Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/28002
Appears in Collections:Faculty of Social Sciences Journal Articles
Peer Review Status: Refereed
Title: Older people's mortality index: Development of a practical model for prediction of mortality in nursing homes (Kahrizak Elderly Study)
Author(s): Sharifi, Farshad
Ghaderpanahi, Maryam
Fakhrzadeh, Hossein
Mirarefin, Mojde
Badamchizadeh, Zohre
Tajalizadekhoob, Yaser
Fadayivatan, Reza
Philp, Ian
Larijani, Bagher
Contact Email: ian.philp@stir.ac.uk
Keywords: Activities of daily living
aged
mortality
nursing home
survival
Issue Date: 31-Jan-2012
Citation: Sharifi F, Ghaderpanahi M, Fakhrzadeh H, Mirarefin M, Badamchizadeh Z, Tajalizadekhoob Y, Fadayivatan R, Philp I & Larijani B (2012) Older people's mortality index: Development of a practical model for prediction of mortality in nursing homes (Kahrizak Elderly Study). Geriatrics and Gerontology International, 12 (1), pp. 36-45. https://doi.org/10.1111/j.1447-0594.2011.00724.x.
Abstract: Aims: In the elderly, mortality prediction models are important for clinical decision‐making and planning of services required. Methods: A total of 247 Kahrizak Charity Foundation (KCF) residents aged ≥65 years were followed up for approximately 39 months. At the baseline, the questionnaires of Barthel Index (BI), Mini‐Mental State Examination, Geriatric Depression Scale, Mini Nutritional Assessment and Norton Index was given. Medical history was recorded and anthropometric values were also measured at the baseline. Fasting blood samples were collected at baseline. Mortality and its causes were recorded during the study. Results: A total of 30% (74) of participants died during the study. The variables that had a significant association with mortality in the Cox regression hazard model were entered into the principal components analysis (PCA). The older people's mortality index (OPMI) was developed by four variables extracted from PCA, including BI, age, hemoglobin and mid‐arm circumference. Cut‐points of these components were calculated using ROC curve analysis. Based on neural network analysis, there was no significant difference in relative importance of OPMI components. OPMI was correlated to mortality (r=−0.351, P=0.000) and survival (r=−0.355, P=0.000). OPMI score was defined as the number of adverse predictors present. An increasing hazard ratio for mortality was observed from scores 1 to 4 (2.10, 4.56, 7.12 and 13.85, respectively). Conclusion: Our suggested model could predict mortality in KCF residents well. The new developed model could be a practical, easy and non‐expensive index for mortality prediction in elderly care facilities. Geriatr Gerontol Int 2012; 12: 36–45.
DOI Link: 10.1111/j.1447-0594.2011.00724.x
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