Application of a practice-based approach in variable selection for a prediction model development study of hospital-induced delirium.

Candidate predictor Delirium Expert judgment Practice-based approach Prediction model Variable selection

Journal

BMC medical informatics and decision making
ISSN: 1472-6947
Titre abrégé: BMC Med Inform Decis Mak
Pays: England
ID NLM: 101088682

Informations de publication

Date de publication:
13 09 2023
Historique:
received: 08 05 2023
accepted: 30 08 2023
medline: 15 9 2023
pubmed: 14 9 2023
entrez: 13 9 2023
Statut: epublish

Résumé

Prognostic models of hospital-induced delirium, that include potential predisposing and precipitating factors, may be used to identify vulnerable patients and inform the implementation of tailored preventive interventions. It is recommended that, in prediction model development studies, candidate predictors are selected on the basis of existing knowledge, including knowledge from clinical practice. The purpose of this article is to describe the process of identifying and operationalizing candidate predictors of hospital-induced delirium for application in a prediction model development study using a practice-based approach. This study is part of a larger, retrospective cohort study that is developing prognostic models of hospital-induced delirium for medical-surgical older adult patients using structured data from administrative and electronic health records. First, we conducted a review of the literature to identify clinical concepts that had been used as candidate predictors in prognostic model development-and-validation studies of hospital-induced delirium. Then, we consulted a multidisciplinary task force of nine members who independently judged whether each clinical concept was associated with hospital-induced delirium. Finally, we mapped the clinical concepts to the administrative and electronic health records and operationalized our candidate predictors. In the review of 34 studies, we identified 504 unique clinical concepts. Two-thirds of the clinical concepts (337/504) were used as candidate predictors only once. The most common clinical concepts included age (31/34), sex (29/34), and alcohol use (22/34). 96% of the clinical concepts (484/504) were judged to be associated with the development of hospital-induced delirium by at least two members of the task force. All of the task force members agreed that 47 or 9% of the 504 clinical concepts were associated with hospital-induced delirium. Heterogeneity among candidate predictors of hospital-induced delirium in the literature suggests a still evolving list of factors that contribute to the development of this complex phenomenon. We demonstrated a practice-based approach to variable selection for our model development study of hospital-induced delirium. Expert judgement of variables enabled us to categorize the variables based on the amount of agreement among the experts and plan for the development of different models, including an expert-model and data-driven model.

Sections du résumé

BACKGROUND
Prognostic models of hospital-induced delirium, that include potential predisposing and precipitating factors, may be used to identify vulnerable patients and inform the implementation of tailored preventive interventions. It is recommended that, in prediction model development studies, candidate predictors are selected on the basis of existing knowledge, including knowledge from clinical practice. The purpose of this article is to describe the process of identifying and operationalizing candidate predictors of hospital-induced delirium for application in a prediction model development study using a practice-based approach.
METHODS
This study is part of a larger, retrospective cohort study that is developing prognostic models of hospital-induced delirium for medical-surgical older adult patients using structured data from administrative and electronic health records. First, we conducted a review of the literature to identify clinical concepts that had been used as candidate predictors in prognostic model development-and-validation studies of hospital-induced delirium. Then, we consulted a multidisciplinary task force of nine members who independently judged whether each clinical concept was associated with hospital-induced delirium. Finally, we mapped the clinical concepts to the administrative and electronic health records and operationalized our candidate predictors.
RESULTS
In the review of 34 studies, we identified 504 unique clinical concepts. Two-thirds of the clinical concepts (337/504) were used as candidate predictors only once. The most common clinical concepts included age (31/34), sex (29/34), and alcohol use (22/34). 96% of the clinical concepts (484/504) were judged to be associated with the development of hospital-induced delirium by at least two members of the task force. All of the task force members agreed that 47 or 9% of the 504 clinical concepts were associated with hospital-induced delirium.
CONCLUSIONS
Heterogeneity among candidate predictors of hospital-induced delirium in the literature suggests a still evolving list of factors that contribute to the development of this complex phenomenon. We demonstrated a practice-based approach to variable selection for our model development study of hospital-induced delirium. Expert judgement of variables enabled us to categorize the variables based on the amount of agreement among the experts and plan for the development of different models, including an expert-model and data-driven model.

Identifiants

pubmed: 37704994
doi: 10.1186/s12911-023-02278-1
pii: 10.1186/s12911-023-02278-1
pmc: PMC10500854
doi:

Types de publication

Review Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

181

Subventions

Organisme : NIA NIH HHS
ID : R33 AG062884
Pays : United States

Informations de copyright

© 2023. BioMed Central Ltd., part of Springer Nature.

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Auteurs

Urszula A Snigurska (UA)

College of Nursing, Department of Family, Community, and Health Systems Science, University of Florida, 1225 Center Drive, PO Box 100197, Gainesville, FL, 32610, United States of America. usnigurska@ufl.edu.

Sarah E Ser (SE)

College of Public Health and Health Professions & College of Medicine, Department of Epidemiology, University of Florida, 2004 Mowry Rd, Gainesville, FL, 32610, United States of America.

Laurence M Solberg (LM)

College of Nursing, Department of Family, Community, and Health Systems Science, University of Florida, 1225 Center Drive, PO Box 100197, Gainesville, FL, 32610, United States of America.
Geriatrics Research, Education, and Clinical Center (GRECC), North Florida/South Georgia Veterans Health System, 1601 SW Archer Rd, Gainesville, FL, 32608, United States of America.
College of Medicine, University of Central Florida, 6850 Lake Nona Blvd, Orlando, FL, 32827, United States of America.

Mattia Prosperi (M)

College of Public Health and Health Professions & College of Medicine, Department of Epidemiology, University of Florida, 2004 Mowry Rd, Gainesville, FL, 32610, United States of America.

Tanja Magoc (T)

Clinical and Translational Science Institute (CTSI), Integrated Data Repository Research Services, University of Florida, 3300 SW Williston Rd, Gainesville, FL, 32608, United States of America.

Zhaoyi Chen (Z)

College of Medicine, Department of Health Outcomes & Biomedical Informatics, University of Florida, 2004 Mowry Rd, Gainesville, FL, 32610, United States of America.

Jiang Bian (J)

College of Medicine, Department of Health Outcomes & Biomedical Informatics, University of Florida, 2004 Mowry Rd, Gainesville, FL, 32610, United States of America.

Ragnhildur I Bjarnadottir (RI)

College of Nursing, Department of Family, Community, and Health Systems Science, University of Florida, 1225 Center Drive, PO Box 100197, Gainesville, FL, 32610, United States of America.

Robert J Lucero (RJ)

College of Nursing, Department of Family, Community, and Health Systems Science, University of Florida, 1225 Center Drive, PO Box 100197, Gainesville, FL, 32610, United States of America.
School of Nursing, University of California Los Angeles, 700 Tiverton Ave, Los Angeles, CA, 90095, United States of America.

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