Prognostic factors of long-term survival in geriatric inpatients. Should we change the recommendations for the oldest people?
- 195 Downloads
Identification of optimal predictors of the 5.5-year survival in former geriatric inpatients.Investigation of the direction and shape of the relationship between mortality risk and its predictors.
Retrospective survival analysis with the application of the Cox proportional hazards model.
Teaching geriatric unit.
478 inpatients (mean age 77.9; +6.8) discharged from geriatric ward during year 2008, without any exclusion criteria.
Comprehensive geriatric assessment of numerous health variables, body mass index (BMI), clinical and biochemical findings, and outcomes of the final diagnosis. Records on the dates of deaths were obtained from the Provincial Office of Population Register.
During the 5.5-year follow-up 209 (43.7%) patients died. In the multivariate setup, six risk factors with an independent impact on mortality were identified: age (p=0.036), cognitive functioning on the Blessed test (p=0.005), score on instrumental ADL (p<0.0001), score on Charlson comorbidity index (p<0.0001), cholesterol level (p<0.0001), BMI (p<0.0001), and hemoglobin level (p=0.02). The latter two predictors exhibited a significant inverted J-shaped association with mortality, i.e., considerably higher risk of death corresponds to the lower values of these variables in comparison to their higher levels.
Older age, worse IADL and cognitive functioning, and higher comorbidity were recognized as endangering one’s long-term survival. On the other hand, moderate obesity (BMI 36), higher cholesterol and the absence of anemia (hemoglobin 13.6 g/dL) are associated with longer survival. Therefore, irrespective of the individualized treatment and physical exercise, nutrient-dense food seems to be a key recommendation to prevent frailty or malnutrition in the oldest and comorbid population.
Key wordsLong-term survival geriatric patients BMI cholesterol hemoglobin
Unable to display preview. Download preview PDF.
- 1.Mladovsky P, Allin S, Masseria C, Hernández-Quevedo C, McDaid D, Mossialos E. Health in European Union: Trends and analysis. European Observatory on Health Systems and Policies. Observatory Studies Series no.19, 2009Google Scholar
- 12.De Ruijter W, Westendorp RGJ, Assendelft WJJ, den Elzen WPJ, de Craen AJM, Cessie S, Jacobijn Gussekloo J. Use of Framingham risk score and new biomarkers to predict cardiovascular mortality in older people: population based observational cohort study. BMJ 2009;338:a3083 doi: 10.1136/bmj.a3083 PubMedCentralCrossRefPubMedGoogle Scholar
- 24.Petursson H, Sigursson JA, Bengtsson C, Nilsen TIL, Getz L. Is the use of cholesterol in mortality risk algorithms in clinical guidelines valid? Ten years prospective data from the Norvegian HUNT 2 study. J Eval Clin Pract 18:159–168Google Scholar
- 27.Mahmood S, Levy D, Vasan RS, Wang T. The Framingham Heart Study and the epidemiology of cardiovascular disease: a historical perspective, 2013. http://dx.doi.org/10.1016/S0140-6736(13)61752-3Google Scholar
- 29.Rubenstein LZ, Wieland D, Bernabei R, Eds. Geriatric assessment technology. Milan: Kurtis, 1995Google Scholar
- 38.Royston P. Multiple imputation of missing values: update. The STATA Journal 2005;5:188–201Google Scholar
- 39.Royston P. Multiple imputation of missing values: update of ice. The STATA Journal 2005;5:527–536Google Scholar