Department of Development and Regeneration, KU Leuven, Leuven, Belgium; Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands.
Department of Development and Regeneration, KU Leuven, Leuven, Belgium.
Eur Urol. 2018 Dec;74(6):796-804. doi: 10.1016/j.eururo.2018.08.038. Epub 2018 Sep 19.
Urologists regularly develop clinical risk prediction models to support clinical decisions. In contrast to traditional performance measures, decision curve analysis (DCA) can assess the utility of models for decision making. DCA plots net benefit (NB) at a range of clinically reasonable risk thresholds.
To provide recommendations on interpreting and reporting DCA when evaluating prediction models.
We informally reviewed the urological literature to determine investigators' understanding of DCA. To illustrate, we use data from 3616 patients to develop risk models for high-grade prostate cancer (n=313, 9%) to decide who should undergo a biopsy. The baseline model includes prostate-specific antigen and digital rectal examination; the extended model adds two predictors based on transrectal ultrasound (TRUS).
We explain risk thresholds, NB, default strategies (treat all, treat no one), and test tradeoff. To use DCA, first determine whether a model is superior to all other strategies across the range of reasonable risk thresholds. If so, that model appears to improve decisions irrespective of threshold. Second, consider if there are important extra costs to using the model. If so, obtain the test tradeoff to check whether the increase in NB versus the best other strategy is worth the additional cost. In our case study, addition of TRUS improved NB by 0.0114, equivalent to 1.1 more detected high-grade prostate cancers per 100 patients. Hence, adding TRUS would be worthwhile if we accept subjecting 88 patients to TRUS to find one additional high-grade prostate cancer or, alternatively, subjecting 10 patients to TRUS to avoid one unnecessary biopsy.
The proposed guidelines can help researchers understand DCA and improve application and reporting.
Decision curve analysis can identify risk models that can help us make better clinical decisions. We illustrate appropriate reporting and interpretation of decision curve analysis.
泌尿科医生经常开发临床风险预测模型以支持临床决策。与传统的性能指标不同，决策曲线分析（DCA）可以评估模型在决策中的效用。DCA 图在一系列临床合理的风险阈值范围内绘制净收益（NB）。
提供评估预测模型时解释和报告 DCA 的建议。
我们非正式地回顾了泌尿科文献，以确定研究人员对 DCA 的理解。为了说明这一点，我们使用来自 3616 名患者的数据分析了用于诊断高级别前列腺癌（n=313，9%）的风险模型，以决定谁应该接受活检。基本模型包括前列腺特异性抗原和直肠指检；扩展模型增加了基于直肠超声（TRUS）的两个预测因子。
我们解释了风险阈值、NB、默认策略（治疗所有，不治疗任何人）和测试权衡。要使用 DCA，首先确定模型是否在整个合理风险阈值范围内优于所有其他策略。如果是这样，那么无论阈值如何，该模型似乎都可以改善决策。其次，考虑使用该模型是否存在重要的额外成本。如果是这样，请获取测试权衡，以检查与最佳其他策略相比，NB 的增加是否值得额外的成本。在我们的案例研究中，增加 TRUS 将 NB 提高了 0.0114，相当于每 100 名患者增加 1.1 例检测到的高级别前列腺癌。因此，如果我们接受让 88 名患者接受 TRUS 以发现额外的 1 例高级别前列腺癌，或者相反，让 10 名患者接受 TRUS 以避免 1 次不必要的活检，那么增加 TRUS 将是值得的。
提出的指南可以帮助研究人员理解 DCA，并提高应用和报告的质量。
决策曲线分析可以确定有助于我们做出更好临床决策的风险模型。我们举例说明了决策曲线分析的适当报告和解释。
