Table_1_Machine Learning–Based Overall Survival Prediction of Elderly Patients With Multiple Myeloma From Multicentre Real-Life Data.docx

ObjectiveTo use machine learning methods to explore overall survival (OS)-related prognostic factors in elderly multiple myeloma (MM) patients.
MethodsData were cleaned and imputed using simple imputation methods. Two data resampling methods were implemented to facilitate model building and cross validation. Four algorithms including the cox proportional hazards model (CPH); DeepSurv; DeepHit; and the random survival forest (RSF) were applied to incorporate 30 parameters, such as baseline data, genetic abnormalities and treatment options, to construct a prognostic model for OS prediction in 338 elderly MM patients (>65 years old) from four hospitals in Beijing. The C-index and the integrated Brier score (IBwere used to evaluate model performances.
ResultsThe 30 variables incorporated in the models comprised MM baseline data, induction treatment data and maintenance therapy data. The variable importance test showed that the OS predictions were largely affected by the maintenance schema variable. Visualizing the survival curves by maintenance schema, we realized that the immunomodulator group had the best survival rate. C-indexes of 0.769, 0.780, 0.785, 0.798 and IBS score of 0.142, 0.112, 0.108, 0.099 were obtained from the CPH model, DeepSurv, DeepHit, and the RSF model respectively. The RSF model yield best scores from the fivefold cross-validation, and the results showed that different data resampling methods did affect our model results.
ConclusionWe established an OS model for elderly MM patients without genomic data based on 30 characteristics and treatment data by machine learning.

研究目的：采用机器学习方法探究老年多发性骨髓瘤（multiple myeloma, MM）患者总生存期（overall survival, OS）相关的预后因素。
研究方法：本研究通过简单插补法完成数据清洗与缺失值插补；为支撑模型构建与交叉验证，采用了两种数据重采样策略。本研究纳入考克斯比例风险模型（cox proportional hazards model, CPH）、DeepSurv、DeepHit及随机生存森林（random survival forest, RSF）共四种算法，结合基线资料、遗传学异常与治疗方案等30项参数，针对来自北京四家医院的338例年龄＞65岁的老年多发性骨髓瘤患者，构建用于总生存期预测的预后模型。采用一致性指数（C-index）与整合Brier分数（integrated Brier score, IBS）对模型性能进行评估。
研究结果：本研究所纳入的30项变量涵盖多发性骨髓瘤基线资料、诱导治疗资料与维持治疗资料。变量重要性检验结果显示，维持治疗方案变量对总生存期预测的影响最为显著。按维持治疗方案绘制生存曲线可见，免疫调节剂组患者的生存率最优。考克斯比例风险模型、DeepSurv、DeepHit与随机生存森林模型的一致性指数分别为0.769、0.780、0.785、0.798，整合Brier分数分别为0.142、0.112、0.108、0.099。五折交叉验证结果表明，随机生存森林模型取得最优性能评分，且不同数据重采样方法确实会对模型结果产生影响。
研究结论：本研究基于30项特征与治疗资料，构建了无需基因组数据的老年多发性骨髓瘤患者总生存期预测模型。