12:00 – 1:00pm
Research Journey: Health Services Research Studies in Women’s Health, Disabilities, Surgical Outcomes, and More – Leveraging Large Observational Data

Speaker: Neil Kamdar, MA

Large administrative claims, electronic medical records, and other large clinical and surgical registries possess a breadth of information about our patient population. While these data sources may not offer the depth that may be afforded through studies involving primary data collection, they represent an important and fundamental strength of large sample sizes and the ability to study vulnerable populations and/or rare diseases. I will go through studies involving several data sources and populations broken into thematic units: vulnerable populations (e.g. disabled and those with diseases of aging), women’s health, and surgical outcomes and quality. This talk is especially geared for those who wish to better understand the breadth of published studies that can be tackled using these data, the key findings, and illustrate some other new areas I have explored with these registries over the last several years. I will finally touch upon some of the new avenues for exploration using these data and the potential synergies between Sheps analytic data assets and resources with our community of researchers.

1:00 – 1:30pm
Follow up discussion

12:00 – 1:00pm
Long COVID Phenotypes Leveraging a Large Primary Care Electronic Medical Record Registry, the American Family Cohort

Speaker: Neil Kamdar, MA

Long COVID or Post-Acute Sequelae following initial SARS-COV-2 (COVID-19) has continued to be a challenge for diagnosing in the primary care setting. Various published studies have incorporated different approaches and methods which have defined some potential opportunities for classification of COVID-19 afflicted patients into relevant sub-groups based on their symptom presentation during the follow-up period. Using a large primary care registry, the American Family Cohort, I will walk through the key existing challenges for classification in the literature, and the incorporation of a latent class analysis approach to examine extant differences in racial composition, sex, and social deprivation index in group membership. Diagnostic phenotypes of potential Long COVID patients and their comparisons to likely influenza-like (ILI) controls will be explored and examined. This talk was presented as a Distinguished Paper at the National American Primary Care Research Group (NAPCRG) in November 2023.

1:00 – 1:30pm
Follow up discussion

12:00 – 1:00pm
Methods Session

An Application and Example of Glassbox Explainable Boosting Machines (EBMs) in a Surgical Cohort Registry

Speaker: Neil Kamdar, MA

Predictive models have become important for postoperative occurrences after surgery, such as readmissions, 30-day reoperation, and other complications. Frequently, the application of predictive models has an opaqueness which remains extraordinarily difficult to achieve stakeholder buy-in, which is usually hospital administrators, clinicians, and other policymakers. In this tutorial-based, mostly graph-based presentation with code snippets, a statement of the key issues surrounding prediction, a description of the sample data source, the research question in-progress, and model outputs including graphs and interaction term interpretations will be discussed using Explainable Boosting Machines (EBMs). Advantages of this modeling approach for these contexts will be discussed, with special attention to some use cases within the Sheps context focusing on surgical and other clinical outcomes.

1:00 – 1:30pm
Follow up discussion