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利用多源数据为霍奇金淋巴瘤患者提供个体化护理。

Harnessing multi-source data for individualized care in Hodgkin Lymphoma.

机构信息

Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, United States of America; Division of Hematology/Oncology, Tufts Medical Center, Boston, MA, United States of America.

Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, United States of America.

出版信息

Blood Rev. 2024 May;65:101170. doi: 10.1016/j.blre.2024.101170. Epub 2024 Jan 14.

Abstract

Hodgkin lymphoma is a rare, but highly curative form of cancer, primarily afflicting adolescents and young adults. Despite multiple seminal trials over the past twenty years, there is no single consensus-based treatment approach beyond use of multi-agency chemotherapy with curative intent. The use of radiation continues to be debated in early-stage disease, as part of combined modality treatment, as well as in salvage, as an important form of consolidation. While short-term disease outcomes have varied little across these different approaches across both early and advanced stage disease, the potential risk of severe, longer-term risk has varied considerably. Over the past decade novel therapeutics have been employed in the retrieval setting in preparation to and as consolidation after autologous stem cell transplant. More recently, these novel therapeutics have moved to the frontline setting, initially compared to standard-of-care treatment and later in a direct head-to-head comparison combined with multi-agent chemotherapy. In 2018, we established the HoLISTIC Consortium, bringing together disease and methods experts to develop clinical decision models based on individual patient data to guide providers, patients, and caregivers in decision-making. In this review, we detail the steps we followed to create the master database of individual patient data from patients treated over the past 20 years, using principles of data science. We then describe different methodological approaches we are taking to clinical decision making, beginning with clinical prediction tools at the time of diagnosis, to multi-state models, incorporating treatments and their response. Finally, we describe how simulation modeling can be used to estimate risks of late effects, based on cumulative exposure from frontline and salvage treatment. The resultant database and tools employed are dynamic with the expectation that they will be updated as better and more complete information becomes available.

摘要

霍奇金淋巴瘤是一种罕见但高度可治愈的癌症,主要影响青少年和年轻人。尽管在过去的二十年中进行了多项开创性试验,但除了采用多机构化疗以达到治愈目的外,没有单一的基于共识的治疗方法。在早期疾病中,放疗的使用仍在联合治疗模式中存在争议,也在挽救治疗中作为重要的巩固形式存在争议。虽然在早期和晚期疾病中,这些不同方法的短期疾病结局差异不大,但严重、长期风险的潜在风险差异很大。在过去的十年中,新型治疗方法已在检索环境中使用,为自体干细胞移植后的巩固治疗做准备。最近,这些新型治疗方法已经进入一线治疗环境,最初是与标准治疗相比,后来是与多机构化疗直接进行头对头比较。2018 年,我们成立了 HoLISTIC 联盟,汇集了疾病和方法专家,基于个体患者数据开发临床决策模型,以指导提供者、患者和护理人员做出决策。在这篇综述中,我们详细介绍了我们遵循的步骤,使用数据科学原理从过去 20 年接受治疗的患者中创建个体患者数据的主数据库。然后,我们描述了我们正在采用的不同方法来进行临床决策,从诊断时的临床预测工具开始,到多状态模型,包括治疗及其反应。最后,我们描述了如何基于一线和挽救治疗的累积暴露来使用模拟建模来估计晚期效应的风险。所使用的数据库和工具是动态的,预计随着更好和更完整的信息的出现,它们将得到更新。

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