Department of Hematology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
Department of Hematology, Beijing Luhe Hospital, Capital Medical University, Beijing, China.
Biomed Res Int. 2019 Apr 8;2019:5652935. doi: 10.1155/2019/5652935. eCollection 2019.
To establish a nomogram for predicting the overall survival (OS) of patients with newly diagnosed multiple myeloma (MM), 304 patients with newly diagnosed MM were recruited between June 1, 2010, and June 30, 2015, from the Beijing Chaoyang Hospital, Capital Medical University, and randomly divided into training (n=214) and validation (n=90) cohorts. The Kaplan-Meier method and the Cox proportional hazards regression model were used to evaluate the prognostic effects of multiple clinical and laboratory parameters on survival. Significant prognostic factors were combined to build a nomogram. The discriminative ability and predictive accuracy of the nomogram were evaluated using the index of concordance (C-index) and calibration curves and compared with the five staging systems currently used for MM. Multivariate analysis of the training cohort revealed that the age at diagnosis, clonal bone marrow plasma cells, serum lactate dehydrogenase, serum 2-microglobulin, and del (17p) were independent risk factors for OS and were used to establish the nomogram. The C-index value of the nomogram for predicting OS was 0.749, which was significantly higher than the C-indices of the five most common staging systems currently used for MM. In the validation cohort, the C-index for nomogram-based predictions was 0.711 for OS, and the nomogram discrimination was better than the above mentioned five staging systems (<0.001). All calibration curves revealed good consistency between predicted and actual survivals. The proposed nomogram is more accurate in predicting the prognoses of patients with newly diagnosed MM.
为建立预测新诊断多发性骨髓瘤(MM)患者总生存期(OS)的列线图,我们于 2010 年 6 月 1 日至 2015 年 6 月 30 日期间从首都医科大学附属北京朝阳医院招募了 304 例新诊断的 MM 患者,并将其随机分为训练队列(n=214)和验证队列(n=90)。我们采用 Kaplan-Meier 方法和 Cox 比例风险回归模型评估了多个临床和实验室参数对生存的预后影响。将显著的预后因素进行组合以构建列线图。通过一致性指数(C-index)和校准曲线评估列线图的判别能力和预测准确性,并与目前用于 MM 的五种分期系统进行比较。对训练队列的多变量分析显示,诊断时的年龄、克隆性骨髓浆细胞、血清乳酸脱氢酶、血清 2-微球蛋白和 del(17p)是 OS 的独立危险因素,并用于建立列线图。用于预测 OS 的列线图的 C-index 值为 0.749,显著高于目前用于 MM 的五种最常见分期系统的 C-index 值。在验证队列中,基于列线图预测的 OS 的 C-index 值为 0.711,列线图的判别能力优于上述五种分期系统(<0.001)。所有校准曲线均显示预测和实际生存之间具有良好的一致性。该列线图在预测新诊断 MM 患者的预后方面更为准确。