Dynamical network model for age-related health deficits and mortality

Taneja, Swadhin; Mitnitski, Arnold; Rockwood, Kenneth; Rutenberg, Andrew D.

doi:10.1103/physreve.93.022309

Cited by 38 publications

(45 citation statements)

References 71 publications

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“…Fried and colleagues attempted to “untangle” these constructs but there is considerable overlap which increases with age [11]. Work on defining health deficit accumulation through network modeling shows that what matters the most is the density of a deficits connections to other deficits which is not captured by simple counting of deficits [49–51]. As an individual ages and accumulates deficits, as would be the case in many older people who are critically ill, the more that frailty and co-morbidity are inextricably intertwined.…”

Section: Discussionmentioning

confidence: 99%

The impact of frailty on intensive care unit outcomes: a systematic review and meta-analysis

et al. 2017

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PurposeFunctional status and chronic health status are important baseline characteristics of critically ill patients. The assessment of frailty on admission to the intensive care unit (ICU) may provide objective, prognostic information on baseline health. To determine the impact of frailty on the outcome of critically ill patients, we performed a systematic review and meta-analysis comparing clinical outcomes in frail and non-frail patients admitted to ICU.MethodsWe searched the Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, PubMed, CINAHL, and Clinicaltrials.gov. All study designs with the exception of narrative reviews, case reports, and editorials were included. Included studies assessed frailty in patients greater than 18 years of age admitted to an ICU and compared outcomes between fit and frail patients. Two reviewers independently applied eligibility criteria, assessed quality, and extracted data. The primary outcomes were hospital and long-term mortality. We also determined the prevalence of frailty, the impact on other patient-centered outcomes such as discharge disposition, and health service utilization such as length of stay.ResultsTen observational studies enrolling a total of 3030 patients (927 frail and 2103 fit patients) were included. The overall quality of studies was moderate. Frailty was associated with higher hospital mortality [relative risk (RR) 1.71; 95% CI 1.43, 2.05; p < 0.00001; I 2 = 32%] and long-term mortality (RR 1.53; 95% CI 1.40, 1.68; p < 0.00001; I 2 = 0%). The pooled prevalence of frailty was 30% (95% CI 29–32%). Frail patients were less likely to be discharged home than fit patients (RR 0.59; 95% CI 0.49, 0.71; p < 0.00001; I 2 = 12%).ConclusionsFrailty is common in patients admitted to ICU and is associated with worsened outcomes. Identification of this previously unrecognized and vulnerable ICU population should act as the impetus for investigating and implementing appropriate care plans for critically ill frail patients. Registration: PROSPERO (ID: CRD42016053910).Electronic supplementary materialThe online version of this article (doi:10.1007/s00134-017-4867-0) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

The impact of frailty on intensive care unit outcomes: a systematic review and meta-analysis

et al. 2017

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“…[13] As a consequence of deficit interaction in and of itself – i.e. absent any explicit age dependence[54] – deficits accumulate across the life course at a constant rate – mercilessly, like compound interest. [55] This understanding makes clear why some studies have found midlife factors (hypertension, obesity) to be important in late-life cognitive decline.…”

Section: Discussionmentioning

confidence: 99%

Changes in Frailty Predict Changes in Cognition in Older Men: The Honolulu-Asia Aging Study

Armstrong

Godin

Launer

et al. 2016

JAD

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Objectives As cognitive decline mostly occurs in late life, where typically it co-exists with many other ailments, it is important to consider frailty in understanding cognitive change. Here, we examined the association of change in frailty status with cognitive trajectories in a well-studied cohort of older Japanese-American men. Methods Using the prospective Honolulu-Asia Aging Study (HAAS), 2817 men of Japanese descent were followed (aged 71–93 at baseline). Starting in 1991 with follow-up health assessments every two to three years, cognition was measured using the Cognitive Abilities Screening Instrument (CASI). For this study, health data was used to construct an accumulation of deficits frailty index (FI). Using six waves of data, multilevel growth curve analyses were constructed to examine simultaneous changes in cognition in relation to changes in the frailty index (FI), controlling for baseline frailty, age, education, and APOE-ε4 status. Results On average, CASI scores declined by 2.0 points per year (95% confidence interval 1.9–2.1). Across six waves, each 10% within-person increase in frailty from baseline was associated with a 5.0 point reduction in CASI scores (95% confidence interval 4.7–5.2). Baseline frailty and age were associated both with lower initial CASI scores and with greater decline across the five follow-up assessments (p < 0.01). Conclusions Cognition is adversely affected by impaired health status in old age. Using a multidimensional measure of frailty, both baseline status and within-person changes in frailty were predictive of cognitive trajectories.

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“…We use observational data for mortality rate and FI vs age to fine-tune the network parameters [12,13]. A systematic exploration of parameters was done in previous work [12,13]. Most of our parameterization (N = 10000, α = 2.27, k = 4, γ − = 6.5) is the same as reported previously [13].…”

Section: A Modelmentioning

confidence: 99%

“…In previous work we developed a stochastic network model of aging with damage accumulation [12,13]. Each individual is modeled as a network of interacting nodes that represent health attributes.…”

Section: Introductionmentioning

confidence: 99%

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Probing the network structure of health deficits in human aging

Farrell

Mitnitski

Theou

et al. 2018

Preprint

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Human aging leads to the stochastic accumulation of damage. We model an aging population using a stochastic network model. Individuals are modeled as a network of interacting nodes, representing health attributes. Nodes in the network stochastically damage and repair, with rates dependent on the state of their neighbors. Damaged nodes represent health deficits. The Frailty Index (FI) assesses age-related damage as the proportion of health deficits an individual has accumulated, from a selection of attributes. Here, we use computational, information-theoretic, and mean-field approaches to show that the degree distribution and degree correlations of the underlying network are important to the model's ability to recover the behavior of observational health data. We use different measures of damage in the network to probe the structure of the network. We find that the behavior of different classes of observational health deficits (laboratory or clinical) is similar to the behavior of nodes of low or high degree in the model, respectively. This explains how damage can propagate within the network, leading towards individual mortality.

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Dynamical network model for age-related health deficits and mortality

Cited by 38 publications

References 71 publications

The impact of frailty on intensive care unit outcomes: a systematic review and meta-analysis

The impact of frailty on intensive care unit outcomes: a systematic review and meta-analysis

Changes in Frailty Predict Changes in Cognition in Older Men: The Honolulu-Asia Aging Study

Probing the network structure of health deficits in human aging

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