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Optimal cholesterol treatment plans and genetic testing strategies for cardiovascular diseases

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Abstract

Atherosclerotic cardiovascular disease (ASCVD) is among the leading causes of death in the US. Although research has shown that ASCVD has genetic elements, the understanding of how genetic testing influences its prevention and treatment has been limited. To this end, we model the health trajectory of patients stochastically and determine treatment and testing decisions simultaneously. Since the cholesterol level of patients is one controllable risk factor for ASCVD events, we model cholesterol treatment plans as Markov decision processes. We determine whether and when patients should receive a genetic test using value of information analysis. By simulating the health trajectory of over 64 million adult patients, we find that 6.73 million patients undergo genetic testing. The optimal treatment plans informed with clinical and genetic information save 5,487 more quality-adjusted life-years while costing $1.18 billion less than the optimal treatment plans informed with clinical information only. As precision medicine becomes increasingly important, understanding the impact of genetic information becomes essential.

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Funding

Wesley J. Marrero’s work on this study was supported in part by the National Science Foundation grant DGE 1256260. Mariel S. Lavieri’s work was supported in part by the National Science Foundation grant CMMI-1552545. Jeremy B. Sussman’s work was supported in part by the United States Department of Veterans Affairs grants VA HSR&D CDA13-021 and VA HSR&D IIR 15-432. The National Science Foundation and the Veterans Affairs had no influence on the study’s design, conduct, and/or reporting.

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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Wesley J. Marrero. The first draft of the manuscript was written by Wesley J. Marrero and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Wesley J. Marrero.

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Appendices

Appendix

1.1 A.1 Progression of Risk Factors Over Time

Table 4 Linear regression models coefficients

A.2 Convergence Analysis

Fig. 8
figure 8

Convergence of QALYs and cost saved in our simulation over the number of health trajectory replications under a single GRS realization per patient. Red line represents the QALYs and cost saved at 2,000 health trajectory replications

Fig. 9
figure 9

Convergence of QALYs and cost saved in our simulation over the number of GRS realizations with a fixed number of heath trajectory replications. Red line represents the QALYs and cost saved at 500 GRS realizations and 500 health trajectory replications

Fig. 10
figure 10

Convergence of QALYs and cost saved in our simulation over the number of health trajectory replications under 100 GRS realizations per patient. Red line represents the QALYs and cost saved at 750 health trajectory replications and 100 GRS realizations

A.3 Results of Sensitivity Analysis by Age Groups

Fig. 11
figure 11

Testing cost sensitivity analysis results by age group. Shaded boxplots represent the base case, asterisks represent outliers, and points in the center of the boxes represent the average testing year

Fig. 12
figure 12

Treatment cost sensitivity analysis results by age group. Shaded boxplots represent the base case, asterisks represent outliers, and points in the center of the boxes represent the average testing year

Fig. 13
figure 13

Treatment disutility sensitivity analysis results by age group. Shaded boxplots represent the base case, asterisks represent outliers, and points in the center of the boxes represent the average testing year

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Marrero, W.J., Lavieri, M.S. & Sussman, J.B. Optimal cholesterol treatment plans and genetic testing strategies for cardiovascular diseases. Health Care Manag Sci 24, 1–25 (2021). https://doi.org/10.1007/s10729-020-09537-x

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