Predicting mortality of elderly patients acutely admitted to the Department of Internal Medicine.

Link to article at PubMed

Predicting mortality of elderly patients acutely admitted to the Department of Internal Medicine.

Int J Clin Pract. 2014 Oct 14;

Authors: Smolin B, Levy Y, Sabbach-Cohen E, Levi L, Mashiach T

AIMS: This study addresses the common practice of providing aggressive treatments of limited clinical benefit and cost-effectiveness to seriously ill and frail elderly. We have created a statistical model of 6-month mortality risk prediction following acute hospitalisation admission, and identified a subset of patients with poorest prognosis that requires comfort-focused care.
METHODS: We have studied electronic medical records of 26,937 patients age 65 years or older, hospitalised in the internal medicine departments of one tertiary-care teaching medical center in Northern Israel from January 1, 2008 through December 31, 2011 and mortality data from the Israeli Internal Ministry Registry. Norton score records were employed for the performance status evaluation. Multivariate logistic regression analysis was used to predict the risk of 6-month mortality.
RESULTS: Variables associated with an increased risk of 6-month mortality included: metastatic cancer, age above 85 years, decreased values of blood albumin and haemoglobin, increased blood urea nitrogen and decreased physical/mental status and activity. The receiver operating characteristic area for the predicted probability of death was 0.845 and 0.847 in external validation cohort. Using predictive values of the logistic regression analysis, the study cohort was stratified into six groups with various predictive mortality risks.
CONCLUSION: The majority of deaths that have occurred within 6 months following the acute hospitalisation could be predicted on patient admission based on a few simple and easily obtained parameters. Earlier recognition of patients nearing the end of their lives may lead to better care and more efficient use of available resource.

PMID: 25311361 [PubMed - as supplied by publisher]

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