Department of Orthopedic Surgery, Tianjin First Central Hospital, Tianjin, China.
Graduate School, Tianjin Medical University, Tianjin, China.
Neurol Res. 2021 Apr;43(4):327-335. doi: 10.1080/01616412.2020.1866244. Epub 2020 Dec 30.
To evaluate the prognostic effect of pre-treatment factors in patients with spinal metastases secondary to lung cancer, and establish a novel predicting nomogram for predicting the survival probability.
A total of 209 patients operated for spinal metastases from lung cancer were consecutively enrolled, and divided into the training and validation samples with a ratio of 7:3, for model establishing and validating, respectively. Basing on the training sample, univariate and multivariate COX proportional hazard models were used for identifying the prognostic effect of pre-treatment factors, following which significant prognostic factors would be listed as items in nomogram to calculate the survival probabilities at 3, 6, 12 and 18 months. Then, the C-indexes and the calibration curves would be figured out to evaluate the discrimination ability and accuracy of the model both for the training and validation samples.
In the multivariate COX analysis, the gender, smoking history, location of spinal metastasis, visceral metastasis, Karnofsky performance status (KPS), adjuvant therapy, lymphocyte percentage and globulin were found to be significantly associated with the overall survival, and a novel nomogram was generated basing on these independent predictors. The C-indexes for the training and validation samples were 0.761 and 0.732, respectively. Favorable consistencies between the predicted and actual survival rates were demonstrated both in the internal and external validations.
Pre-treatment characteristics, including gender, smoking history, location of spinal metastasis, visceral metastasis, KPS, adjuvant therapy, percentage of lymphocyte, and serum globulin level, were identified to be significantly associated with overall survival of patients living with spinal metastases derived from lung cancer, and a user-friendly nomogram was established using these independent predictors.
评估肺癌脊柱转移患者治疗前因素的预后影响，并建立一种新的预测列线图来预测生存概率。
连续纳入 209 例接受肺癌脊柱转移手术的患者，按照 7:3 的比例分为训练和验证样本，分别用于模型建立和验证。基于训练样本，采用单因素和多因素 COX 比例风险模型来确定治疗前因素的预后影响，然后将显著的预后因素列为列线图中的项目，以计算 3、6、12 和 18 个月的生存概率。然后，计算 C 指数和校准曲线，以评估模型在训练和验证样本中的区分能力和准确性。
多因素 COX 分析显示，性别、吸烟史、脊柱转移部位、内脏转移、卡氏功能状态（KPS）、辅助治疗、淋巴细胞百分比和球蛋白与总生存率显著相关，并基于这些独立预测因素生成了一个新的列线图。训练和验证样本的 C 指数分别为 0.761 和 0.732。内部和外部验证均显示预测和实际生存率之间具有良好的一致性。
治疗前特征，包括性别、吸烟史、脊柱转移部位、内脏转移、KPS、辅助治疗、淋巴细胞百分比和血清球蛋白水平，与肺癌脊柱转移患者的总生存率显著相关，并使用这些独立预测因素建立了一个易于使用的列线图。
