Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2026
Cost-Effectiveness and Clinical Utility of a Retinal Cardiovascular AI-Guided Strategy for Statin Initiation: A South Korean Multicenter Prospective Study.
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Nam Dongjin, Kim Man S, Park Hwan-Cheol, Liu Lei, Oh Si Nae, Park Youngmin, Han Jae Yong, Lee Kwanghyun, Kim Jung Hyun, Hwang Sunjin
Аннотация
OBJECTIVES: To evaluate the clinical utility and long-term cost-effectiveness of artificial intelligence (AI)-guided retinal cardiovascular risk assessment, compared with standard of care (SOC), for guiding statin therapy in primary cardiovascular disease prevention in South Korea.
METHODS: In this multicenter study, 274 prospectively enrolled AI-guided participants were compared with 274 propensity score-matched retrospective SOC controls; guideline concordance was adjudicated by an independent evaluator. Long-term cost-effectiveness was assessed from a healthcare-system perspective using a decision tree and lifetime Markov microsimulation of 10,000 hypothetical individuals aged 63 years. Costs and quality-adjusted life-years (QALYs) were discounted at 4.5%; deterministic and probabilistic sensitivity analyses included 10,000 Monte Carlo iterations.
RESULTS: AI-guided care was associated with higher guideline compliance (82.8% vs 61.3%) and appropriate statin initiation (53.3% vs 4.7%) and lower inappropriate non-initiation (11.7% vs 35.8%; all P<.001), which were directly observed decision-level outcomes. In the model, the AI-guided strategy was projected to be dominant, yielding more QALYs (12.57 vs 11.39) at lower lifetime cost ($123,271 vs $140,865) and an incremental cost-effectiveness ratio of -$14,972/QALY. Incremental net monetary benefit was $43,389 at 30,000,000 KRW/QALY ($21,952), and the strategy was cost-effective in every probabilistic iteration and all examined sensitivity analyses, including sex-stratified analyses.
CONCLUSIONS: Linking retinal cardiovascular AI outputs to guideline-based preventive care was associated with more guideline-concordant statin decisions and, in a lifetime model, was projected to be economically dominant over usual care. Long-term outcomes were projected, not observed, and require confirmation in other settings.
METHODS: In this multicenter study, 274 prospectively enrolled AI-guided participants were compared with 274 propensity score-matched retrospective SOC controls; guideline concordance was adjudicated by an independent evaluator. Long-term cost-effectiveness was assessed from a healthcare-system perspective using a decision tree and lifetime Markov microsimulation of 10,000 hypothetical individuals aged 63 years. Costs and quality-adjusted life-years (QALYs) were discounted at 4.5%; deterministic and probabilistic sensitivity analyses included 10,000 Monte Carlo iterations.
RESULTS: AI-guided care was associated with higher guideline compliance (82.8% vs 61.3%) and appropriate statin initiation (53.3% vs 4.7%) and lower inappropriate non-initiation (11.7% vs 35.8%; all P<.001), which were directly observed decision-level outcomes. In the model, the AI-guided strategy was projected to be dominant, yielding more QALYs (12.57 vs 11.39) at lower lifetime cost ($123,271 vs $140,865) and an incremental cost-effectiveness ratio of -$14,972/QALY. Incremental net monetary benefit was $43,389 at 30,000,000 KRW/QALY ($21,952), and the strategy was cost-effective in every probabilistic iteration and all examined sensitivity analyses, including sex-stratified analyses.
CONCLUSIONS: Linking retinal cardiovascular AI outputs to guideline-based preventive care was associated with more guideline-concordant statin decisions and, in a lifetime model, was projected to be economically dominant over usual care. Long-term outcomes were projected, not observed, and require confirmation in other settings.