Trends in Postpartum Hemorrhage Prevalence and Comorbidity Burden Among Women: Insights from the ENACT Network Aggregated Electronic Health Records Data

Presentation Time: 12:45 PM - 01:00 PM

Abstract Keywords: Clinical Decision Support for Translational/Data Science Interventions, Data-Driven Research and Discovery, Secondary Use of EHR Data, Reproducible Research Methods and Tools
Primary Track: Clinical Research Informatics
Programmatic Theme: Real-World Evidence in Informatics: Bridging the Gap between Research and Practice

The goal of this study was to assess trends in postpartum hemorrhage (PPH), its risk factors, and maternal comorbidity burden in the United States using aggregate data from the Evolve to Next-Gen Accrual of patients to Clinical Trials (ENACT) network obtained via Shared Health Research Information Network (SHRINE) and Integrating Biology & the Bedside (i2b2) tools. We conducted repeated annual cross-sectional analyses to evaluate PPH occurrence and comorbidities across various ethnoracial and sociodemographic groups from 2005 to 2022. During this time, there was a statistically significant increasing trend in the prevalence of PPH, rising from 5,634 to 10,504 PPH per 100,000 deliveries (Ptrend <0.001). Our findings revealed a continuous upward trend in PPH rates that remained consistent among individuals with ≥1 comorbid conditions (Ptrend <0.001) and those with ≥1 maternal risk factor (Ptrend <0.001). This result aligns with prior studies and extends beyond the time period previously reported. Overall, Native Hawaiian or Other Pacific Islander women had the highest PPH incidence (~13%), followed by Asian women (10%), American Indian or Alaska Native women (9%), and Black or African American women (8%). The top PPH risk factor identified was Placenta Previa or Accreta, while the top comorbidity was Antepartum Hemorrhage (APH)/Placental Abruption. The most common cause of PPH, namely uterine atony, was prevalent in ENACT data. Our analysis highlights significant ethnoracial disparities and underscores the need for targeted preventative interventions.
Speaker(s):
Olga Kravchenko, PhD
University of Pittsbugh

Author(s):
Malarkodi Samayamuthu, MD - University of Pittsbugh; Weihsuan Jenny Lo-Ciganic, PhD - University of Florida; Eugene Sadhu, MD - University of Pittsburgh; Seonkyeong Yang, PhD - University of Pittsburgh; Shyam Visweswaran, MD PhD - University of Pittsburgh; Vanathi Gopalakrishnan, PhD - University of Pittsburgh; Olga Kravchenko, PhD - University of Pittsbugh;

Sequential Data Mining Applications in the All of Us Research Program

Presentation Time: 01:00 PM - 01:15 PM

Abstract Keywords: Secondary Use of EHR Data, Data Mining and Knowledge Discovery, EHR-based Phenotyping, Data-Driven Research and Discovery
Primary Track: Clinical Research Informatics
Programmatic Theme: Real-World Evidence in Informatics: Bridging the Gap between Research and Practice

Electronic health record (EHR) data inherently possess a sequential nature, reflecting the chronological progression of patient care diagnoses, treatments, and health outcomes. We applied EHR temporal sequencing methods to the All of Us Research Program dataset. We highlight two exemplars of this approach from our own work related to identifying participants with multiple chronic conditions and mapping contraceptive prescriptions over time. Sequential arrangement of EHR data supports researchers in investigating a wide variety of questions.
Speaker(s):
Xintong Li, Bachelor of Science
University of Rochester

Author(s):
Caitlin Dreisbach, PhD, RN - University of Rochester; Xintong Li, Bachelor of Science - University of Rochester; Theresa Koleck, PhD, BSN, RN - University of Pittsburgh;

Relationship between COVID-19 Reinfections and Long COVID—An Observational Study from RECOVER and N3C

Presentation Time: 01:15 PM - 01:30 PM

Abstract Keywords: Secondary Use of EHR Data, EHR-based Phenotyping, Informatics Research/Biomedical Informatics Research Methods
Primary Track: Clinical Research Informatics
Programmatic Theme: Health Data Science and Artificial Intelligence Innovation: From Single-Center to Multi-Site

COVID-19 reinfections in the Omicron era have become commonplace, and while reinfections are often not severe, their impact on the risk of developing long COVID is not clear. Using observational health records from the National Covid Cohort Collaborative (N3C) for 174,917 reinfected individuals, and as many matched controls, we find that experiencing a reinfection significantly increases risk of LC (relative risk: 1.32, 95% CI 1.28-1.37).
Speaker(s):
Richard Moffitt, Ph.D.
Emory University

Author(s):
Daniel Brannock, MS - RTI International; Emily Hadley, MS - RTI International; Alexander Preiss, MS - RTI International; Megan Fitzgerald, PhD - Patient-Led Research Collaborative; Richard Moffitt, Ph.D. - Emory University;

Quantifying EHR Continuity in the All of Us Research Program

Presentation Time: 01:30 PM - 01:45 PM

Abstract Keywords: Data Quality, Fairness and Disparity Research in Health Informatics, Informatics Research/Biomedical Informatics Research Methods
Primary Track: Data Science/Artificial Intelligence
Programmatic Theme: Emerging Best Practices for Clinical Research Informatics Operations

Electronic Health Records (EHR) completeness is crucial for credible biomedical research. We adapted i2b2 loyalty score algorithm, EHR continuity and completeness score, for OMOP and applied it to the All of Us dataset, analyzing EHR data from 64 sites. We compared loyalty scores across sites, demographics, socioeconomic factors, and against conformance, plausibility, and completeness scores. Higher loyalty scores were found in white participants, those with incomes over 50K, and those with college education or higher.
Speaker(s):
Lina Sulieman, PhD
Vanderbilt University Medical Center

Author(s):
Xinzhuo Jiang, MS - Columbia University Department of Biomedical Informatics; Jason Patterson, BSc - Columbia University Irving Medical Center; Karthik Natarajan, PhD - Columbia University Dept of Biomedical Informatics;

Automating Reusable Data Fitness Assessments: Applications in PCORnet®

Presentation Time: 01:45 PM - 02:00 PM

Abstract Keywords: Data Quality, Reproducible Research Methods and Tools, Secondary Use of EHR Data
Primary Track: Clinical Research Informatics
Programmatic Theme: Implementation Science and Deployment in Informatics: Enabling Clinical and Translational Research

Secondary use of clinical data has been criticized because of data quality (DQ) issues that cause significant problems in conducting research, even for large multi-institutional networks with sophisticated programs that evaluate data quality systematically. We sought to address this gap between broad network DQ and analysis-specific DQ by developing a set of modules that systematically address data fitness, which we piloted in PCORnet using a test cohort definition of patients with Type 2 Diabetes. Using a previously developed framework, we specified and automated 12 DQ modules that can be applied to any study context at any point in the study lifecycle with simple configurations by the user. We distributed the modules to 17 network partners in PCORnet, of which 8 are primarily pediatric health care systems. The modules tested cohort attrition, quality of specialty data, density of patient clinical data, presence of expected variables over time, concept set distributions, and patient clinical event sequencing. Results illuminated significant differences in population, data available for study, and clinical behavior among network partners' data. In this work, we describe how general modules can be tailored to a study-specific use case. We further describe how these anomalies may impact research design decisions. Importantly, we modularized a range of data quality assessments that can be seamlessly integrated into feasibility queries across PCORnet and other multi-institutional networks.
Speaker(s):
Hanieh Razzaghi, PhD
Children's Hospital of Philadelphia

Author(s):
Kaleigh Wieand; Kimberley Dickinson - Children's Hospital of Philadelphia; Michael Kahn, MD - University of Colorado; Jason Roy, PhD - Rutgers University; Charles Bailey - Children's Hospital of Philadelphia;