[R03] Clinical Informatics of Xerostomia: Characterizing Comorbidities, Social Risk Factors, and Predicting Oral Health Outcomes Using Structured and Unstructured EHR Data

Ente: National Institute of Dental and Craniofacial Research

Scadenza: 2028-08-27

Importo max: 328.000 EUR

Paese: US

Descrizione

Project Summary Xerostomia (dry mouth) is a widespread and underrecognized oral health condition that affects an estimated 46% of adults in the United States and 22% globally, with the highest prevalence among older adults aged 50– 90. Etiologies include polypharmacy, autoimmune diseases such as Sjögren’s syndrome, prior head and neck radiation or chemotherapy, and other systemic conditions. Xerostomia significantly impairs oral function and quality of life, leading to poor oral hygiene, rampant dental caries, periodontal disease, tooth loss, fungal infections, halitosis, problems with denture retention or fit, and difficulty with chewing, swallowing, tasting, and speaking. Patients with xerostomia also experience 33% higher dental utilization and expenditures, highlighting its importance as a major dental public health burden. Despite its clinical and economic impact, a critical gap exists in our ability to identify xerostomia patients most at risk for severe oral health complications. Current diagnostic and research approaches rely heavily on structured data such as ICD codes, which have poor sensitivity for xerostomia. In practice, xerostomia is more often documented in unstructured clinical notes, making it largely invisible to traditional analytic methods. Recent advances in natural language processing (NLP) now enable the extraction of patient-reported symptoms and social risk factors from clinical text, providing new opportunities for more accurate case identification and comprehensive phenotyping. The objective of this R03 project is to develop and validate a novel EHR-based phenotype of xerostomia and use it to build a preliminary AI-driven risk prediction model of oral health complications. We will analyze a large real-world dataset from UCSF’s De-Identified Clinical Data Warehouse, which contains more than 4.5 million patients and 198 million clinical notes. Aim 1 will characterize xerostomia and related oral health outcomes using AI-enabled multimodal phenotyping that integrates structured data, clinical notes, and social factors. Aim 2 will develop and internally validate a preliminary risk prediction model using logistic regression and machine learning methods to estimate oral health complication risk over time. The expected outcomes of this project are: (1) a rigorously validated, scalable EHR phenotype of xerostomia, and (2) a preliminary, interpretable risk model for oral health complications. These deliverables will lay the groundwork for future R01/R21/R34 applications to design and test an EHR-integrated Clinical Decision Support (CDS) tool, with the ultimate goal of providing personalized prevention and treatment plans for xerostomia and related oral health complications, and advancing the quality of life of patients with xerostomia. Istituzione: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO PI: Sepideh Banava Progetto: 1R03DE036394-01

Settori: National Institute of Dental and Craniofacial Research

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