External Validation and Cost Effectiveness Analysis of the Non-LB Framingham Cardiovascular Disease Risk Assessment Algorithm in the Atherosclerosis Risk in Communities Dataset
Date of Award
Open Access Dissertation
Doctor of Philosophy (PhD)
Background: In recent years, non-Laboratory based (non-LB) risk assessment algorithms have been developed to facilitate absolute cardiovascular disease (CVD) risk assessment in resource constrained primary care settings. The non-LB Framingham algorithm, which substitutes body mass index (BMI) for lipids, has the best discrimination and calibration among the published algorithms, but its external validity and cost-effectiveness have not been determined.
Purpose: External validation and comparative effectiveness analysis of the non-LB versus laboratory based (LB) Framingham algorithm in a racially diverse population, and simulated cost-effectiveness analysis focusing on a black sample.
Methods: Secondary data analysis was performed using the Atherosclerosis Risk in Communities (ARIC) dataset. Cox regression models including the non-LB and LB Framingham covariates were developed. Model discrimination was assessed using the C statistic, calibration using the goodness-of-fit test, and equivalence of regression coefficients using the z-test. Algorithms based on the models were developed and their performance assessed using the area under receiver operating characteristic curve (AUROC), and agreement using kappa statistics. Analyses using simulated incremental cost-effectiveness ratios (ICER) were focused on the black sample. IRB approval was obtained. Data were analyzed using Stata© software version 14.
Results: Among 11,601 individuals (mean age 53.9 ± 5.7 years, 55% female, 24% black), the non-LB versus LB models performed as follows: C statistic (0.75 vs 0.76 for women, & 0.67 vs 0.68 for men); goodness-of-fit (14.2 vs 10.5 for women, & 25.8 vs 21.8 for men) respectively. In the black sample, regression coefficients of all covariates were similar to those generated in Framingham (z = ±1.96). The two algorithms based on the models had a kappa statistic of 0.76. When used to stratify risk in the entire ARIC sample, the non-LB and LB Framingham algorithms had AUROC of 0.706 vs 0.710 respectively. Prevention program guided by the non-LB Framingham dominated those guided by individual risk factors and LB Framingham algorithm.
Conclusions: These results demonstrate the validity and cost-effectiveness of the non-LB Framingham algorithm. This approach could provide a valuable and efficient alternative to the traditional LB approaches in the ongoing efforts to address the high burden of CVD in underserved communities.
Kariuki, Jacob K., "External Validation and Cost Effectiveness Analysis of the Non-LB Framingham Cardiovascular Disease Risk Assessment Algorithm in the Atherosclerosis Risk in Communities Dataset" (2016). Graduate Doctoral Dissertations. 280.