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Predictive performance of four frailty measures in an older Australian population.

ABSTRACT: There are several different frailty measures available for identifying the frail elderly. However, their predictive performance in an Australian population has not been examined.To examine the predictive performance of four internationally validated frailty measures in an older Australian population.A retrospective study in the Australian Longitudinal Study of Ageing (ALSA) with 2,087 participants. Frailty was measured at baseline using frailty phenotype (FP), simplified frailty phenotype (SFP), frailty index (FI) and prognostic frailty score (PFS). Odds ratios (OR) were calculated to measure the association between frailty and outcomes at Wave 3 including mortality, hospitalisation, nursing home admission, fall and a combination of all outcomes. Predictive performance was measured by assessing sensitivity, specificity, positive and negative predictive values (PPV and NPV) and likelihood ratio (LR). Area under the curve (AUC) of dichotomised and the multilevel or continuous model of the measures was examined.Prevalence of frailty varied from 2% up to 49% between the measures. Frailty was significantly associated with an increased risk of any outcome, OR (95% confidence interval) for FP: 1.9 (1.4-2.8), SFP: 3.6 (1.5-8.8), FI: 3.4 (2.7-4.3) and PFS: 2.3 (1.8-2.8). PFS had high sensitivity across all outcomes (sensitivity: 55.2-77.1%). The PPV for any outcome was highest for SFP and FI (70.8 and 69.7%, respectively). Only FI had acceptable accuracy in predicting outcomes, AUC: 0.59-0.70.Being identified as frail by any of the four measures was associated with an increased risk of outcomes; however, their predictive accuracy varied.

SUBMITTER: Widagdo IS

PROVIDER: S-EPMC4621238 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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Predictive performance of four frailty measures in an older Australian population.

Widagdo Imaina S IS Pratt Nicole N Russell Mary M Roughead Elizabeth E EE

Age and ageing 20151101 6

<h4>Background</h4>There are several different frailty measures available for identifying the frail elderly. However, their predictive performance in an Australian population has not been examined.<h4>Objective</h4>To examine the predictive performance of four internationally validated frailty measures in an older Australian population.<h4>Methods</h4>A retrospective study in the Australian Longitudinal Study of Ageing (ALSA) with 2,087 participants. Frailty was measured at baseline using frailt ...[more]

PMID: 26504118

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Project description:Given that the global population is aging, the number of age-related syndromes, such as frailty, is expected to rise in conjunction. Frailty is characterized by the loss of homeostatic reserve, rendering the individual vulnerable to poor health outcomes. Many biological mechanisms have been proposed to contribute to frailty. However, few studies have assessed the associations between frailty and brain diseases or neuroimaging biomarkers. Aims: The aims of this study were to measure the prevalence of frailty in a memory clinic and to examine associations between frailty and brain changes found on magnetic resonance imaging (MRI) and 18-F deoxyglucose (FDG) positron emission tomography (PET) in memory clinic attendees. Methods: A 54-items Frailty Index was retrospectively assessed for all clinic attendees from 2014. Frailty was defined as FI > 0.25. MR images were analyzed for stroke, cerebral small vessel disease [CSVD, including cerebral microbleeds (CMBs), cortical superficial siderosis (CSS), and white matter hyperintensity (WMH)], and neurodegenerative changes [MRI: mesial temporal atrophy (MTA), FDG-PET: regional hypometabolism], blind to clinical findings. Results: There were 209 clinic attendees in 2014, of whom 121 had MRI performed. The prevalence of frailty (using FI) in the memory clinic in 2014 was 38.3% overall (patients without MRI: 43.2%, patients with MRI 34.7%, p = 0.25). Frailty was associated with presence of deep WMH, increased severity of periventricular WMH, and presence of CSS, but not neurodegeneration markers (MTA atrophy/FDG-PET hypometabolism). Conclusion: The findings support the idea that previously reported associations between frailty and imaging evidence of CSVD in other cohorts are also relevant to the Australian clinic setting. Given that a large proportion of memory clinic attendees are frail, there may be opportunities for interventions to reduce preventable adverse health outcomes, such as falls and fractures, and reduce the prevalence and impact of frailty in this cohort.

Project description:Background and purposePolygenic risk scores (PRSs) can be used to predict ischemic stroke (IS). However, further validation of PRS performance is required in independent populations, particularly older adults in whom the majority of strokes occur.MethodsWe predicted risk of incident IS events in a population of 12 792 healthy older individuals enrolled in the ASPREE trial (Aspirin in Reducing Events in the Elderly). The PRS was calculated using 3.6 million genetic variants. Participants had no previous history of cardiovascular events, dementia, or persistent physical disability at enrollment. The primary outcome was IS over 5 years, with stroke subtypes as secondary outcomes. A multivariable model including conventional risk factors was applied and reevaluated after adding PRS. Area under the curve and net reclassification were evaluated.ResultsAt baseline, mean population age was 75 years. In total, 173 incident IS events occurred over a median follow-up of 4.7 years. When PRS was added to the multivariable model as a continuous variable, it was independently associated with IS (hazard ratio, 1.41 [95% CI, 1.20–1.65] per SD of the PRS; P<0.001). The PRS alone was a better discriminator for IS events than most conventional risk factors. PRS as a categorical variable was a significant predictor in the highest tertile (hazard ratio, 1.74; P=0.004) compared with the lowest. The area under the curve of the conventional model was 66.6% (95% CI, 62.2–71.1) and after inclusion of the PRS, improved to 68.5 ([95% CI, 64.0–73.0] P=0.095). In subgroup analysis, the continuous PRS remained an independent predictor for large vessel and cardioembolic stroke subtypes but not for small vessel stroke. Reclassification was improved, as the continuous net reclassification index after adding PRS to the conventional model was 0.25 (95% CI, 0.17–0.43).ConclusionsPRS predicts incident IS in a healthy older population but only moderately improves prediction over conventional risk factors.RegistrationURL: https://www.clinicaltrials.gov; Unique identifier: NCT01038583.

Project description:ObjectivesDetermine the effects of missing data in frailty identification and risk prediction.DesignAnalysis of the National Health in Aging Trends Study.SettingCommunity.ParticipantsAbout 6206 older adults.MeasurementsA 41-variable frailty index (FI) was constructed with the following domains: comorbidities, activities of daily living (ADLs), instrumental activities of daily living, self-reported physical limitations, physical performance, and neuropsychiatric tests. We evaluated discrimination after removing single and multiple domains, comparing C-statistics for predicting 5-year risk of mortality and 1-year risks of disability and falls.ResultsThe full FI yielded a mean of .18 and C-statistics of .72 (95% confidence interval, .70-.74) for mortality, .80 (.77-.82) for disability, and .66 (.64-.68) for falls. Removal of any single domain shifted the FI distribution, resulting in a mean FI ranging from .13 (removing comorbidities) to .20 (removing ADLs) and frailty prevalence (FI ≥ .25) from 16.0% to 28.7%. Among robust participants models missing ADLs misclassified most often, (19% as pre-frail). Among pre-frail and frail participants missing comorbidities misclassified most often(69.2% from pre-frail to robust, 24% from frail to pre-frail, and 4.9% from frail to robust). Removal of any single domain minimally changed C-statistics: mortality, .71-.73; disability, .79-.80; and falls, .64-.66. Removing neuropsychiatric testing and physical performance yielded comparable C-statistics of .70, .78, and .66 for mortality, ADLs, and falls, respectively. However, removal of three or four domains based on likely availability decreased C-statistics for mortality (.69, .66),disability (.75, .70), and falls (.64, .63), respectively.ConclusionWhile FI discrimination is robust to missing information in any single domain, risk prediction is affected by absence of multiple domains. This work informs the application of FI as a clinical and research tool. J Am Geriatr Soc 68:1771-1777, 2020.

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Predictive performance of four frailty measures in an older Australian population.

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Predictive performance of four frailty measures in an older Australian population.

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