Xu Lei, Yang Zhihao, Chen Huawei, Sun Chengjun, Tu Chuanjian, Gu Zhiwei, Luo Ming
Department of Neurosurgery, Shaoxing Central Hospital, The Central Affiliated Hospital, Shaoxing University, Shaoxing, China.
Front Med (Lausanne). 2024 Sep 6;11:1443157. doi: 10.3389/fmed.2024.1443157. eCollection 2024.
Conditional survival (CS) considers the duration since the initial diagnosis and can provide supplementary informative insights. Our objective was to evaluate CS among gliosarcoma (GSM) patients and develop a CS-incorporated nomogram to predict the conditional probability of survival.
This retrospective study using the Surveillance, Epidemiology, and End Results (SEER) database included patients with GSM between 2000 and 2017. The CS was defined as the probability of surviving additional y years after already surviving for x years. The formula utilized for CS was: CS(y|x) = S(y + x)/S(x), where S(x) denotes the overall survival at x years. Univariate Cox regression, best subset regression (BSR) and the least absolute shrinkage and selection operator (LASSO) were used for significant prognostic factors screening. Following this, backward stepwise multivariable Cox regression was utilized to refine predictor selection. Finally, a novel CS-integrated nomogram model was developed and we also employed diverse evaluation methods to assess its performance.
This study included a total of 1,015 GSM patients, comprising 710 patients in training cohort and 305 patients in validation cohort. CS analysis indicated a gradual increase in the probability of achieving a 5-year survival, ascending from 5% at diagnosis to 13, 31, 56, and 74% with each subsequent year survived after 1, 2, 3, and 4 years post-diagnosis, respectively. Following variable screening through univariate Cox regression, BSR, and LASSO analysis, five factors-age, tumor stage, tumor size, radiotherapy, and chemotherapy-were ultimately identified for constructing the CS-nomogram model. The performance of the nomogram model was validated through discrimination and calibration assessments in both the training and validation cohorts. Furthermore, we confirmed that the effectiveness of the CS-nomogram in stratifying GSM patient risk status.
This nationwide study delineated the CS of patients diagnosed with GSM. Utilizing national data, a CS-nomogram could provide valuable guidance for patient counseling during follow-up and risk stratification.
条件生存(CS)考虑自初次诊断以来的持续时间,并能提供补充性的有用见解。我们的目的是评估胶质肉瘤(GSM)患者的条件生存情况,并开发一个纳入条件生存的列线图来预测生存的条件概率。
这项使用监测、流行病学和最终结果(SEER)数据库的回顾性研究纳入了2000年至2017年间的GSM患者。条件生存被定义为在已经存活x年之后再存活y年的概率。用于条件生存的公式为:CS(y|x)=S(y + x)/S(x),其中S(x)表示x年时的总生存率。单因素Cox回归、最佳子集回归(BSR)和最小绝对收缩和选择算子(LASSO)用于筛选显著的预后因素。在此之后,采用向后逐步多变量Cox回归来优化预测因子的选择。最后,开发了一种新的纳入条件生存的列线图模型,并且我们还采用了多种评估方法来评估其性能。
本研究共纳入1015例GSM患者,其中训练队列710例,验证队列305例。条件生存分析表明,实现5年生存的概率逐渐增加,从诊断时的5%上升到诊断后1、2、3和4年每多存活一年后的13%、31%、56%和74%。通过单因素Cox回归、BSR和LASSO分析进行变量筛选后,最终确定了年龄、肿瘤分期、肿瘤大小、放疗和化疗这五个因素用于构建条件生存列线图模型。通过在训练队列和验证队列中的区分度和校准评估验证了列线图模型的性能。此外,我们证实了条件生存列线图在分层GSM患者风险状态方面的有效性。
这项全国性研究描绘了诊断为GSM患者的条件生存情况。利用国家数据,条件生存列线图可为随访期间的患者咨询和风险分层提供有价值的指导。