Clinical Prediction Models for Hospital-Induced Delirium Using Structured and Unstructured Electronic Health Record Data: Protocol for a Development and Validation Study.

Autor:	Ser SE; Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, United States., Shear K; Department of Family, Community, and Health Systems Science, College of Nursing, University of Florida, Gainesville, FL, United States., Snigurska UA; Department of Family, Community, and Health Systems Science, College of Nursing, University of Florida, Gainesville, FL, United States., Prosperi M; Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, United States., Wu Y; Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States., Magoc T; Integrated Data Repository Research Services, University of Florida, Gainesville, FL, United States., Bjarnadottir RI; Department of Family, Community, and Health Systems Science, College of Nursing, University of Florida, Gainesville, FL, United States., Lucero RJ; Department of Family, Community, and Health Systems Science, College of Nursing, University of Florida, Gainesville, FL, United States.; School of Nursing, University of California Los Angeles, Los Angeles, CA, United States.
Jazyk:	angličtina
Zdroj:	JMIR research protocols [JMIR Res Protoc] 2023 Nov 09; Vol. 12, pp. e48521. Date of Electronic Publication: 2023 Nov 09.
DOI:	10.2196/48521
Abstrakt:	Background: Hospital-induced delirium is one of the most common and costly iatrogenic conditions, and its incidence is predicted to increase as the population of the United States ages. An academic and clinical interdisciplinary systems approach is needed to reduce the frequency and impact of hospital-induced delirium. Objective: The long-term goal of our research is to enhance the safety of hospitalized older adults by reducing iatrogenic conditions through an effective learning health system. In this study, we will develop models for predicting hospital-induced delirium. In order to accomplish this objective, we will create a computable phenotype for our outcome (hospital-induced delirium), design an expert-based traditional logistic regression model, leverage machine learning techniques to generate a model using structured data, and use machine learning and natural language processing to produce an integrated model with components from both structured data and text data. Methods: This study will explore text-based data, such as nursing notes, to improve the predictive capability of prognostic models for hospital-induced delirium. By using supervised and unsupervised text mining in addition to structured data, we will examine multiple types of information in electronic health record data to predict medical-surgical patient risk of developing delirium. Development and validation will be compliant to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement. Results: Work on this project will take place through March 2024. For this study, we will use data from approximately 332,230 encounters that occurred between January 2012 to May 2021. Findings from this project will be disseminated at scientific conferences and in peer-reviewed journals. Conclusions: Success in this study will yield a durable, high-performing research-data infrastructure that will process, extract, and analyze clinical text data in near real time. This model has the potential to be integrated into the electronic health record and provide point-of-care decision support to prevent harm and improve quality of care. International Registered Report Identifier (irrid): DERR1-10.2196/48521. (©Sarah E Ser, Kristen Shear, Urszula A Snigurska, Mattia Prosperi, Yonghui Wu, Tanja Magoc, Ragnhildur I Bjarnadottir, Robert J Lucero. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 09.11.2023.)
Databáze:	MEDLINE
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