Data_Sheet_1_External Validation of Radiation-Induced Dyspnea Models on Esophageal Cancer Radiotherapy Patients.docx

Purpose: Radiation-induced lung disease (RILD), defined as dyspnea in this study, is a risk for patients receiving high-dose thoracic irradiation. This study is a TRIPOD (Transparent Reporting of A Multivariable Prediction Model for Individual Prognosis or Diagnosis) Type 4 validation of previously-published dyspnea models via secondary analysis of esophageal cancer SCOPE1 trial data. We quantify the predictive performance of these two models for predicting the maximal dyspnea grade ≥ 2 within 6 months after the end of high-dose chemo-radiotherapy for primary esophageal cancer.
Materials and methods: We tested the performance of two previously published dyspnea risk models using baseline, treatment and follow-up data on 258 esophageal cancer patients in the UK enrolled into the SCOPE1 multi-center trial. The tested models were developed from lung cancer patients treated at MAASTRO Clinic (The Netherlands) from the period 2002 to 2011. The adverse event of interest was dyspnea ≥ Grade 2 (CTCAE v3) within 6 months after the end of radiotherapy. As some variables were missing randomly and cannot be imputed, 212 patients in the SCOPE1 were used for validation of model 1 and 255 patients were used for validation of model 2. The model parameter Forced Expiratory Volume in 1 s (FEV1), as a predictor to both validated models, was imputed using the WHO performance status. External validation was performed using an automated, decentralized approach, without exchange of individual patient data.
Results: Out of 258 patients with esophageal cancer in SCOPE1 trial data, 38 patients (14.7%) developed radiation-induced dyspnea (≥ Grade 2) within 6 months after chemo-radiotherapy. The discrimination performance of the models in esophageal cancer patients treated with high-dose external beam radiotherapy was moderate, area under curve (AUC) of 0.68 (95% CI 0.55–0.76) and 0.70 (95% CI 0.58–0.77), respectively. The curves and AUCs derived by distributed learning were identical to the results from validation on a local host.
Conclusion: We have externally validated previously published dyspnea models using an esophageal cancer dataset. FEV1 that is not routinely measured for esophageal cancer was imputed using WHO performance status. Prediction performance was not statistically different from previous training and validation sets. Risk estimates were dominated by WHO score in Model 1 and baseline dyspnea in Model 2. The distributed learning approach gave the same answer as local processing, and could be performed without accessing a validation site's individual patients-level data.

研究目的：本研究中定义为呼吸困难的放射性肺疾病（Radiation-induced lung disease, RILD），是接受大剂量胸部放疗患者的常见风险。本研究属于TRIPOD（多变量预后或诊断预测模型透明报告规范，Transparent Reporting of A Multivariable Prediction Model for Individual Prognosis or Diagnosis）第4类验证研究，通过对食管癌SCOPE1试验数据的二次分析，对已发表的呼吸困难预测模型进行验证。本研究旨在量化这两种模型在原发性食管癌患者接受大剂量放化疗结束后6个月内，预测最大呼吸困难等级≥2级的预测性能。

材料与方法：本研究使用英国SCOPE1多中心试验中258例食管癌患者的基线、治疗及随访数据，对两种已发表的呼吸困难风险预测模型进行性能测试。本次测试的模型基于2002年至2011年荷兰马斯特罗诊所（MAASTRO Clinic）收治的肺癌患者数据开发。本研究关注的不良事件为放疗结束后6个月内出现≥2级呼吸困难（不良事件通用术语标准第3版，CTCAE v3）。由于部分变量存在随机缺失且无法进行插补，最终分别有212例SCOPE1试验患者用于模型1的验证，255例患者用于模型2的验证。针对两个验证模型均使用的预测变量——第一秒用力呼气容积（FEV1），本研究采用WHO体能状态评分进行插补。本研究采用自动化去中心化方法开展外部验证，无需交换患者个体水平数据。

结果：SCOPE1试验的258例食管癌患者中，共有38例（14.7%）在放化疗后6个月内出现≥2级放射性呼吸困难。在接受大剂量外照射放疗的食管癌患者中，两款模型的区分性能中等，曲线下面积（AUC）分别为0.68（95%置信区间0.55~0.76）与0.70（95%置信区间0.58~0.77）。通过分布式学习得到的曲线与曲线下面积结果，与本地主机验证得到的结果完全一致。

结论：本研究通过食管癌数据集对已发表的呼吸困难预测模型进行了外部验证。针对食管癌患者常规未检测的FEV1，我们采用WHO体能状态评分进行插补。两款模型的预测性能与既往训练及验证队列相比，无统计学差异。模型1的风险估计主要由WHO体能状态评分决定，模型2则主要由基线呼吸困难水平决定。分布式学习方法可得到与本地处理完全一致的结果，且无需访问验证机构的患者个体水平数据即可开展。