Wicky Alexandre, Gatta Roberto, Latifyan Sofiya, Micheli Rita De, Gerard Camille, Pradervand Sylvain, Michielin Olivier, Cuendet Michel A
Precision Oncology Center, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland.
Dipartimento di Scienze Cliniche e Sperimentali dell'Università degli Studi di Brescia, Brescia, Italy.
Front Oncol. 2023 Mar 21;13:1043683. doi: 10.3389/fonc.2023.1043683. eCollection 2023.
The growing availability of clinical real-world data (RWD) represents a formidable opportunity to complement evidence from randomized clinical trials and observe how oncological treatments perform in real-life conditions. In particular, RWD can provide insights on questions for which no clinical trials exist, such as comparing outcomes from different sequences of treatments. To this end, process mining is a particularly suitable methodology for analyzing different treatment paths and their associated outcomes. Here, we describe an implementation of process mining algorithms directly within our hospital information system with an interactive application that allows oncologists to compare sequences of treatments in terms of overall survival, progression-free survival and best overall response. As an application example, we first performed a RWD descriptive analysis of 303 patients with advanced melanoma and reproduced findings observed in two notorious clinical trials: CheckMate-067 and DREAMseq. Then, we explored the outcomes of an immune-checkpoint inhibitor rechallenge after a first progression on immunotherapy versus switching to a BRAF targeted treatment. By using interactive process-oriented RWD analysis, we observed that patients still derive long-term survival benefits from immune-checkpoint inhibitors rechallenge, which could have direct implications on treatment guidelines for patients able to carry on immune-checkpoint therapy, if confirmed by external RWD and randomized clinical trials. Overall, our results highlight how an interactive implementation of process mining can lead to clinically relevant insights from RWD with a framework that can be ported to other centers or networks of centers.
临床真实世界数据（RWD）的可得性不断提高，这为补充随机临床试验的证据以及观察肿瘤治疗在现实生活中的表现提供了巨大机遇。特别是，RWD可以为那些尚无临床试验的问题提供见解，比如比较不同治疗顺序的结果。为此，过程挖掘是一种特别适合用于分析不同治疗路径及其相关结果的方法。在此，我们描述了一种在我们的医院信息系统中直接实现过程挖掘算法的方法，并通过一个交互式应用程序，使肿瘤学家能够在总生存期、无进展生存期和最佳总体反应方面比较治疗顺序。作为一个应用实例，我们首先对303例晚期黑色素瘤患者进行了RWD描述性分析，并重现了在两项著名临床试验（CheckMate - 067和DREAMseq）中观察到的结果。然后，我们探讨了在免疫治疗首次进展后重新使用免疫检查点抑制剂与转而使用BRAF靶向治疗的结果。通过使用交互式的面向过程的RWD分析，我们观察到患者仍能从免疫检查点抑制剂重新使用中获得长期生存益处，如果经外部RWD和随机临床试验证实，这可能对能够进行免疫检查点治疗的患者的治疗指南产生直接影响。总体而言，我们的结果突出了过程挖掘的交互式实现如何能够通过一个可移植到其他中心或中心网络的框架，从RWD中获得与临床相关的见解。
