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. 2006 Oct;18(5):381-7.
doi: 10.1007/BF03324834.

Predictors of mortality after hip fracture: results from 1-year follow-up

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Predictors of mortality after hip fracture: results from 1-year follow-up

Giulio Pioli et al. Aging Clin Exp Res. 2006 Oct.

Abstract

Background and aims: The study investigates one-year mortality risk associated with hip fracture in elderly people, and pre-fracture characteristics and events occurring during the acute phase which may represent significant predictors for acute and long-term mortality.

Methods: The study is a prospective cohort study of 252 patients aged 70 and older, consecutively admitted with hip fracture to the Division of Orthopedic Surgery of the Galliera Hospital of Genoa, Italy. At admission, each subject received a standardized diagnostic evaluation, including demographic variables, biochemical markers of nutritional status and basic medical, functional and cognitive assessment. Patients were followed by telephone interviews at three months, six months and one year after fracture. The relationship between mortality and the risk factors recorded was assessed using logistic regression models.

Results: 248 patients were eligible. Cumulative mortality was 4.8% during hospital stay, and 12.5% at 3, 18.9% at 6 and 24% at 12 months. The risk factors significantly associated with mortality were: sex, Acute Physiology Score (APS), comorbidity, functional and cognitive status, and albumin levels. In multivariate models, albumin below 3 g/dL remained the only significant predictor of in-hospital mortality (OR 6,8, 95% CI 1.56-29,7, p<0.001); functional status and comorbidity were significant risk factors of mortality after 6 and 12 months.

Conclusions: These findings confirm the important role of serum albumin in assessing in-hospital health status and defining its role as a strong predictor of early and late mortality after hospital discharge. They also emphasize the effects of comorbidity and functional impairment on long-term mortality after hip fracture. Identifying these predictive factors may be helpful in improving case management during hospital stay and more accurate discharge planning.

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