Cost-Effectiveness of Genotype-Guided Acenocoumarol Therapy in Atrial Fibrillation: A Pharmacogenomic Simulation Study in the Chilean Population

Cost-Effectiveness of Genotype-Guided Acenocoumarol Therapy in Atrial Fibrillation: A Pharmacogenomic Simulation Study in the Chilean Population | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Cost-Effectiveness of Genotype-Guided Acenocoumarol Therapy in Atrial Fibrillation: A Pharmacogenomic Simulation Study in the Chilean Population Luis Quinones, Maximiliano Mena, Matías Carrasco, Leslie Cerpa, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7411548/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Apr, 2026 Read the published version in The Pharmacogenomics Journal → Version 1 posted 7 You are reading this latest preprint version Abstract Cardiovascular diseases are the leading cause of death in Chile and worldwide. In atrial fibrillation, anticoagulation is essential, and in Chile acenocoumarol rather than warfarin, used in most countries, is the standard agent. Its dosing shows substantial interindividual variability due to CYP2C9 and VKORC1 polymorphisms. We developed a cohort-based Markov model to compare standard care, genotype-guided dosing, and genotype-guided dosing adjusted for population-level adherence in 123 Chilean patients and 123 matched simulated individuals. Outcomes were measured as quality-adjusted life years (QALYs) and direct medical costs, with cost-effectiveness assessed at a willingness-to-pay threshold of US$17,093. Genotype-guided dosing achieved the highest effectiveness (2938.34 QALYs) with an incremental cost-effectiveness ratio of US$436.86/QALY versus standard care, remaining cost-effective in sensitivity analyses up to test prices far exceeding the current US$190. The adherence-adjusted strategy was weakly dominated. These results strongly support implementing pharmacogenetic testing for acenocoumarol dosing to optimize anticoagulation safety, efficacy, and cost-effectiveness in Chile Health sciences/Biomarkers/Predictive markers Health sciences/Health care/Health policy Health sciences/Health care/Public health pharmacogenetics acenocoumarol cost-effectiveness anticoagulation Chile atrial fibrillation Markov model ICER CYP2C9 VKORC1 Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Cardiovascular diseases (CVDs) are the leading cause of death worldwide, accounting for approximately 17.9 million deaths annually 1 . These conditions encompass a broad spectrum of disorders affecting the heart and blood vessels, including coronary artery disease, stroke, and peripheral arterial disease. In Chile, CVDs are also the primary cause of mortality, responsible for 24.47% of all deaths in 2022 2 . Among CVDs, coagulation-related disorders are of public concern due to the severe consequences they may have on affected individuals and their families if left untreated. These consequences can range from minor medical incidents to severe outcomes such as disability from a cerebrovascular accident or even death. Therefore, preventive therapies—especially anticoagulation—are essential. However, these treatments must be individualized to help patients reach their optimal therapeutic range as quickly and safely as possible. This interindividual variability is strongly influenced by genetic polymorphisms in key metabolic enzymes, particularly CYP2C9 and VKORC1 , which are critical in the metabolism of vitamin K antagonists such as warfarin and acenocoumarol. These genetic variants can significantly alter drug response, making pharmacogenetically guided dosing a promising strategy to personalize therapy and reduce the risks of haemorrhagic or thrombotic complications 3 , 4 . In this context, genotype-based dosing algorithms are gaining momentum and becoming more commonly used due to their clinical benefits, including faster attainment of the therapeutic INR range and reduced risk of adverse events 5 .Nevertheless, their widespread adoption is often met with scepticism due to the higher initial costs compared to standard care. The overall effectiveness of these pharmacogenetic strategies depends on three key dimensions: (i) the clinical efficacy of the medication, (ii) patient adherence to the prescribed treatment, and (iii) the associated costs of therapy. In 2020, we published the first Latin American pharmacogenetic dosing algorithm for acenocoumarol 6 . The main objective was to reduce the time variability in achieving therapeutic INR levels due to genetic differences among Chilean patients. The resulting algorithm explained 49.99% of the variability in therapeutic dose requirements within the studied population. To assess the economic dimension—specifically, whether investing in pharmacogenetic-guided therapy is justified—various analytic strategies have been developed that integrate both clinical effectiveness and treatment costs. Among these, state-transition models have proven particularly useful for simulating the behavior of patient cohorts within specific time periods. The most applied models in this context are Markov models, which are widely used in economic evaluations in other countries 7 and thus serve as the methodological foundation of this study. Genotype-guided therapy, particularly in the management of anticoagulation, is increasingly recognized for its clinical and economic advantages. Testing for CYP2C9 and VKORC1 genomic variants, which influence acenocoumarol metabolism, has been shown to shorten the time to reach stable INR levels and reduce the incidence of adverse events such as bleeding or thrombosis. These pharmacogenetic tests are both reliable and cost-effective, with prices ranging from U $ 250 to U $ 630 8,9 . At the regional level, some studies have explored the implementation of genotype-guided therapy, but few have translated this approach into applied economic evaluations. This study aims to address that gap by evaluating the cost-effectiveness of genotype-guided acenocoumarol therapy in the Chilean context. Three strategies were compared: (i) standard dosing, (ii) genotype-guided dosing with partial adherence (reflecting the general population), and (iii) genotype-guided dosing assuming full adherence. In all scenarios, relevant demographic variables such as BMI, gender, and age were incorporated to reflect patient heterogeneity. Materials and Methods The cost-effectiveness analysis was performed from the perspective of the Chilean public healthcare system, incorporating cost data from the National Health Fund (FONASA) and the Digital Hospital platform of the Chilean Ministry of Health 10 , 11 . This perspective was chosen as it reflects the healthcare access and financing structure for most atrial fibrillation patients in Chile. The model considered only direct medical costs, including: (i) anticoagulant medication; (ii) surgery and hospitalization due to major adverse events such as ischemic stroke (USD 11,796) or haemorrhagic stroke (USD 1,604); (iii) INR monitoring and outpatient follow-up; and (iv) genetic testing for CYP2C9 and VKORC1 polymorphisms. All costs were expressed in U.S. dollars (USD) and adjusted to 2025 values 12 , 13 . This approach aims to inform public health decision-making based on both clinical outcomes and economic effectiveness. The model used in this study is a state-transition Markov model 14 designed to evaluate three anticoagulation strategies: (i) standard of care; (ii) genotype-guided dosing; and (iii) genotype-guided dosing adjusted for real-world population adherence parameters 15 . The model tracks the proportion of the cohort transitioning through distinct health states over time, with transition probabilities and key input parameters derived from the literature and our cohort data 6 . A detailed list of the transition values and model inputs including probabilities of clinical events, QALY weights, and unit costs is provided in Supplementary Table 1 . At the end of the simulation, key outcomes including incremental cost-effectiveness ratios (ICERs) and quality-adjusted life years (QALYs) were calculated for each strategy. These parameters incorporate both mortality and morbidity across the defined time horizon. The analysis used a willingness-to-pay threshold equivalent to the Chilean per capita gross domestic product (GDP), set at U $ 17,093 16 . The input data were obtained from a non-public database associated with a master's thesis on pharmacogenetic-guided acenocoumarol dosing 17 . Patient-level follow-up data were supplemented with relevant demographic characteristics such as gender, age, and body mass index (BMI), as these variables influence individual survival probabilities and therefore affect the simulated outcomes. Adherence to therapy was incorporated using modifiers that affected the time to therapeutic range (TTR), increasing it in the general population arm to reflect the reduced compliance typically observed in unsupervised real-world settings. The Markov model simulated a total cohort of 246 patients undergoing anticoagulation therapy. Of these, 123 were real patients whose data were obtained from the acenocoumarol algorithm study; their sex, age, and initial INR values were used to initialize the model. All eligible patients at baseline were included, regardless of whether they completed the full duration of the original study. The remaining 123 patients were simulated to match the demographic profile of the real cohort, maintaining similar distributions of key characteristics. The total population was divided into two primary treatment groups: genotype-guided dosing and standard care. In the genotype-guided arm, 54 patients were managed using the pharmacogenetic algorithm, while 69 patients in the standard of care arm received anticoagulation without genetic testing. Patients in both groups were further categorized into three INR outcome groups according to their initial therapeutic response: subtherapeutic (INR 3). In the genotype-guided group, 18 patients (33.3%) were classified as subtherapeutic, 16 (29.6%) as therapeutic, and 20 (37.0%) as supratherapeutic. In the standard care group, 23 patients (33.3%) were subtherapeutic, 30 (43.5%) therapeutic, and 16 (23.2%) supratherapeutic, as shown in Table 1 . Table 1 Distribution of patients INR Group Genotype-Guided Standard Care 3 (supratherapeutic) 20 (37.0%) 16 (23.2%) Total 54 69 INR: International Normalized Ratio These proportions reflect differences in dosing accuracy between groups and serve as the foundation for defining the transition probabilities in the first cycle of the Markov model. The INR level distribution is a critical determinant, as it influences the likelihood of progressing into health states associated with adverse events (e.g., bleeding or thrombosis), stabilization, or continued suboptimal anticoagulation. Thus, the simulation captures both the clinical variability and the therapeutic impact of pharmacogenetic guidance. The model is run through 180 cycles where each simulated individual moves specifically between the nodes according to their initial health state and the probabilities of transition associated with each state on the Markov model. To build the Markov transition model four distinct stats were defined: a) Full Health: The individual can produce 1 (QALY) per cycle and is affected only by the normal mortality related to his age, gender and IMC; b) Partial health: The individual is producing a value lower than 1 QALY per cycle, affected by the sequels of his ischemic stroke and is more likely to die in the next cycle; c) Partial health after ischemic stroke: The individual is producing a value lower than 1 QALY per cycle, affected by the sequels of his Haemorrhagic stroke and is more likely to die in the next cycle; d) Death: The individual dies due to natural causes, consequences of a deteriorated health status or from a fatal ischemic/haemorrhagic event. Figure 1 presents the nodes of the Markov Chain Model associated to the three strategies and the states that the population can transit into. The main output of the model will be a Cost-Effectiveness Analysis where the ICER will capture both the cost and effectiveness of both strategies using this formula: Where, C 1 and E 1 represent the total cost and effectiveness (e.g., QALYs) of the standard care strategy, and C 2 and E 2 correspond to those of the genotype-guided therapy being evaluated. Given the presence of two genotype-guided strategies—one assuming full adherence and another with population-level adherence—additional ICERs were calculated using: C 2A and E 2A : genotype-guided therapy with full adherence C 2B and E 2B : genotype-guided therapy with population-level adherence Each genotype-guided strategy was compared independently against standard care The analysis was performed using the software Amua® (v 0.3.1) 18 . Results The cost-effectiveness analysis compared three strategies: standard of care, genotype-guided therapy, and genotype-guided therapy with population-level adherence. For the 246-size simulated cohort who use acenocoumarol the results of the cost-effectiveness analysis for the three strategies are presented in Table 2 . Table 2 Cost-effectiveness results for the three strategies Strategy Costs QALY ICER Notes Standard of Care U $ 722 217 2777.39 --- Baseline Genotype-Guide (Population Adherence) U $ 763 003 2783.38 --- Weakly Dominated Genotype-guide Therapy U $ 792 526 2938.34 U $ 436.86 QALY: quality-adjusted life year; ICER: Incremental Cost-Effectiveness Ratio The Standard care strategy had the lowest cost (U $ 722,217) but also the lowest effectiveness (2777.39 QALYs) and was therefore used as the baseline comparator. The genotype-guided strategy considering population-level adherence to treatment showed a total cost of U $ 763,003 and an effectiveness of 2783.38 QALYs. This strategy is slightly more expensive than the cost of standard care while still being better, as it provides a higher effectiveness over the baseline but lower than the genotype-guided therapy strategy, offering fewer QALYs at a lower but not proportionally reduced cost. The genotype-guided therapy strategy resulted in the highest effectiveness, with 2938.34 QALYs, at a total cost of U $ 792,526. Compared to standard care, it yielded an incremental cost-effectiveness ratio (ICER) of U $ 436.86 per QALY. The analysis for incremental cost-effectiveness is presented in Table 3 , it summarizes the incremental cost-effectiveness results after removing dominated strategies. Genotype-guided therapy provided an additional 160.95 QALY compared to standard care at an incremental cost of U $ 70,309, yielding an ICER of U $ 436.86 per QALY. Table 3 Incremental cost-effectiveness analysis Strategy Costs* QALY Δ Cost vs Previous Δ QALY vs Previous ICER Standard Care U $ 722 217 2777.39 --- ---- —- Genotype-Guide (Population Adherence) U $ 763 003 2783.38 U $ 70 309 160.95 U $ 436.86 QALY: quality-adjusted life year; ICER: Incremental Cost-Effectiveness Ratio *Costs were obtained using data of supplementary material The cost-effectiveness plane (Fig. 2 ) shows that the genotype-guided therapy lies on the efficiency frontier, indicating higher effectiveness and acceptable incremental cost. The calculated ICER for this strategy was U $ 436.86 per QALY gained compared to standard care. The population adherence strategy lies below the frontier and is therefore weakly dominated. The sensitivity Analysis for the three strategies (Fig. 3 ) in relation to the cost of the Genetic Testing shows how the overall cost of the strategies increases as does the cost of the Genetic Testing. On the other hand, the sensitivity analysis for the ICER-to-cost ratio of genotype-guided therapy (Fig. 4 ) shows a linear increase as the cost of genetic testing rises, since this expense only impacts the initial stage of the simulation Discussion We developed a state-transition Markov cohort model to evaluate the cost-effectiveness of three therapeutic strategies for anticoagulation with acenocoumarol in patients with atrial fibrillation. The model considered: (i) standard care assuming typical INR control times; (ii) genotype-guided dosing using a validated algorithm to accelerate achievement of the therapeutic INR range; and (iii) a genotype-guided strategy adjusted for real-world medication adherence, introducing penalties for delayed therapeutic attainment 15 . This design reflects both ideal and pragmatic clinical scenarios and allows the model to simulate realistic outcomes under varying adherence conditions, which are known to significantly affect anticoagulation efficacy 19 . After executing the simulation, the results demonstrated that the genotype-guided therapy was the most cost-effective option. Its ICER of U $ 436.86 per QALY gained falls well below the commonly used cost-effectiveness threshold based on Chile’s per capita GDP (~ U $ 17,093 in 2023 16 ), supporting its high value as a public health investment. This reinforces the positioning of genotype-guided therapy on the efficiency frontier as both more effective and economically attractive compared to standard care and adherence-only strategies. As shown in the one-way sensitivity analysis (Fig. 3 ), we explored the impact of varying the genetic test cost—a potential cost driver in pharmacogenomics. The analysis demonstrated that although higher test prices increased the overall cost, the genotype-guided therapy maintained its cost-effectiveness up to a test cost of nearly U $ 12,000. Compared to the current cost in Chile (~ U $ 190), this margin suggests a high tolerance to fluctuations in implementation cost. Moreover, no reversal in strategy ranking occurred, indicating that the cost of the genetic test is not the primary determinant of cost-effectiveness in this context. When incorporating reduced adherence into the model—simulating a real-world scenario where patients do not consistently follow prescribed treatment—the ICER increased to U $ 6,797 per QALY. Although still under the cost-effectiveness threshold, this represents a substantial reduction in value relative to the ideal adherence scenario. These findings highlight the crucial importance of patient engagement and education in maximizing the clinical and economic benefits of pharmacogenomic interventions 20 . Importantly, genotype-guided therapy offers not only economic benefits but also substantial clinical advantages by reducing the risk of major adverse events, such as ischemic stroke and hemorrhage, which are associated with high immediate treatment costs, prolonged hospitalization, and long-term reductions in quality of life 21 . Our findings align conceptually with recent international studies in other therapeutic areas where pharmacogenomic-guided interventions have demonstrated economic and clinical value. For instance, Fragoulakis et al . (2023) conducted a cost-utility analysis of pharmacogenomic-guided therapy in colorectal cancer patients receiving fluoropyrimidines or irinotecan, showing that pre-emptive DPYD and UGT1A1 testing reduced severe toxicity, hospitalization, and overall treatment cost 22 . Although their study focused on oncology and a European population, both analyses underscore a shared principle: pharmacogenomic stratification improves therapeutic outcomes while containing healthcare expenditures. Unlike these prior works, our study provides real-world evidence specific to anticoagulation therapy in the Chilean context, where genotype-guided dosing showed cost-effectiveness well below the national WTP threshold and tangible clinical benefit in preventing adverse outcomes. Despite the robustness of our findings, several important limitations must be acknowledged. Foremost, the Markov model’s transition probabilities and cost parameters were derived from international literature due to the lack of local Chilean data on pharmacogenomic-guided anticoagulation. This limitation reflects on the novelty of this work, which represents the first cost-effectiveness analysis of genotype-guided acenocoumarol dosing in Chile. Consequently, key inputs such as INR control trajectories, adverse event rates, and healthcare utilization patterns may not fully capture the clinical realities of the Chilean population, potentially affecting model precision. Furthermore, the model was not formally validated—either internally or externally—due to the unavailability of national datasets with longitudinal outcomes. In addition, patient adherence was incorporated using a generalized penalty based on indirect assumptions, rather than real-world adherence data. Future research should focus on validating the model using prospective or retrospective local cohorts, and on generating Chilean-specific data to improve parameter accuracy and enhance the model’s applicability and policy relevance. Conducting cost-effectiveness studies that evaluate the clinical and economic value of pharmacogenomic-guided interventions is of utmost importance for Latin America, a region characterized by constrained public healthcare budgets, significant heterogeneity in political and administrative health governance, and limited infrastructure for pharmacovigilance and real-world outcome monitoring. In many Latin American countries, adverse drug reactions and therapeutic inefficacy are underreported or poorly documented, which hampers the development of policies to improve medication safety and efficacy. Furthermore, Latin America's rich ethnic and genetic diversity adds another layer of complexity to drug response variability, underscoring the need for locally generated evidence. Many pharmacogenomic markers validated in populations of European or Asian ancestry may not fully apply to admixed Latin American populations, where allele frequencies and haplotypic structures differ significantly 23 , 24 , 25 . Without region-specific data, there is a risk of perpetuating health disparities and limiting the benefits of precision medicine. Pharmacogenomics holds great promise for optimizing drug therapy by reducing adverse events and enhancing treatment response, but its implementation in resource-limited settings requires rigorous economic evaluations tailored to local contexts 26 . This first pharmacogenomic cost-effectiveness study conducted in Chile represents a foundational step toward generating locally relevant evidence, offering a methodological framework and initial economic benchmarks that can inform similar research across the region. By demonstrating that genotype-guided therapy can be both clinically beneficial and economically feasible within the constraints of the Chilean public healthcare system, this study provides critical insights for policymakers in other Latin American countries seeking to evaluate and prioritize personalized medicine strategies. Such regional evidence is essential to bridge the translational gap and move pharmacogenomics from bench to bedside in Latin America, where equity, sustainability, cost-efficiency, and ethnically appropriate solutions are imperative for long-term adoption 27 , 28 . Conclusions This study provides evidence that genotype-guided dosing of acenocoumarol has the potential to be a cost-effective strategy for anticoagulation therapy in patients with atrial fibrillation within the Chilean public healthcare system. Using a validated pharmacogenetic algorithm within a state-transition Markov model, the analysis showed that genotype-guided therapy yielded greater health benefits—measured in QALYs—at an incremental cost corresponding to an ICER of U $ 436.86. This value is substantially lower than Chile’s per capita gross domestic product (GDP) in 2023 (~ U $ 17,093), which is commonly used as a proxy willingness-to-pay (WTP) threshold in the absence of a nationally defined value. These findings suggest that, under this benchmark, genotype-guided dosing would be considered an economically attractive intervention, warranting further evaluation for policy adoption. Sensitivity analyses confirmed the robustness of this strategy even under varying genetic testing costs and adherence scenarios. While reduced adherence diminished the overall cost-effectiveness, the intervention remained economically favourable. This highlights the critical role of patient engagement and healthcare system support in maximizing the value of pharmacogenomic interventions. Beyond economic metrics, genotype-guided therapy offers tangible clinical benefits by reducing the likelihood of serious adverse events such as stroke and haemorrhage, thereby improving quality of life and long-term outcomes. These findings provide the first economic evidence supporting pharmacogenomic implementation in Chile and serve as a valuable reference for other Latin American countries facing similar challenges in healthcare resource allocation. Overall, the integration of pharmacogenomics into anticoagulation management represents a scientifically sound, economically viable, and ethically justified advance toward personalized medicine in Latin America. Policymakers and health systems are encouraged to consider the adoption of genotype-guided strategies, especially in populations with high interindividual variability and limited healthcare budgets. Declarations Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Author Contributions LAQ. Conceived the subject, the conception of the research, writing the manuscript and obtaining the financial support; MM. Analysis of data and edition of the manuscript; JPC and MC Analysis of data; LCC and VT edited the manuscript and gave valuable scientific input. Lastly, all authors reviewed and approved the manuscript. Funding This work was partially financed by FONDEF Grant IT2010003. 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An updated examination of the perception of barriers for pharmacogenomics implementation and the usefulness of drug/gene pairs in Latin America and the Caribbean. Front Pharmacol. 2023;14:1175737. doi:10.3389/fphar.2023.1175737 Additional Declarations There is NO conflict of interest to disclose. Supplementary Files SupplementaryTable1.docx Supplementary Table 1. Model transition values and input parameters for the cost-effectiveness analysis. Cite Share Download PDF Status: Published Journal Publication published 20 Apr, 2026 Read the published version in The Pharmacogenomics Journal → Version 1 posted Editorial decision: revise 15 Sep, 2025 Review # 1 received at journal 08 Sep, 2025 Reviewer # 1 agreed at journal 22 Aug, 2025 Reviewers invited by journal 20 Aug, 2025 Submission checks completed at journal 20 Aug, 2025 First submitted to journal 19 Aug, 2025 Editor assigned by journal 19 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7411548","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":503194539,"identity":"9264ca33-4add-43c0-8747-89487a7eac50","order_by":0,"name":"Luis Quinones","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYBACPiA+AGHyMBx4wGADZDA2HsCnhQ1FSwJDGkhLA0EtDDAtDAkMh8FM/FrYcwwPF1TckTM4fvbggYSK83Zr2w8DbamxicapheeNweEZZ54ZG5zJSziQcOZ28rYziUAtx9JyG3BpkcgxOMzbdjhxww0egwOJbbeTzQ4AtTA2HCaopR6i5d+5ZLPzD4nTkmAA1tJwwM7sBiFbeJ4VHOY5c9hw5pkcgwMJx5ITzG4AbUnA4xd+9uTNn3kqDsvzHT9j/OFDjZ292fn0hw8+1Njg1AKKCxSQ2IBFEL8We7yKR8EoGAWjYEQCAO1gab2Z3cMUAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-7967-5320","institution":"University of Chile","correspondingAuthor":true,"prefix":"","firstName":"Luis","middleName":"","lastName":"Quinones","suffix":""},{"id":503194540,"identity":"4a9911d7-f723-40a1-b25f-edd124170cfa","order_by":1,"name":"Maximiliano Mena","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Maximiliano","middleName":"","lastName":"Mena","suffix":""},{"id":503194541,"identity":"cb9f2dfb-0774-43e0-9b96-720061cabd19","order_by":2,"name":"Matías Carrasco","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Matías","middleName":"","lastName":"Carrasco","suffix":""},{"id":503194542,"identity":"e64ddfcf-636d-483e-b199-16f89276a84b","order_by":3,"name":"Leslie Cerpa","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Leslie","middleName":"","lastName":"Cerpa","suffix":""},{"id":503194543,"identity":"8f73e7b9-ba0b-4512-b00b-a31539820cd5","order_by":4,"name":"Juan Cayún","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Cayún","suffix":""},{"id":503194544,"identity":"289d4353-abfb-4d1e-9c92-47fa0d93194d","order_by":5,"name":"Valentina Torres","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Valentina","middleName":"","lastName":"Torres","suffix":""}],"badges":[],"createdAt":"2025-08-19 20:10:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7411548/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7411548/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41397-026-00411-7","type":"published","date":"2026-04-20T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90311603,"identity":"76c749cd-fdcb-4130-afd2-de160a06b86a","added_by":"auto","created_at":"2025-09-01 09:52:21","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":149592,"visible":true,"origin":"","legend":"\u003cp\u003eDecision tree (Markov Model) comparing standard care, genotype-guided therapy, and genotype-guided therapy with partial adherence (Pop. Adherence). C1: chance node for clinical outcomes based on INR (International Normalized Ratio): within range (1.5–2), low (\u0026lt;1.5), high (\u0026gt;2), death, post-hemorrhage, and post-ischemic stroke.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7411548/v1/65ea75c38616f424d63ff11f.jpg"},{"id":90310602,"identity":"e27a7143-04b7-475b-8bec-6c95aeda4656","added_by":"auto","created_at":"2025-09-01 09:44:21","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":67327,"visible":true,"origin":"","legend":"\u003cp\u003eCost-Effectiveness Plane for three strategies (Genotype-guide Therapy, Standard Care, Genotype-Guide Therapy (Under Population adherence to therapy). \u003cem\u003eQALY: quality-adjusted life year\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7411548/v1/62f454e6209eab20c5cdb54f.jpg"},{"id":90311604,"identity":"178759aa-794a-44f3-95a1-f9a10f531083","added_by":"auto","created_at":"2025-09-01 09:52:21","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":196454,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity Analysis for the Expected Value cost of the three strategies (Genotype-guide Therapy, Standard Care, Genotype-Guide Therapy (Under Population adherence to therapy) vs Cost of the Genetic Test. EV: Expected Value.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7411548/v1/950571afd7fbe1a5ccf8066a.jpg"},{"id":90308068,"identity":"572a7b96-8f47-4dfb-be46-7361e19673b5","added_by":"auto","created_at":"2025-09-01 09:36:21","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":180838,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity Analysis for Genotype-guide Therapy ICER/U$ ratio vs Cost of the Genetic Test. \u003cem\u003eICER: Incremental Cost-Effectiveness Ratio\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7411548/v1/cde8f56d2def90bcaeaca939.jpg"},{"id":107399110,"identity":"e15f799a-5f42-43c6-94b7-157f745489e4","added_by":"auto","created_at":"2026-04-21 07:13:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":960387,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7411548/v1/f6044964-4789-408a-9308-0b44b2f01b5d.pdf"},{"id":90308055,"identity":"4af1c0df-0ee5-4aec-84de-488cf201d153","added_by":"auto","created_at":"2025-09-01 09:36:21","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18497,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table 1. Model transition values and input parameters for the cost-effectiveness analysis.\u003c/p\u003e","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7411548/v1/12d423da6476ce39755e7b82.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Cost-Effectiveness of Genotype-Guided Acenocoumarol Therapy in Atrial Fibrillation: A Pharmacogenomic Simulation Study in the Chilean Population","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiovascular diseases (CVDs) are the leading cause of death worldwide, accounting for approximately 17.9\u0026nbsp;million deaths annually\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. These conditions encompass a broad spectrum of disorders affecting the heart and blood vessels, including coronary artery disease, stroke, and peripheral arterial disease. In Chile, CVDs are also the primary cause of mortality, responsible for 24.47% of all deaths in 2022\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAmong CVDs, coagulation-related disorders are of public concern due to the severe consequences they may have on affected individuals and their families if left untreated. These consequences can range from minor medical incidents to severe outcomes such as disability from a cerebrovascular accident or even death. Therefore, preventive therapies\u0026mdash;especially anticoagulation\u0026mdash;are essential. However, these treatments must be individualized to help patients reach their optimal therapeutic range as quickly and safely as possible. This interindividual variability is strongly influenced by genetic polymorphisms in key metabolic enzymes, particularly \u003cem\u003eCYP2C9\u003c/em\u003e and \u003cem\u003eVKORC1\u003c/em\u003e, which are critical in the metabolism of vitamin K antagonists such as warfarin and acenocoumarol. These genetic variants can significantly alter drug response, making pharmacogenetically guided dosing a promising strategy to personalize therapy and reduce the risks of haemorrhagic or thrombotic complications\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn this context, genotype-based dosing algorithms are gaining momentum and becoming more commonly used due to their clinical benefits, including faster attainment of the therapeutic INR range and reduced risk of adverse events\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.Nevertheless, their widespread adoption is often met with scepticism due to the higher initial costs compared to standard care. The overall effectiveness of these pharmacogenetic strategies depends on three key dimensions: (i) the clinical efficacy of the medication, (ii) patient adherence to the prescribed treatment, and (iii) the associated costs of therapy.\u003c/p\u003e\u003cp\u003eIn 2020, we published the first Latin American pharmacogenetic dosing algorithm for acenocoumarol\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The main objective was to reduce the time variability in achieving therapeutic INR levels due to genetic differences among Chilean patients. The resulting algorithm explained 49.99% of the variability in therapeutic dose requirements within the studied population.\u003c/p\u003e\u003cp\u003eTo assess the economic dimension\u0026mdash;specifically, whether investing in pharmacogenetic-guided therapy is justified\u0026mdash;various analytic strategies have been developed that integrate both clinical effectiveness and treatment costs. Among these, state-transition models have proven particularly useful for simulating the behavior of patient cohorts within specific time periods. The most applied models in this context are Markov models, which are widely used in economic evaluations in other countries\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e and thus serve as the methodological foundation of this study.\u003c/p\u003e\u003cp\u003eGenotype-guided therapy, particularly in the management of anticoagulation, is increasingly recognized for its clinical and economic advantages. Testing for \u003cem\u003eCYP2C9\u003c/em\u003e and \u003cem\u003eVKORC1\u003c/em\u003e genomic variants, which influence acenocoumarol metabolism, has been shown to shorten the time to reach stable INR levels and reduce the incidence of adverse events such as bleeding or thrombosis. These pharmacogenetic tests are both reliable and cost-effective, with prices ranging from U\u003cspan\u003e$\u003c/span\u003e250 to U\u003cspan\u003e$\u003c/span\u003e630\u003csup\u003e8,9\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAt the regional level, some studies have explored the implementation of genotype-guided therapy, but few have translated this approach into applied economic evaluations. This study aims to address that gap by evaluating the cost-effectiveness of genotype-guided acenocoumarol therapy in the Chilean context. Three strategies were compared: (i) standard dosing, (ii) genotype-guided dosing with partial adherence (reflecting the general population), and (iii) genotype-guided dosing assuming full adherence. In all scenarios, relevant demographic variables such as BMI, gender, and age were incorporated to reflect patient heterogeneity.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eThe cost-effectiveness analysis was performed from the perspective of the Chilean public healthcare system, incorporating cost data from the National Health Fund (FONASA) and the Digital Hospital platform of the Chilean Ministry of Health\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. This perspective was chosen as it reflects the healthcare access and financing structure for most atrial fibrillation patients in Chile. The model considered only direct medical costs, including: (i) anticoagulant medication; (ii) surgery and hospitalization due to major adverse events such as ischemic stroke (USD 11,796) or haemorrhagic stroke (USD 1,604); (iii) INR monitoring and outpatient follow-up; and (iv) genetic testing for \u003cem\u003eCYP2C9\u003c/em\u003e and \u003cem\u003eVKORC1\u003c/em\u003e polymorphisms. All costs were expressed in U.S. dollars (USD) and adjusted to 2025 values\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. This approach aims to inform public health decision-making based on both clinical outcomes and economic effectiveness.\u003c/p\u003e\n\u003cp\u003eThe model used in this study is a state-transition Markov model\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e designed to evaluate three anticoagulation strategies: (i) standard of care; (ii) genotype-guided dosing; and (iii) genotype-guided dosing adjusted for real-world population adherence parameters\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The model tracks the proportion of the cohort transitioning through distinct health states over time, with transition probabilities and key input parameters derived from the literature and our cohort data\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. A detailed list of the transition values and model inputs including probabilities of clinical events, QALY weights, and unit costs is provided in \u003cem\u003eSupplementary Table\u0026nbsp;1\u003c/em\u003e. At the end of the simulation, key outcomes including incremental cost-effectiveness ratios (ICERs) and quality-adjusted life years (QALYs) were calculated for each strategy. These parameters incorporate both mortality and morbidity across the defined time horizon. The analysis used a willingness-to-pay threshold equivalent to the Chilean per capita gross domestic product (GDP), set at U\u003cspan\u003e$\u003c/span\u003e17,093\u003csup\u003e16\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe input data were obtained from a non-public database associated with a master\u0026apos;s thesis on pharmacogenetic-guided acenocoumarol dosing\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Patient-level follow-up data were supplemented with relevant demographic characteristics such as gender, age, and body mass index (BMI), as these variables influence individual survival probabilities and therefore affect the simulated outcomes.\u003c/p\u003e\n\u003cp\u003eAdherence to therapy was incorporated using modifiers that affected the time to therapeutic range (TTR), increasing it in the general population arm to reflect the reduced compliance typically observed in unsupervised real-world settings.\u003c/p\u003e\n\u003cp\u003eThe Markov model simulated a total cohort of 246 patients undergoing anticoagulation therapy. Of these, 123 were real patients whose data were obtained from the acenocoumarol algorithm study; their sex, age, and initial INR values were used to initialize the model. All eligible patients at baseline were included, regardless of whether they completed the full duration of the original study. The remaining 123 patients were simulated to match the demographic profile of the real cohort, maintaining similar distributions of key characteristics.\u003c/p\u003e\n\u003cp\u003eThe total population was divided into two primary treatment groups: genotype-guided dosing and standard care. In the genotype-guided arm, 54 patients were managed using the pharmacogenetic algorithm, while 69 patients in the standard of care arm received anticoagulation without genetic testing. Patients in both groups were further categorized into three INR outcome groups according to their initial therapeutic response: subtherapeutic (INR\u0026thinsp;\u0026lt;\u0026thinsp;2), therapeutic (INR 2\u0026ndash;3), and supratherapeutic (INR\u0026thinsp;\u0026gt;\u0026thinsp;3).\u003c/p\u003e\n\u003cp\u003eIn the genotype-guided group, 18 patients (33.3%) were classified as subtherapeutic, 16 (29.6%) as therapeutic, and 20 (37.0%) as supratherapeutic. In the standard care group, 23 patients (33.3%) were subtherapeutic, 30 (43.5%) therapeutic, and 16 (23.2%) supratherapeutic, as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDistribution of patients\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eINR Group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGenotype-Guided\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandard Care\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;2 (subtherapeutic)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (33.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u0026ndash;3 (therapeutic range)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (29.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (43.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;\u0026thinsp;3 (supratherapeutic)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (37.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (23.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e54\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e69\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003eINR: International Normalized Ratio\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThese proportions reflect differences in dosing accuracy between groups and serve as the foundation for defining the transition probabilities in the first cycle of the Markov model. The INR level distribution is a critical determinant, as it influences the likelihood of progressing into health states associated with adverse events (e.g., bleeding or thrombosis), stabilization, or continued suboptimal anticoagulation. Thus, the simulation captures both the clinical variability and the therapeutic impact of pharmacogenetic guidance.\u003c/p\u003e\n\u003cp\u003eThe model is run through 180 cycles where each simulated individual moves specifically between the nodes according to their initial health state and the probabilities of transition associated with each state on the Markov model. To build the Markov transition model four distinct stats were defined: a) Full Health: The individual can produce 1 (QALY) per cycle and is affected only by the normal mortality related to his age, gender and IMC; b) Partial health: The individual is producing a value lower than 1 QALY per cycle, affected by the sequels of his ischemic stroke and is more likely to die in the next cycle; c) Partial health after ischemic stroke: The individual is producing a value lower than 1 QALY per cycle, affected by the sequels of his Haemorrhagic stroke and is more likely to die in the next cycle; d) Death: The individual dies due to natural causes, consequences of a deteriorated health status or from a fatal ischemic/haemorrhagic event. Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the nodes of the Markov Chain Model associated to the three strategies and the states that the population can transit into.\u003c/p\u003e\n\u003cp\u003eThe main output of the model will be a Cost-Effectiveness Analysis where the ICER will capture both the cost and effectiveness of both strategies using this formula:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003eWhere, C\u003csub\u003e1\u003c/sub\u003e and E\u003csub\u003e1\u003c/sub\u003e represent the total cost and effectiveness (e.g., QALYs) of the \u003cstrong\u003estandard care\u003c/strong\u003e strategy, and C\u003csub\u003e2\u003c/sub\u003e and E\u003csub\u003e2\u003c/sub\u003e correspond to those of the \u003cstrong\u003egenotype-guided therapy\u003c/strong\u003e being evaluated. Given the presence of two genotype-guided strategies\u0026mdash;one assuming full adherence and another with population-level adherence\u0026mdash;additional ICERs were calculated using:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eC\u003csub\u003e2A\u003c/sub\u003e and E\u003csub\u003e2A\u003c/sub\u003e: genotype-guided therapy with full adherence\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eC\u003csub\u003e2B\u003c/sub\u003e and E\u003csub\u003e2B\u003c/sub\u003e: genotype-guided therapy with population-level adherence\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eEach genotype-guided strategy was compared independently against standard care\u003c/p\u003e\n\u003cp\u003eThe analysis was performed using the software Amua\u0026reg; (v 0.3.1)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe cost-effectiveness analysis compared three strategies: standard of care, genotype-guided therapy, and genotype-guided therapy with population-level adherence. For the 246-size simulated cohort who use acenocoumarol the results of the cost-effectiveness analysis for the three strategies are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCost-effectiveness results for the three strategies\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStrategy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCosts\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQALY\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eICER\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNotes\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eStandard of Care\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU\u003cspan\u003e$\u003c/span\u003e 722 217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2777.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBaseline\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGenotype-Guide (Population Adherence)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU\u003cspan\u003e$\u003c/span\u003e 763 003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2783.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWeakly Dominated\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGenotype-guide Therapy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU\u003cspan\u003e$\u003c/span\u003e 792 526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2938.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eU\u003cspan\u003e$\u003c/span\u003e436.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eQALY: quality-adjusted life year; ICER: Incremental Cost-Effectiveness Ratio\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe Standard care strategy had the lowest cost (U\u003cspan\u003e$\u003c/span\u003e722,217) but also the lowest effectiveness (2777.39 QALYs) and was therefore used as the baseline comparator.\u003c/p\u003e\u003cp\u003eThe genotype-guided strategy considering population-level adherence to treatment showed a total cost of U\u003cspan\u003e$\u003c/span\u003e763,003 and an effectiveness of 2783.38 QALYs. This strategy is slightly more expensive than the cost of standard care while still being better, as it provides a higher effectiveness over the baseline but lower than the genotype-guided therapy strategy, offering fewer QALYs at a lower but not proportionally reduced cost.\u003c/p\u003e\u003cp\u003eThe genotype-guided therapy strategy resulted in the highest effectiveness, with 2938.34 QALYs, at a total cost of U\u003cspan\u003e$\u003c/span\u003e792,526. Compared to standard care, it yielded an incremental cost-effectiveness ratio (ICER) of U\u003cspan\u003e$\u003c/span\u003e436.86 per QALY. The analysis for incremental cost-effectiveness is presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, it summarizes the incremental cost-effectiveness results after removing dominated strategies. Genotype-guided therapy provided an additional 160.95 QALY compared to standard care at an incremental cost of U\u003cspan\u003e$\u003c/span\u003e70,309, yielding an ICER of U\u003cspan\u003e$\u003c/span\u003e436.86 per QALY.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eIncremental cost-effectiveness analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStrategy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCosts*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQALY\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eΔ Cost vs Previous\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eΔ QALY vs Previous\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eICER\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eStandard Care\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU\u003cspan\u003e$\u003c/span\u003e 722 217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2777.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e---\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e----\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026mdash;-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGenotype-Guide (Population Adherence)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU\u003cspan\u003e$\u003c/span\u003e 763 003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2783.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eU\u003cspan\u003e$\u003c/span\u003e70 309\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e160.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eU\u003cspan\u003e$\u003c/span\u003e436.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eQALY: quality-adjusted life year; ICER: Incremental Cost-Effectiveness Ratio\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e*Costs were obtained using data of supplementary material\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe cost-effectiveness plane (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) shows that the genotype-guided therapy lies on the efficiency frontier, indicating higher effectiveness and acceptable incremental cost. The calculated ICER for this strategy was U\u003cspan\u003e$\u003c/span\u003e436.86 per QALY gained compared to standard care. The population adherence strategy lies below the frontier and is therefore weakly dominated.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe sensitivity Analysis for the three strategies (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) in relation to the cost of the Genetic Testing shows how the overall cost of the strategies increases as does the cost of the Genetic Testing.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOn the other hand, the sensitivity analysis for the ICER-to-cost ratio of genotype-guided therapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) shows a linear increase as the cost of genetic testing rises, since this expense only impacts the initial stage of the simulation\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe developed a state-transition Markov cohort model to evaluate the cost-effectiveness of three therapeutic strategies for anticoagulation with acenocoumarol in patients with atrial fibrillation. The model considered: (i) standard care assuming typical INR control times; (ii) genotype-guided dosing using a validated algorithm to accelerate achievement of the therapeutic INR range; and (iii) a genotype-guided strategy adjusted for real-world medication adherence, introducing penalties for delayed therapeutic attainment\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. This design reflects both ideal and pragmatic clinical scenarios and allows the model to simulate realistic outcomes under varying adherence conditions, which are known to significantly affect anticoagulation efficacy\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAfter executing the simulation, the results demonstrated that the genotype-guided therapy was the most cost-effective option. Its ICER of U\u003cspan\u003e$\u003c/span\u003e436.86 per QALY gained falls well below the commonly used cost-effectiveness threshold based on Chile\u0026rsquo;s per capita GDP (~\u0026thinsp;U\u003cspan\u003e$\u003c/span\u003e17,093 in 2023\u003csup\u003e16\u003c/sup\u003e), supporting its high value as a public health investment. This reinforces the positioning of genotype-guided therapy on the efficiency frontier as both more effective and economically attractive compared to standard care and adherence-only strategies.\u003c/p\u003e\u003cp\u003eAs shown in the one-way sensitivity analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), we explored the impact of varying the genetic test cost\u0026mdash;a potential cost driver in pharmacogenomics. The analysis demonstrated that although higher test prices increased the overall cost, the genotype-guided therapy maintained its cost-effectiveness up to a test cost of nearly U\u003cspan\u003e$\u003c/span\u003e12,000. Compared to the current cost in Chile (~\u0026thinsp;U\u003cspan\u003e$\u003c/span\u003e190), this margin suggests a high tolerance to fluctuations in implementation cost. Moreover, no reversal in strategy ranking occurred, indicating that the cost of the genetic test is not the primary determinant of cost-effectiveness in this context.\u003c/p\u003e\u003cp\u003eWhen incorporating reduced adherence into the model\u0026mdash;simulating a real-world scenario where patients do not consistently follow prescribed treatment\u0026mdash;the ICER increased to U\u003cspan\u003e$\u003c/span\u003e6,797 per QALY. Although still under the cost-effectiveness threshold, this represents a substantial reduction in value relative to the ideal adherence scenario. These findings highlight the crucial importance of patient engagement and education in maximizing the clinical and economic benefits of pharmacogenomic interventions\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eImportantly, genotype-guided therapy offers not only economic benefits but also substantial clinical advantages by reducing the risk of major adverse events, such as ischemic stroke and hemorrhage, which are associated with high immediate treatment costs, prolonged hospitalization, and long-term reductions in quality of life\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Our findings align conceptually with recent international studies in other therapeutic areas where pharmacogenomic-guided interventions have demonstrated economic and clinical value. For instance, Fragoulakis \u003cem\u003eet al\u003c/em\u003e. (2023) conducted a cost-utility analysis of pharmacogenomic-guided therapy in colorectal cancer patients receiving fluoropyrimidines or irinotecan, showing that pre-emptive \u003cem\u003eDPYD\u003c/em\u003e and \u003cem\u003eUGT1A1\u003c/em\u003e testing reduced severe toxicity, hospitalization, and overall treatment cost\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Although their study focused on oncology and a European population, both analyses underscore a shared principle: pharmacogenomic stratification improves therapeutic outcomes while containing healthcare expenditures. Unlike these prior works, our study provides real-world evidence specific to anticoagulation therapy in the Chilean context, where genotype-guided dosing showed cost-effectiveness well below the national WTP threshold and tangible clinical benefit in preventing adverse outcomes.\u003c/p\u003e\u003cp\u003eDespite the robustness of our findings, several important limitations must be acknowledged. Foremost, the Markov model\u0026rsquo;s transition probabilities and cost parameters were derived from international literature due to the lack of local Chilean data on pharmacogenomic-guided anticoagulation. This limitation reflects on the novelty of this work, which represents the first cost-effectiveness analysis of genotype-guided acenocoumarol dosing in Chile. Consequently, key inputs such as INR control trajectories, adverse event rates, and healthcare utilization patterns may not fully capture the clinical realities of the Chilean population, potentially affecting model precision. Furthermore, the model was not formally validated\u0026mdash;either internally or externally\u0026mdash;due to the unavailability of national datasets with longitudinal outcomes. In addition, patient adherence was incorporated using a generalized penalty based on indirect assumptions, rather than real-world adherence data. Future research should focus on validating the model using prospective or retrospective local cohorts, and on generating Chilean-specific data to improve parameter accuracy and enhance the model\u0026rsquo;s applicability and policy relevance.\u003c/p\u003e\u003cp\u003e Conducting cost-effectiveness studies that evaluate the clinical and economic value of pharmacogenomic-guided interventions is of utmost importance for Latin America, a region characterized by constrained public healthcare budgets, significant heterogeneity in political and administrative health governance, and limited infrastructure for pharmacovigilance and real-world outcome monitoring. In many Latin American countries, adverse drug reactions and therapeutic inefficacy are underreported or poorly documented, which hampers the development of policies to improve medication safety and efficacy. Furthermore, Latin America's rich ethnic and genetic diversity adds another layer of complexity to drug response variability, underscoring the need for locally generated evidence. Many pharmacogenomic markers validated in populations of European or Asian ancestry may not fully apply to admixed Latin American populations, where allele frequencies and haplotypic structures differ significantly\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Without region-specific data, there is a risk of perpetuating health disparities and limiting the benefits of precision medicine. Pharmacogenomics holds great promise for optimizing drug therapy by reducing adverse events and enhancing treatment response, but its implementation in resource-limited settings requires rigorous economic evaluations tailored to local contexts\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. This first pharmacogenomic cost-effectiveness study conducted in Chile represents a foundational step toward generating locally relevant evidence, offering a methodological framework and initial economic benchmarks that can inform similar research across the region. By demonstrating that genotype-guided therapy can be both clinically beneficial and economically feasible within the constraints of the Chilean public healthcare system, this study provides critical insights for policymakers in other Latin American countries seeking to evaluate and prioritize personalized medicine strategies. Such regional evidence is essential to bridge the translational gap and move pharmacogenomics from bench to bedside in Latin America, where equity, sustainability, cost-efficiency, and ethnically appropriate solutions are imperative for long-term adoption\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003e This study provides evidence that genotype-guided dosing of acenocoumarol has the potential to be a cost-effective strategy for anticoagulation therapy in patients with atrial fibrillation within the Chilean public healthcare system. Using a validated pharmacogenetic algorithm within a state-transition Markov model, the analysis showed that genotype-guided therapy yielded greater health benefits\u0026mdash;measured in QALYs\u0026mdash;at an incremental cost corresponding to an ICER of U\u003cspan\u003e$\u003c/span\u003e436.86. This value is substantially lower than Chile\u0026rsquo;s per capita gross domestic product (GDP) in 2023 (~\u0026thinsp;U\u003cspan\u003e$\u003c/span\u003e17,093), which is commonly used as a proxy willingness-to-pay (WTP) threshold in the absence of a nationally defined value. These findings suggest that, under this benchmark, genotype-guided dosing would be considered an economically attractive intervention, warranting further evaluation for policy adoption.\u003c/p\u003e\u003cp\u003eSensitivity analyses confirmed the robustness of this strategy even under varying genetic testing costs and adherence scenarios. While reduced adherence diminished the overall cost-effectiveness, the intervention remained economically favourable. This highlights the critical role of patient engagement and healthcare system support in maximizing the value of pharmacogenomic interventions.\u003c/p\u003e\u003cp\u003eBeyond economic metrics, genotype-guided therapy offers tangible clinical benefits by reducing the likelihood of serious adverse events such as stroke and haemorrhage, thereby improving quality of life and long-term outcomes. These findings provide the first economic evidence supporting pharmacogenomic implementation in Chile and serve as a valuable reference for other Latin American countries facing similar challenges in healthcare resource allocation.\u003c/p\u003e\u003cp\u003e Overall, the integration of pharmacogenomics into anticoagulation management represents a scientifically sound, economically viable, and ethically justified advance toward personalized medicine in Latin America. Policymakers and health systems are encouraged to consider the adoption of genotype-guided strategies, especially in populations with high interindividual variability and limited healthcare budgets.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLAQ. Conceived the subject, the conception of the research, writing the manuscript and obtaining the financial support; MM. Analysis of data and edition of the manuscript; JPC and MC Analysis of data; LCC and VT edited the manuscript and gave valuable scientific input. Lastly, all authors reviewed and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was partially financed by FONDEF Grant IT2010003.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to thank the Latin American Society of Pharmacogenomics and Personalized Medicine (SOLFAGEM) for sponsoring this article, and Christina Mitropoulou from the Golden Helix Foundation, London, UK, for her critical review and improvement of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eWorld Health Organization. Cardiovascular diseases (CVDs). 2021. Available from: https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-%28cvds%29\u003c/li\u003e\n \u003cli\u003eMinisterio de Salud, Departamento de Estad\u0026iacute;sticas e Informaci\u0026oacute;n de Salud (DEIS). 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Pharm Policy Law. 2014;16(3\u0026ndash;4):289\u0026ndash;312. doi:10.3233/PPL-140390\u003c/li\u003e\n \u003cli\u003eSalas-Hern\u0026aacute;ndez A, Galleguillos M, Carrasco M, L\u0026oacute;pez-Cort\u0026eacute;s A, Redal MA, Fonseca-Mendoza D, et al. An updated examination of the perception of barriers for pharmacogenomics implementation and the usefulness of drug/gene pairs in Latin America and the Caribbean. Front Pharmacol. 2023;14:1175737. doi:10.3389/fphar.2023.1175737\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-pharmacogenomics-journal","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tpj","sideBox":"Learn more about [The Pharmacogenomics Journal](http://www.nature.com/tpj/)","snPcode":"41397","submissionUrl":"https://mts-tpj.nature.com/cgi-bin/main.plex","title":"The Pharmacogenomics Journal","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"pharmacogenetics, acenocoumarol, cost-effectiveness, anticoagulation, Chile, atrial fibrillation, Markov model, ICER, CYP2C9, VKORC1","lastPublishedDoi":"10.21203/rs.3.rs-7411548/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7411548/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCardiovascular diseases are the leading cause of death in Chile and worldwide. In atrial fibrillation, anticoagulation is essential, and in Chile acenocoumarol rather than warfarin, used in most countries, is the standard agent. Its dosing shows substantial interindividual variability due to \u003cem\u003eCYP2C9\u003c/em\u003e and \u003cem\u003eVKORC1\u003c/em\u003epolymorphisms. We developed a cohort-based Markov model to compare standard care, genotype-guided dosing, and genotype-guided dosing adjusted for population-level adherence in 123 Chilean patients and 123 matched simulated individuals. Outcomes were measured as quality-adjusted life years (QALYs) and direct medical costs, with cost-effectiveness assessed at a willingness-to-pay threshold of US$17,093. Genotype-guided dosing achieved the highest effectiveness (2938.34 QALYs) with an incremental cost-effectiveness ratio of US$436.86/QALY versus standard care, remaining cost-effective in sensitivity analyses up to test prices far exceeding the current US$190. The adherence-adjusted strategy was weakly dominated. These results strongly support implementing pharmacogenetic testing for acenocoumarol dosing to optimize anticoagulation safety, efficacy, and cost-effectiveness in Chile\u003c/p\u003e","manuscriptTitle":"Cost-Effectiveness of Genotype-Guided Acenocoumarol Therapy in Atrial Fibrillation: A Pharmacogenomic Simulation Study in the Chilean Population","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 09:36:16","doi":"10.21203/rs.3.rs-7411548/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2025-09-15T17:16:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-09-08T07:10:42+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-08-22T10:09:00+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2025-08-20T14:44:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-20T14:29:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"The Pharmacogenomics Journal","date":"2025-08-19T20:09:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-19T20:09:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-pharmacogenomics-journal","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tpj","sideBox":"Learn more about [The Pharmacogenomics Journal](http://www.nature.com/tpj/)","snPcode":"41397","submissionUrl":"https://mts-tpj.nature.com/cgi-bin/main.plex","title":"The Pharmacogenomics Journal","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ad27c0fc-a8ce-4488-8802-7f9d6f4997b3","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":53456840,"name":"Health sciences/Biomarkers/Predictive markers"},{"id":53456841,"name":"Health sciences/Health care/Health policy"},{"id":53456842,"name":"Health sciences/Health care/Public health"}],"tags":[],"updatedAt":"2026-04-21T07:13:35+00:00","versionOfRecord":{"articleIdentity":"rs-7411548","link":"https://doi.org/10.1038/s41397-026-00411-7","journal":{"identity":"the-pharmacogenomics-journal","isVorOnly":false,"title":"The Pharmacogenomics Journal"},"publishedOn":"2026-04-20 04:00:00","publishedOnDateReadable":"April 20th, 2026"},"versionCreatedAt":"2025-09-01 09:36:16","video":"","vorDoi":"10.1038/s41397-026-00411-7","vorDoiUrl":"https://doi.org/10.1038/s41397-026-00411-7","workflowStages":[]},"version":"v1","identity":"rs-7411548","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7411548","identity":"rs-7411548","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}