Relative Fitness and Frailty of Elderly Men and Women in Developed Countries and Their Relationship with Mortality

Mitnitski, Arnold; Song, Xiaowei; Skoog, Ingmar; Broe, GA; Cox, Jafna L.; Grunfeld, Eva; Rockwood, Kenneth

doi:10.1111/j.1532-5415.2005.00506.x

Cited by 490 publications

(495 citation statements)

References 37 publications

(54 reference statements)

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“…This approach recognizes that frailty is a continuum—it is not all or none; the more deficits a person has, across more organ systems and physiologic parameters, the more likely that person is to be frail. Although the idea and approach are relatively simple, the results yielded by the FI have been consistent across many settings, even though not every FI considers the same deficits, or even the same number of deficits 10, 18, 19. The prognostic implications of FI have been demonstrated not only in a variety of different chronic conditions (osteoporosis,20 human immunodeficiency virus and AIDS,21 kidney disease22) but also in acute disease states (trauma23).…”

Section: Discussionmentioning

confidence: 94%

Frailty and Outcomes After Myocardial Infarction: Insights From the CONCORDANCE Registry

Patel

Goodman

Yan

et al. 2018

JAHA

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BackgroundLittle is known about the prognostic implications of frailty, a state of susceptibility to stressors and poor recovery to homeostasis in older people, after myocardial infarction (MI).Methods and ResultsWe studied 3944 MI patients aged ≥65 years treated at 41 Australian hospitals from 2009 to 2016 in the CONCORDANCE (Australian Cooperative National Registry of Acute Coronary Care, Guideline Adherence and Clinical Events) registry. Frailty index (FI) was determined using the health deficit accumulation method. All‐cause and cardiac‐specific mortality at 6 months were compared between frail (FI >0.25) and nonfrail (FI ≤0.25) patients. Among 1275 patients with ST‐segment–elevation MI (STEMI), 192 (15%) were frail, and among 2669 non‐STEMI (NSTEMI) patients, 902 (34%) were frail. Compared with nonfrail counterparts, frail STEMI patients received 30% less reperfusion therapy and 22% less revascularization during index hospitalization; frail NSTEMI patients received 30% less diagnostic angiography and 39% less revascularization. Unadjusted 6‐month all‐cause mortality (STEMI: 13% versus 3%; NSTEMI: 13% versus 4%) and cardiac‐specific mortality (STEMI: 6% versus 1.4%, NSTEMI: 3.2% versus 1.2%) were higher among frail patients. After adjustment for known prognosticators, FI was significantly associated with higher 6‐month all‐cause (STEMI: odds ratio: 1.74 per 0.1 FI [95% confidence interval, 1.37–2.22], P<0.001; NSTEMI: odds ratio: 1.62 per 0.1 FI [95% confidence interval, 1.40–1.87], P<0.001) but not cardiac‐specific mortality (STEMI: P=0.99; NSTEMI: P=0.93).ConclusionsFrail patients receive lower rates of invasive cardiac care during MI hospitalization. Increased frailty was independently associated with increased postdischarge all‐cause mortality but not cardiac‐specific mortality. These findings inform identification of frailty during MI hospitalization as a potential opportunity to address competing risks for mortality in this high‐risk population.

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Section: Discussionmentioning

confidence: 94%

Frailty and Outcomes After Myocardial Infarction: Insights From the CONCORDANCE Registry

Patel

Goodman

Yan

et al. 2018

JAHA

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“…Related analyses classified individuals' likely lifespans according to trajectories of physiological indices (Yashin et al ., 2006, 2010). Further, a landmark series of studies of the accumulation of age‐related functional deficits in individuals over time identified a snowball effect where individuals with physiological deficits are more likely to accumulate further deficits over time and are exponentially more likely to die (Mitnitski et al ., 2005, 2006). Taken together, these studies indicate that the trajectory of an individuals' physiological state over time provides a rich context for understanding future mortality risk.…”

Section: Introductionmentioning

confidence: 99%

Predicting all‐cause mortality from basic physiology in the Framingham Heart Study

Zhang

Pincus

2015

Aging Cell

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SummaryUsing longitudinal data from a cohort of 1349 participants in the Framingham Heart Study, we show that as early as 28–38 years of age, almost 10% of variation in future lifespan can be predicted from simple clinical parameters. Specifically, we found diastolic and systolic blood pressure, blood glucose, weight, and body mass index (BMI) to be relevant to lifespan. These and similar parameters have been well‐characterized as risk factors in the relatively narrow context of cardiovascular disease and mortality in middle to old age. In contrast, we demonstrate here that such measures can be used to predict all‐cause mortality from mid‐adulthood onward. Further, we find that different clinical measurements are predictive of lifespan in different age regimes. Specifically, blood pressure and BMI are predictive of all‐cause mortality from ages 35 to 60, while blood glucose is predictive from ages 57 to 73. Moreover, we find that several of these parameters are best considered as measures of a rate of ‘damage accrual’, such that total historical exposure, rather than current measurement values, is the most relevant risk factor (as with pack‐years of cigarette smoking). In short, we show that simple physiological measurements have broader lifespan‐predictive value than indicated by previous work and that incorporating information from multiple time points can significantly increase that predictive capacity. In general, our results apply equally to both men and women, although some differences exist.

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“…In such cases the cumulative indices of health/well-being disorders (DIs) (also known as "frailty indices, FI") represent a reasonable alternative. Statistical properties of such indices have been recently investigated using different survey data (Goggins et al 2005;Kulminski et al 2006a,b;Mitnitski and Rockwood 2006;Mitnitski et al 2005). The results of these analyses suggest that the DIs can describe the aging-related changes in health/ well-being/survival status in humans.…”

Section: Introductionmentioning

confidence: 99%

Health decline, aging and mortality: how are they related?

et al. 2007

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The deterioration of human health with age is manifested in changes of thousands of physiological and biological variables. The contribution of some of such changes to the mortality risk may be small and cannot be reliably detected by existing statistical methods. A cumulative index of health/wellbeing disorders, which counts changes in observed variables on the way of losing health, may be an appropriate way to take the effects of such variables into account. In this paper we investigate regularities of the aging-related changes in human health/well-being/survival status described by such an index using the new version of the quadratic hazard model of human aging and mortality. We found that the shape and the location of the mortality risk, considered as a function of the introduced health-related index, changes with age reflecting the decline in stress resistance and the age-dependence of the "optimal" health/well-being status. Comparison of these results with findings from early studies using the Cox's-like model of risk function indicates that the results are likely to describe regularities of deterioration in human health during the aging process.

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Relative Fitness and Frailty of Elderly Men and Women in Developed Countries and Their Relationship with Mortality

Cited by 490 publications

References 37 publications

Frailty and Outcomes After Myocardial Infarction: Insights From the CONCORDANCE Registry

Frailty and Outcomes After Myocardial Infarction: Insights From the CONCORDANCE Registry

Predicting all‐cause mortality from basic physiology in the Framingham Heart Study

Health decline, aging and mortality: how are they related?

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