A standard procedure for creating a frailty index

Samuel D Searle, Arnold Mitnitski, Evelyne A Gahbauer, Thomas M Gill, Kenneth Rockwood, Samuel D Searle, Arnold Mitnitski, Evelyne A Gahbauer, Thomas M Gill, Kenneth Rockwood

Abstract

Background: Frailty can be measured in relation to the accumulation of deficits using a frailty index. A frailty index can be developed from most ageing databases. Our objective is to systematically describe a standard procedure for constructing a frailty index.

Methods: This is a secondary analysis of the Yale Precipitating Events Project cohort study, based in New Haven CT. Non-disabled people aged 70 years or older (n = 754) were enrolled and re-contacted every 18 months. The database includes variables on function, cognition, co-morbidity, health attitudes and practices and physical performance measures. Data came from the baseline cohort and those available at the first 18-month follow-up assessment.

Results: Procedures for selecting health variables as candidate deficits were applied to yield 40 deficits. Recoding procedures were applied for categorical, ordinal and interval variables such that they could be mapped to the interval 0-1, where 0 = absence of a deficit, and 1= full expression of the deficit. These individual deficit scores were combined in an index, where 0= no deficit present, and 1= all 40 deficits present. The values of the index were well fit by a gamma distribution. Between the baseline and follow-up cohorts, the age-related slope of deficit accumulation increased from 0.020 (95% confidence interval, 0.014-0.026) to 0.026 (0.020-0.032). The 99% limit to deficit accumulation was 0.6 in the baseline cohort and 0.7 in the follow-up cohort. Multivariate Cox analysis showed the frailty index, age and sex to be significant predictors of mortality.

Conclusion: A systematic process for creating a frailty index, which relates deficit accumulation to the individual risk of death, showed reproducible properties in the Yale Precipitating Events Project cohort study. This method of quantifying frailty can aid our understanding of frailty-related health characteristics in older adults.

Figures

Figure 1
Figure 1
Frailty Index Distribution. Gamma distribution fit (lines) of the observed distribution of the frailty index (bar) in the baseline (red) and 18 month follow up (blue) sample.
Figure 2
Figure 2
Frailty Index versus Age Plot. Frailty index versus age plot of baseline (light and dark red) and 18 month follow up (light and dark blue). Shown here are the average (dark blue/red) and the observed 99th percentile (light blue/red) lines. The slope of the best fit curves shows no accumulation of deficits in the most impaired (99th) of the sample. By contrast the follow up average curve has 2.6% deficit accumulation per year. The baseline average curve has a 2.0% deficit accumulation per year; the 99th percentile slope also shows no accumulation of deficits with age.
Figure 3
Figure 3
Variance in the Slope of the Frailty Index. The Bootstrapping of the frailty index. The frailty index was created and plotted 1000 times, each time randomly picking 80% of the variables of the index. Twenty iterations are shown here. The experimental and best fit regression lines of the average index values are shown in the baseline (red) and follow up (blue).

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