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在缺乏完美参考测试的情况下评估新测试的准确性和经济价值。

Evaluating the accuracy and economic value of a new test in the absence of a perfect reference test.

机构信息

Technology Assessment Unit, McGill University Health Centre, Montréal, Quebec, Canada.

Toronto Health Economics and Technology Assessment Collaborative, Leslie Dan Pharmacy, University of Toronto, Toronto, Ontario, Canada.

出版信息

Res Synth Methods. 2017 Sep;8(3):321-332. doi: 10.1002/jrsm.1243. Epub 2017 May 23.

Abstract

BACKGROUND

Streptococcus pneumoniae (SP) pneumonia is often treated empirically as diagnosis is challenging because of the lack of a perfect test. Using BinaxNOW-SP, a urinary antigen test, as an add-on to standard cultures may not only increase diagnostic yield but also increase costs.

OBJECTIVE

To estimate the sensitivity and specificity of BinaxNOW-SP and subsequently estimate the cost-effectiveness of adding BinaxNOW-SP to the diagnostic work-up.

DESIGN

We fit a Bayesian latent-class meta-analysis model to obtain estimates of BinaxNOW-SP accuracy that adjust for the imperfect accuracy of culture. Meta-analysis results were combined with information on prevalence of SP pneumonia to estimate the number of patients who are correctly classified under competing diagnostic strategies. Taking into consideration the cost of antibiotics, we determined the incremental cost of adding BinaxNOW-SP to the work-up per case correctly diagnosed.

RESULTS

The BinaxNOW-SP test had a pooled sensitivity of 0.74 (95% credible interval [CrI], 0.67-0.83) and a pooled specificity of 0.96 (95% CrI, 0.92-0.99). An overall increase in diagnostic accuracy of 6.2% due to the addition of BinaxNOW-SP corresponded to an incremental cost per case correctly classified of $582 Canadian dollars.

CONCLUSIONS

The methods we have described allow us to evaluate the accuracy and economic value of a new test in the absence of a perfect reference test using an evidence-based approach.

摘要

背景

由于缺乏完美的检测方法,肺炎链球菌(SP)肺炎的诊断具有挑战性,通常需要经验性治疗。使用 BinaxNOW-SP,一种尿抗原检测方法,作为标准培养的附加物,不仅可以提高诊断效果,还可能增加成本。

目的

评估 BinaxNOW-SP 的敏感性和特异性,进而估计将 BinaxNOW-SP 添加到诊断方案中的成本效益。

设计

我们拟合了贝叶斯潜在类别荟萃分析模型,以获得调整培养不完美准确性的 BinaxNOW-SP 准确性估计值。荟萃分析结果与 SP 肺炎的流行率信息相结合,以估计在竞争诊断策略下正确分类的患者数量。考虑到抗生素的成本,我们确定了每个正确诊断病例添加 BinaxNOW-SP 到工作流程中的增量成本。

结果

BinaxNOW-SP 测试的合并敏感性为 0.74(95%可信区间 [CrI],0.67-0.83),合并特异性为 0.96(95% CrI,0.92-0.99)。由于添加了 BinaxNOW-SP,诊断准确性总体提高了 6.2%,每个正确分类病例的增量成本为 582 加元。

结论

我们描述的方法允许我们在缺乏完美参考测试的情况下,使用循证方法评估新测试的准确性和经济价值。

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