Scoring the Clinical Frailty Scale in the Emergency Department: The Home FIRsT Experience

Owen Thorpe, Elva McCabe, Elena Marie Herrero, William Ormiston Doyle, Aoife Dillon, Lucinda Edge, Sinéad Flynn, Anna Mullen, Aisling Davis, Aoife Molamphy, Anna Kirwan, Robert Briggs, Amanda H Lavan, Darragh Shields, Geraldine McMahon, Arthur Hennessy, Una Kennedy, Paul Staunton, Emer Kidney, Sarah-Jane Yeung, Deirdre Glynn, Frances Horgan, Conal Cunningham, Roman Romero-Ortuno, Owen Thorpe, Elva McCabe, Elena Marie Herrero, William Ormiston Doyle, Aoife Dillon, Lucinda Edge, Sinéad Flynn, Anna Mullen, Aisling Davis, Aoife Molamphy, Anna Kirwan, Robert Briggs, Amanda H Lavan, Darragh Shields, Geraldine McMahon, Arthur Hennessy, Una Kennedy, Paul Staunton, Emer Kidney, Sarah-Jane Yeung, Deirdre Glynn, Frances Horgan, Conal Cunningham, Roman Romero-Ortuno

Abstract

We evaluated predictors of the Clinical Frailty Scale (CFS) scored by an interdisciplinary team (Home FIRsT) performing comprehensive geriatric assessment (CGA) in our Emergency Department (ED). This was a retrospective observational study (service evaluation) utilising ED-based CGA data routinely collected by Home FIRsT between January and October 2020. A linear regression model was computed to establish independent predictors of CFS. This was complemented by a classification and regression tree (CRT) to evaluate the main predictors. There were 799 Home FIRsT episodes, of which 740 were unique patients. The CFS was scored on 658 (89%) (median 4, range 1-8; mean age 81 years, 61% women). Independent predictors of higher CFS were older age (p<0.001), history of dementia (p<0.001), mobility (p≤0.007), disability (p<0.001), and higher acuity of illness (p=0.009). Disability and mobility were the main classifiers in the CRT. Results suggest appropriate CFS scoring informed by functional baseline.

Keywords: Clinical Frailty Scale; Disability; Emergency Department; Geriatric Assessment; Mobility.

Copyright: © 2022 Hylonome Publications.

Figures

Figure 1
Figure 1
Results of the classification and regression tree (method: exhaustive Chi-square automatic interaction detection method) to predict Clinical Frailty Scale (CFS) score. In each node, the predicted score is highlighted.

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