Development of a Claims-based Frailty Indicator Anchored to a Well-established Frailty Phenotype

Datos Bibliográficos

ID9104544
AutoresJodi B Segal (0000-0003-3978-9662, Department of Medicine, Johns Hopkins University School of Medicine, autor de correspondencia), Hsien-Yen Chang (Health Policy and Management), Hsien‐Yen Chang (0000-0002-7997-4822), Yu Du (0000-0002-4798-1649, Biostatistics), Jeremy D Walston (Department of Medicine, Johns Hopkins University School of Medicine), Jeremy Walston (0000-0002-6965-2723, Johns Hopkins University, autor de correspondencia), Michelle C Carlson (0000-0003-2465-7421, Mental Health, Johns Hopkins Bloomberg School of Public Health), Ravi Varadhan (0000-0002-8434-1034, Biostatistics)
Año2017
Volumen55
Número7
Páginas716-722
Fecha de publicación2017-07-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaMedical Care (JOURNAL)
Identificadores de la revistaISSN: 0025-7079 • E-ISSN: 1537-1948
EditorialOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/mlr.0000000000000729
PMID28437320
OpenAlexW2606119962
IdiomaEN
Citas recibidas8
Referencias citadas33

BACKGROUND: Fried and colleagues described a frailty phenotype measured in the Cardiovascular Health Study (CHS). This phenotype is manifest when ≥3 of the following are present: low grip strength, low energy, slowed waking speed, low physical activity, or unintentional weight loss. We sought to approximate frailty phenotype using only administrative claims data to enable frailty to be assessed without physical performance measures. STUDY DESIGN: We used the CHS cohort data linked to participants Medicare claims. The reference standard was the frailty phenotype measured at visits 5 and 9. With penalized logistic regression, we developed a parsimonious index for predicting the frailty phenotype using a linear combination of diagnoses, operationalized with claims data. We assessed the predictive validity of frailty index by examining how well it predicted common aging-related outcomes including hospitalization, disability, and death. RESULTS: There were 4454 CHS participants from 4 clinical sites. In total, 84% were white, 58% were women and their mean age was 72 years at enrollment. Approximately 11% of the cohort was frail. The model had an area under the receiver operating curve of 0.75 to concurrently predict a frailty phenotype. This Claims-based Frailty Indicator significantly predicted death (odds ratio, 1.84), time to death (hazards ratio, 1.71), number of hospital admissions (incidence rate ratio, 1.74), and nursing home admission (odds ratio, 1.47) in models adjusted for age and sex. CONCLUSIONS: Claims data alone can be used to classify individuals as frail and nonfrail. The Claims-based Frailty Indicator might be used in research with large datasets for confounding adjustment or risk prediction. The indicator might also be used for emergency preparedness for identification of regions enriched with frail individuals

Cohort · Cohort study · Confounding · Frailty Index · Grip strength · Logistic regression · Odds · Odds ratio · Physical therapy · Chronic Disease Management Strategies · Demography · Frailty in Older Adults · Internal Medicine · Medicine · Nutrition and Health in Aging · Gerontology