Department of Orthopedic Oncology, Changzheng Hospital, Second Military Medical University, Shanghai, China.
Spine (Phila Pa 1976). 2021 Mar 15;46(6):E364-E373. doi: 10.1097/BRS.0000000000003888.
A retrospective study of 84 patients with spinal metastasis from prostate cancer (SMPCa) was performed.
The aim of this study was to predict the survival of patients with SMPCa by establishing an effective prognostic nomogram model, associating with the affecting factors and compare its efficacy with the existing scoring models.
Prostate cancer (PCa) is the second most frequently malignant cancer causing death in men, and the spine is the most common site of bone metastatic burden. The aim of this study was to establish a prognostic nomogram for survival prediction of patients with SMPCa, explore associated factors, and compare the effectiveness of the new nomogram prediction model with the existing scoring systems.
Included in this study were 84 SMPCa patients who were admitted in our spinal tumor center between 2006 and 2018. Their clinical data were retrospectively analyzed by univariate and multivariate analyses to identify independent variables that enabled to predict prognosis. A nomogram, named Changzheng Nomogram for Survival Prediction (CNSP), was established on the basis of preoperative independent variables, and then subjected to bootstrap re-samples for internal validation. The predictive accuracy and discriminative ability were measured by concordance index (C-index). Receiver-operating characteristic (ROC) analysis with the corresponding area under the ROC was used to estimate the prediction efficacy of CNSP and compare it with the four existing prognostic models Tomita, Tokuhashi, Bauer, and Crnalic.
A total of seven independent variables including Gleason score (P = 0.001), hormone refractory (P < 0.001), visceral metastasis (P < 0.001), lymphocyte to monocyte ratio (P = 0.009), prostate-specific antigen (P = 0.018), fPSA/tPSA (P = 0.029), Karnofsky Performance Status (P = 0.039) were identified after accurate analysis, and then entered the nomogram with the C-index of 0.87 (95% confidence interval, 0.84-0.90). The calibration curves for probability of 12-, 24-, and 36-month overall survival (OS) showed good consistency between the predictive risk and the actual risk. Compared with the previous prognostic models, the CNSP model was significantly more effective than the four existing prognostic models in predicting OS of the SMPCa patients (p < 0.05).
The overall performance of the CNSP model was satisfactory and could be used to estimate the survival outcome of individual patients more precisely and thus help clinicians design more specific and individualized therapeutic regimens.Level of Evidence: 4.
回顾性分析了 84 例前列腺癌脊柱转移患者（SMPCa）的临床资料。
本研究旨在建立一种有效的预后列线图模型，预测 SMPCa 患者的生存情况，并探讨其与影响因素的关系，与现有的评分模型进行比较。
前列腺癌（PCa）是男性第二大常见的致死性恶性肿瘤，脊柱是最常见的骨转移部位。本研究旨在建立 SMPCa 患者生存预测的预后列线图模型，探讨相关因素，并比较新的列线图预测模型与现有的评分系统的有效性。
回顾性分析 2006 年至 2018 年在我院脊柱肿瘤中心就诊的 84 例 SMPCa 患者的临床资料，采用单因素和多因素分析确定预测预后的独立变量。基于术前独立变量建立列线图，命名为“长征医院生存预测列线图（CNSP）”，并进行 bootstrap 重采样内部验证。通过一致性指数（C-index）评估预测准确性和区分能力。通过接收者操作特征（ROC）分析及其相应的 ROC 曲线下面积（AUC）来评估 CNSP 的预测效能，并与 Tomita、Tokuhashi、Bauer 和 Crnalic 等 4 种现有的预后模型进行比较。
经过准确分析，共确定了 7 个独立变量，包括 Gleason 评分（P=0.001）、激素抵抗（P<0.001）、内脏转移（P<0.001）、淋巴细胞与单核细胞比值（P=0.009）、前列腺特异性抗原（P=0.018）、fPSA/tPSA（P=0.029）和卡氏功能状态评分（P=0.039），并将其纳入 C-index 为 0.87（95%置信区间，0.84-0.90）的列线图。12、24 和 36 个月总生存（OS）概率的校准曲线显示，预测风险与实际风险之间具有良好的一致性。与既往的预后模型相比，CNSP 模型在预测 SMPCa 患者 OS 方面明显优于 4 种现有的预后模型（p<0.05）。
CNSP 模型的整体性能令人满意，能够更准确地估计个体患者的生存结果，从而帮助临床医生制定更具体和个体化的治疗方案。
4 级
