External Laboratory Data Integration and Impact: Hepatitis C Registry and Care Continuum Classification Before and After

Presentation Time: 08:30 AM - 08:45 AM

Abstract Keywords: Interoperability and Health Information Exchange, Infectious Diseases and Epidemiology, Clinical Decision Support, Population Health
Primary Track: Applications
Programmatic Theme: Public Health Informatics

To meet the upcoming New York mandate on universal HCV screening and to improve HCV interoperability, we discretely mapped 342,598 HCV diagnostic tests into a homegrown HCV patient registry and created a novel HCV care continuum classification system. With more complete and accurate HCV testing data available at the point of care, we found a substantial increase in patients identified as tested or cured for HCV, reducing unnecessary testing and prioritizing patient outreach.

Speaker(s):
Zeyu Li, MPH
NYC Health + Hospitals

Author(s):
Kruti Gala, Masters of Public Health - NYC Health + Hospitals; Emma Kaplan-Lewis, MD - NYC Health + Hospitals; Eunice Casey, MPH, MIA - NYC Health + Hospitals; Steve Park, NA - New York Medical College; Gabriel Cohen, MD - NYC Health + Hospitals;

Automated Data Collection Tool for Real-World Cohort Studies of Chronic Hepatitis B Virus Infection: Leveraging AI and Large Language Model (LLM) Technologies for Improved Efficiency

Presentation Time: 08:45 AM - 09:00 AM

Abstract Keywords: Global Health, Large Language Models (LLMs), Interoperability and Health Information Exchange, Clinical Decision Support
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics

This study aimed to develop a patient screening and data collection tool based on AI-LLMs for real-world retrospective studies on chronic hepatitis B patients. The tool assisted researchers in quickly identifying patient information that met the study requirements from one hospital system from Southern China, while also assisting with processing unstructured EMR data. The introduction of AI-LLMs contributed to increased efficiency and accuracy to extract key information from a large amount of unstructured data.

Speaker(s):
Jaime Smith, PhD
Parexel International, LLC

Author(s):
Xiaomei Zhou, MS - Information center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Tao Zeng, MD - Department of Infectious Diseases, The Third Affiliated Hospital of Sun Yat-sen University, Guangdong Key Laboratory of Liver Disease, Guangzhou, China;Department of Infectious Diseases, The First People’s Hospital of Kashi Prefecture, Kashi, China; Yingying Liao, MS - Department of Infectious Diseases, The Third Affiliated Hospital of Sun Yat-sen University, Guangdong Key Laboratory of Liver Disease, Guangzhou, China; Yibo Zhang, MS - Department of Infectious Diseases, The Third Affiliated Hospital of Sun Yat-sen University, Guangdong Key Laboratory of Liver Disease, Guangzhou, China; Lin Zhang, MD, PhD - PAREXEL China Co., Ltd. Shanghai, China; Chao Wang, MS - PAREXEL China Co., Ltd. Shanghai, China; Xia Jin, PhD - PAREXEL China Co., Ltd. Shanghai, China; Qinghai Li, MS - KingPoint Data Technology Co., Ltd. Guangzhou, China.; Dongbo Wu, MD - West China Hospital of Sichuan University, Chengdu, China.; Yutian Chong, MD - Department of Infectious Diseases, The Third Affiliated Hospital of Sun Yat-sen University, Guangdong Key Laboratory of Liver Disease, Guangzhou, China; Xinhua Li, Xinhua Li - Department of Infectious Diseases, The Third Affiliated Hospital of Sun Yat-sen University, Guangdong Key Laboratory of Liver Disease, Guangzhou, China;

Antimicrobial Resistance Patterns in an Urban County: a Spatiotemporal Exploration

Presentation Time: 09:00 AM - 09:15 AM

Abstract Keywords: Health Equity, Infectious Diseases and Epidemiology, Population Health
Primary Track: Applications
Programmatic Theme: Public Health Informatics

The Center for Disease Control has raised national alarm over five Antimicrobial Resistant Organisms (AMROs) considered urgent or serious threats to public safety. Understanding the prevalence and distribution of AMROs at a local level can inform the unique infection risks facing our communities. We conducted a retrospective, spatiotemporal analysis of AMRO prevalence across Tarrant County, Texas from 2010-2019. Using spatial autocorrelation tests, we identified that across the five AMRO subtypes, the Western half of Tarrant County experienced more hotspots than the Eastern half. Our Space-Time Permutation Models identified 35 unique AMRO clusters. Using bivariate logistic regression models, we found significant associations between Area Deprivation Index, a measure of socioeconomic disparity, and most AMRO clusters. These findings underscore the critical influence of socioeconomic factors on health outcomes and highlight the necessity for targeted public health interventions to address these disparities.

Speaker(s):
Tanvi Ingle, BS
UT Southwestern Medical Center

Author(s):
Lauren Cooper, MS - University of Texas Southwestern Medical Center; Alaina Beauchamp, PhD, MPH - University of Texas Southwestern Medical Center; Abdi Wakene, BS - University of Texas Southwestern Medical Center; Christoph Lehmann, MD, FAAP, FACMI, FIAHSI - UT Southwestern; Richard Medford, MD - UT Southwestern Medical Center;

Automated Stop Orders to Reduce Catheter Associated Urinary Tract Infections

Presentation Time: 09:15 AM - 09:30 AM

Abstract Keywords: Clinical Decision Support, Infectious Diseases and Epidemiology, Patient Safety
Primary Track: Applications
Programmatic Theme: Clinical Informatics

A multicenter randomized controlled trial was conducted to assess the efficacy stop orders as part of clinical decision support to reduce catheter associated urinary tract infections (CAUTIs). Patient encounters randomized to automated stop orders in the electronic health record had a reduction in urinary catheter dwell time and lower risk of developing CAUTI over time.

Speaker(s):
Courtney Diamond, MA, MPhil
Columbia University

Author(s):
Danielle Carter, MD, MS - Columbia University; Donald Dietz, MD, MS - Weill Cornell Medical Center; Gregory Hruby, PhD - Mount Sinai Health System; Shing Lee, PhD - Columbia Mailman School of Public Health; Min Qian, PhD - Columbia Mailman School of Public Health; Jacqueline Gaston-Kim, BS - NewYork-Presbyterian Hospital; Jo R. Applebaum, MPH - Columbia University Medical Center; Salvatore Crusco, MD - NewYork-Presbyterian Hospital; Jiayao Sun, MS - Columbia Mailman School of Public Health; Benjamin Ranard, MD, MSHP - Columbia University Medical Center; David Calfee, MD, MS - Weill Cornell Medical Center; Yoko Furuya, MD - Columbia University Medical Center; Jason Adelman, MD, MS - Columbia University Medical Center;

Emulation of a Target Trial to Estimate the Effect of Selective Serotonin Reuptake Inhibitors on the Development of Antimicrobial-Resistant Infections using Electronic Health Record Data and Causal Machine Learning

Presentation Time: 09:30 AM - 09:45 AM

Abstract Keywords: Infectious Diseases and Epidemiology, Causal Inference, Machine Learning
Primary Track: Applications

Antimicrobial resistance is a significant public health concern. Selective serotonin reuptake inhibitors (SSRIs) are medications commonly prescribed to treat depression, anxiety, and other psychiatric disorders; their use is increasing. Previous in vitro studies have shown that bacteria can become resistant to antibiotics when exposed to SSRIs. In this study, we emulated a target trial to estimate the effect of SSRI usage on the incidence of antibiotic-resistant infection. Our study population consisted of patients with mood, anxiety, or stress-related disorders, and a record of previous antimicrobial susceptibility testing or diagnosis of bacterial infection. Univariable, multivariable survival regression, and causal survival forest analyses all showed that patients treated with SSRIs had a higher risk of developing an antibiotic-resistant infection than those not treated with SSRIs. This study confirms the in vitro findings and may provide insights for future studies exploring the relationship of treatment with SSRIs and subsequent antibiotic-resistant infection.

Speaker(s):
Sarah Ser, MS
University of Florida

Author(s):
Sarah Ser, MS - University of Florida; Urszula Snigurska, BSN, RN - University of Florida College of Nursing; Scott Cohen, MPH - University of Florida; Inyoung Jun - The University of Florida; Ragnhildur Bjarnadottir, MPH, PhD, RN - University of Florida; Robert Lucero, PhD, MPH, RN, FAAN, FACMI - University of California, Los Angeles; Simone Marini, PhD - University of Florida; Jiang Bian, PhD - University of Florida; Mattia Prosperi, PhD, FAMIA - University of Florida;

Enhancing Antibiotic Stewardship: A Machine Learning Approach to Predicting Antibiotic Resistance in Inpatient Care

Presentation Time: 09:45 AM - 10:00 AM

Abstract Keywords: Precision Medicine, Machine Learning, Infectious Diseases and Epidemiology, Population Health, Evaluation
Primary Track: Applications

Antibiotics have been crucial in advancing medical treatments, but the growing threat of antibiotic resistance challenges these achievements and emphasizes the need for innovative stewardship strategies. In this study, we developed machine learning models, 'personalized antibiograms', to predict antibiotic resistance across five key antibiotics using Stanford’s electronic health record data of 49,872 urine, blood, and respiratory infections. We aimed to ascertain the efficacy of these models in predicting antibiotic susceptibility and identify the clinical factors most indicative of resistance. Employing LightGBM, we incorporated demographics, prior resistance, prescriptions, and comorbidities as features. The models demonstrated notable discriminative ability, with AUROCs between 0.74 and 0.78, and highlighted prior resistance and prescriptions as significant predictive factors. The high specificity demonstrates machine learning models’ potential to inform antibiotic de-escalation, aiding stewardship without risking safety. By leveraging machine learning with relevant clinical features, we show that it is feasible to improve empirical antibiotic prescribing.

Speaker(s):
Fateme Nateghi Haredasht, PhD
Stanford University

Author(s):
Fateme Nateghi Haredasht, PhD - Stanford University;