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. 2025 Aug 21;20(8):e0330425. doi: 10.1371/journal.pone.0330425

Cost-utility analysis of Coronary Artery Calcium screening to guide statin prescription among intermediate-risk patients in Thailand

Pakpoom Wongyikul 1, Phichayut Phinyo 2,*,#, Pannipa Suwannasom 3, Apichat Tantraworasin 4, Surasak Saokaew 5,6,7,8,*,#
Editor: Forgive Avorgbedor9
PMCID: PMC12370023  PMID: 40839608

Abstract

Objective

This study aims to evaluate the cost-utility of Coronary Artery Calcium (CAC) screening for primary prevention in Thai patients with intermediate cardiovascular disease (CVD) risk, compared to the current practice according to the ACC/AHA 2019 guideline recommendation without the use of a CAC score.

Methods

A hybrid model combining a decision tree and a Markov model was constructed to compare costs and QALYs from a societal perspective. The model evaluated a target population of statin-naïve individuals aged 40–75 with intermediate CVD risk. We assessed the impact of statin initiation for primary prevention based on the ACC/AHA 2019 guideline with CAC screening compared to without CAC screening over a 35-year time horizon. The service costs and related household expenses were based on the Thai setting. The incremental cost-effectiveness ratio (ICER) was compared against the official willingness-to-pay threshold of Thailand (160,000 THB, approximately 4,400 USD per QALY). Probabilistic and additional one-way sensitivity analyses were performed to assess the robustness of the model and evaluate how variations in key assumptions impact the results. These analyses help determine the reliability of the findings by exploring the extent to which changes in input parameters influence the overall conclusions.

Results

The CAC screening strategy required an incremental cost of 10,091 THB to gain 0.62 QALYs per person, resulting in an ICER of 16,308 THB per QALY gained. For the probabilistic sensitivity analysis, at the official Thai threshold, the probability of cost-effectiveness was 71% for CAC screening. Sensitivity analyses based on varying the effect of drug adherence, drug cost, incidence of CVD events, and the distribution of CAC scores demonstrated robust cost-effectiveness favouring CAC screening.

Conclusion

CAC screening strategy is cost-effective in the Thai context, especially when the cost of screening and high-potency statins is low.

Introduction

Atherosclerotic cardiovascular diseases (ASCVD), caused by the gradual buildup of cholesterol plaques inside the arteries, are the leading cause of death globally, with ischaemic heart disease and cerebrovascular disease being the two major sources of disability [1,2]. Many studies have demonstrated the heavy economic burden of ASCVD, particularly in low- and middle-income countries (LMICs) compared to high-income countries (HICs) [3,4]. Effective management of dyslipidaemia, a disorder characterised by abnormal levels of lipids in any form, is crucial for lowering the incidence of ASCVD. Nevertheless, numerous LMICs encounter substantial difficulties in executing effective and comprehensive national strategies for the primary prevention of ASCVD [5,6].

In subclinical individuals, primary prevention of ASCVD is based on the predicted 10-year risk of a CVD event. In Thailand, patients are assessed using Thai cardiovascular (CV) risk tools, which estimate their 10-year risk as a percentage [7]. Statin therapy is not recommended for individuals at low risk (<10% 10-year risk), while it is recommended for those at high ASCVD risk (>20% 10-year risk) [8]. However, for individuals at intermediate risk (10% to 20% 10-year risk), treatment is generally only recommended if serum cholesterol levels exceed a defined threshold or if there are other ASCVD risk enhancers present, such as elevated blood pressure, metabolic syndrome, or chronic kidney disease [8–11]. Evidence indicates a substantial heterogeneous baseline risk within the intermediate risk category [12,13]. Shared decision-making between doctor and patient is, therefore, the principle guiding whether a patient in this risk group should initiate statin therapy. However, in Thailand, the treatment initiation rate for dyslipidaemia is notably lower compared to diabetes and hypertension [6].

The Coronary Artery Calcium (CAC) score, introduced in the late 1990s, is a non-invasive imaging technique used to assess coronary artery calcification [14]. It quantifies calcified atherosclerotic plaques within the coronary arteries and has been strongly associated with CVD events [15,16]. Extensive evidence strongly supports the potential of Coronary Artery Calcium (CAC) scores as an accurate tool for cardiovascular risk stratification [17,18]. Previous health economic studies have demonstrated the cost-effectiveness of CAC screening for primary prevention [19–24]. Based on the American College of Cardiology/American Heart Association (ACC/AHA) Cholesterol Management Guideline 2019, CAC screening for informed decisions in intermediate-risk patients appears reasonable [9]. However, the Thailand Clinical Practice Guideline on Pharmacologic Therapy of Dyslipidaemia for Atherosclerotic Cardiovascular Disease (ASCVD) Prevention 2016 did not implement the supplemental use of CAC scores for risk stratification [8]. Additionally, there is a lack of local evidence to support the cost-effectiveness of CAC screening in the Thai context. Consequently, the use of CAC screening in the country is currently guided by the 2019 ACC/AHA recommendations [9]. The inclusion of CAC for ASCVD risk screening in the publicly financed health insurance scheme needs to be justified [25]. This study aims to evaluate the cost-effectiveness of using a CAC screening strategy to guide statin initiation for primary prevention in Thai patients with intermediate ASCVD risk, compared to current practice based on the ACC/AHA 2019 guidelines without incorporating CAC screening.

Methods

Model overview

A hybrid model combining a decision tree and a Markov model was constructed (Figs 1 and 2) using Microsoft Excel with the Plant-A-Tree add-in [26]. Quality-adjusted life-years (QALYs) were used as the outcome measure. The model evaluated the target population of statin-naïve individuals aged 40–75 with intermediate cardiovascular (CV) risk, assessed using Thai CV risk tools [9], who were free of known CVD events (myocardial infarction (MI), stroke, or CV death).

Fig 1. Decision tree diagram showing two screening strategies.

Fig 1

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Abbreviations: CAC, coronary artery calcium score; CV, cardiovascular; LDL-C, low-density lipoprotein cholesterol; “M” signs at the end of the decision tree, Markov model.

Fig 2. State-transition diagram for Markov model.

Fig 2

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Abbreviations: CVD, cardiovascular diseases; MI, myocardial infarction.

This study was conducted in compliance with the process of Thai health technology assessment (HTA) guidelines, which consider transparency, accountability, inclusiveness, timeline, quality, consistency, and contestability for the HTA process. In addition, for economic evaluation, we followed the Thai guidelines and the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) guidelines [27,28]. The study was granted ethical approval from the Institutional Review Board and Ethics Committee of the Faculty of Medicine, Chiang Mai University (HOS 2566-0099).

Model structure and strategies

A decision tree was used to compare the costs and consequences of two screening strategies: (1) CAC screening and (2) current practice (Fig 1). For the CAC screening strategy, individual patients are assessed for their CAC score to guide statin therapy. A moderate-potency statin is initiated for patients with a CAC score of 1–99, and a high-potency statin is initiated for patients with a CAC score of ≥100. Patients with a CAC score of zero are recommended to reassess the score every 5 years until a positive CAC score is detected [6]. All simulated individuals are assumed to receive lifestyle advice.

The current practice reflects the implementation of the ACC/AHA guideline 2019 without incorporating CAC screening [6], where statin therapy is initiated under the following conditions: for a low-density lipoprotein cholesterol (LDL-C) of 70–189 mg/dL, a moderate-potency statin is provided, and for an LDL-C of ≥190 mg/dL, a high-potency statin is provided. All patients are assumed to maintain their statin potency level without stepping down. We did not directly compare the CAC screening strategy to the Thai clinical guidelines 2016 [8] because they are likely underutilised due to being outdated and may not reflect current practices.

The Markov model was used to assess natural disease progression in each simulated patient with different baselines. The model tracked quality of life, cost, and time spent in one of the following eight health states: (1) No CVD, (2) Post non-fatal MI, (3) Post recurrent non-fatal MI, (4) Post non-fatal stroke, (5) Post recurrent non-fatal stroke, (6) Post non-fatal MI and stroke, (7) Post recurrent non-fatal MI and stroke, and (8) Dead. Each simulated patient started in the “No CVD” state at age 40 and was followed until age 75 (time horizon: 35 years) (Fig 2) [6].

The probabilities of experiencing an acute non-fatal MI, acute non-fatal stroke, CVD death, and death from any cause were used to determine transitions to other health states in each one-year cycle. Patients in the post non-fatal MI state could experience a recurrent non-fatal MI, acute non-fatal stroke, or death, leading to transitions to the post recurrent non-fatal MI, post non-fatal MI and stroke, or dead state. Similar events could occur for patients in the post non-fatal stroke and post non-fatal MI and stroke states. After experiencing any CVD event, patients would be prescribed a high-potency statin. The model structures were reviewed and validated by cardiologists and health economists.

Data sources

Prevalence, effectiveness of statin therapy, cost, and transition probabilities were retrieved from published literature and primary data collection, summarised in Tables 1 and 2. Since the aim of this analysis was to inform policymakers in Thailand, the identified sources were most relevant to the Thai context. When local data were not available, the most relevant international publications were used [29,30,36–38].

Table 1. Selected input parameters for the decision tree model and corresponding treatment strategy.
Input parameter: probabilities Base-case value
mean (SE)		 Receiving treatment
Current practice strategy
Incidence of initial LDL-C ≥ 190 mg/dL 0.018 (0.013) High potency statin
Incidence of initial LDL-C 70–189 mg/dL 0.759 (0.04) Moderate potency statin
Incidence of initial LDL-C < 70 mg/dL 0.223 (0.04) No statin
CAC screening strategy
Incidence of initial CAC ≥ 100 0.36 (0.013) High potency statin
Incidence of initial CAC 1–99 0.39 (0.013) Moderate potency statin
Incidence of initial CAC = 0 0.26 (0.012) No statin
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*The input data were sourced from the hospital database from 2012 to 2022. A beta distribution was selected for these input parameters in the probabilistic sensitivity analysis. Abbreviations: CAC, coronary artery calcium score; LDL-C, low-density lipoprotein cholesterol; SE, standard error.

Table 2. Selected input parameters for the Markov model.
Input parameters Base-case value mean (SE) Distribution References
Transitional probability of the CVD event following each reclassification groups
No CVD state
Incidence of non-fatal MI in patient without statin therapy (Person-year) 0.00558
(0.00039)		 Beta [13,29]
Incidence of non-fatal stroke in patient without statin therapy (Person-year) 0.01348
(0.00106)		 Beta [13,29]
Incidence of CVD death in patient without statin therapy (Person-year) 0.00148
(0.00013)		 Beta [13,29]
Incidence of non-fatal MI in patient with CAC = 0 (Person-year) 0.00325
(0.00050)		 Beta [13]
Incidence of non-fatal MI in patient with CAC 1–99 (Person-year) 0.00340
(0.00042)		 Beta [13]
Incidence of non-fatal MI in patient with CAC ≥ 100 (Person-year) 0.00626
(0.00064)		 Beta [13]
Incidence of non-fatal stroke in patient with CAC = 0 (Person-year) 0.00883
(0.00137)		 Beta [13]
Incidence of non-fatal stroke in patient with CAC 1–99 (Person-year) 0.00918
(0.00114)		 Beta [13]
Incidence of non-fatal stroke in patient with CAC ≥ 100 (Person-year) 0.01689
(0.00175)		 Beta [13]
Incidence of CVD death in patient with CAC = 0 (Person-year) 0.00105
(0.00016)		 Beta [13]
Incidence of CVD death in patient with CAC 1–99 (Person-year) 0.00103
(0.00014)		 Beta [13]
Incidence of CVD death in patient with CAC ≥ 100 (Person-year) 0.00190
(0.00021)		 Beta [13]
Non-fatal MI state
Probability of developing
first-year recurrent non-fatal MI		 0.1000
(0.0010)		 Beta [30]
Probability of developing
first-year non-fatal stroke		 0.0240
(0.0004)		 Beta [30]
Probability of developing
first-year CVD death		 0.0810
(0.0010)		 Beta [30]
Non-fatal stroke state
Probability of developing
first-year non-fatal MI		 0.0095
(0.0004)		 Beta [31]
Probability of developing
first-year recurrent non-fatal stroke		 0.0900
(0.0010)		 Beta [31]
Probability of developing
first-year CVD death		 0.0340
(0.0040)		 Beta [31]
CAC progression per 5 years in patient previously CAC = 0
45–49 developing score 1–99, ≥ 100 0.341, 0.158 Hospital database 2012–2022
50–54 developing score 1–99, ≥ 100 0.338, 0.195 Hospital database 2012–2022
55–59 developing score 1–99, ≥ 100 0.331, 0.237 Hospital database 2012–2022
60–64 developing score 1–99, ≥ 100 0.319, 0.284 Hospital database 2012–2022
65–69 developing score 1–99, ≥ 100 0.302, 0.336 Hospital database 2012–2022
70–74 developing score 1–99, ≥ 100 0.281, 0.390 Hospital database 2012–2022
Statin efficacy
RR of moderate potency vs. no statin
on incident of non-fatal MI		 0.76
(0.026)		 Beta [29]
RR of moderate potency vs. no statin
on incident of non-fatal stroke		 0.86
(0.041)		 Beta [29]
RR of moderate potency vs. no statin
on incident of CVD death		 0.88
(0.020)		 Beta [29]
RR of high potency vs. no statin
on incident of non-fatal MI		 0.58
(0.026)		 Beta [29]
RR of high potency vs. no statin
on incident of non-fatal stroke		 0.74
(0.041)		 Beta [29]
RR of high potency vs. no statin
on incident of CVD death		 0.77
(0.020)		 Beta [29]
Average Cost per year (THB) (approximately 36 THB = 1 US$ in 2024)
Direct Medical Cost
Moderate potency statin 2,589
(259*)		 Gamma Hospital database 2021–2024
[32]
High potency statin 9,071
(907*)		 Gamma Hospital database 2021–2024
[32]
CAC test 4,000
(400*)		 Gamma [33]
Treatment of first-year non-fatal MI 149,285
(68,107)		 Gamma [34]
Treatment of first-year recurrent
non-fatal MI		 149,285
(68,107)		 Gamma [34]
Treatment of annual follow-up non-fatal MI 29,340
(2,934)		 Gamma [34]
Treatment of first year non-fatal stroke 191,467
(54,378)		 Gamma [35]
Treatment of first-year recurrent
non-fatal stroke		 92,396
(7,670)		 Gamma [35]
Treatment of annual follow-up non-fatal stroke 37,7812
(3,198)		 Gamma [35]
Direct Non-medical Cost
Non-medical cost of first-year non-fatal MI patient 3,667
(367*)		 Gamma [34]
Non-medical cost of annual follow-up
non-fatal MI patient		 5,304
(530*)		 Gamma [34]
Non-medical cost of first-year non-fatal stroke patient 72,309
(2279)		 Gamma [35]
Non-medical cost of annual follow-up
non-fatal stroke patient		 46,390
(1,470)		 Gamma [35]
Utility for each health state
Post non-fatal MI 0.828
(0.038)		 Beta [36]
Post non-fatal stroke 0.69
(0.062)		 Beta [36]
Post non-fatal stroke and MI 0.678
(0.036)		 Beta [37]
Disutility due to recurrent non-fatal MI 0.147
(0.015*)		 Beta [38]
Disutility due to recurrent non-fatal stroke 0.226
(0.022*)		 Beta [38]
Disutility due to recurrent non-fatal MI or stroke in patient with previous non-fatal CVD event 0.187
(0.019*)		 Beta [38]
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*Assume SE was 10% from mean value. The utility of No CVD health state was provided in S1 File, Table S4. Abbreviations: CAC, coronary artery calcium score; CT, computed tomography; CVD, cardiovascular disease; MI, myocardial infarction; RR, relative risk; SE, standard error.

Due to the lack of nationally representative published evidence, the initial incidence of LDL-C levels and CAC scores was retrieved from a Chiang Mai University hospital database covering the period from 2012 to 2022 (Table 1). All data were retrieved from standard electronic medical records. Data were collected from June 2023 to August 2023. Patient identity was viewed only during data collection and was not recorded. Details on the baseline characteristics of the cohort are available in Table S2 in S1 File.

The assumptions applied in the Markov model are based on literature [13,29–38]. Since LDL-C levels change dynamically over time, the transition probability for the first CVD event in the current guideline was assumed to be the same regardless of LDL-C level. The benefits of statin therapy, in terms of risk reduction for MI, stroke, and CVD mortality, were obtained from meta-analyses and considered equal for men and women [29].

We employed a societal perspective, incorporating direct medical and non-medical costs (including transportation, food, accommodation, and opportunity costs) into the model. Direct medical costs comprised the average annual cost of statin therapy, non-contrast cardiac CT (once if CAC is present, and every five years if CAC is absent), first-year specific CVD event-related treatment, and annual follow-up for CVD event-related treatment. Costs for non-contrast cardiac CT (CAC screening) and statins were based on the national healthcare reimbursement rate in 2023 [33] and retail prices from online national drug information (NDI) in 2024 [32]. An average price per unit of each statin potency was calculated from prescribing proportions in the hospital database from 2021 to 2024 (Table S5 in S1 File). Cost data were converted to 2024 values using the Thai consumer price index (CPI) [39] and presented in Thai Baht (THB) (approximately 36 THB = 1 US$ in 2024) [40]. Future costs and QALYs beyond the first year were discounted by 3% annually.

Cost-effectiveness analysis

The cost-effectiveness was evaluated in terms of incremental cost and incremental QALYs over a 35-year time horizon using base-case values and represented as the incremental cost-effectiveness ratio (ICER). The ICER was then compared against the official willingness-to-pay threshold in Thailand, which is set at 160,000 THB per QALY [27].

Sensitivity analyses

A probabilistic sensitivity analysis (PSA) was conducted to assess the uncertainty of our model. Each ICER value was calculated based on a random value drawn from each parameter distribution, and this process was repeated for 1,000 simulations. A beta distribution was selected for probability and utility parameters, a log-normal distribution was used for risk ratio parameters, and a gamma distribution was used for all cost parameters. In cases where parameter standard error (SE) was unavailable, we assumed an SE of 10% of the mean values. The simulated ICERs were presented in cost-effectiveness acceptability curves (CEAC) for willingness-to-pay thresholds ranging from 0 to 600,000 THB/QALY to evaluate the probability that a strategy was cost-effective.

Beyond the PSA, we performed several additional sensitivity analyses to assess the impact of important assumptions. The following input parameters were evaluated for their ICER response by varying upper and lower range values and were presented using a tornado diagram: the incidence of initial LDL-C levels and CAC scores, transition probability of the first CVD event, statin price, treatment effectiveness, health state utilities, and discount rate (between 0% and 5%) [41].

For other types of sensitivity analyses, we performed scenario analyses by changing specific parameters of the model. First, we considered the effect of varying adherence to statin therapy in the current practice and CAC screening, based on awareness of their CAC score [41,42]. Adherence rates were considered as 19–52% [42–46] for the current practice strategy and 52% and 56% for CAC scores of 1–99 and ≥100, respectively [47]. Second, we evaluated the effect of changing the mean cost of high-potency statins to 15, 20, 30, and 35 THB per unit, and the mean cost of a CAC test to 2,000, 6,000, and 8,000 THB. Both interventions were also assessed for the value that exceeds the Thailand willingness-to-pay threshold. Third, we explored the impact of CAC progression by changing the CAC progression probability using a prediction model from the CAC Consortium, a large multicentre cohort of low-CV patients in the USA [48] (details on the probabilities by age are provided in Table S3 in S1 File).

Fourth, since we adopted the transition probabilities from Tiansuwan N. et al. [13], which included non-naïve statin patients and a significant proportion of DM patients, the baseline CV risk tends to be higher than in the general population. This may explain why patients with a CAC score of zero carried a similar risk to those with a CAC score of 1–99 (Table 2) and had a risk twice as high as reported in previous studies [19,21,24]. We considered reducing all CVD event rates in patients with a CAC score of zero to half of the base-case value. Lastly, we considered the effect of changing the incidence of initial CAC scores from Tiansuwan N.-derived incidences [13] to those of a lower-risk population. Therefore, we compared our base-case set with models that assumed incidence rates from the Multi-Ethnic Study of Atherosclerosis (MESA) cohort [11], a large, ongoing medical research study in the United States that aims to investigate the prevalence, causes, and progression of subclinical CVD in a diverse population. MESA, initiated in 2000, includes over 6,800 participants aged 45–84 from four major ethnic groups (White, African American, Hispanic, and Chinese American) who were free of cardiovascular disease at enrollment. The study's focus on advanced imaging, long-term follow-up, and diverse populations has significantly enhanced the understanding of early cardiovascular disease and improved risk prediction. The post-hoc sensitivity analysis using the healthcare provider's perspective was performed to account for the impact of direct non-medical costs.

Results

Base-case analysis

The comparison of total costs, life years, QALYs, and ICER from the base-case analyses is summarised in Table 3. The CAC screening strategy required an incremental cost of 10,091 THB to gain 0.62 QALYs per person over a 35-year time horizon, resulting in an ICER of 16,308 THB per QALY gained. Overall, the CAC screening strategy incurred higher costs but proved more effective.

Table 3. Costs, Utility, and Cost-effectiveness of each strategy in base-case analysis.

Current practice CAC screening
Costs (THB) 309,357 319,448
Life years (year) 19.37 19.88
QALYs (year) 16.55 17.17
Incremental Costs (THB) Reference 10,091
Incremental QALYs (year) Reference 0.62
ICER per QALY gained Reference 16,308
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Approximately 36 THB = 1 US$ in 2024. Abbreviations: CAC, coronary artery calcium score; ICER, incremental cost-effectiveness ratio; QALYs, Quality adjusted life years.

Sensitivity analyses

The PSA result showed that CAC screening became cost-effective at a willingness-to-pay threshold of 40,000 THB per QALY gained. At the Thai threshold of 160,000 THB per QALY gained, the probability of cost-effectiveness was 71% for CAC screening (Fig 3).

Fig 3. Cost-effectiveness acceptability curve.

Fig 3

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Abbreviations: CAC, coronary artery calcium score; QALYs, Quality adjusted life years.

The tornado diagram highlighted the most influential parameter as the incidence of non-fatal stroke in patients without statin therapy. Variations in this parameter led to changes in the ICER from 146,112 THB to −32,522 THB, compared to the base-case ICER of 16,308 THB. The next most influential parameters included the cost of high-potency statins, the incidence of non-fatal stroke in patients with CAC scores of 1–99 and CAC ≥ 100, and the incidence of initial CAC scores ≥100 (Fig 4).

Fig 4. Tornado diagram of one-way sensitivity analysis.

Fig 4

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Discount rate was varied between 0 −5%. Abbreviations: CAC, coronary artery calcium score; CT, computed tomography; CVD, cardiovascular disease; LDL, low-density lipoprotein; MI, myocardial infarction; RR, relative risk; ICER, incremental cost-effectiveness ratio.

In scenario analyses, varying statin adherence assumptions (ranging from 19% to 52% in the current practice strategy versus approximately 55% in CAC screening) showed that CAC screening remained cost-effective at the Thai threshold of 160,000 THB per QALY gained (Table 4). Lower adherence to the current practice strategy favoured the cost-effectiveness of CAC screening. In terms of intervention cost, when the price of high-potency statins dropped to 20 THB per unit or less (Table 4), CAC screening was dominant. Conversely, rising high-potency statin prices led to increased incremental costs. When the annual cost of statin therapy was above 23,596 THB (equivalent to 65 THB per unit), CAC screening was not cost-effective.

Table 4. Sensitivity analysis based on multiple scenarios.

Scenario Incremental
Cost (THB)		 Incremental QALYs (years) ICER Decision*
Drug adherence
Current practice CAC screening
19% 36% for CAC =0
52% for CAC 1-99
56% for CAC ≥ 100		 -69,370 1.07 -65,090 CAC is dominant
30% -45,636 0.88 -51,834 CAC is dominant
52% 5,106 0.49 10,433 CAC is cost-effective
100% (base case) 100% (base case) 10,091 0.62 16,308 CAC is cost-effective
Cost parameter (THB)
High potency statin CAC test
5,479 (15 per unit) 4,000 (base case) -11,901 0.62 -19,235 CAC is dominant
7,305 (20 per unit) 4,000 (base case) -722 0.62 -1,167 CAC is dominant
9,071 (25 base case) 4,000 (base case) 10,091 0.62 16,308 CAC is cost-effective
10,956 (30 per unit) 4,000 (base case) 21,636 0.62 34,968 CAC is cost-effective
12784 (35 per unit) 4,000 (base case) 32,816 0.62 53,036 CAC is cost-effective
23,596 (64 per unit) 4,000 (base case) 98,999 0.62 160,000 CAC is not cost-effective
0.62
9,071 (base-case) 2,000 8,071 0.62 13,044 CAC is dominant
9,071 (base-case) 4,000 (base case) 10,091 0.62 16,308 CAC is cost-effective
9,071 (base-case) 6,000 12,111 0.62 19,573 CAC is cost-effective
9,071 (base-case) 8,000 14,131 0.62 22,838 CAC is cost-effective
9,071 (base-case) 92,028 98,999 0.62 160,000 CAC is not cost-effective
Incident of CVD event in patient with CAC score of 0
Incidence of non-fatal MI (Person-year)
0.00160 8,883 0.65 13,699 CAC is cost-effective
0.00325 (base-case) 10,091 0.62 16,308 CAC is cost-effective
Incidence of non-fatal stroke (Person-year)
0.00440 -443 0.70 -634 CAC is dominant
0.00883 (base-case) 10,091 0.62 16,308 CAC is cost-effective
Incidence of non-fatal CVD death (Person-year)
0.00052 10,340 0.64 16,284 CAC is cost-effective
0.00104 (base-case) 10,091 0.62 16,308 CAC is cost-effective
Incident of initial CAC score (Probability)
MESA cohort [11]
CAC ≥ 100 0.24 -4,902 0.48 -10,136 CAC is dominant
CAC 1-99 0.26
CAC = 0 0.50
Tiansuwan, N. (base-case) [7]
CAC ≥ 100 0.36 10,091 0.62 16,308 CAC is cost-effective
CAC 1-99 0.39
CAC = 0 0.26
CAC progression parameter
Based on CAC Consortium [45] 13 0.64 20 CAC is cost-effective
Based on Hospital data (base-case) 10,091 0.62 16,308 CAC is cost-effective
Perspective
Health care provider perspective 24,762 0.62 40,020 CAC is cost-effective
Societal perspective (base-case) 10,091 0.62 16,308 CAC is cost-effective
Discounting rate
0% 8,772 1.15 7,608 CAC is cost-effective
3% (base-case) 10,091 0.62 16,308 CAC is cost-effective
5% 10,165 0.44 23,352 CAC is cost-effective
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*Cost-effectiveness was interpreted under the Thai willingness-to-pay threshold of 160,000 THB per QALY gained. Approximately 36 THB = 1 US$ in 2024. Abbreviations: CAC, coronary artery calcium score; ICER, incremental cost-effectiveness ratio; QALYs, Quality adjusted life years.

A similar trend was observed with varying costs of the CAC test (Table 4), with the cost threshold being 92,028 THB per test. The results showed that slower CAC progression makes the CAC screening strategy more cost-effective, as fewer patients developed CAC scores of 1–99 and ≥100 (Table 4).

Decreasing the incidence of CVD events in patients with CAC = 0 by half of the base-case value made CAC screening more cost-effective (Table 4). Furthermore, simulations using initial CAC incidence data from the MESA cohort, where 50%, 26%, and 24% of the cohort had CAC scores of 0, 1–99, and ≥100, respectively, yielded similar conclusions (Table 4). The post-hoc sensitivity analysis using the healthcare provider's perspective demonstrated that CAC screening remains a cost-effective strategy (Table 4).

Discussion

This study assessed the comparative cost-effectiveness of the CAC screening strategy and the current practice strategy. Our model indicated that the CAC screening strategy is cost-effective among patients with intermediate CV risk. The differences in cost and QALYs were minimal, with an ICER of 16,308 THB per QALY gained in the base-case analysis. At a willingness-to-pay threshold of 160,000 THB/QALY, CAC screening is likely to be cost-effective in 71% of simulations.

Based on our analysis, approximately one-third of patients who followed the CAC screening strategy received moderate- or high-potency statins at the beginning, while the majority of patients in the current practice received moderate-potency statins (76%). Moderate-potency statins appear to be the predominant potency in clinical practice according to the ACC/AHA guideline 2019 [9,43]. We found that when the annual cost of high-potency statins increased, CAC screening became more costly. Conversely, with the rising cost of moderate-potency statins, the CAC screening strategy became more cost-effective. This result demonstrated the differing impact of drug cost and its distribution among strategies. Since the cost of moderate-potency statins affects the current practice significantly more than CAC screening, an increase in moderate-potency statin costs leads to a relatively lower overall cost for CAC screening, making it more cost-effective. However, in most real-world situations, the cost of statins tends to be lower due to the country's health insurance coverage and drug reimbursement policies [49]. We found that when the cost of high-potency statins was below 20 THB, the CAC screening strategy became less costly and more effective.

Drug adherence also plays a key role in the cost-effectiveness of the strategy. Some patients prefer to avoid taking daily preventive medication [50]. Thus, treating all patients with statins may not be an appropriate strategy. In our analysis, higher adherence rates in the CAC screening group showed lower overall costs. With minimal costs for performing CAC testing, CAC scores can enhance the shared decision-making process through more accurate risk prediction. This helps reduce low-value pharmacological therapy and guides treatment decisions toward a patient-centred strategy [24]. Since the CAC test is not covered by universal coverage in our country, the price tends to be higher than the national healthcare reimbursement rate. However, we found that the CAC test would need to cost approximately 92,000 THB to render the CAC screening strategy not cost-effective at a willingness-to-pay threshold of 160,000 THB per QALY.

It is important to note that our simulated patients with a CAC score of zero had twice the risk of a CVD event compared to previous health economic studies [19,21,24]. The results of our model were also highly sensitive to the incidence of non-fatal stroke. Our analysis, which assumed a 50% reduction in CVD events from the base-case value in patients with a CAC score of zero, demonstrated a favourable outcome for CAC screening. In our base-case analysis, the proportion of the population placed on statins was similar for both the current practice and the CAC screening strategy (approximately 78% vs 75%). We also modelled the incidence of initial CAC scores based on the MESA cohort [11], which had a higher proportion of patients with a CAC score of zero, resulting in fewer patients being placed on statins. The simulation results similarly concluded that CAC screening is less costly and more effective. CAC screening allows for more efficient allocation of pharmacotherapy, requiring statin use in fewer patients to achieve the same reduction in events [17].

Strengths and limitations

It was previously revealed that many patients who had non-fatal MI or stroke went on to experience other major hard events within a year [30,31]. In the real world, the transition probabilities, utility, and costs within post non-fatal MI and stroke states probably differ from other health states. Additionally, CAC scores naturally progress with age. Our study's strength lies in incorporating these health states and the CAC progression parameter into the Markov model, ensuring a more realistic representation of patient outcomes. Although it is difficult to compare with previous studies due to different models, assumptions, and target populations, the demonstration of the dominant CAC screening strategy adds weight to prior studies [19–21,23,24]. Our study is the first to address a common clinical scenario for Thai patients and clinicians in deciding on the initiation of long-term statin therapy for patients at intermediate risk for CVD using the ACC/AHA cholesterol guideline 2019 [9]. Our findings indicated that the CAC screening strategy is probably more cost-effective and strengthens healthcare providers’ ability to follow CVD prevention guidelines.

There are some limitations regarding the design and data used in this study. First, our analysis may underestimate costs as we did not include expenses related to patient time lost due to diagnostic testing or physician visits prior to a CVD event. Second, we did not account for potential cancer-related risks associated with CAC screening radiation. However, such complications are rare [51] and unlikely to significantly influence outcomes [19]. Third, we did not account for potential disutility and complications associated with statin use due to limited local data. We attempted to address this through scenario analysis, assuming varying levels of drug adherence among patients. In a scenario where there was a higher adherence rate in the CAC screening group, the strategy became dominant and less costly than the base case.

Fourth, some input parameters were derived from data specific to our centre, which may not be fully representative of the Thai population. We recommend rigorously collecting large national datasets for these parameters in future budget impact analyses before implementation. Finally, we did not consider the potential synergistic benefits of statins with other modalities such as anti-diabetic or anti-hypertensive therapies. The literature referenced in our study includes a mix of intermediate CV risk patients with comorbidities like diabetes mellitus and hypertension, which may overestimate the CVD event rate. Future research could incorporate the effects of these additional therapies into the model to reflect reality. However, our study focuses on statin-naïve patients who may be healthier than those simulated in our model. In our scenario analysis, which assumed a 50% reduction in CVD events from the base-case value for patients with a CAC score of zero, the CAC screening strategy demonstrated a reduction in incremental cost, making it a dominant strategy.

Conclusion

The CAC screening strategy as part of ASCVD primary prevention among Thai patients with intermediate risk is probably cost-effective at a willingness-to-pay threshold of 160,000 THB per QALY gained. CAC screening appears to be the dominant strategy under a wide range of scenarios, especially when the costs of CAC screening and high-potency statins are low. Our study highlights the potential for implementing CAC screening in the Thai context. However, further budget impact analysis studies should be conducted to assess the affordability of healthcare technologies when making policy decisions.

Supporting information

S1 File. Model summary.

(DOCX)

pone.0330425.s001.docx (132.1KB, docx)
S1 Fig. Cost-effectiveness plane of probabilistic sensitivity analysis.

(PNG)

pone.0330425.s002.png (119.8KB, png)

Acknowledgments

This study was partially supported by the Faculty of Medicine, Chiang Mai University and Chiang Mai University itself, the University of Phayao, and the Thailand Science Research and Innovation Fund. We extend our deep gratitude to cardiologists Songsak Kiatchoosakun, Suphot Srimahachota, Arintaya Phrommintikul, and Yotsawee Chotechuang for their valuable review and validation of the model. Additionally, we thank Wilarat Saiyarat, a pharmacist at Chiang Mai University Hospital, for providing essential information on input parameters.

Data Availability

All relevant data are within the paper.

Funding Statement

This study was partially funded by the University of Phayao and Thailand Science Research and Innovation Fund (Fundamental Fund 2025, Grant No. 5017/2567).The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Reviewer #2: Yes

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Reviewer #1: The authors aim to evaluate the cost-effectiveness of using Coronary Artery Calcium (CAC) screening as an alternative to the current screening guidelines in Thailand. However, I recommend that this study be rejected for publication in PLOS ONE.

The primary concern is that the relative effect between CAC screening and the current guidelines has not been appropriately estimated. There are two key issues:

Initial Incidence Data: The initial incidence of LDL-C and CAC levels is derived from only 112 patients at a single hospital. This small, non-representative sample cannot adequately reflect the Thai population. Additionally, the inclusion of patients who must have CAC testing introduces selection bias.

Long-Term Effect Estimation: The long-term effect of CAC screening is based on the estimated progression of CAC by age, derived from a retrospective study without a direct comparator. The progression data for comparator is sourced from another trial, making the relative effect unreliable.

This flaw is fundamental to the cost-effectiveness analysis and cannot be resolved through revision; substantial additional data on the relative effect is required.

Reviewer #2: Thank you for the thoughtful research. Here are some specific and general comments:

Abstract:

-The statement in the methods “Different potencies of statin were initiated based on CAC score and ACC/AHA guidelines 2019 recommendation” is not clear to me. From the introduction my understanding is that this was comparing CAC screening plus treatment to standard treatment as directed by guidelines. Without reading the body of the paper, it’s confusing how many different interventions are being compared.

-Stating that the old practice has a 29% of being cost-effective compared to the CAC intervention having a probability of 71% is redundant; in a comparison of two interventions the probability that the other is cost-effective is going to be 100-(probability other is cost-effective). This could be removed for space and replaced with other information.

-Recommend rounding all dollar figures, including ICERs, to the nearest whole number (i.e. no decimal places). What is the Thai WTP threshold? It might be useful to provide a conversion to USD in the abstract for international readers.

Introduction:

-Perhaps provide a short definition in lay terms of dyslipidemia and atherosclerosis.

-What is meant by “subclinical”

-Suggest modifying the sentence: “This study aims to evaluate the cost-effectiveness of CAC screening for primary prevention in Thai patients with intermediate ASCVD risk, compared to the current guidelines according to the ACC/AHA guidelines 2019 recommendation” to be clearer. Propose something like: “This study aims to evaluate the cost-effectiveness of including CAC screening for primary prevention in Thai patients with intermediate ASCVD risk as per the ACC/AHA guidelines 2019 recommendation, compared to the current Thai guidelines which do not advise screening in this population.”

Model Overview:

-Revise first sentence “A hybrid model combining a decision tree and a hybrid model combining a decision tree and Markov model was constructed (Fig 1 and 2)” – sounds like “hybrid model combining a decision tree” is repeated?

- “Quality-adjusted life-years (QALYs) were used as the outcome utility measure.” I think you can delete “utility” and just say outcome measure.

-Can a reference be provided for the Thai guidelines?

Data Sources

-“Patient identity was access only during the data collection and was not collected.” Suggest “viewed only during data collection and was not recorded”

Table 1: since all have the same distribution and source, these columns could be combined/moved to a footnote of the table.

Table 2: Personally I think five decimal places is a lot, I think displaying four in the table would be OK. As commented for the abstract rounding costs to whole number is advised. What is the utility for the “No CVD” health state?

Sensitivity Analysis:

-What about a scenario analysis where the discount rate is varied (or set to zero)?

-“MESA” acronym – needs to be defined and described

Results

Table 4: suggest revising the title, it is currently not clear/grammatically correct. I don’t think stating something is “more dominant” or “less dominant” is appropriate. CAC is not dominant in the base case. I think just stating whether it is or is not dominant is appropriate.

Discussion

-Using “dominant” to describe the results is not appropriate. To me this terminology is only used when an intervention is both less costly and more effective. The CAC strategy is cost-effective but not dominant. If it becomes dominant in a scenario analysis that can be stated, but stating “more dominant” or “less dominant”, does not align with my understanding of the base case findings.

-It's interesting to me that the cost of the statins seems to be driving the higher cost of the CAC strategy. The way the discussion phrases it, it doesn’t seem like there is a benefit to the higher dose of statins, that it is simply an extra cost to be rationalized but doesn’t lead to avoidance of downstream costs or increase in utility. Is this the intention of the discussion section on statin costs?

-Is there any research on cost-effectiveness of CAC screening from other settings where this has already been included in guidelines?

Other comments:

Figure 4 needs abbreviations added. Also, the maximum and minimum ICER bars all being to one side of the base case does not make sense to me. How can this be correct? I’ve never seen a tornado diagram do that.

**********

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Reviewer #1: No

Reviewer #2: Yes:  Rebecca Hancock-Howard

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PLoS One. 2025 Aug 21;20(8):e0330425. doi: 10.1371/journal.pone.0330425.r002

Author response to Decision Letter 1

21 Oct 2024

Reviewers comment on the manuscript

Entitles: “Cost-utility analysis of Coronary Artery Calcium screening to guide statin prescription among intermediate risk patient in Thailand”

Dear Editor and reviewers,

We would like to thank you for your valuable reviews and comments. It is our great pleasure to have an opportunity to revise our manuscript. We have revised and modified our manuscript with some additional information (track change) as suggested by reviewers’ comment. We hope that our revisions will improve the quality of the manuscript and give a clearer vision of research methodology to meet qualification for publication in PLOS ONE. Please inform us if further information or clarification needs to be addressed. We are looking forward to your reviews and would be extremely grateful to make our response.

____________________________________________________________________________

Reviewer #1: The authors aim to evaluate the cost-effectiveness of using Coronary Artery Calcium (CAC) screening as an alternative to the current screening guidelines in Thailand. However, I recommend that this study be rejected for publication in PLOS ONE.

The primary concern is that the relative effect between CAC screening and the current guidelines has not been appropriately estimated. There are two key issues:

1. Initial Incidence Data: The initial incidence of LDL-C and CAC levels is derived from only 112 patients at a single hospital. This small, non-representative sample cannot adequately reflect the Thai population. Additionally, the inclusion of patients who must have CAC testing introduces selection bias.

Answer: We appreciated your feed back and would like to address your concern. We acknowledge that our data may not fully represent the true population of our country. However, the included patients were rigorously screened to ensure they were at intermediate cardiovascular risk and had never received statin therapy. We specifically selected patients who underwent CAC screening because our goal was to assess LDL distribution in individuals for whom CAC might be used to guide statin therapy.

We have provided incidence data for LDL categories based on previous evidence that closely aligns with our target patient population.

Table 1.

Study Country ASCVD 10-year risk Incidence distribution Inclusion criteria Disadvantage

Kevin E Kip

2024 [1] USA Intermediate Total N =32,801

LDL < 70 = 8.8 %

LDL 70-189 = 88.6 %

LDL >= 190 = 2.6% patients aged 50–89 years from tertiary care

and was restricted to primary prevention

(not on statin therapy at baseline or within 1year of follow-up.) Other country

Matthew E Gold

2020 [2] USA Mixed Total N =4,623,851

LDL >= 190 = 2.9% included adults (age ≥18 years) with LDL-C ≥190 mg/dL, at least one LDL-C level drawn from 255 health systems participatin Other country

Mixed ASCVD risk

Mixed population of patient receive statin

Tainsuwan N2023 [3]

Thai intermediate Total N= 1,427

LDL Mean ±SD

127.28 ±(36.8)

Patient underwent CAC without previous MACE event Mixed population of patient receive statin

Aekplakorn W

2009 [4] Thai Mixed Total N= 19,021

LDL Mean (SE)

128.7 (1.1) NHES 2009 Mixed population of patient receive statin

This study Thai intermediate Total N= 112

LDL median (IQR)

106 (81, 135)

LDL < 70 = 22.8 %

LDL 70-189 = 75.9 %

LDL >= 190 = 1.8% Chiang Mai University Hospital database from 2012 -2022

Patients were free of CVD, and not started statin therapy. Small sample size

Our data might be the best available data that align with our target population at the current time. The true LDL-C levels of population could be higher or lower than the values used in the base case. Sensitivity analyses were then conducted to account for uncertainties in these parameters. In the one-way sensitivity analysis, the incidence of initial LDL levels at 70 and 70-189 mg/dL showed minimal impact on the ICER, while LDL levels above 190 mg/dL emerged as a sensitive input parameter. We varied the probability of this incidence between 0% and 5% (the upper or lower limit were based on standard error), and the ICER demonstrated a trend toward negative values as the incidence increased (see red box in Figure 1). Importantly, the ICER did not exceed the willingness-to-pay (WTP) threshold of 160,000 THB when 0 was input, indicating that the conclusion for this parameter remains robust. However, we encourage the use of national data that specifically focus on collecting these input parameters in future budget impact analyses before implementation.

We have added the text regarding to this limitation in line 341-344 as follow: “Fourth, some input parameters were derived from data specific to our center with relatively have small sample size, which may not be fully representative of the Thai population. However, sensitivity analysis accounted for this parameter uncertainties showed robust outcome. We recommend rigorously collecting large national datasets for these parameters in future budget impact analyses before implementation.”

Figure 1. Tornado diagram of one-way sensitivity analysis

Reference

1. Kip KE, Diamond D, Mulukutla S, et al. Is LDL cholesterol associated with long-term mortality among primary prevention adults? A retrospective cohort study from a large healthcare system. BMJ Open2024;14:e077949. doi:10.1136/bmjopen-2023-077949

2. Gold, M. E., Nanna, M. G., Doerfler, S. M., Schibler, T., Wojdyla, D., Peterson, E. D., & Navar, A. M. (2020). Prevalence, treatment, and control of severe hyperlipidemia. American journal of preventive cardiology, 3, 100079. https://doi.org/10.1016/j.ajpc.2020.100079

3. Tiansuwan, N., Sasiprapha, T., Jongjirasiri, S., Unwanatham, N., Thakkinstian, A., Laothamatas, J., & Limpijankit, T. (2023). Utility of coronary artery calcium in refining 10-year ASCVD risk prediction using a Thai CV risk score. Frontiers in cardiovascular medicine, 10, 1264640. https://doi.org/10.3389/fcvm.2023.1264640

4. Aekplakorn W, Taneepanichskul S, Kessomboon P, et al. Prevalence of Dyslipidemia and Management in the Thai Population, National Health Examination Survey IV, 2009. J Lipids. 2014;2014:249584. doi:10.1155/2014/249584

2. Long-Term Effect Estimation: The long-term effect of CAC screening is based on the estimated progression of CAC by age, derived from a retrospective study without a direct comparator. The progression data for comparator is sourced from another trial, making the relative effect unreliable.

Answer: We appreciated your feedback and would like to address your concern. Our model evaluated the impact of statin initiation for primary prevention based on the 2019 ACC/AHA guideline with CAC screening compared to without CAC screening over a 35-year time horizon. CAC progression is an input parameter used to predict the likelihood of progression to CAC 1-99 or >100 in patients who initially have no detectable CAC. This parameter was not addressed in previous models [1-5], which assumed the CAC score remained constant over time. To account for the natural progression of the disease, we included this parameter in the model to construct a more realistic cost-effectiveness analysis and to ensure fairness for patients who initially have no detectable CAC.

The observational studies that assess long-term CAC progression in Thailand is limited, as these types of studies require considerable time and resources. Estimating CAC progression every five years using retrospective data from the Chiang Mai University Hospital database (2012-2022, N=112) may be the most relevant evidence available for our target population.

The efficacy of moderate- or high-potency statins for reduction the risk of major cardiovascular events is based on an average pooled effect from meta-analysis of multiple trials, with a median follow-up of up to 5 years [6]. As a result, we assumed that the long-term effect of statin therapy remains constant across all age groups.

Additionally, we conducted scenario analysis using CAC progression data estimated from a prediction model based on the CAC Consortium [7], a multicenter cohort study involving four high-volume centers in the United States (Total N= 22,346 patients) (Table 2), and a scenario where CAC progression parameters were not used in the model. The results showed that slower CAC progression makes the CAC screening strategy more cost-effective, as fewer patients developed CAC 1-99, and ≥ 100 (Table 3). However, the probability estimates from the CAC Consortium may not be proper to be use in base-case, as they are based on a population not closely aligned with our target group.

We have added this analysis in our appendix and addressed this issue as our strength as follow:

Method section: in line 207-210

“Third, we explored the impact of CAC progression by changing the CAC progression probability using prediction model from the CAC Consortium, a large multicenter cohort of low CV patients in the USA [45] (details on the probabilities by age are provided in Supplementary Table S3).”

Result section: in line 263-265

“The results showed that slower CAC progression makes the CAC screening strategy more cost-effective, as fewer patients developed CAC 1-99, and ≥ 100 (Table 4).”

Discussion section: in line 320-325

“It was previously revealed that many patients who had non-fatal MI or stroke went on to experience other major hard events within a year [27, 28]. In the real world, the transition probabilities, utility, and costs within post non-fatal MI & stroke probably differ from other health states. Additionally, CAC scores naturally progress with age. Our study's strength lies in incorporating these health states and the CAC progression parameter into the Markov model, ensuring a more realistic representation of patient outcomes.”

Table 2. Comparison of the probability of CAC progression over age among patient who initially have no detectable CAC

Baseline characteristic Hospital data based 2012-2022 CAC Consortium [7]

Age (years) Mean± SD: 63.4±7.62

Intermediate risk: 100%

CAC score categories (%): 0 (25.7), 1-99 (31.2), ≥100 (43.1) Age (years) Mean± SD: 43.5±4.5

Intermediate risk: 2.6%

CAC score categories (%): 0 (65.6), 1-99 (27.2), ≥100 (7.2)

Age Probability of having CAC

1-99, ≥ 100 Probability of having CAC

≥ 0 Probability of having CAC

≥ 0

45 – 49 0.341, 0.158 0.50 0.17

50 – 54 0.338, 0.195 0.53 0.26

55 – 59 0.331, 0.237 0.57 0.38

60 – 64 0.319, 0.284 0.60 0.50

65 – 69 0.302, 0.336 0.64 0.63

70 – 74 0.281, 0.390 0.67 0.74

Scenario Incremental

Cost (THB) Incremental QALYs (years) ICER Decision*

CAC progression parameter

Absence -6,866 0.62 -11,014 CAC is dominant

Based on CAC Consortium [7] 13 0.64 20 CAC is cost-effective

Based on Hospital data 10,091 0.62 16,308 CAC is cost-effective

Table 3. Scenario analysis

*Cost-effectiveness was interpreted under the Thai willingness-to-pay threshold of 160,000 THB per QALY gained. Approximately 36 THB = 1 US$ in 2024. Abbreviation: CAC, coronary calcium score; ICER, incremental cost-effectiveness ratio; QALYs, Quality adjusted life years.

Reference

1. van Kempen BJ, Spronk S, Koller MT, et al. Comparative effectiveness and cost-effectiveness of computed tomography screening for coronary artery calcium in asymptomatic individuals. J Am Coll Cardiol 2011;58:1690–701.

2. Roberts ET, Horne A, Martin SS, et al. Cost-effectiveness of coronary artery calcium testing for coronary heart and cardiovascular disease risk prediction to guide statin allocation: the Multi-Ethnic Study of Atherosclerosis (MESA). PLoS One 2015;10:e0116377.

3. van Kempen BJ, Ferket BS, Steyerberg EW, Max W, Myriam Hunink MG, Fleischmann KE. Comparing the cost-effectiveness of four novel risk markers for screening asymptomatic individuals to prevent cardiovascular disease (CVD) in the US population. Int J Cardiol 2016;203: 422–31.

4. Hong JC, Blankstein R, Shaw LJ, Padula WV, Arrieta A, Fialkow JA, Blumenthal RS, Blaha MJ, Krumholz HM, Nasir K. Implications of Coronary Artery Calcium Testing for Treatment Decisions Among Statin Candidates According to the ACC/AHA Cholesterol Management Guidelines: A Cost-Effectiveness Analysis. JACC Cardiovasc Imaging. 2017 Aug;10(8):938-952. doi: 10.1016/j.jcmg.2017.04.014. PMID: 28797417.

5. Galper BZ, Wang YC, Einstein AJ. Strategies for primary prevention of coronary heart disease based on risk stratification by the ACC/ AHA lipid guidelines, ATP III guidelines, coronary calcium scoring, and C-reactive protein, and a global treat-all strategy: a comparativeeffectiveness modeling study. PLoS One 2015; 10:e0138092.

6. Cholesterol Treatment Trialists' (CTT) Collaborators, Mihaylova, B., Emberson, J., Blackwell, L., Keech, A., Simes, J., Barnes, E. H., Voysey, M., Gray, A., Collins, R., & Baigent, C. (2012). The effects of lowering LDL cholesterol with statin therapy in people at low risk of vascular disease: meta-analysis of individual data from 27 randomised trials. Lancet (London, England), 380(9841), 581–590. https://doi.org/10.1016/S0140-6736(12)60367-5

7. Dzaye, O., Razavi, A. C., Dardari, Z. A., Shaw, L. J., Berman, D. S., Budoff, M. J., Miedema, M. D., Nasir, K., Rozanski, A., Rumberger, J. A., Orringer, C. E., Smith, S. C., Jr, Blankstein, R., Whelton, S. P., Mortensen, M. B., & Blaha, M. J. (2021). Modeling the Recommended Age for Initiating Coronary Artery Calcium Testing Among At-Risk Young Adults. Journal of the American College of Cardiology, 78(16), 1573–1583. https://doi.org/10.1016/j.jacc.2021.08.019

Reviewer #2: Thank you for the thoughtful research. Here are some specific and general comments:

Abstract:

1. The statement in the methods “Different potencies of statin were initiated based on CAC score and ACC/AHA guidelines 2019 recommendation” is not clear to me. From the introduction my understanding is that this was comparing CAC screening plus treatment to standard treatment as directed by guidelines. Without reading the body of the paper, it’s confusing how many different interventions are being compared.

Answer: In our study, statin therapy was initiated at either high or moderate potency based on the severity of each individual’s cardiovascular risk. Recent studies show that the CAC score improves cardiovascular risk assessment [1], helping to better identify patients needing intensive statin therapy or those who may not benefit. Our aim is to compare the cost-effectiveness of incorporating the CAC score as a cardiovascular risk assessment tool, based on the 2019 ACC/AHA guidelines, against not using it for the primary prevention of ASCVD. For the CAC screening strategy, individual patients are assessed based on their CAC score to guide statin therapy. A moderate-potency statin is prescribed for patients with a CAC score of 1-99, while a high-potency statin is recommended for those with a CAC score of ≥100. Patients with a CAC score of zero are advised to reassess their score every five years until a positive CAC score is detected.

Under the 2019 ACC/AHA guidelines, without using the CAC score, patients with low-density lipoprotein cholesterol (LDL-C) levels between 70 and 189 mg/dL are prescribed a moderate-potency statin, while those with LDL-C levels of 190 mg/dL or higher are prescribed a high-potency statin.

For more clarity, we have expanded the detailed on the intervention in line 35-47 as follow: “Objective: This study aims to evaluate the cost-utility of Coronary Artery Calcium (CAC) screening for primary prevention in Thai patients with intermediate cardiovascular diseases (CVD) risk, compared to the current practice according to the ACC/AHA guideline 2019 recommendation without using of CAC score.

Methods: A hybrid model combining a decision tree and Markov model was constructed to compare cost and QALYs from a societal perspective. The model evaluated the target population of statin-naïve individuals aged 40-75 with intermediate CVD risk. We assessed the impact of statin initiation for primary prevention based on the ACC/AHA guideline 2019 with CAC screening compared to without CAC screening over a 35-year time horizon.”

References

1. Tiansuwan, N., Sasiprapha, T., Jongjirasiri, S., Unwanatham, N., Thakkinstian, A., Laothamatas, J., & Limpijankit, T. (20

Attachment

Submitted filename: response letter 20Oct24_SS.docx

pone.0330425.s004.docx (138.2KB, docx)

Decision Letter 1

Andreas Zirlik

28 Feb 2025

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Academic Editor

PLOS ONE

Additional Editor Comments:

There are numerous instances of incorrect grammar, it would be an enormous effort to correct them all as a reviewer. I note that the journal does not copyedit submissions but this should be done before proceeding with publication.

Here are more substantive comments:

Abstract: Should read “$160,00 BAHT per QALY” not QALYs

The statement: “Probabilistic, and additional one-way sensitivity analyses were performed to account for the model’s important assumption and robustness” is not clear. Suggest revising to say it is assessing robustness and testing the impact of assumptions.

Suggest rounding all monetary figures to the nearest whole value (i.e. remove decimals) throughout text.

Introduction line 69 and Methods line 101: what are the Thai risk assessment tools? They are not described in detail so it is hard to know what they involve.

Introduction line 79: Can a brief description of what CAC involves and what it assesses be provided?

Methods line 116: I think the terminal nodes for the different strategies in the decision trees should be described. My understanding is that after screening, a patient can be on no statin, moderate potency statin, or high potency statin. This is clear in the figure but not described in the text. I think Table 1 could also be revised with a column added to show how these classifications relate to no/moderate/high potency statins. This will make it more clear for the reader.

Methods: Can more details be provided about who the cardiologists and health economists were who reviewed the model, and what the review process involved?

Table formatting looks off in my version. Will need to be revised to common font and layout in publication.

Table 2: What was cost of CAC testing? I don’t think this is shown in the costs section?

Table 2: Perhaps put non-medical costs in their own category so they are more obvious. Will also need to describe how these were gathered. Is it from a patient survey? There is very little detail on how these costs were obtained. Perhaps conducting a scenario analysis from the health system perspective would also be of interest.

Table 2: Relative risk compared to placebo – I think placebo is what it was compared to in the source trials, but here shouldn’t it be compared to “no statin”?

Table 2 and methods: reference 31 used for costs is from 2006. How were these costs inflated to present values? What year is the currency presented in? This is a requested item in the CHEERS list. Perhaps add a completed CHEERS checklist as an appendix.

Discussion Line 288: “Based on our analysis, approximately one-third of patients who followed the CAC screening strategy received moderate- or high-potency statins at the beginning, while the majority of patients in the current practice received moderate-potency statins (76%).“ As discussed in the methods feedback, would it be possible to show how many patients are on the different therapies after the decision tree portion of the model? It would make the differences in the treatment of the two groups more obvious and understandable.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #2: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #2: No

**********

Reviewer #2: There are numerous instances of incorrect grammar, it would be an enormous effort to correct them all as a reviewer. I note that the journal does not copyedit submissions but this should be done before proceeding with publication.

Here are more substantive comments:

Abstract: Should read “$160,00 BAHT per QALY” not QALYs

The statement: “Probabilistic, and additional one-way sensitivity analyses were performed to account for the model’s important assumption and robustness” is not clear. Suggest revising to say it is assessing robustness and testing the impact of assumptions.

Suggest rounding all monetary figures to the nearest whole value (i.e. remove decimals) throughout text.

Introduction line 69 and Methods line 101: what are the Thai risk assessment tools? They are not described in detail so it is hard to know what they involve.

Introduction line 79: Can a brief description of what CAC involves and what it assesses be provided?

Methods line 116: I think the terminal nodes for the different strategies in the decision trees should be described. My understanding is that after screening, a patient can be on no statin, moderate potency statin, or high potency statin. This is clear in the figure but not described in the text. I think Table 1 could also be revised with a column added to show how these classifications relate to no/moderate/high potency statins. This will make it more clear for the reader.

Methods: Can more details be provided about who the cardiologists and health economists were who reviewed the model, and what the review process involved?

Table formatting looks off in my version. Will need to be revised to common font and layout in publication.

Table 2: What was cost of CAC testing? I don’t think this is shown in the costs section?

Table 2: Perhaps put non-medical costs in their own category so they are more obvious. Will also need to describe how these were gathered. Is it from a patient survey? There is very little detail on how these costs were obtained. Perhaps conducting a scenario analysis from the health system perspective would also be of interest.

Table 2: Relative risk compared to placebo – I think placebo is what it was compared to in the source trials, but here shouldn’t it be compared to “no statin”?

Table 2 and methods: reference 31 used for costs is from 2006. How were these costs inflated to present values? What year is the currency presented in? This is a requested item in the CHEERS list. Perhaps add a completed CHEERS checklist as an appendix.

Discussion Line 288: “Based on our analysis, approximately one-third of patients who followed the CAC screening strategy received moderate- or high-potency statins at the beginning, while the majority of patients in the current practice received moderate-potency statins (76%).“ As discussed in the methods feedback, would it be possible to show how many patients are on the different therapies after the decision tree portion of the model? It would make the differences in the treatment of the two groups more obvious and understandable.

**********

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If you choose “no”, your identity will remain anonymous but your review may still be made public.

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Reviewer #2: No

**********

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PLoS One. 2025 Aug 21;20(8):e0330425. doi: 10.1371/journal.pone.0330425.r004

Author response to Decision Letter 2

19 Mar 2025

Reviewers comment on the manuscript #2

Entitles: “Cost-utility analysis of Coronary Artery Calcium screening to guide statin prescription among intermediate risk patient in Thailand”

Dear Editor and reviewers,

We would like to thank you for your valuable reviews and comments. It is our great pleasure to have an opportunity to revise our manuscript. We have revised and modified our manuscript with some additional information (track change) as suggested by reviewers’ comment. We hope that our revisions will improve the quality of the manuscript and give a clearer vision of research methodology to meet qualification for publication in PLOS ONE. Please inform us if further information or clarification needs to be addressed. We are looking forward to your reviews and would be extremely grateful to make our response.

Reviewer #2

There are numerous instances of incorrect grammar, it would be an enormous effort to correct them all as a reviewer. I note that the journal does not copyedit submissions but this should be done before proceeding with publication.

Response: Thank you for your comments. We have carefully reviewed the manuscript and corrected all grammatical errors as suggested.

1. Abstract: Should read “$160,00 BAHT per QALY” not QALYs

Answer: Thank you for your suggestions. We have made a necessary edit throughout the manuscript.

2. The statement: “Probabilistic, and additional one-way sensitivity analyses were performed to account for the model’s important assumption and robustness” is not clear. Suggest revising to say it is assessing robustness and testing the impact of assumptions.

Answer: Thank you for your suggestions. We have edited according to your suggestion in line 46-49 as follows: “Probabilistic and one-way sensitivity analyses were conducted to assess the robustness of the model and evaluate how variations in key assumptions impact the results. These analyses help determine the reliability of the findings by exploring the extent to which changes in input parameters influence the overall conclusions.”

3. Suggest rounding all monetary figures to the nearest whole value (i.e. remove decimals) throughout text.

Answer: Thank you for your suggestions. We have made a necessary edit throughout the manuscript.

4. Introduction line 69 and Methods line 101: what are the Thai risk assessment tools? They are not described in detail so it is hard to know what they involve.

Answer: We appreciated your feedback. Thai risk assessment tool is called Thai CV risk tools [1]. The tool predicted 10-year risk of having cardiovascular event as percentage.

For more clarify, we have added more detail of the tools in line 68-72 as follows: “In subclinical individuals, primary prevention of ASCVD is based on the predicted 10-year risk of a CVD event. In Thailand, patients are assessed using Thai cardiovascular (CV) risk tools, which estimate their 10-year risk as a percentage [7]. Statin therapy is not recommended for individuals at low risk (<10% 10-year risk), while it is recommended for those at high ASCVD risk (>20% 10-year risk) [8].”

Reference

1. Vathesatogkit, P., Woodward, M., Tanomsup, S., Ratanachaiwong, W., Vanavanan, S., Yamwong, S., & Sritara, P. (2012). Cohort profile: the electricity generating authority of Thailand study. International journal of epidemiology, 41(2), 359–365. https://doi.org/10.1093/ije/dyq218

5. Introduction line 79: Can a brief description of what CAC involves and what it assesses be provided?

Answer: Thank you for your suggestions. We have added the short description of CAC in line 80-84 as follows: “The Coronary Artery Calcium (CAC) score, introduced in the late 1990s, is a non-invasive imaging technique used to assess coronary artery calcification [14]. It quantifies calcified atherosclerotic plaques within the coronary arteries and has been strongly associated with CVD events [15, 16]. Extensive evidence strongly supports the potential of Coronary Artery Calcium (CAC) scores as an accurate tool for cardiovascular risk stratification [17, 18].”

6. Methods line 116: I think the terminal nodes for the different strategies in the decision trees should be described. My understanding is that after screening, a patient can be on no statin, moderate potency statin, or high potency statin. This is clear in the figure but not described in the text. I think Table 1 could also be revised with a column added to show how these classifications relate to no/moderate/high potency statins. This will make it more clear for the reader.

Answer: Thank you for your suggestions. To ensure greater clarity and comprehensibility, we have added the corresponding treatment column for each terminal node in decision tree within Table 1, as presented below.

Table 1. Selected input parameters to the decision tree model and corresponding treatment strategy

Input parameter: probabilities Base-case value

mean (SE) Receiving treatment

Current practice strategy

Incidence of initial LDL-C ≥ 190 mg/dL 0.018 (0.013) High potency statin

Incidence of initial LDL-C 70-189 mg/dL 0.759 (0.04) Moderate potency statin

Incidence of initial LDL-C <70 mg/dL 0.223 (0.04) No statin

CAC screening strategy

Incidence of initial CAC ≥ 100 0.36 (0.013) High potency statin

Incidence of initial CAC 1-99 0.39 (0.013) Moderate potency statin

Incidence of initial CAC = 0 0.26 (0.012) No statin

*The input data were sourced from the hospital database from 2012 to 2022. A beta distribution was selected for these input parameters in the probabilistic sensitivity analyses. Abbreviation: CAC, coronary calcium score; LDL-C, low-density lipoprotein cholesterol; SE, standard error

7. Discussion Line 288: “Based on our analysis, approximately one-third of patients who followed the CAC screening strategy received moderate- or high-potency statins at the beginning, while the majority of patients in the current practice received moderate-potency statins (76%).“ As discussed in the methods feedback, would it be possible to show how many patients are on the different therapies after the decision tree portion of the model? It would make the differences in the treatment of the two groups more obvious and understandable.

Answer: Thank you for your suggestions. We have added the corresponding treatment column for each terminal node in decision tree within Table 1, as presented below.

Table 1. Selected input parameters to the decision tree model and corresponding treatment strategy

Input parameter: probabilities Base-case value

mean (SE) Receiving treatment

Current practice strategy

Incidence of initial LDL-C ≥ 190 mg/dL 0.018 (0.013) High potency statin

Incidence of initial LDL-C 70-189 mg/dL 0.759 (0.04) Moderate potency statin

Incidence of initial LDL-C <70 mg/dL 0.223 (0.04) No statin

CAC screening strategy

Incidence of initial CAC ≥ 100 0.36 (0.013) High potency statin

Incidence of initial CAC 1-99 0.39 (0.013) Moderate potency statin

Incidence of initial CAC = 0 0.26 (0.012) No statin

*The input data were sourced from the hospital database from 2012 to 2022. A beta distribution was selected for these input parameters in the probabilistic sensitivity analyses. Abbreviation: CAC, coronary calcium score; LDL-C, low-density lipoprotein cholesterol; SE, standard error

8. Methods: Can more details be provided about who the cardiologists and health economists were who reviewed the model, and what the review process involved?

Answer: The conceptualisation and initial drafting of the model structure were carried out with the valuable contributions of cardiologist Pannipa Suwannasom. The study’s model was then reviewed and validated by external cardiologists, including Songsak Kiatchoosakun, Suphot Srimahachota, Arintaya Phrommintikul, and Yotsawee Chotechuang. Furthermore, the economic aspects of the model were thoroughly reviewed by Surasak Saokaew. The process involved multiple meetings, beginning with designing the model structure and validating the data used for input parameters. Follow-up meetings were then conducted to review and discuss both the preliminary and final results.

We have acknowledged the external experts who assisted in this study in the Acknowledgements section, line 378 - 382, as follows: “This study was partially supported by the Faculty of Medicine, Chiang Mai University, the University of Phayao, and the Thailand Science Research and Innovation Fund. We extend our deep gratitude to cardiologists Songsak Kiatchoosakun, Suphot Srimahachota, Arintaya Phrommintikul, and Yotsawee Chotechuang for their valuable review and validation of the model.”

9. Table formatting looks off in my version. Will need to be revised to common font and layout in publication.

Answer: We appreciate your feedback. The font in all tables has been revised accordingly.

10. Table 2: What was cost of CAC testing? I don’t think this is shown in the costs section?

Answer: We appreciate your feedback and sincerely apologise for any confusion in our manuscript. The cost of CAC testing is provided in Table 2. However, we initially used the term "CT coronary calcium scan," which may not be familiar to experts outside the field. To enhance clarity and consistency, we have revised it to "CAC screening" throughout the manuscript.

11. Table 2: Perhaps put non-medical costs in their own category so they are more obvious. Will also need to describe how these were gathered. Is it from a patient survey? There is very little detail on how these costs were obtained. Perhaps conducting a scenario analysis from the health system perspective would also be of interest.

Answer: We appreciate your feedback and would like to address your concern regarding the non-medical costs associated with non-fatal MI. These costs were sourced from Anukoolsawat et al. (2006) [1], where interview records from 193 ACS patients were used to estimate direct non-medical expenses. The non-fatal stroke costs was based on data from Rattanavipapong et al. (2022) [2]. This study utilized retrospective patient data from a multicentre stroke unit, incorporating key parameters such as inpatient length of stay, number of follow-up visits, and travel distance to estimate non-medical costs. Furthermore, transportation, caregiver, food, and accommodation expenses incurred during treatment were referenced from Singhpoo K et al. (2009) [3]. To enhance transparency, we have added these details into the supplementary data on page 8-9, as outlined below.: “Details of Cost Parameters from Anukoolsawat et al. (2006) and Rattanavipapong et.al. (2022)

Our study derived costs for secondary treatment and prevention in populations who experienced non-fatal MI costs, fatal MI costs, and direct non-medical costs from Anukoolsawat et al. (2006) [9]. This study used data from the Thai Acute Coronary Syndrome (ACS) registry to estimate lifetime costs of ACS. Medical records of 330 ACS patients were used to calculate direct medical costs (pre-event and MI-related costs of non-fatal MI), while interview records of 193 ACS patients were used to estimate direct non-medical costs. For the costs of non-fatal stroke and recurrent non-fatal stroke (both direct and indirect), our study utilized data from Rattanavipapong et al. (2022) [8] due to its recency and comprehensive. This study utilized retrospective patient data from a multicentre stroke unit, incorporating key parameters such as inpatient length of stay, number of follow-up visits, and travel distance to estimate non-medical costs. Furthermore, transportation, caregiver, food, and accommodation expenses incurred during treatment were referenced from Singhpoo K et al. (2009) [11]. Detailed information on direct and non-medical costs and their components can be found in the supplementary materials of the original article.”

We performed sensitivity analysis using health care providers perspective. The incremental cost, QALYs, and ICER for CAC screening are presented in the table below. CAC screening remains cost-effective under Thailand’s willingness-to-pay threshold of 160,000 THB per QALY gained.

Scenario Incremental

Cost (THB) Incremental QALYs (years) ICER Decision*

Perspective

Healthcare provider perspective 24,762 0.62 40,020 CAC is cost-effective

Societal perspective (base-case) 10,091 0.62 16,308 CAC is cost-effective

We have added the relevant text addressing this issue in the Methods section, line 236-237, as follows: “The post-hoc sensitivity analysis using the healthcare provider’s perspective was performed to account for the impact of direct non-medical costs.”

And in result section, line 286-288 as follows: “The post-hoc sensitivity analysis using the healthcare provider’s perspective demonstrated that CAC screening remains a cost-effective strategy (Table 4).”

1. Anukoolsawat, Pongchai, Piyamitr Sritara and Yot Teerawattananon. “Costs of Lifetime Treatment of Acute Coronary Syndrome at Ramathibodi Hospital.” (2006).

2. Rattanavipapong W, Worakijthamrongchai T, Soboon B, et al. Economic evaluation of endovascular treatment for acute ischaemic stroke in Thailand. BMJ Open2022;12:e064403. doi:10.1136/bmjopen-2022-064403

3. Singhpoo K, Tiamkao S, Ariyanuchitkul S, Sangpongsanon S, Kamsa-ard S, Lekbunyasin O, Soommart Y. The expenditures of stroke outpatients at Srinagarind hospital. Srinagarind Medical Journal. 2009;24(1):54-9.

12. Table 2: Relative risk compared to placebo – I think placebo is what it was compared to in the source trials, but here shouldn’t it be compared to “no statin”?

Answer: Thank you for your suggestions. We have edited according to your suggestions.

13. Table 2 and methods: reference 31 used for costs is from 2006. How were these costs inflated to present values? What year is the currency presented in? This is a requested item in the CHEERS list. Perhaps add a completed CHEERS checklist as an appendix.

Answer: We appreciate your feedback and would like to address your concern. All Cost data was converted to 2024 values using the Thai consumer price index (CPI) [1] and are expressed in Thai Baht (THB) (approximately 36 THB = 1 US$ in 2024) [2]. To account for time preference in the model, future costs and QALYs beyond the first year were discounted by 3% annually. This information has been explicitly stated in method section, line 185-188 as follows: “Cost data were converted to 2024 values using the Thai consumer price index (CPI) [39] and presented in Thai Baht (THB) (approximately 36 THB = 1 US$ in 2024) [40]. Future costs and QALYs beyond the first year were discounted by 3% annually.” According to your suggestion, we have added the CHEERS checklist as a supplementary file.

Reference

1. Bureau of Trade & Economics Indices Ministry of Commerce. Consumer price index of Thailand year 2024 base year 2006, 2018, 2021, and 2023. Available: http://www.price.moc.go.th/price/cpi/index_new_all.asp [Accessed 8 January 2024].

2. International Monetary Fund. World Economic Outlook Database for October 2007. Available from: https://www.imf.org/external/np/fin/ert/GUI/Pages/Report.aspx [Accessed 10 May , 2024].

Attachment

Submitted filename: Response to reviewer.docx

pone.0330425.s005.docx (30.7KB, docx)

Decision Letter 2

Forgive Avorgbedor

24 Jul 2025

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Reviewer #2: Congratulations on the hard work, this version is an improvement on the first one and reads very well. A few specific comments:

Line 50: has an extra zero been added to the incremental cost? Should be $10,000 not $100,000?

Line 177: What are the indirect costs based on?

Line 221: My jurisdiction’s guidelines also suggest a scenario analysis where the discount rate is set to zero (and one were it’s increased above the base case value). Could consider adding this but may not be expected in your jurisdiction.

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PLoS One. 2025 Aug 21;20(8):e0330425. doi: 10.1371/journal.pone.0330425.r006

Author response to Decision Letter 3

28 Jul 2025

This study was partially funded by the University of Phayao and Thailand Science Research and Innovation Fund (Fundamental Fund 2025, Grant No. 5017/2567).The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

We have removed declarative statements, such as Data Availability, Competing/Conflict of Interest, Funding/Financial Disclosure, Consent to publish statements, etc. from the manuscript files.

Best,

Phichayut

Decision Letter 3

Forgive Avorgbedor

1 Aug 2025

Cost-utility analysis of Coronary Artery Calcium screening to guide statin prescription among intermediate-risk patients in Thailand

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    pone.0330425.s001.docx (132.1KB, docx)
    S1 Fig. Cost-effectiveness plane of probabilistic sensitivity analysis.

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    Attachment

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