BACKGROUND Phase III trial NCT04560894 showed finotonlimab plus bevacizumab biosimilar SCT510 (FIN-BEV) improved survival over sorafenib in advanced hepatocellular carcinoma (HCC). However, the cost-effectiveness of FIN-BEV relative to sorafenib remains uncertain. This study evaluates the cost-effectiveness of FIN-BEV as a first-line treatment for advanced HCC from the perspective of the Chinese healthcare system, compared to sorafenib. METHODS A partitioned survival model with three health states was built in TreeAge Pro 2022 using NCT04560894 data. Drug costs were calculated based on national bid-winning prices, while other costs and utility values were obtained from published Chinese literature. Sensitivity analyses were conducted to assess the robustness of the model. RESULTS FIN-BEV yielded 2.43 QALYs at $66,740.79 versus sorafenib's 1.63 QALYs at $14,241.13. The incremental cost-effectiveness ratio was $65,717.80/QALY, exceeding the willingness-to-pay threshold of $27,906/QALY. The probability of FIN-BEV being cost-effective as a first-line treatment for advanced HCC was 0.2%. Key factors influencing the model results included the utility value of progression-free survival, the cost of SCT510, body weight, the utility value of progressive disease, and the cost of finotonlimab. CONCLUSION From the perspective of the Chinese healthcare system, FIN-BEV does not represent a cost-effective first-line treatment for advanced HCC when compared to sorafenib.
BACKGROUND Adding milrinone to dobutamine (dobutamine-milrinone escalation) in cardiogenic shock (CS) is reimbursed for a limited duration under Indonesia's national health insurance, but the cost structure of escalation remains poorly characterized. This study quantified direct medical charges associated with escalation and the contribution of drug acquisition. RESEARCH DESIGN AND METHODS Retrospective cohort of 76 adult CS survivors (20 escalation, 56 dobutamine monotherapy) at Indonesia's national cardiovascular referral center, 2020-2024. Covariate Balancing Propensity Score-based inverse probability of treatment weighting, using pre-treatment covariates, estimated adjusted cost ratios (CR) for intensive cardiovascular care unit (ICVCU) and total hospital costs from institutional billing records. RESULTS Escalation was associated with higher ICVCU costs (CR 2.711; 95% CI 1.912-3.844; p < 0.001) and total hospital costs (CR 2.321; 95% CI 1.372-3.929; p = 0.002). Combined inotrope costs represented 4.3% of ICVCU expenditure in the escalation group, compared with 30.9% for therapeutic and interventional procedures. Baseline vasoactive-inotropic score correlated with total hospital cost (ρ=0.597; p < 0.001). CONCLUSIONS Drug acquisition represented a minor share of costs associated with dobutamine-milrinone escalation. The findings support consideration of severity- and resource-based approaches to reimbursement rather than focusing solely on duration limits for individual inotropes. Interpretation is limited by the retrospective, single-center, survivor-only design.
BACKGROUND Health economic (HE) evaluations are essential for healthcare decision-making, yet the rapid pace of innovation demands accelerated HE evaluations. Integrating Generative AI (GenAI), particularly Large Language Models (LLMs), into HE simulation modeling offers a promising opportunity to enhance model development efficiency and speed. RESEARCH DESIGN AND METHODS We propose a framework for automating HE model development through expert-AI co-creation. This framework enables GenAI to generate simulation models in a structured and transparent manner. The proposed approach is expert-led and incorporates iterative prompt-response cycles. GPT5-mini and GPT-5.2-codex are employed using zero-shot prompting. As LLM results are non-deterministic, we introduced a Prompt Robustness Test (PRT) to assess the stability of LLM-generated outputs, indicating whether a prompt is sufficiently reliable to support automated model development. The methodology is applied to replicate a published discrete-event simulation comparing two post-surgery treatment strategies for colon cancer. The original model is complex and representative of real-world HE practice. RESULTS Using our framework, we replicated the model in Python, achieving cost and QALY estimates within 2% of the original. While the overall pathway structure was successfully reconstructed, several logic discrepancies remained compared with the original model. CONCLUSIONS Our framework provides methodological guidance on the expert-AI co-creation of complex HE models, emphasizing that expert oversight is essential throughout the process.
BACKGROUND This study evaluates the cost-effectiveness of dostarlimab plus chemotherapy in contrast to chemotherapy as first-line treatment of metastatic non-small cell lung cancer (NSCLC) from the perspective of healthcare payers in the U.S.A. RESEARCH DESIGN AND METHODS An economic evaluation using a three‑state partitioned survival model assessed the cost-effectiveness of dostarlimab plus chemotherapy compared to chemotherapy alone. The Kaplan-Meier curves for overall survival (OS) and progression-free survival (PFS) from clinical trials were digitally extracted, and the log‑normal model with network meta-analysis (NMA) was applied at the end of the trial to extrapolate long-term survival. The cost and health utility data were sourced from the published literature. RESULTS The anticipated expenditure for dostarlimab plus chemotherapy therapy exceeded the cost for chemotherapy alone therapy (366,887.33 USD vs 102,503.32 USD). The estimated utility was also greater compared to that of chemotherapy alone treatment (3.74 QALYs vs 1.90 QALYs). The ICER was 143,856.30 USD/QALY. CONCLUSIONS Dostarlimab plus chemotherapy was cost-effective compared to chemotherapy alone as a first-line treatment for advanced or metastatic NSCLC patients at the WTP threshold of 150,000 USD/QALY in the U.S.A.
