Kammer Michael N, Rowe Dianna J, Deppen Stephen A, Grogan Eric L, Kaizer Alexander M, Barón Anna E, Maldonado Fabien
Vanderbilt University Medical Center, Nashville, Tennessee.
Tennessee Valley Healthcare Administration Nashville Campus, Nashville, Tennessee.
Cancer Epidemiol Biomarkers Prev. 2022 Sep 2;31(9):1752-1759. doi: 10.1158/1055-9965.EPI-22-0190.
Diagnostic prediction models are useful guides when considering lesions suspicious for cancer, as they provide a quantitative estimate of the probability that a lesion is malignant. However, the decision to intervene ultimately rests on patient and physician preferences. The appropriate intervention in many clinical situations is typically defined by clinically relevant, actionable subgroups based upon the probability of malignancy. However, the "all-or-nothing" approach of threshold-based decisions is in practice incorrect.
Here, we present a novel approach to understanding clinical decision-making, the intervention probability curve (IPC). The IPC models the likelihood that an intervention will be chosen as a continuous function of the probability of disease. We propose the cumulative distribution function as a suitable model. The IPC is explored using the National Lung Screening Trial and the Prostate Lung Colorectal and Ovarian Screening Trial datasets.
Fitting the IPC results in a continuous curve as a function of pretest probability of cancer with high correlation (R2 > 0.97 for each) with fitted parameters closely aligned with professional society guidelines.
The IPC allows analysis of intervention decisions in a continuous, rather than threshold-based, approach to further understand the role of biomarkers and risk models in clinical practice.
We propose that consideration of IPCs will yield significant insights into the practical relevance of threshold-based management strategies and could provide a novel method to estimate the actual clinical utility of novel biomarkers.
诊断预测模型在考虑疑似癌症病变时是有用的指南，因为它们能对病变为恶性的概率提供定量估计。然而，最终的干预决策取决于患者和医生的偏好。在许多临床情况下，适当的干预通常由基于恶性概率的临床相关、可操作的亚组来定义。然而，基于阈值的“全有或全无”决策方法在实践中是不正确的。
在此，我们提出一种理解临床决策的新方法——干预概率曲线（IPC）。IPC将选择干预的可能性建模为疾病概率的连续函数。我们提出累积分布函数作为合适的模型。使用国家肺癌筛查试验和前列腺、肺、结肠和卵巢癌筛查试验数据集对IPC进行探索。
拟合IPC得到一条作为癌症预测试概率函数的连续曲线，相关性很高（每个数据集的R2>0.97），拟合参数与专业学会指南密切一致。
IPC允许以连续而非基于阈值的方法分析干预决策，以进一步理解生物标志物和风险模型在临床实践中的作用。
我们提出，考虑IPC将对基于阈值的管理策略的实际相关性产生重大见解，并可为估计新型生物标志物的实际临床效用提供一种新方法。
