Off-campus UMass Amherst users: To download campus access dissertations, please use the following link to log into our proxy server with your UMass Amherst user name and password.
Non-UMass Amherst users: Please talk to your librarian about requesting this dissertation through interlibrary loan.
Dissertations that have an embargo placed on them will not be available to anyone until the embargo expires.
ORCID
https://orcid.org/0000-0002-4023-5926
Access Type
Open Access Thesis
Document Type
thesis
Degree Program
Public Health
Degree Type
Master of Science (M.S.)
Year Degree Awarded
2022
Month Degree Awarded
May
Abstract
Frailty is a multifaceted, geriatric syndrome that is associated with age-related declines in functional reserves resulting in increased risks of in-hospital death, readmissions and discharge to nursing homes. The risks associated with frailty highlights the need for providers to be able to quickly, and accurately, assess someone’s frailty level. Previous studies have shown that bedside clinician assessment is not a reliable or valid way to determine frailty, meaning that a more reliable, valid and concise method is needed. We developed a prediction model using discharge ICD-9/ICD-10 diagnostic codes and other demographic variables to predict Reported Edmonton Frail Scale scores. Participants were from the Baystate Frailty Study, a prospective cohort design study among elderly patients greater than 65 years old who were admitted to a single academic medical center between 2014 and 2016. Three different predictive models were completed utilizing the LASSO approach. The adjusted r-square increased across the three models indicating an increase in the predictive ability of the models. In this study of 762 hospitalized patients over the age of 65 years old, we found that a frailty prediction model that included ICD codes only had a poor prediction ability (adjusted r-square=0.10). The prediction ability improved 2-fold after adding demographic information, a comorbidity score and interaction terms (adjusted r-square=0.26). This study provided additional insights into the development of an automatic frailty assessment, something which is currently missing from clinical care.
DOI
https://doi.org/10.7275/28355453
First Advisor
Brian W. Whitcomb
Second Advisor
Paul Visintainer
Third Advisor
Mihaela Stefan
Recommended Citation
Poronsky, Kye, "Development of a Predictive Model for Frailty Utilizing Electronic Health Records" (2022). Masters Theses. 1213.
https://doi.org/10.7275/28355453
https://scholarworks.umass.edu/masters_theses_2/1213