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Observational Study
. 2019 Jul 12;74(8):1282-1288.
doi: 10.1093/gerona/gly224.

Evaluation of Frailty as an Unmeasured Confounder in Observational Studies of Antidiabetic Medications

Affiliations
Observational Study

Evaluation of Frailty as an Unmeasured Confounder in Observational Studies of Antidiabetic Medications

Caroline A Presley et al. J Gerontol A Biol Sci Med Sci. .

Abstract

Background: It is unknown whether observational studies evaluating the association between antidiabetic medications and mortality adequately account for frailty. Our objectives were to evaluate if frailty was a potential confounder in the relationship between antidiabetic medication regimen and mortality and how well administrative and clinical electronic health record (EHR) data account for frailty.

Methods: We conducted a retrospective cohort study in a single Veterans Health Administration (VHA) healthcare system of 500 hospitalizations-the majority due to heart failure-of Veterans who received regular VHA care and initiated type 2 diabetes treatment from 2001 to 2008. We measured frailty using a modified frailty index (FI, >0.21 frail). We obtained antidiabetic medication regimen and time-to-death from administrative sources. We compared FI among patients on different antidiabetic regimens. Stepwise Cox proportional hazards regression estimated time-to-death by demographic, administrative, clinical EHR, and FI data.

Results: Median FI was 0.22 (interquartile range 0.18, 0.27). Frailty differed across antidiabetic regimens (p < .001). An FI increase of 0.05 was associated with an increased risk of death (hazard ratio 1.45, 95% confidence interval 1.32, 1.60). Cox proportional hazards model for time-to-death including demographic, administrative, and clinical EHR data had a c-statistic of 0.70; adding FI showed marginal improvement (c-statistic 0.72).

Conclusions: Frailty was associated with antidiabetic regimen and death, and may confound that relationship. Demographic, administrative, and clinical EHR data, commonly used to balance differences among exposure groups, performed moderately well in assessing risk of death, with minimal gain from adding frailty. Study design and analytic techniques can help minimize potential confounding by frailty in observational studies.

Keywords: Diabetes; Drug-related; Epidemiology; Frailty.

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Figures

Figure 1.
Figure 1.
Relationship of patient frailty (frailty index) with antidiabetic medication regimen (exposure) and mortality (outcome), unadjusted analyses. (A) Boxplot of patient frailty (FI) by antidiabetic medication regimen categories. (B) Hazard ratio of outcome, time-to-death, by frailty index relative to median patient frailty (FI = 0.22) – unadjusted Cox proportional hazards model and boxplot of patient FI.
Figure 2.
Figure 2.
Receiver operating characteristic curves for stepwise Cox proportional hazards models using administrative variables and patient frailty (FI) to predict time-to-death at median survival time of 3.4 years. Step 0 predicts time-to-death by chart-abstracted frailty unadjusted for any covariates. Additional steps build sequentially including: demographics (Step 1); administrative data (Step 2); clinical electronic health record (EHR) data (Step 3); chart-abstracted frailty index (Step 4). C-statistics for each step: Step 0 = 0.67 (frailty alone); Step 1 = 0.64; Step 2 = 0.70; Step 3 = 0.70; Step 4 = 0.72.
Figure 3.
Figure 3.
Hazard ratio of frailty index (FI) upon time-to-death when comparing mean FI of patients on metformin plus insulin versus the mean FI of patients on metformin plus sulfonylurea. Three analytic strategies were undertaken to control for confounding: (a) unadjusted for any confounding variables, (b) direct covariate adjustment for Step 3 variables (except for antidiabetic medication regimen) reduced to the first five principal components, and (c) the same direct adjustment using first five principal components on a propensity score weighted cohort.

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