiguity and lack of interoperability in the context of computable phenotyping. To test this hypothesis, I compared the performance of two variants of two computable phenotypes (for depression and rheumatoid arthritis, respectively). The first variant of each phenotype used a list of ICD-10-CM codes to define the condition; the second variant used ontology concepts from SNOMED and the Human Phenotype Ontology (HPO). After executing each variant of each phenotype against a clinical data repository, I compared the patients matched in each case to see where the different variants overlapped and diverged. Both the ontologies and the clinical data were stored in an RDF triplestore to allow me to assess the interoperability advantages of the RDF format for clinical data. All tested methods successfully identified cohorts in the data store, with differing rates of overlap and divergence between variants. Depending on the phenotyping use case, SNOMED and HPO’s ability to more broadly define many conditions due to complex relationships between their concepts may be seen as an advantage or a disadvantage. I also found that RDF triplestores do indeed provide interoperability advantages, despite being far less commonly used in clinical data applications than relational databases. Despite the fact that these methods and technologies are not “one-size-fits-all,” the experimental results are encouraging enough for them to (1) be put into practice in combination with existing phenotyping methods or (2) be used on their own for particularly well-suited use cases.Doctor of Philosoph

The Lasting Effects of Learning Communities

A majority of the research on the impact of learning communities has focused on the positive outcomes for students in their first year of study (Andrade, 2007; Goldman, 2012; Laverick, 2018; Wathington, Pretlow, & Mitchell, 2010). Less is known about the impact of learning community involvement as students complete their enrollment and persist through their next three (or more) years of education. Recent studies have addressed learning community involvement using qualitative measures. This article adds to the literature on learning community impact by describing an investigation of how juniors and seniors characterize the influence of their first-year learning community participation. Findings from the study illuminated the importance of faculty involvement and preparation, the use of High-Impact Practices (HIPS), and ways we might attend to peer dynamics in our learning community classrooms. The practice of following students to determine the possible lasting effects of learning communities has informed our work, and we argue that this practice should be included in learning community program assessment

The Effect of Computer and Internet Attitudes and Anxiety on e-Health Search Behaviors

While the need for health information is a seemingly universal concept, comfort using computers and the Internet is not. Yet studies have shown that users within a large range of years of computer experience search the Internet for health information (“e-health information”) at ever-increasing rates. The purpose of the current study is to discover how a searcher’s attitudes toward and self-perception of their computer and Internet competence affect his or her e-health information-seeking behaviors. An online survey was distributed with questions that served to measure participants’ computer and Internet anxiety, as well as questions pertaining to their e-health attitudes and search behaviors. Participants’ anxiety levels had a statistically significant effect on participants’ (1) feeling that their e-health searches are generally successful (or unsuccessful), (2) satisfaction with the information obtained, and (3) tendency to share e-health information with a health care provider

FDTool: a Python application to mine for functional dependencies and candidate keys in tabular data [version 1; peer review: 2 approved]

Functional dependencies (FDs) and candidate keys are essential for table decomposition, database normalization, and data cleansing. In this paper, we present FDTool, a command line Python application to discover minimal FDs in tabular datasets and infer equivalent attribute sets and candidate keys from them. The runtime and memory costs associated with seven published FD discovery algorithms are given with an overview of their theoretical foundations. We conclude that FD_Mine is the most efficient FD discovery algorithm when applied to datasets with many rows (> 100,000 rows) and few columns (< 14 columns). This puts it in a special position to rule mine clinical and demographic datasets, which often consist of long and narrow sets of participant records. The structure of FD Mine is described and supplemented with a formal proof of the equivalence pruning method used. FDTool is a re-implementation of FD Mine with additional features added to improve performance and automate typical processes in database architecture. The experimental results of applying FDTool to 12 datasets of different dimensions are summarized in terms of the number of FDs checked, the number of FDs found, and the time it takes for the code to terminate. We find that the number of attributes in a dataset has a much greater effect on the runtime and memory costs of FDTool than does row count. The last section explains in detail how the FDTool application can be accessed, executed, and further developed

Clinical Data: Sources and Types, Regulatory Constraints, Applications.

Access to clinical data is critical for the advancement of translational research. However, the numerous regulations and policies that surround the use of clinical data, although critical to ensure patient privacy and protect against misuse, often present challenges to data access and sharing. In this article, we provide an overview of clinical data types and associated regulatory constraints and inferential limitations. We highlight several novel approaches that our team has developed for openly exposing clinical data

Leveraging Open Electronic Health Record Data and Environmental Exposures Data to Derive Insights Into Rare Pulmonary Disease

Research on rare diseases has received increasing attention, in part due to the realized profitability of orphan drugs. Biomedical informatics holds promise in accelerating translational research on rare disease, yet challenges remain, including the lack of diagnostic codes for rare diseases and privacy concerns that prevent research access to electronic health records when few patients exist. The Integrated Clinical and Environmental Exposures Service (ICEES) provides regulatory-compliant open access to electronic health record data that have been integrated with environmental exposures data, as well as analytic tools to explore the integrated data. We describe a proof-of-concept application of ICEES to examine demographics, clinical characteristics, environmental exposures, and health outcomes among a cohort of patients enriched for phenotypes associated with cystic fibrosis (CF), idiopathic bronchiectasis (IB), and primary ciliary dyskinesia (PCD). We then focus on a subset of patients with CF, leveraging the availability of a diagnostic code for CF and serving as a benchmark for our development work. We use ICEES to examine select demographics, co-diagnoses, and environmental exposures that may contribute to poor health outcomes among patients with CF, defined as emergency department or inpatient visits for respiratory issues. We replicate current understanding of the pathogenesis and clinical manifestations of CF by identifying co-diagnoses of asthma, chronic nasal congestion, cough, middle ear disease, and pneumonia as factors that differentiate patients with poor health outcomes from those with better health outcomes. We conclude by discussing our preliminary findings in relation to other published work, the strengths and limitations of our approach, and our future directions

Mapping the Human Memory B Cell and Serum Neutralizing Antibody Responses to Dengue Virus Serotype 4 Infection and Vaccination

ABSTRACT The four dengue virus (DENV) serotypes are mosquito-borne flaviviruses responsible for dengue fever and dengue hemorrhagic fever. People exposed to DENV develop antibodies (Abs) that strongly neutralize the serotype responsible for infection. Historically, infection with DENV serotype 4 (DENV4) has been less common and less studied than infections with the other three serotypes. However, DENV4 has been responsible for recent large and sustained epidemics in Asia and Latin America. The neutralizing antibody responses and the epitopes targeted against DENV4 have not been characterized in human infection. In this study, we mapped and characterized epitopes on DENV4 recognized by neutralizing antibodies in people previously exposed to DENV4 infections or to a live attenuated DENV4 vaccine. To study the fine specificity of DENV4 neutralizing human antibodies, B cells from two people exposed to DENV4 were immortalized and screened to identify DENV-specific clones. Two human monoclonal antibodies (MAbs) that neutralized DENV4 were isolated, and their epitopes were finely mapped using recombinant viruses and alanine scan mutation array techniques. Both antibodies bound to quaternary structure epitopes near the hinge region between envelope protein domain I (EDI) and EDII. In parallel, to characterize the serum neutralizing antibody responses, convalescence-phase serum samples from people previously exposed to primary DENV4 natural infections or a monovalent DENV4 vaccine were analyzed. Natural infection and vaccination also induced serum-neutralizing antibodies that targeted similar epitope domains at the EDI/II hinge region. These studies defined a target of neutralizing antigenic site on DENV4 targeted by human antibodies following natural infection or vaccination. IMPORTANCE The four serotypes of dengue virus are the causative agents of dengue fever and dengue hemorrhagic fever. People exposed to primary DENV infections develop long-term neutralizing antibody responses, but these principally recognize only the infecting serotype. An effective vaccine against dengue should elicit long-lasting protective antibody responses to all four serotypes simultaneously. We and others have defined antigenic sites on the envelope (E) protein of viruses of dengue virus serotypes 1, 2, and 3 targeted by human neutralizing antibodies. The epitopes on DENV4 E protein targeted by the human neutralizing antibodies and the mechanisms of serotype 4 neutralization are poorly understood. Here, we report the properties of human antibodies that neutralize dengue virus serotype 4. People exposed to serotype 4 infections or a live attenuated serotype 4 vaccine developed neutralizing antibodies that bound to similar sites on the viral E protein. These studies have provided a foundation for developing and evaluating DENV4 vaccines

Alloimmunization is associated with older age of transfused red blood cells in sickle cell disease

Red blood cell (RBC) alloimmunization is a significant clinical complication of sickle cell disease (SCD). It can lead to difficulty with cross-matching for future transfusions and may sometimes trigger life-threatening delayed hemolytic transfusion reactions. We conducted a retrospective study to explore the association of clinical complications and age of RBC with alloimmunization in patients with SCD followed at a single institution from 2005 to 2012. One hundred and sixty six patients with a total of 488 RBC transfusions were evaluated. Nineteen patients (11%) developed new alloantibodies following blood transfusions during the period of review. The median age of RBC units was 20 days (interquartile range: 14-27 days). RBC antibody formation was significantly associated with the age of RBC units (P = 0.002), with a hazard ratio of 3.5 (95% CI: 1.71-7.11) for a RBC unit that was 7 days old and 9.8 (95% CI: 2.66-35.97) for a unit that was 35 days old, 28 days after the blood transfusion. No association was observed between RBC alloimmunization and acute vaso-occlusive complications. Although increased echocardiography-derived tricuspid regurgitant jet velocity (TRV) was associated with the presence of RBC alloantibodies (P = 0.02), TRV was not significantly associated with alloimmunization when adjusted for patient age and number of transfused RBC units. Our study suggests that RBC antibody formation is significantly associated with older age of RBCs at the time of transfusion. Prospective studies in patients with SCD are required to confirm this finding

Pre-existing autoimmunity is associated with increased severity of COVID-19: A retrospective cohort study using data from the National COVID Cohort Collaborative (N3C)

Identifying individuals with a higher risk of developing severe COVID-19 outcomes will inform targeted or more intensive clinical monitoring and management. To date, there is mixed evidence regarding the impact of pre-existing autoimmune disease (AID) diagnosis and/or immunosuppressant (IS) exposure on developing severe COVID-19 outcomes.A retrospective cohort of adults diagnosed with COVID-19 was created in the National COVID Cohort Collaborative enclave. Two outcomes, life-threatening disease, and hospitalization were evaluated by using logistic regression models with and without adjustment for demographics and comorbidities.Of the 2,453,799 adults diagnosed with COVID-19, 191,520 (7.81%) had a pre-existing AID diagnosis and 278,095 (11.33%) had a pre-existing IS exposure. Logistic regression models adjusted for demographics and comorbidities demonstrated that individuals with a pre-existing AID (OR = 1.13, 95% CI 1.09 - 1.17; P&lt; 0.001), IS (OR= 1.27, 95% CI 1.24 - 1.30; P&lt; 0.001), or both (OR = 1.35, 95% CI 1.29 - 1.40; P&lt; 0.001) were more likely to have a life-threatening COVID-19 disease. These results were consistent when evaluating hospitalization. A sensitivity analysis evaluating specific IS revealed that TNF inhibitors were protective against life-threatening disease (OR = 0.80, 95% CI 0.66- 0.96; P=0.017) and hospitalization (OR = 0.80, 95% CI 0.73 - 0.89; P&lt; 0.001).Patients with pre-existing AID, exposure to IS, or both are more likely to have a life-threatening disease or hospitalization. These patients may thus require tailored monitoring and preventative measures to minimize negative consequences of COVID-19

An efficient approach for surveillance of childhood diabetes by type derived from electronic health record data: the SEARCH for Diabetes in Youth Study

Objective To develop an efficient surveillance approach for childhood diabetes by type across 2 large US health care systems, using phenotyping algorithms derived from electronic health record (EHR) data