Doctor of Philosophy

dissertationPrenatal and early childhood exposures are potential risk factors for childhood cancers. Previous studies that have assessed these factors have relied on self-reports or are characterized by inconsistent findings. In addition, the available epidemiologic research investigating the association between childhood cancer and infections is sparse. The purpose of this dissertation was to investigate prenatal, maternal, birth, and parental birth factors that may be associated with childhood cancers using the Utah Population Database (UPDB). Furthermore, this dissertation also examined the potential relationship between infections and childhood leukemia, lymphoma, and central nervous system (CNS) tumors. A population-based case-control study design was utilized in investigating these relationships. Large-for-gestational age (LGA) birth weight was associated with an increased risk for childhood lymphoma (OR=1.59, 95%CI: 1.08 - 2.33). An overall dose response was observed between birth weight-for-gestational age (BWGA) and childhood CNS cancer risk (p=0.020). High birth weight newborns and childhood CNS cancer risk were associated with high birth weight parents (OR=3.11, 95% CI 1.02 - 9.55). Additionally, an overall dose response was observed between maternal BWGA and childhood CNS cancer risk (p=0.048). The risk of childhood lymphoma was associated with the following infection-related diagnosis groups: acute respiratory infections (OR=2.15, 95% CI: 1.39 - 3.31), pneumonia or influenza (OR=2.61, 95% CI: 1.10 - 6.18), and sweating iv fever (OR=3.56, 95% CI: 1.66 - 7.66). Dose response relationships were also observed between the number of infections and childhood lymphoma risk, including viral or respiratory disease (p=0.014). Furthermore, the risk of childhood lymphoma associated with sweating fever further increased in magnitude when limiting diagnoses to those that occurred within the first year of life (OR 6.53, 95% CI: 1.45 - 29.53). Having a tonsillectomy or adenoidectomy was associated with an increase in the risk of childhood leukemia (OR=2.57, 95% CI: 1.33 - 4.61). Finally, individuals who had undergone a myringotomy or tympanostomy were at a greater risk for childhood lymphoma (OR=3.90, 95% CI: 1.26 - 12.05). Our findings suggest that the prenatal growth environment and repeated exposure to common infections, particularly early in life, may play an important role in the etiology of childhood cancers

Models solely using claims-based administrative data are poor predictors of rheumatoid arthritis disease activity

Abstract Background This study developed and validated a claims-based statistical model to predict rheumatoid arthritis (RA) disease activity, measured by the 28-joint count Disease Activity Score (DAS28). Method Veterans enrolled in the Veterans Affairs Rheumatoid Arthritis (VARA) registry with one year of data available for review before being assessed by the DAS28, were studied. Three models were developed based on initial selection of variables for analyses. The first model was based on clinically defined variables, the second leveraged grouping systems for high dimensional data and the third approach prescreened all possible predictors based on a significant bivariate association with the DAS28. The least absolute shrinkage and selection operator (LASSO) with fivefold cross-validation was used for variable selection and model development. Models were also compared for patients with 5.1) activity. Results There were 1582 Veterans who fulfilled inclusion criteria. The adjusted r-square for the three models tested ranged from 0.221 to 0.223. The models performed slightly better for patients with <5 years of RA disease than for patients with ≥5 years of RA disease. Correct classification of DAS28 categories ranged from 39.9% to 40.5% for the three models. Conclusion The multiple models tested showed weak overall predictive accuracy in measuring DAS28. The models performed poorly at predicting patients with remission and high disease activity. Future research should investigate components of disease activity measures directly from medical records and incorporate additional laboratory and other clinical data

Usability Testing a Potentially Inappropriate Medication Dashboard: A Core Component of the Dashboard Development Process.

BACKGROUND: With the increased usage of dashboard reporting systems to monitor and track patient panels by clinical users, developers must ensure that the information displays they produce are accurate and intuitive. When evaluating usability of a clinical dashboard among potential end users, developers oftentimes rely on methods such as questionnaires as opposed to other, more time-intensive strategies that incorporate direct observation. OBJECTIVES: Prior to release of the potentially inappropriate medication (PIM) clinical dashboard, designed to facilitate completion of a quality improvement project by clinician scholars enrolled in the Veterans Affairs (VA) workforce development Geriatric Scholars Program (GSP), we evaluated the usability of the system. This article describes the process of usability testing a dashboard reporting system with clinicians using direct observation and think-aloud moderating techniques. METHODS: We developed a structured interview protocol that combines virtual observation, think-aloud moderating techniques, and retrospective questioning of the overall user experience, including use of the System Usability Scale (SUS). Thematic analysis was used to analyze field notes from the interviews of three GSP alumni. RESULTS: Our structured approach to usability testing identified specific functional problems with the dashboard reporting system that were missed by results from the SUS. Usability testing lead to overall improvements in the intuitive use of the system, increased data transparency, and clarification of the dashboards purpose. CONCLUSION: Reliance solely on questionnaires and surveys at the end stages of dashboard development can mask potential functional problems that will impede proper usage and lead to misinterpretation of results. A structured approach to usability testing in the developmental phase is an important tool for developers of clinician friendly systems for displaying easily digested information and tracking outcomes for the purpose of quality improvement

Developing the VA Geriatric Scholars Programs Clinical Dashboards Using the PDSA Framework for Quality Improvement.

BACKGROUND: Involving clinician end users in the development process of clinical dashboards is important to ensure that user needs are adequately met prior to releasing the dashboard for use. The challenge with following this approach is that clinician end users can undergo periodic turnover, meaning, the clinicians that played a role in the initial development process may not be the same individuals that use the dashboard in future. OBJECTIVES: Here, we summarize our Plan, Do, Study, Act (PDSA)-guided clinical dashboard development process for the VA Geriatric Scholars Program (GSP) and the value of continuous, iterative development. We summarize dashboard adaptations that resulted from two PDSA cycles of improvement for the potentially inappropriate medication dashboard (PIMD), one of many Geriatric Scholars clinical dashboards. We also present the evaluative performance of the PIMD. METHODS: Evaluation of the PIMD was performed using the system usability scale (SUS) and through review of user interaction logs. Routine end users that were Geriatric Scholars and had evidence of 5 or more dashboard views were invited to complete an electronic form that contained the 10-item SUS. RESULTS: The proportion of Geriatric Scholars that utilized the PIMD increased for each iterative dashboard version that was produced as a byproduct from feedback (31.0% in 2017 to 60.2% in 2019). The overall usability of the PIMD among routine users was found to be above average (SUS score: 75.2 [95% CI 70.5-79.8]) in comparison to the recommended standard of acceptability (SUS score: 68) CONCLUSION:  The solicitation of feedback during dashboard orientations led to iterative adaptations of the PIMD that broadened its intended use. The presented PDSA-guided process to clinical dashboard development for the VA GSP can serve as a valuable framework for development teams seeking to produce well-adopted and usable health information technology (IT) innovations

Predicting Survival in Veterans with Follicular Lymphoma Using Structured Electronic Health Record Information and Machine Learning

The most accurate prognostic approach for follicular lymphoma (FL), progression of disease at 24 months (POD24), requires two years’ observation after initiating first-line therapy (L1) to predict outcomes. We applied machine learning to structured electronic health record (EHR) data to predict individual survival at L1 initiation. We grouped 523 observations and 1933 variables from a nationwide cohort of FL patients diagnosed 2006–2014 in the Veterans Health Administration into traditionally used prognostic variables (“curated”), commonly measured labs (“labs”), and International Classification of Diseases diagnostic codes (“ICD”) sets. We compared performance of random survival forests (RSF) vs. traditional Cox model using four datasets: curated, curated + labs, curated + ICD, and curated + ICD + labs, also using Cox on curated + POD24. We evaluated variable importance and partial dependence plots with area under the receiver operating characteristic curve (AUC). RSF with curated + labs performed best, with mean AUC 0.73 (95% CI: 0.71–0.75). It approximated, but did not surpass, Cox with POD24 (mean AUC 0.74 [95% CI: 0.71–0.77]). RSF using EHR data achieved better performance than traditional prognostic variables, setting the foundation for the incorporation of our algorithm into the EHR. It also provides for possible future scenarios in which clinicians could be provided an EHR-based tool which approximates the predictive ability of the most accurate known indicator, using information available 24 months earlier

Treatment Patterns and Outcomes in a Nationwide Cohort of Older and Younger Veterans with Waldenström Macroglobulinemia, 2006–2019

Little is known about real-world treatment patterns and outcomes in Waldenström macroglobulinemia (WM) following the recent introduction of newer treatments, especially among older adults. We describe patterns of first-line (1 L) WM treatment in early (2006–2012) and modern (2013–2019) eras and report outcomes (overall response rate (ORR), overall survival (OS), progression-free survival (PFS), and adverse event (AE)-related discontinuation) in younger (≤70 years) and older (&gt;70 years) populations. We followed 166 younger and 152 older WM patients who received 1 L treatment between January 2006 and April 2019 in the Veterans Health Administration. Median follow-up was 43.5 months (range: 0.6–147.2 months). Compared to the early era, older patients in the modern era achieved improved ORRs (early: 63.8%, modern: 72.3%) and 41% lower risk of death/progression (hazard ratio (HR) for PFS: 0.59, 95% CI (confidence interval): 0.36–0.95), with little change in AE-related discontinuation between eras (HR: 0.82, 95% CI: 0.4–1.7). In younger patients, the AE-related discontinuation risk increased almost fourfold (HR: 3.9, 95% CI: 1.1–14), whereas treatment effects did not change between eras (HR for OS: 1.4, 95% CI: 0.66–2.8; HR for PFS: 1.1, 95% CI: 0.67–1.7). Marked improvements in survival among older adults accompanied a profound shift in 1 L treatment patterns for WM