| Autor: |
Pilotto A; 1Department of Geriatric Care, Orthogeriatrics and Rehabilitation, EO Galliera Hospital, National Relevance and High Specialization Hospital, Genova, Italy., Veronese N; 1Department of Geriatric Care, Orthogeriatrics and Rehabilitation, EO Galliera Hospital, National Relevance and High Specialization Hospital, Genova, Italy., Quispe Guerrero KL; 1Department of Geriatric Care, Orthogeriatrics and Rehabilitation, EO Galliera Hospital, National Relevance and High Specialization Hospital, Genova, Italy., Zora S; 1Department of Geriatric Care, Orthogeriatrics and Rehabilitation, EO Galliera Hospital, National Relevance and High Specialization Hospital, Genova, Italy., Boone ALD; 2FICYT Foundation for Applied Scientific Research and Technology in Asturias, Oviedo, Spain., Puntoni M; 3Scientific Coordination Unit, EO Galliera Hospital, Genova, Italy., Giorgeschi A; 1Department of Geriatric Care, Orthogeriatrics and Rehabilitation, EO Galliera Hospital, National Relevance and High Specialization Hospital, Genova, Italy., Cella A; 1Department of Geriatric Care, Orthogeriatrics and Rehabilitation, EO Galliera Hospital, National Relevance and High Specialization Hospital, Genova, Italy., Rey Hidalgo I; 2FICYT Foundation for Applied Scientific Research and Technology in Asturias, Oviedo, Spain., Pers YM; 4Clinical Immunology and Osteoarticular Diseases Therapeutic Unit, Rheumatology Department, Lapeyronie University Hospital, Montpellier, France., Ferri A; 1Department of Geriatric Care, Orthogeriatrics and Rehabilitation, EO Galliera Hospital, National Relevance and High Specialization Hospital, Genova, Italy., Fernandez JRH; 5Health Promotion Service, Public Health General Directorate, Principality of Asturias, Spain., Pisano Gonzalez M; 6SESPA, Health Service of the Principality of Asturias, Oviedo, Spain. |
| Abstrakt: |
The multidimensional prognostic index (MPI) is a comprehensive geriatric assessment (CGA)-based tool that accurately predicts negative health outcomes in older subjects with different diseases and settings. To calculate the MPI several validated tools are assessed by health care professionals according to the CGA, whereas self-reported information by the patients is not available, but it could be of importance for the early identification of frailty. We aimed to develop and validate a self-administered MPI (SELFY-MPI) in community-dwelling subjects. For this reason, we enrolled 167 subjects (mean age = 67.3, range = 20-88 years, 51% = men). All subjects underwent a CGA-based assessment to calculate the MPI and the SELFY-MPI. The SELFY-MPI included the assessment of (1) basic and instrumental activities of daily living, (2) mobility, (3) memory, (4) nutrition, (5) comorbidity, (6) number of medications, and (7) socioeconomic situation. The Bland-Altman methodology was used to measure the agreement between MPI and SELFY-MPI. The mean MPI and SELFY-MPI values were 0.147 and 0.145, respectively. The mean difference was +0.002 ± standard deviation of 0.07. Lower and upper 95% limits of agreement were -0.135 and +0.139, respectively, with only 5 of 167 (3%) of observations outside the limits. Stratified analysis by age provided similar results for younger (≤65 years old, n = 45) and older subjects (>65 years, n = 122). The analysis of variances in subjects subdivided according to different year decades showed no differences of agreement according to age. In conclusion, the SELFY-MPI can be used as a prognostic tool in subjects of different ages. |
