Pflieger Lance T, Mason Clinton C, Facelli Julio C
Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
Department of Pediatrics, University of Utah, Salt Lake City, UT, USA.
J Clin Transl Sci. 2017 Feb;1(1):53-59. doi: 10.1017/cts.2016.9. Epub 2017 Jan 20.
Family health history (FHx) is an important factor in breast and ovarian cancer risk assessment. As such, multiple risk prediction models rely strongly on FHx data when identifying a patient's risk. These models were developed using verified information and when translated into a clinical setting assume that a patient's FHx is accurate and complete. However, FHx information collected in a typical clinical setting is known to be imprecise and it is not well understood how this uncertainty may affect predictions in clinical settings. Using Monte Carlo simulations and existing measurements of uncertainty of self-reported FHx, we show how uncertainty in FHx information can alter risk classification when used in typical clinical settings. We found that various models ranged from 52% to 64% for correct tier-level classification of pedigrees under a set of contrived uncertain conditions, but that significant misclassification are not negligible. Our work implies that (i) uncertainty quantification needs to be considered when transferring tools from a controlled research environment to a more uncertain environment (i.e, a health clinic) and (ii) better FHx collection methods are needed to reduce uncertainty in breast cancer risk prediction in clinical settings.
家族健康史（FHx）是乳腺癌和卵巢癌风险评估中的一个重要因素。因此，多种风险预测模型在识别患者风险时严重依赖FHx数据。这些模型是使用经过验证的信息开发的，在转化为临床应用时，假定患者的FHx准确且完整。然而，在典型临床环境中收集的FHx信息已知是不准确的，而且这种不确定性如何影响临床环境中的预测还不太清楚。通过蒙特卡洛模拟和自我报告的FHx不确定性的现有测量方法，我们展示了在典型临床环境中使用时，FHx信息的不确定性如何改变风险分类。我们发现，在一组人为设定的不确定条件下，各种模型对系谱进行正确层级分类的比例在52%至64%之间，但显著的错误分类不可忽视。我们的工作表明：（i）当将工具从受控的研究环境转移到更不确定的环境（即健康诊所）时，需要考虑不确定性量化；（ii）需要更好的FHx收集方法来减少临床环境中乳腺癌风险预测的不确定性。
