Mathematical Institute, Leiden University, Leiden, The Netherlands
Mathematical Institute, Leiden University, Leiden, The Netherlands.
BMJ Open. 2020 Oct 12;10(10):e036376. doi: 10.1136/bmjopen-2019-036376.
This study aimed at developing a dynamic prediction model for patients with Ewing sarcoma (ES) to provide predictions at different follow-up times. During follow-up, disease-related information becomes available, which has an impact on a patient's prognosis. Many prediction models include predictors available at baseline and do not consider the evolution of disease over time.
In the analysis, 979 patients with ES from the Gesellschaft für Pädiatrische Onkologie und Hämatologie registry, who underwent surgery and treatment between 1999 and 2009, were included.
A dynamic prediction model was developed to predict updated 5-year survival probabilities from different prediction time points during follow-up. Time-dependent variables, such as local recurrence (LR) and distant metastasis (DM), as well as covariates measured at baseline, were included in the model. The time effects of covariates were investigated by using interaction terms between each variable and time.
Developing LR, DM in the lungs (DMp) or extrapulmonary DM (DMo) has a strong effect on the probability of surviving an additional 5 years with HRs and 95% CIs equal to 20.881 (14.365 to 30.353), 6.759 (4.465 to 10.230) and 17.532 (13.210 to 23.268), respectively. The effects of primary tumour location, postoperative radiotherapy (PORT), histological response and disease extent at diagnosis on survival were found to change over time. The HR of PORT versus no PORT at the time of surgery is equal to 0.774 (0.594 to 1.008). One year after surgery, the HR is equal to 1.091 (0.851 to 1.397).
The time-varying effects of several baseline variables, as well as the strong impact of time-dependent variables, show the importance of including updated information collected during follow-up in the prediction model to provide accurate predictions of survival.
本研究旨在为尤文肉瘤（ES）患者开发一种动态预测模型，以提供不同随访时间的预测。在随访期间，疾病相关信息会不断出现，这会影响患者的预后。许多预测模型包含基线时可用的预测因子，但并未考虑疾病随时间的演变。
在分析中，纳入了 1999 年至 2009 年期间在德国儿科肿瘤和血液学学会注册中心接受手术和治疗的 979 名 ES 患者。
开发了一种动态预测模型，以预测从随访期间不同预测时间点更新的 5 年生存率。该模型纳入了时间依赖性变量，如局部复发（LR）和远处转移（DM），以及基线时测量的协变量。通过每个变量与时间之间的交互项，研究了协变量的时间效应。
发生 LR、肺部 DM（DMp）或肺外 DM（DMo）对额外存活 5 年的概率有很大影响，HRs 和 95%CI 分别为 20.881（14.365 至 30.353）、6.759（4.465 至 10.230）和 17.532（13.210 至 23.268）。原发肿瘤位置、术后放疗（PORT）、组织学反应和诊断时疾病范围对生存的影响被发现随时间而变化。手术时 PORT 与无 PORT 的 HR 等于 0.774（0.594 至 1.008）。手术后 1 年，HR 等于 1.091（0.851 至 1.397）。
几个基线变量的时变效应，以及时间依赖性变量的强烈影响，表明在预测模型中纳入随访期间收集的更新信息对于提供生存的准确预测非常重要。
