The association between geriatric syndromes and survival.
ABSTRACT To ascertain the effect on survival of eight common geriatric syndromes (multiple comorbidities, cognitive impairment, frailty, disability, sarcopenia, malnutrition, homeostenosis, and chronic inflammation), identified by an expert panel of academic geriatricians.
A systematic literature review sought studies from a variety of sources to compare survival and life expectancy of individuals with geriatric syndromes with those of the general population.
Studies used reflected the general population.
Community-dwelling persons aged 65 and older.
Eight geriatric syndromes (multiple definitions) and survival.
Two thousand three hundred seventy-four publications were retrieved, and 509 publications of 123 studies were included. Seven geriatric syndromes (multiple comorbidities, cognitive impairment, frailty, disability, malnutrition, impaired homeostasis, and chronic inflammation) were associated with poor survival. In each case, the prevalence of a syndrome was negatively associated with mortality. Malnutrition and impaired homeostasis exerted twice the influence of factors such as multiple comorbidities and frailty. From age 65 to 74, only those who are very ill or frail (e.g., impaired homeostasis, low body mass index, or advanced dementia) have a higher risk of mortality than average older adults. In the old-old, particularly aged 90 and older, the added value of predicting survival beyond 1 year is minimal.
Geriatric syndrome information is helpful to understanding survival for younger old persons but provides little information about survival for the very old. Complex survival models add comparatively little benefit to more simply measured and calculated models.
Conference Paper: Maximizing Expected Model Change for Active Learning in Regression[Show abstract] [Hide abstract]
ABSTRACT: Active learning is well-motivated in many supervised learning tasks where unlabeled data may be abundant but labeled examples are expensive to obtain. The goal of active learning is to maximize the performance of a learning model using as few labeled training data as possible, thereby minimizing the cost of data annotation. So far, there is still very limited work on active learning for regression. In this paper, we propose a new active learning framework for regression called Expected Model Change Maximization (EMCM), which aims to choose the examples that lead to the largest change to the current model. The model change is measured as the difference between the current model parameters and the updated parameters after training with the enlarged training set. Inspired by the Stochastic Gradient Descent (SGD) update rule, the change is estimated as the gradient of the loss with respect to a candidate example for active learning. Under this framework, we derive novel active learning algorithms for both linear regression and nonlinear regression to select the most informative examples. Extensive experimental results on the benchmark data sets from UCI machine learning repository have demonstrated that the proposed algorithms are highly effective in choosing the most informative examples and robust to various types of data distributions.2013 IEEE International Conference on Data Mining (ICDM); 12/2013
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ABSTRACT: In clinical practice, geriatric nutritional assessment usually includes nutritional screening, a simple anthropometric assessment, measurement of various biochemical parameters, such as serum albumin, and sometimes (not always) body composition analysis (BCA). However, there is a high prevalence of undiagnosed malnutrition in patients with dementia. Several factors contribute to this situation; probably, the most notable is the methodology used to assess body composition (BC). In this regard, for BCA, techniques are needed that are noninvasive, affordable, safe, simple and that require the minimum possible collaboration by the elderly patient. Consequently, body mass index (BMI) and waist circumference (WC) are widely used as indicators of overall and central adiposity, respectively; however, there is no consensus on the cutoffs for the elderly, and changes in BC (especially muscle-mass depletion) are masked by normal values of BMI and WC. Bioimpedance analysis is a simple, cost-effective and precise method for BCA, provided that cross-validated equations are used. Its main disadvantage is that it is highly sensitive to changes in body water (overhydration or dehydration), leading to substantial errors in BC estimates. However, using Bioelectrical Impedance Vector Analysis errors are minimized, as there is no need for the subject to be normally hydrated and it does not require the use of predictive models.European Journal of Clinical Nutrition advance online publication, 13 August 2014; doi:10.1038/ejcn.2014.168.European Journal of Clinical Nutrition 08/2014; · 2.95 Impact Factor
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ABSTRACT: The relationship between mortality and impaired cognitive function has not been thoroughly investigated in a very elderly community-dwelling population, and little is known about the association of disease-specific mortality with Mini-Mental State Examination (MMSE) subscale scores. Here we evaluated these data in Japanese community-dwelling elderly. In 2003, 85 year-olds (n=207) were enrolled; 205 completed the MMSE for cognitive function and were followed-up for 10 years, during which time 120 participants died, 70 survived, and 17 were lost to follow-up. Thirty-eight deaths were due to cardiovascular disease, 22 to senility, 21 to respiratory disease, and 16 to cancer. All-cause mortality decreased by 4.3% with a 1-point increase in the global MMSE score without adjustment, and it decreased by 6.3% with adjustment for both sex and length of education. Cardiovascular mortality decreased by 7.6% and senility mortality decreased by 9.2% with a 1-point increase in the global MMSE score with adjustment for sex and education. No association was found between respiratory diseases or cancer mortality and global MMSE score. All-cause mortality also decreased with increases in MMSE subscale scores for time orientation, place orientation, delayed recall, naming objects, and listening and obeying. Cardiovascular mortality was also associated with the MMSE subscale of naming objects, and senility mortality was associated with the subscales of time orientation and place orientation. Thus, we found that impaired cognitive function determined by global MMSE score and some MMSE subscale scores were independent predictors of all-cause mortality or mortality due to cardiovascular disease or senility in 85 year-olds.Clinical Interventions in Aging 01/2014; 9:1691-9. · 2.65 Impact Factor