Neuroradiology Medical Director, Harborview Medical Center, University of Washington, Seattle, Washington.
University of Washington, Seattle, Washington.
J Am Coll Radiol. 2024 Sep;21(9):1489-1496. doi: 10.1016/j.jacr.2023.11.029. Epub 2024 Mar 26.
Osteoporotic vertebral compression fractures (OVCFs) are a highly prevalent source of morbidity and mortality, and preventive treatment has been demonstrated to be both effective and cost effective. To take advantage of the information available on existing chest and abdominal radiographs, the authors' study group has developed software to access these radiographs for OVCFs with high sensitivity and specificity using an established artificial intelligence deep learning algorithm. The aim of this analysis was to assess the potential cost-effectiveness of implementing this software.
A deterministic expected-value cost-utility model was created, combining a tree model and a Markov model, to compare the strategies of opportunistic screening for OVCFs against usual care. Total costs and total quality-adjusted life-years were calculated for each strategy. Screening and treatment costs were considered from a limited societal perspective, at 2022 prices.
In the base case, assuming a cost of software implantation of $10 per patient screened, the screening strategy dominated the nonscreening strategy: it resulted in lower cost and increased quality-adjusted life-years. The lower cost was due primarily to the decreased costs associated with fracture treatment and decreased probability of requiring long-term care in patients who received preventive treatment. The screening strategy was dominant up to a cost of $46 per patient screened.
Artificial intelligence-based opportunistic screening for OVCFs on existing radiographs can be cost effective from a societal perspective.
骨质疏松性椎体压缩性骨折（OVCFs）是一种高发病率和死亡率的疾病，预防治疗已被证明既有效又具有成本效益。为了利用现有胸部和腹部 X 光片上的信息，作者的研究小组开发了一种软件，该软件使用经过验证的人工智能深度学习算法，以高灵敏度和特异性来检测这些 X 光片中的 OVCFs。本分析旨在评估实施该软件的潜在成本效益。
创建了一个确定性预期价值成本效用模型，结合树模型和马尔可夫模型，比较了对 OVCFs 进行机会性筛查的策略与常规护理。为每种策略计算了总成本和总质量调整生命年。从有限的社会角度考虑了筛查和治疗成本，以 2022 年的价格计算。
在基础情况下，假设每位接受筛查的患者的软件植入成本为 10 美元，筛查策略优于非筛查策略：它降低了成本并增加了质量调整生命年。较低的成本主要归因于接受预防性治疗的患者骨折治疗成本降低和长期护理需求概率降低。在每位患者筛查成本为 46 美元时，筛查策略仍然具有优势。
从社会角度来看，基于人工智能的现有 X 光片上 OVCFs 的机会性筛查具有成本效益。
