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Access Type

Open Access Thesis

Document Type


Degree Program

Public Health

Degree Type

Master of Science (M.S.)

Year Degree Awarded


Month Degree Awarded



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.


First Advisor

Brian W. Whitcomb

Second Advisor

Paul Visintainer

Third Advisor

Mihaela Stefan