利用深度学习进行先天性心脏病产前诊断的成本效益分析。

IF 1.7 4区医学 Q3 HEALTH POLICY & SERVICES

Cost Effectiveness and Resource Allocation Pub Date : 2024-05-22 DOI:10.1186/s12962-024-00550-3

Gary M Ginsberg, Lior Drukker, Uri Pollak, Mayer Brezis

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引用次数: 0

摘要

背景：深度学习（DL）是一项新技术，可协助产前超声（US）检测产前先天性心脏病（CHD）。因此，需要进行经济流行病学评估（又称成本效用分析），以帮助决策者决定是否采用这项新技术：方法：在目前提供 US 加脉搏血氧仪（POX）的基础上增加 DL 辅助超声（DL-US）的增量成本效用比（CUR），是通过建立一个电子表格模型计算得出的，该模型综合了人口统计学、经济流行病学、医疗服务利用率、筛查效果、存活率和终生生活质量等数据，并基于标准公式：CUR = 增加的干预成本 - 减少的治疗成本在 US 和 POX 的基础上增加 DL 所避免的 QALY 损失 US 筛查数据基于真实世界的常规操作报告（而非研究）。DL 筛查成本 145 美元是基于以色列 US 成本加上 20.54 美元的屏幕阅读和记录费用：结果：添加 DL 辅助 US 后，灵敏度提高（95% 对 58.1%），未确诊婴儿的数量也大大减少（560 名和 659 名新生儿中，未确诊婴儿分别为 16 名和 102 名[或 2.9% 对 15.4%]）。采用 DL-US 技术将增加 1,204 QALYs，增加的筛查成本为 2,250 万美元，大部分被减少的治疗成本（2,040 万美元）所抵消。因此，新的 DL-US 技术被认为 "非常具有成本效益"，每 QALY 的成本仅为 1,720 美元。对于大多数性能组合（灵敏度 > 80%，特异性 > 90%），采用 DL-US 要么具有成本效益，要么非常具有成本效益。对于特异性大于 98%（灵敏度大于 94%）的情况，DL-US（& POX）以较低的成本提供了更多的 QALYs，可以说 "主导 "了 US（& POX）：我们的探索性 CUA 计算表明，DL-US 至少具有成本效益。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Cost-utility analysis of prenatal diagnosis of congenital cardiac diseases using deep learning.

Background: Deep learning (DL) is a new technology that can assist prenatal ultrasound (US) in the detection of congenital heart disease (CHD) at the prenatal stage. Hence, an economic-epidemiologic evaluation (aka Cost-Utility Analysis) is required to assist policymakers in deciding whether to adopt the new technology.

Methods: The incremental cost-utility ratios (CUR), of adding DL assisted ultrasound (DL-US) to the current provision of US plus pulse oximetry (POX), was calculated by building a spreadsheet model that integrated demographic, economic epidemiological, health service utilization, screening performance, survival and lifetime quality of life data based on the standard formula: $CUR = \frac{Increase in Intervention Costs - Decrease in Treatment costs}{Averted QALY losses of adding DL to US & POX}$ US screening data were based on real-world operational routine reports (as opposed to research studies). The DL screening cost of 145 USD was based on Israeli US costs plus 20.54 USD for reading and recording screens.

Results: The addition of DL assisted US, which is associated with increased sensitivity (95% vs 58.1%), resulted in far fewer undiagnosed infants (16 vs 102 [or 2.9% vs 15.4%] of the 560 and 659 births, respectively). Adoption of DL-US will add 1,204 QALYs. with increased screening costs 22.5 million USD largely offset by decreased treatment costs (20.4 million USD). Therefore, the new DL-US technology is considered "very cost-effective", costing only 1,720 USD per QALY. For most performance combinations (sensitivity > 80%, specificity > 90%), the adoption of DL-US is either cost effective or very cost effective. For specificities greater than 98% (with sensitivities above 94%), DL-US (& POX) is said to "dominate" US (& POX) by providing more QALYs at a lower cost.

Conclusion: Our exploratory CUA calculations indicate the feasibility of DL-US as being at least cost-effective.

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来源期刊

Cost Effectiveness and Resource Allocation HEALTH POLICY & SERVICES-

CiteScore

3.40

自引率

4.30%

发文量

审稿时长

34 weeks

期刊介绍： Cost Effectiveness and Resource Allocation is an Open Access, peer-reviewed, online journal that considers manuscripts on all aspects of cost-effectiveness analysis, including conceptual or methodological work, economic evaluations, and policy analysis related to resource allocation at a national or international level. Cost Effectiveness and Resource Allocation is aimed at health economists, health services researchers, and policy-makers with an interest in enhancing the flow and transfer of knowledge relating to efficiency in the health sector. Manuscripts are encouraged from researchers based in low- and middle-income countries, with a view to increasing the international economic evidence base for health.