Deborah A Lekan1, Debra C Wallace1, Thomas P McCoy1, Jie Hu2, Susan G Silva3, Heather E Whitson4,5. 1. 1 School of Nursing, University of North Carolina at Greensboro, Greensboro, NC, USA. 2. 2 College of Nursing, The Ohio State University, Columbus, OH, USA. 3. 3 School of Nursing, Duke University, Durham, NC, USA. 4. 4 Departments of Medicine and Opthalmology, School of Medicine, Duke University, Durham, NC, USA. 5. 5 Durham VA Geriatrics Research Education and Clinical Center (GRECC), Durham, NC, USA.
Abstract
INTRODUCTION: Frailty, a clinical syndrome of decreased physiologic reserve and dysregulation in multiple physiologic systems, is associated with increased risk for adverse outcomes. PURPOSE: The aim of this retrospective, cross-sectional, correlational study was to characterize frailty in older adults admitted to a tertiary-care hospital using a biopsychosocial frailty assessment and to determine associations between frailty and time to in-hospital mortality and 30-day rehospitalization. METHODS: The sample included 278 patients ≥55 years old admitted to medicine units. Frailty was determined using clinical data from the electronic health record (EHR) for symptoms, syndromes, and conditions and laboratory data for four serum biomarkers. A frailty risk score (FRS) was created from 16 risk factors, and relationships between the FRS and outcomes were examined. RESULTS: The mean age of the sample was 70.2 years and mean FRS was 9.4 ( SD, 2.2). Increased FRS was significantly associated with increased risk of death (hazard ratio = 1.77-2.27 for 3 days ≤ length of stay (LOS) ≤7 days), but depended upon LOS ( p < .001). Frailty was marginally associated with rehospitalization for those who did not die in hospital (adjusted odds ratio = 1.18, p = .086, area under the curve [AUC] = 0.66, 95% confidence interval for AUC = [0.57, 0.76]). DISCUSSION: Clinical data in the EHR can be used for frailty assessment. Informatics may facilitate data aggregation and decision support. Because frailty is potentially preventable and treatable, early detection is crucial to delivery of tailored interventions and optimal patient outcomes.
INTRODUCTION: Frailty, a clinical syndrome of decreased physiologic reserve and dysregulation in multiple physiologic systems, is associated with increased risk for adverse outcomes. PURPOSE: The aim of this retrospective, cross-sectional, correlational study was to characterize frailty in older adults admitted to a tertiary-care hospital using a biopsychosocial frailty assessment and to determine associations between frailty and time to in-hospital mortality and 30-day rehospitalization. METHODS: The sample included 278 patients ≥55 years old admitted to medicine units. Frailty was determined using clinical data from the electronic health record (EHR) for symptoms, syndromes, and conditions and laboratory data for four serum biomarkers. A frailty risk score (FRS) was created from 16 risk factors, and relationships between the FRS and outcomes were examined. RESULTS: The mean age of the sample was 70.2 years and mean FRS was 9.4 ( SD, 2.2). Increased FRS was significantly associated with increased risk of death (hazard ratio = 1.77-2.27 for 3 days ≤ length of stay (LOS) ≤7 days), but depended upon LOS ( p < .001). Frailty was marginally associated with rehospitalization for those who did not die in hospital (adjusted odds ratio = 1.18, p = .086, area under the curve [AUC] = 0.66, 95% confidence interval for AUC = [0.57, 0.76]). DISCUSSION: Clinical data in the EHR can be used for frailty assessment. Informatics may facilitate data aggregation and decision support. Because frailty is potentially preventable and treatable, early detection is crucial to delivery of tailored interventions and optimal patient outcomes.
Entities:
Keywords:
biomarkers; electronic health record; frail elderly; hospitalization; risk assessment
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