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Frailty Index in Europeans: association with determinants of health.

ABSTRACT: The Frailty Index (FI) summarizes differences in health status within individuals, and the determinants of health drive that variability. The aim of the present study was to investigate the influence of education, income, smoking, alcohol intake and parental longevity on the FI variability in subjects of the same chronological age group.Analyses were based on a 40-item FI based on the first wave of the Survey of Health, Aging and Retirement in Europe (SHARE, http://www.share-project.org/), including 29?905 participants aged ?50 years from 12 countries. For each sex, the sample was divided into age categories (50s, 60s, 70s, 80s and ?90), and FI quartiles within age categories were calculated. Multivariate ordinal regressions were computed to assess the relative contribution of the health determinants on the FI quartiles in each age group.In women, the most significant multivariate predictors were years of education (odds ratios [OR] around 0.9), and difficulties making ends meet (OR between 1.8 and 2.1). In men, the most significant multivariate predictors were years of education (OR around 0.9), difficulties making ends meet (OR between 1.6 and 2.1), mother's age at death (OR under 1), and father's age at death (OR under 1).Consistently with the literature, education and income explained, in both sexes, cross-sectional variability in FI in subjects of the same chronological age group. The influence of parental longevity seemed to be greater in men, which mirrors previous studies showing that genetic factors might have a higher impact on longevity in men.

SUBMITTER: Romero-Ortuno R

PROVIDER: S-EPMC3843989 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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Frailty Index in Europeans: association with determinants of health.

Romero-Ortuno Roman R

Geriatrics & gerontology international 20130723 2

<h4>Aim</h4>The Frailty Index (FI) summarizes differences in health status within individuals, and the determinants of health drive that variability. The aim of the present study was to investigate the influence of education, income, smoking, alcohol intake and parental longevity on the FI variability in subjects of the same chronological age group.<h4>Methods</h4>Analyses were based on a 40-item FI based on the first wave of the Survey of Health, Aging and Retirement in Europe (SHARE, http://ww ...[more]

PMID: 23879634

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Project description:ImportanceFrail patients are known to have poor perioperative outcomes. There is a paucity of literature investigating how the Modified Frailty Index (mFI), a validated measure of frailty, is associated with unplanned readmission among military veterans following surgery.ObjectiveTo understand the association between frailty and 30-day postoperative unplanned readmission.Design, setting, and participantsA retrospective cohort study was conducted among adult patients who underwent surgery and were discharged alive from Veterans Affairs hospitals for orthopedic, general, and vascular conditions between October 1, 2007, and September 30, 2014, with a postoperative length of stay between 2 and 30 days.ExposureFrailty, as calculated by the 11 variables on the mFI.Main outcomes and measuresThe primary outcome of interest is 30-day unplanned readmission. Secondary outcomes included any 30-day predischarge or postdischarge complication, 30-day postdischarge mortality, and 30-day emergency department visit.ResultsThe study sample included 236 957 surgical procedures (among 223 877 men and 13 080 women; mean [SD] age, 64.0 [11.3] years) from high-volume surgical specialties: 101 348 procedures (42.8%) in orthopedic surgery, 92 808 procedures (39.2%) in general surgery, and 42 801 procedures (18.1%) in vascular surgery. The mFI was associated with readmission (odds ratio [OR], 1.11; 95% CI, 1.10-1.12; R2 = 10.3%; C statistic, 0.71). Unadjusted rates of overall 30-day readmission (26 262 [11.1%]), postdischarge emergency department visit (34 204 [14.4%]), any predischarge (13 855 [5.9%]) or postdischarge (14 836 [6.3%]) complication, and postdischarge mortality (1985 [0.8%]) varied by frailty in a dose-dependent fashion. In analysis by individual mFI components using Harrell ranking, impaired functional status, identified as nonindependent functional status (OR, 1.16; 95% CI, 1.11-1.21; P < .01) or having a residual deficit from a prior cerebrovascular accident (OR, 1.17; 95% CI, 1.11-1.22; P < .01), contributed most to the ability of the mFI to anticipate readmission compared with the other components. Acutely impaired sensorium (OR, 1.12; 95% CI, 0.99-1.27; P = .08) and history of a myocardial infarction within 6 months (OR, 0.93; 95% CI, 0.81-1.06; P = .28) were not significantly associated with readmission.Conclusions and relevanceThe mFI is associated with poor surgical outcomes, including readmission, primarily due to impaired functional status. Targeting potentially modifiable aspects of frailty preoperatively, such as improving functional status, may improve perioperative outcomes and decrease readmissions.

Project description:Importance:Objective preoperative risk assessment tools, such as the Modified Frailty Index (mFI), may inform patient and physician decision making when considering total laryngectomy. Estimation of outcomes may help to set realistic expectations about recovery and outcomes and facilitate optimal resource management. Objective:To evaluate the association between the mFI score as a measure of frailty and outcomes following total laryngectomy. Design, Setting, and Participants:Retrospective evaluation using the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP), a risk- and case-mix-adjusted national quality assessment program. The ACS NSQIP database identified 595 patients who underwent total laryngectomy between 2006 and 2012. Patients were assessed for demographics and comorbidity and were stratified on the basis of calculated mFI score. Outcomes, including postoperative complications, length of hospitalization, and discharge destination, were evaluated as a function of increasing frailty using multivariable logistic regression and Cox proportional hazards regression models. Main Outcomes and Measures:Risk of postoperative complications, length of hospitalization, and discharge disposition. Results:After exclusion of patients who experienced significant deviation from standard care protocols and those with missing or incomplete data, 343 individuals were included in the analysis. Of these, 278 (81.0%) were men, and the mean age was 63 years (95% CI, 61.9-64.4 years). Increasing frailty resulted in a nonlinear but progressive rise in incidence of postoperative adverse events. Overall, 96 (28.0%) patients experienced a postoperative complication, and patients with an mFI score of 3 or higher were more likely to develop postoperative complications than were patients with an mFI score of 0 (50.0% vs 16.7%; OR, 3.83; 95% CI, 1.72- 8.51). Patients in the highest frailty group experienced a longer mean duration of hospitalization (14.2 vs 9.5 days; difference, 4.7; 95% CI, 1.3-8.1 days) and were more likely to require skilled care after discharge (33.3% vs 3.2%; difference, 30.1%; 95% CI, 7.4%-52.9%). Conclusions and Relevance:An mFI score of 3 or higher is associated with increased risk for postoperative complications, longer hospitalization, and need for postdischarge skilled care following total laryngectomy. The mFI provides a personalized risk assessment to better inform patients, physicians, and payers when planning a total laryngectomy.

Project description:BackgroundFew studies have made longitudinal comparisons between frailty phenotype (FP) and frailty index (FI) changes. We aimed to investigate frailty status changes defined by FP and FI concurrently, and to compare the associated factors and incident disability among different combination of FI and FP trajectory groups.MethodsData on respondents aged over 50 who completed the 1999, 2003 and 2007 Taiwan Longitudinal Study on Aging (TLSA) surveys (n = 2807) were excerpted. Changes of FI, FP and major time-dependent variables were constructed by group-based trajectory modeling. Logistic regression was used to investigate the associated factors and relationships with incident disability among different frailty trajectories.ResultsWe identified four FP trajectories - stably robust, worsened frailty, improved frailty, and stably frail and three FI trajectories - stable FI, moderate increase FI and rapid increase FI. Lower self-rated health, mobility impairment, and depressed mood were associated with unfavorable FP and FI changes (all p < 0.001). Regardless of FP trajectory groups, the moderate and rapid increase FI group had significantly more comorbidities than the stable FI group, and more visual, hearing, oral intake impairment, more difficulty in meeting living expenses, and poorer cognitive function in ≥65-year-olds (all p < 0.05). In addition, the worsened frailty, improved frailty, and stably frail groups had ORs for incident disability of 10.5, 3.0, and 13.4, respectively, compared with the stably robust group (all p < 0.01); the moderate and rapid increase FI groups had 8.4-fold and 77.5-fold higher risk than the stable FI group (both p < 0.001). When combining FI and FP trajectories, risk increased with FI trajectory steepness, independent of FP change (all p < 0.01 in rapid increase FI vs stable FI).ConclusionsFour FP trajectories (stably robust, worsened frailty, improved frailty, and stably frail) and three FI trajectories (stable FI, moderate increase FI and rapid increase FI) were identified. Lower self-rated health, mobility impairment, and depressed mood were associated with both unfavorable FP and FI trajectories. Nevertheless, even for individuals in stably robust or improved frailty FP groups, moderate or rapid increase in FI, either due to comorbidities, sensory impairment, cognitive deficits, or financial challenges, may still increase the risk of incident disability.

Project description:INTRODUCTION:Frailty is a geriatric syndrome that has been defined differently with various indices. Without a uniform definition, it remains unclear how to interpret and compare different frailty indices (FIs). With the advances in index mining, we find it necessary to review the implicit assumptions about the creation of FIs. We are concerned the processing of frailty data may introduce measurement error and bias. We aim to review the assumptions, interpretability and predictive power of FIs regarding mortality. METHODS:Three FIs, the Functional Domains Model proposed by Strawbridge et al. (1998), the Burden Model by Rockwood et al. (2007) and the Biologic Syndrome Model by Fried et al. (2004), were directly compared using the data from the Health and Retirement Study (HRS), a longitudinal study since 1996 mainly following up Americans aged 50 years and over. The FIs were reproduced according to Cigolle et al. (2009) and interpreted with their input variables through forward-stepwise regression. Biases were the residuals of the FIs that could not be explained by own input variables. Any four of the input variables were used to create alternative indices. Discrete-time survival analysis was conducted to compare the predictive power of FIs, input variables and alternative indices on mortality. RESULTS:We found frailty a syndrome not unique to the elderly. The FIs were produced with different degrees of bias. The FIs could not be fully interpreted with the theory-based input variables. The bias induced by the Biological Syndrome Model better predicted mortality than frailty status. A complicated FI, the Burden Model, could be simplified. The input variables better predicted mortality than the FIs. The continuous FIs predicted mortality better than the frailty statuses. At least 6865 alternative indices better predicted mortality than the FIs. CONCLUSION:FIs have been used as outcome in clinical trials and need to be reviewed for adequacy based on our findings. The three FIs are not closely linked to the theories because of bias introduced by data manipulation and excessive numbers of input variables. We are developing new algorithms to develop and validate innovative indices.

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Frailty Index in Europeans: association with determinants of health.

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Frailty Index in Europeans: association with determinants of health.

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