Cadham Christopher J, Reicher Joshua, Muelly Michael, Hutton David W
Department of Health Management and Policy, School of Public Health, University of Michigan, 1420 Washington Heights, Ann Arbor, MI, 48109-2013, USA.
Imvaria, Inc, Berkeley, CA, USA.
BMC Health Serv Res. 2025 Mar 15;25(1):385. doi: 10.1186/s12913-025-12506-1.
Novel non-invasive machine learning algorithms may improve accuracy and reduce the need for biopsy when diagnosing idiopathic pulmonary fibrosis (IPF). We conducted a cost-effectiveness analysis of diagnostic strategies for IPF.
We developed a decision analytic model to evaluate diagnostic strategies for IPF in the United States. To assess the full spectrum of costs and benefits, we compared four interventions: a machine learning diagnostic algorithm, a genomic classifier, a biopsy-all strategy, and a treat-all strategy. The analysis was conducted from the health sector perspective with a lifetime horizon. The primary outcome measures were costs, Quality-Adjusted Life-Years (QALYs) gained, and Incremental Cost-Effectiveness Ratios (ICERs) based on the average of 10,000 probabilistic runs of the model.
Compared to a biopsy-all strategy the machine learning algorithm and genomic classifer reduced diagnostic-related costs by $14,876 and $3,884, respectively. Use of the machine learning algorithm consistently reduced diagnostic costs. When including downstream treatment costs and benefits of anti-fibrotic treatment, the machine learning algorithm had an ICER of $331,069 per QALY gained compared to the biopsy-all strategy. The genomic classifier had a higher ICER of $390,043 per QALY gained, while the treat-all strategy had the highest ICER of $3,245,403 per QALY gained. Results were sensitive to changes in various input parameters including IPF treatment costs, sensitivity and specificity of novel screening tools, and the rate of additional diagnostics following inconclusive results. High treatment costs were found to drive overall cost regardless of the diagnostic method. As treatment costs lowered, the supplemental diagnostic tools became increasingly cost-effective.
Novel tools for diagnosing IPF reduced diagnostic costs, while overall incremental cost-effectiveness ratios were high due to treatment costs. New IPF diagnosis approaches may become more favourable with lower-cost treatments for IPF.
新型非侵入性机器学习算法可能提高特发性肺纤维化（IPF）诊断的准确性并减少活检需求。我们对IPF的诊断策略进行了成本效益分析。
我们开发了一个决策分析模型，以评估美国IPF的诊断策略。为了评估成本和效益的全貌，我们比较了四种干预措施：机器学习诊断算法、基因组分类器、全活检策略和全治疗策略。分析从卫生部门的角度进行，为期一生。主要结果指标是成本、获得的质量调整生命年（QALY）以及基于该模型10,000次概率运行平均值的增量成本效益比（ICER）。
与全活检策略相比，机器学习算法和基因组分类器分别将诊断相关成本降低了14,876美元和3,884美元。使用机器学习算法持续降低了诊断成本。当纳入下游治疗成本和抗纤维化治疗的效益时，与全活检策略相比，机器学习算法每获得一个QALY的ICER为331,069美元。基因组分类器每获得一个QALY的ICER更高，为390,043美元，而全治疗策略每获得一个QALY的ICER最高，为3,245,403美元。结果对各种输入参数的变化敏感，包括IPF治疗成本、新型筛查工具的敏感性和特异性以及不确定结果后的额外诊断率。发现高治疗成本驱动了总体成本，而与诊断方法无关。随着治疗成本降低，补充诊断工具变得越来越具有成本效益。
用于诊断IPF的新型工具降低了诊断成本，而由于治疗成本，总体增量成本效益比很高。随着IPF治疗成本降低，新的IPF诊断方法可能会变得更有利。
