Abstract Background The health economic evidence about the value and optimal targeting of genetic testing in the prevention of coronary heart disease (CHD) events has remained limited and ambiguous. The objective of this study is to optimize the population-level use and targeting of genetic testing alongside traditional risk factors in the prevention of CHD events and, thereby, to assess the cost-benefit of genetic testing. Methods and findings We compare several strategies for using traditional and genetic testing in the prevention of CHD through statin therapy. The targeting of tests to different patient segments within these strategies is optimized by using a decision-analytic model, in which a patient’s estimated risk of CHD is updated based on test results using Bayesian methods. We adopt the perspective of healthcare sector. The data for the model is exceptionally wide and combined from national healthcare registers, the Finnish Institute for Molecular Medicine, and published literature. Our results suggest that targeting genetic testing in an optimal way to those patients about which traditional risk factors do not provide sufficiently accurate information results in the highest expected net benefit. In particular, compared to the use of traditional risk factors only, the optimal use of genetic testing would decrease the expected costs of an average patient aged 45 years or more by 2.54€ in a 10-year follow-up period while maintaining the level of the expected health outcome. Thus, genetic testing is found to be a part of a cost-beneficial testing strategy alongside traditional risk factors. This conclusion is robust to reasonable changes in model inputs. Conclusions If targeted optimally, the use of genetic testing alongside traditional risk factors is cost-beneficial in the prevention of CHD.

Citation: Hynninen Y, Linna M, Vilkkumaa E (2019) Value of genetic testing in the prevention of coronary heart disease events. PLoS ONE 14(1): e0210010. https://doi.org/10.1371/journal.pone.0210010 Editor: Benjamin Peter Geisler, Massachusetts General Hospital, UNITED STATES Received: June 13, 2018; Accepted: December 14, 2018; Published: January 15, 2019 Copyright: © 2019 Hynninen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All relevant data except data on tests’technical performance are within the paper and its Supporting Information files. Data on tests’ technical performance (the conditional probabilities of obtaining any test result given the patient’s state of health (CHD or no CHD)) are from the study "Genomic prediction of coronary heart disease” (https://doi.org/10.1093/eurheartj/ehw450) whose authors may be contacted at the Institute for Molecular Medicine Finland (FIMM)). In order to obtain the conditional probabilities, the authors of "Genomic prediction of coronary heart disease” have carried out both traditional and genetic risk scores for the samples of the national FINRISK study (1992, 1997, 2002, 2007). In order to access the samples, one may contact THL Biobank (National Institute for Health and Welfare (THL); https://thl.fi/en/web/thl-biobank/for-researchers) which administrates the samples. In order to access the conditional probabilities, one may contact the authors of "Genomic prediction of coronary heart disease” at the Institute for Molecular Medicine Finland (FIMM). Either way, one needs to apply for the permission of the THL Biobank (https://thl.fi/en/web/thl-biobank/for-researchers/apply). We confirm that others are able to access these data in the same manner as we have. We also confirm that we did not have any special access privileges that others would not have. Funding: Financial support for this study was provided in part by a contract with Aalto University (YH, ML, EV), in part by a grant from the SalWe Research Program for GET IT DONE (Tekes – the Finnish Funding Agency for Technology and Innovation grant 534/14; https://www.tekes.fi/) (ML), and in part by a grant from the Academy of Finland’s IMPRO project (https://www.stnimpro.fi/) (ML). This research has been partly supported by the PVN project, funding decision 293446 of the Strategic Research Council of the Academy of Finland. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist.

Introduction Coronary heart diseases (CHD) are the leading global cause of death, accounting for more than 7 million deaths per year [1]. Reducing the burden of CHDs and other cardiovascular diseases (CVD) with appropriate prevention is one of the objectives of the World Health Organization [2]. In order to target preventive interventions such as statin treatment in a cost-effective way, it is important to obtain reliable prognostic information on the patients’ state of health. Traditionally different risk measures based on clinical factors and lipid measurements, such as the Framingham Risk Score [3,4] or FINRISK function [5], have been used for this purpose. However, these measures are far from perfect: it has been estimated that more than half of all coronary heart disease events occur in individuals with estimated risk at low or average levels [6]. Over the last decade, research efforts have increased to discover the potential benefits of using genome information in the prevention of CVD [6–9]. Many studies have focused on one or a few candidate genes for CVD [10] and, more recently, genome-wide studies have tested tens of thousands of single-nucleotide polymorphisms (SNPs) in an effort to identify associations with CVD [7]. However, evidence—particularly health economic evidence—about the value of genetic testing has remained limited and ambiguous. On the one hand, it has been suggested that genetic testing strategies for cardiovascular diseases are more likely to be cost-effective than clinical tests alone [11,12] but, on the other hand, that the extent of cardiovascular disease risk reclassification would be small [7]. Specifically, economic evaluations of genetic testing have focused on intermediate outcomes such as cost per cases detected [13,14]. Studies which have explored the economic value of testing based on, e.g., cost-utility analysis are still relatively scarce [14]. A recent study evaluates the cost-effectiveness of a 27-SNP cardiovascular genetic risk score and concludes that genetic testing is generally not a cost-effective approach for targeting statin therapy for low- to intermediate-risk patients [15]. Yet, even in these more comprehensive analyses, the focus has been mainly on predetermined strategies in which, for instance, patient segments to be tested have been fixed in advance [15]. Additional challenges for reliable health economic analyses have been posed by inadequate data. In this paper, we develop a decision-analytic model for optimizing the population-wide use of traditional risk factors and genetic testing in the prevention of CHD. We model a testing strategy as a decision tree in which the probability of a patient having CHD is updated based on test results using Bayesian methods. Based on optimal testing strategies, the cost-benefit of genetic testing compared to the use of traditional risk factors only can be reliably assessed. We utilize an extensive data set collected for the GeneRISK study [16] which includes, for instance, genetic test results of wide FINRISK cohorts and average CHD outcomes from national registries. We show that the use of genetic testing—if targeted optimally—is cost-beneficial in the prevention of CHD.

Discussion In this paper we built a decision-analytic model to optimize the use of traditional and genetic testing to support the targeting of statin medication treatment in preventing coronary heart disease. Using the model, we were able to assess the cost-benefit of genetic testing from the perspective of healthcare sector. Our results suggest that genetic testing is a promisingly cost-effective technology in the prevention of CHD: Compared to the optimal use of traditional risk factors only, the inclusion of genetic testing would decrease the expected costs of a single patient by 2.54€ during a 10-year follow-up period while maintaining approximately the level of the expected health outcome. In a Finnish population of 100,000 patients aged 45 or more, genetic testing would be expected to be carried out for 2,910 patients, decreasing the total costs by 254,000€ and increasing the health outcome by 1.7 QALYs. Our work has some limitations. First, our model is static in that it considers the risk of a patient having a CHD event in the following ten years without explicitly modeling the deterioration in the patients’ health over time. In this respect, combining our decision-analytic model with a Markov model would increase the relevance of our results by supporting the optimal timing of interventions as well. Second, our model did not account for information provided by genetic testing about other diseases besides CHD, or about the state of health of the testees’ family members who share the same genetic traits. In addition, we did not account for the impact that the acquisition of genetic risk information might have on the patients’ adherence or motivation to improve their lifestyle habits. Third, we considered statin therapy as the only treatment option, although other options for mitigating the risk for CHD are available, such as health coaching interventions (albeit data about their effectiveness might be difficult to obtain). In particular, patients with a high risk of CHD tend to be on multiple drugs alongside statin, including angiotensin-converting enzyme inhibitors for treating hypertension. Ideally, the model should be extended to accommodate the costs and health-related impacts of all relevant interventions. Yet, in the absence of such data, preliminary conclusions about the use of multiple drugs can be drawn from sensitivity analyses on the cost of medication. Specifically, if the cost of medication increases due to the use of multiple drugs, then a smaller segment of patients will be treated, whereby the importance of a correct targeting of treatment is emphasized even more. Consequently, the incremental net benefit of being able to improve prognostic accuracy through genetic testing increases. For instance, when the annual cost of medication and monitoring is doubled from 226€ to 452€, then the optimal share of patients tested with GRS increases from 3% to 4%. Fourth, the GRS used in this study has recently been superseded by multiple groups [40,41], whereby the overall benefits of genetic testing in the optimal targeting of preventive treatment options may have been crudely underestimated. Finally, the price of genome sequencing continues to decline, which further increases its potential usefulness in the prevention and treatment of not only CHD but other chronic diseases as well. To our knowledge, this paper is the first to optimize the targeting of genetic tests to different patient segments in the prevention of CHD. As illustrated by the comparison between optimized and predetermined, non-optimized testing strategies, optimization is crucially important in assessing the cost-benefit of testing technologies: The three strategies with the highest expected net monetary benefits were all optimized strategies, whereas the four non-optimized strategies were clearly the most expensive but did not result in a substantial increase in health outcomes. Consequently, to obtain justifiable results about the cost-benefit of new tests in the prevention of CHD or other diseases, it is necessary to optimize the entire population-level testing strategy instead of, e.g., following the conventional approach of targeting these new tests to some predetermined risk categories. If the required data can be obtained, the model presented in this paper can be readily applied to optimize the use of genetic testing (or other prognostic/diagnostic innovations) in the context of any disease. The contribution of this study is further strengthened by the access to extensive patient data obtained from the Finnish Institute for Molecular Medicine and national healthcare registers. This data made it possible to estimate the performance of traditional and genetic risk scores as well as the outcomes and costs of CHD events. Combining information from various sources is indeed a prerequisite for obtaining reliable assessments about the cost-effectiveness of different measures taken to prevent CHD or other diseases. For this purpose, among others, the Finnish government decided in 2016 to invest 17 million euros in a new national center which integrates all genome, biobank, and healthcare utilization registers [42]. Data collected by this center would enable the acquisition of more accurate risk estimates for individual patients which, in turn, would result in more accurate cost-effectiveness analyses about new tests. The development of predictive models that can be used to map these data into patients’ risk estimates provides an interesting topic for future work.