Dataset Information

Functional Aging Index Complements Frailty in Prediction of Entry Into Care and Mortality.

ABSTRACT:

Background

The aim of this study was to develop a functional aging index (FAI) that taps four body systems: sensory (vision and hearing), pulmonary, strength (grip strength), and movement (gait speed) and to test the predictive value of FAI for entry into care and mortality.

Method

Growth curve models and Cox regression models were applied to data from 1,695 individuals from three Swedish longitudinal studies of aging. Participants were aged 45-93 at intake and data from up to eight follow-up waves were available.

Results

The rate of change in FAI was twice as fast after age 75 as before, women demonstrated higher mean FAI, but no sex differences in rates of change with chronological age were identified. FAI predicted entry into care and mortality, even when chronological age and a frailty index were included in the models. Hazard ratios indicated that FAI was a more important predictor of entry into care for men than women, whereas it was a stronger predictor of mortality for men than women.

Conclusions

Measures of biological aging and functional aging differ in their predictive value for entry into care and mortality for men and women, suggesting that both are necessary for a complete picture of the aging process across genders.

SUBMITTER: Finkel D

PROVIDER: S-EPMC7357456 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Publications

Functional Aging Index Complements Frailty in Prediction of Entry Into Care and Mortality.

Finkel Deborah D Sternäng Ola O Jylhävä Juulia J Bai Ge G Pedersen Nancy L NL

The journals of gerontology. Series A, Biological sciences and medical sciences 20191101 12

<h4>Background</h4>The aim of this study was to develop a functional aging index (FAI) that taps four body systems: sensory (vision and hearing), pulmonary, strength (grip strength), and movement (gait speed) and to test the predictive value of FAI for entry into care and mortality.<h4>Method</h4>Growth curve models and Cox regression models were applied to data from 1,695 individuals from three Swedish longitudinal studies of aging. Participants were aged 45-93 at intake and data from up to eig ...[more]

PMID: 31222213

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Project description:The prompt identification of frailty in primary care is the first step to offer personalized care to older individuals. We aimed to detect and quantify frailty among primary care older patients, by developing and validating a primary care frailty index (PC-FI) based on routinely collected health records and providing sex-specific frailty charts. The PC-FI was developed using data from 308,280 primary care patients ≥ 60 years old part of the Health Search Database (HSD) in Italy (baseline 2013-2019) and validated in the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K; baseline 2001-2004), a well-characterized population-based cohort including 3363 individuals ≥ 60 years old. Potential health deficits part of the PC-FI were identified through ICD-9, ATC, and exemption codes and selected through an optimization algorithm (i.e., genetic algorithm), using all-cause mortality as the main outcome for the PC-FI development. The PC-FI association at 1, 3 and 5 years, and discriminative ability for mortality and hospitalization were tested in Cox models. The convergent validity with frailty-related measures was verified in SNAC-K. The following cut-offs were used to define absent, mild, moderate and severe frailty: < 0.07, 0.07-0.14, 0.14-0.21, and ≥ 0.21. Mean age of HSD and SNAC-K participants was 71.0 years (55.4% females). The PC-FI included 25 health deficits and showed an independent association with mortality (hazard ratio range 2.03-2.27; p < 0.05) and hospitalization (hazard ratio range 1.25-1.64; p < 0.05) and a fair-to-good discriminative ability (c-statistics range 0.74-0.84 for mortality and 0.59-0.69 for hospitalization). In HSD 34.2%, 10.9% and 3.8% were deemed mildly, moderately, and severely frail, respectively. In the SNAC-K cohort, the associations between PC-FI and mortality and hospitalization were stronger than in the HSD and PC-FI scores were associated with physical frailty (odds ratio 4.25 for each 0.1 increase; p < 0.05; area under the curve 0.84), poor physical performance, disability, injurious falls, and dementia. Almost 15% of primary care patients ≥ 60 years old are affected by moderate or severe frailty in Italy. We propose a reliable, automated, and easily implementable frailty index that can be used to screen the primary care population for frailty.

Project description:BackgroundDuring the COVID-19 pandemic, older patients in primary care were triaged based on their frailty or assumed vulnerability for poor outcomes, while evidence on the prognostic value of vulnerability measures in COVID-19 patients in primary care was lacking. Still, knowledge on the role of vulnerability is pivotal in understanding the resilience of older people during acute illness, and hence important for future pandemic preparedness. Therefore, we assessed the predictive value of different routine care-based vulnerability measures in addition to age and sex for 28-day mortality in an older primary care population of patients with COVID-19.MethodsFrom primary care medical records using three routinely collected Dutch primary care databases, we included all patients aged 70 years or older with a COVID-19 diagnosis registration in 2020 and 2021. All-cause mortality was predicted using logistic regression based on age and sex only (basic model), and separately adding six vulnerability measures: renal function, cognitive impairment, number of chronic drugs, Charlson Comorbidity Index, Chronic Comorbidity Score, and a Frailty Index. Predictive performance of the basic model and the six vulnerability models was compared in terms of area under the receiver operator characteristic curve (AUC), index of prediction accuracy and the distribution of predicted risks.ResultsOf the 4,065 included patients, 9% died within 28 days after COVID-19 diagnosis. Predicted mortality risk ranged between 7-26% for the basic model including age and sex, changing to 4-41% by addition of comorbidity-based vulnerability measures (Charlson Comorbidity Index, Chronic Comorbidity Score), more reflecting impaired organ functioning. Similarly, the AUC of the basic model slightly increased from 0.69 (95%CI 0.66 - 0.72) to 0.74 (95%CI 0.71 - 0.76) by addition of either of these comorbidity scores. Addition of a Frailty Index, renal function, the number of chronic drugs or cognitive impairment yielded no substantial change in predictions.ConclusionIn our dataset of older COVID-19 patients in primary care, the 28-day mortality fraction was substantial at 9%. Six different vulnerability measures had little incremental predictive value in addition to age and sex in predicting short-term mortality.

Dataset Information

Functional Aging Index Complements Frailty in Prediction of Entry Into Care and Mortality.

Background

Method

Results

Conclusions

Publications

Functional Aging Index Complements Frailty in Prediction of Entry Into Care and Mortality.

Similar Datasets

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