Li Haopeng, Chen Xiaofeng, Jing Xubin, Wu Chaofen, Zeng Yicheng, Wang Muqing, Zeng Weilong, Zhang Shaohui, Xu Xueqiang, Cai Xianbin
Department of Gastroenterology, The First Affiliated Hospital of Shantou, University Medical College, Shantou, Guangdong, People's Republic of China.
Department of Pathology, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, People's Republic of China.
BMC Public Health. 2025 Mar 31;25(1):1204. doi: 10.1186/s12889-025-22361-8.
Cancer is the leading cause of death globally(1). According to the WHO's 2020 Global Cancer Report(2), China represented 23.7% of new cancer cases and 30.2% of cancer-related deaths worldwide in 2020. From 2015 to 2020, cancer cases made up 18.4% of the global total. Recent statistics show that in China, malignant tumors accounted for 23.91% of all deaths, with both incidence and mortality rates on the rise. Hospice patients in China often lack the measurement of laboratory indicators, which poses difficulties in their survival prediction. This is because almost all current survival prediction models include laboratory parameters. This study established a lab-free prediction model with an accuracy of approximately 73%-75% to predict the survival rates of patients at 30 days, 45 days, and 60 days. An online version has also been developed for wide applications.
We conducted a retrospective analysis of data from patients who received hospice care between January 2008 and December 2018. A total of 4,229 patients were divided into a training set (70%) and a test set (30%). The training group was used to develop the nomogram and a web-based calculator using the least absolute shrinkage and selection operator (LASSO) technique. The test group was used to validate the nomogram, using metrics such as the area under the receiver operating characteristic curve, calibration curve, and decision curve analysis.
Our analysis included 4,299 patients, with 3,163 in the training group and 1,066 in the test group. Using the LASSO algorithm, we identified eight predictors, namely quality of life, Karnofsky performance score, gender, pain duration, anorexia, abdominal distention, tachypnea, and edema. A nomogram with an online version was constructed to predict survival rates at 30, 45, and 60 days for hospice patients with advanced cancer. In the test set, the area under the curve (AUC) values were 0.7538, 0.7342, and 0.7324 for 30-day, 45-day, and 60-day survival, respectively. The nomogram demonstrated excellent calibration, and the decision curve analysis (DCA) showed a significant clinical net benefit.
This study developed a laboratory-free nomogram and a web-based calculator for accurately predicting survival in hospice patients with terminal cancer.
癌症是全球主要的死亡原因（1）。根据世界卫生组织《2020年全球癌症报告》（2），2020年中国的新增癌症病例占全球的23.7%，癌症相关死亡占全球的30.2%。2015年至2020年，癌症病例占全球总数的18.4%。最近的统计数据显示，在中国，恶性肿瘤占所有死亡人数的23.91%，发病率和死亡率均呈上升趋势。中国的临终关怀患者往往缺乏实验室指标的检测，这给生存预测带来了困难。这是因为几乎所有现有的生存预测模型都包含实验室参数。本研究建立了一个无实验室指标的预测模型，准确率约为73%-75%，用于预测患者30天、45天和60天的生存率。还开发了一个在线版本以供广泛应用。
我们对2008年1月至2018年12月期间接受临终关怀的患者数据进行了回顾性分析。总共4229名患者被分为训练集（70%）和测试集（30%）。训练组用于使用最小绝对收缩和选择算子（LASSO）技术开发列线图和基于网络的计算器。测试组用于验证列线图，使用受试者操作特征曲线下面积、校准曲线和决策曲线分析等指标。
我们的分析包括4299名患者，其中训练组3163名，测试组1066名。使用LASSO算法，我们确定了八个预测因素，即生活质量、卡氏功能状态评分、性别、疼痛持续时间、厌食、腹胀、呼吸急促和水肿。构建了一个带有在线版本的列线图，用于预测晚期癌症临终关怀患者30天、45天和60天的生存率。在测试集中，30天、45天和60天生存的曲线下面积（AUC）值分别为0.7538、0.7342和0.7324。列线图显示出良好的校准，决策曲线分析（DCA）显示出显著的临床净效益。
本研究开发了一种无实验室指标的列线图和基于网络的计算器，用于准确预测晚期癌症临终关怀患者的生存情况。
