Longo Roberta, Baxter Paul, Hall Peter, Hewison Jenny, Afshar Mehran, Hall Geoff, McCabe Christopher
Leeds Institute of Health Sciences, University of Leeds, Leeds, UK.
Pharmacoeconomics. 2014 Apr;32(4):327-34. doi: 10.1007/s40273-014-0134-1.
The arrival of personalized medicine in the clinic means that treatment decisions will increasingly rely on test results. The challenge of limited healthcare resources means that the dissemination of these technologies will be dependent on their value in relation to their cost, i.e., their cost effectiveness. Phelps and Mushlin have described how to optimize tests to meet a cost-effectiveness target. However, when tests are applied repeatedly the case mix of the patients tested changes with each administration, and this impacts upon the value of each subsequent test administration. In this article, we present a modification of Phelps and Mushlin's framework for diagnostic tests; to identify the cost-effective cut-off for monitoring tests. Using the Ca125 test monitoring for relapse in ovarian cancer, we show how the repeated use of the initial cut-off can lead to a substantially increased false-negative rate compared with the monitoring cut-off-over 4% higher than in this example-with the associated harms for individual and population health.
个性化医疗进入临床意味着治疗决策将越来越依赖检测结果。医疗资源有限带来的挑战意味着这些技术的推广将取决于其成本效益，即与成本相关的价值。费尔普斯和穆斯林描述了如何优化检测以达到成本效益目标。然而，当检测重复进行时，接受检测患者的病例组合每次都会发生变化，这会影响随后每次检测的价值。在本文中，我们对费尔普斯和穆斯林的诊断检测框架进行了修改；以确定监测检测的成本效益临界值。通过使用Ca125检测监测卵巢癌复发，我们展示了与监测临界值相比，重复使用初始临界值如何导致假阴性率大幅增加——比本示例高出4%以上——并对个体和群体健康造成相关危害。
