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J Gerontol A Biol Sci Med Sci ; 59(4): 350-4, 2004 Apr.
Article in English | MEDLINE | ID: mdl-15071078

ABSTRACT

OBJECTIVE: To assess the effects of delirium on the institutionalization rate, taking into account geriatric syndromes and nutritional status. METHODS: This population-based study took place in an acute care unit and included participants older than 75 years, arriving from home and later discharged. Confusion Assessment Method (CAM) symptoms were recorded by the nurses within 24 hours after admission and every 3 days. Delirium was defined using the CAM algorithm, and subsyndromal delirium responded to symptoms not fulfilling the CAM algorithm. These delirium categories were either present at admission (prevalent) or occurred during the hospital stay (incident). Participants were classified as having a low dietary intake when energy intake was at any time lower than 600 kcal/d. Age, sex, known cognitive impairment, weight, functional dependency, and laboratory testing as well as diagnoses were also recorded. Step-by-step backward logistic regression was used to identify predictors of institutionalization. RESULTS: Among 427 patients, 310 (72.6%) were discharged and were compared with 117 (27.4%) participants admitted to an institution. Female sex (odds ratio [OR]: OR 2.15, 95% confidence interval [CI]: CI 1.22-3.78), prevalent delirium (OR 3.19, 95% CI 1.33-7.64), subsyndromal delirium (OR 2.72, 95% CI 1.48-5.01), incident subsyndromal delirium (OR 4.27, 95% CI 2.17-8.39), low dietary intake (OR 2.50, 95% CI 1.35-4.63), and a fall (OR 2.16, 95% CI 1.22-3.84) or a diagnosis of stroke (OR 2.03, 95% CI 1.04-3.94) were independent predictors of institutionalization. CONCLUSIONS: Symptoms of delirium and severe nutritional impairment led patients to geriatric institutions. Therefore, these institutions need to implement policies that address both of these issues.


Subject(s)
Delirium/epidemiology , Institutionalization/statistics & numerical data , Nutritional Status , Aged , Aged, 80 and over , Delirium/diagnosis , Female , Geriatric Assessment , Humans , Incidence , Logistic Models , Male , Predictive Value of Tests , Prevalence , Prospective Studies , Risk Factors , Statistics, Nonparametric
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