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Aging Clin Exp Res ; 15(1): 32-7, 2003 Feb.
Article in English | MEDLINE | ID: mdl-12841416

ABSTRACT

BACKGROUND AND AIMS: This study was carried out to detect the incidence of adverse outcomes in hospitalized older patients and the role of multidimensional assessment in identifying predictors of adverse outcomes, which include in-hospital mortality and the need for admission to rehabilitation units or nursing homes after hospital discharge. METHODS: 923 patients at least 65 years old (mean age 78.7 +/- 7.2, 49% women) admitted to the acute care geriatric ward of an internal medicine department were included in the study. On admission, each patient underwent a comprehensive medical, functional, neuropsychological, sociodemographic and nutritional assessment. RESULTS: Incidence of mortality and other adverse outcomes was 6 and 16%, respectively. In-hospital mortality was independently predicted by older age (OR per year: 1.07, 95% CI: 1.02-1.12), male gender (OR: 3.97, 1.99-7.95), higher comorbidity (OR: 2.09, 1.01-4.33), higher heart rate (OR: 2.87, 1.30-6.35), lower systolic blood pressure (OR: 2.22, 1.03-4.81), lower serum albumin values (OR: 3.20, 1.63-6.29) and a lower MMSE score at hospital admission (OR: 5.51, 2.34-12.9). Adverse outcomes were independently predicted by older age (OR per year: 1.03, 1.01-1.06), higher comorbidity (OR: 2.00, 1.35-2.94), lower serum albumin values (OR: 2.57, 1.69-3.90) and a lower admission MMSE score (OR: 2.49, 1.68-3.71). CONCLUSIONS: A multidimensional geriatric assessment should be comprised of simple parameters available early, like serum albumin and MMSE score, that are predictive of adverse outcomes in older hospitalized patients, along with other health Indicators (comorbidity, heart rate and blood pressure).


Subject(s)
Acute Disease/mortality , Geriatric Assessment , Hospital Mortality , Nursing Homes/statistics & numerical data , Rehabilitation Centers/statistics & numerical data , Age Distribution , Aged , Aged, 80 and over , Comorbidity , Female , Humans , Incidence , Male , Multivariate Analysis , Sex Distribution
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