Department of Gastroenterology, 485866The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, P.R. China.
Department of Hospice, 485866The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, P.R. China.
Cancer Control. 2022 Jan-Dec;29:10732748221124519. doi: 10.1177/10732748221124519.
The aim of the present study was to develop a nomogram for prognostic prediction of patients with lung cancer in hospice.
The data was collected from 1106 lung cancer patients in hospice between January 2008 and December 2018. The data were split into a training set, which was used to identify the most important prognostic factors by the least absolute shrinkage and selection operator (LASSO) and to build the nomogram, while the testing set was used to validate the nomogram. The performance of the nomogram was assessed by c-index, calibration curve and the decision curve analysis (DCA).
A total of 1106 patients, including 835 (75%) from the training set and 271 (25%) from testing set, were retrospectively analyzed in this study. Using the LASSO regression, 5 most important prognostic predictors that included sex, Karnofsky Performance Scale (KPS), quality-of-life (QOL), edema and anorexia, were selected out of 28 variables. Validated c-indexes of training set at 15, 30, and 90 days were .778 [.737-.818], .776 [.743-.809], and .751 [.713-.790], respectively. Similarly, the validated c-indexes of testing set at 15, 30, and 90 days were .789 [.714-.864], .748 [.685-.811], and .757 [.691-.823], respectively. The nomogram-predicted survival was well calibrated, as the predicted probabilities were close to the expected probabilities. Moreover, the DCA curve showed that nomogram received superior standardized net benefit at a broad threshold.
The study built a non-lab nomogram with important predictor to analyze the clinical parameters using LASSO. It may be a useful tool to allow clinicians to easily estimate the prognosis of the patients with lung cancer in hospice.
本研究旨在为临终关怀肺癌患者建立一种预后预测的列线图。
从 2008 年 1 月至 2018 年 12 月期间在临终关怀的 1106 例肺癌患者中收集数据。该数据被分为训练集，通过最小绝对收缩和选择算子（LASSO）确定最重要的预后因素，并建立列线图，而测试集则用于验证列线图。通过 C 指数、校准曲线和决策曲线分析（DCA）评估列线图的性能。
本研究共回顾性分析了 1106 例患者，其中 835 例（75%）来自训练集，271 例（25%）来自测试集。使用 LASSO 回归，从 28 个变量中筛选出 5 个最重要的预后预测因素，包括性别、卡诺夫斯基表现量表（KPS）、生活质量（QOL）、水肿和厌食。在训练集和测试集中，15、30 和 90 天的验证 C 指数分别为 0.778 [0.737-0.818]、0.776 [0.743-0.809]和 0.751 [0.713-0.790]。同样，在训练集和测试集中，15、30 和 90 天的验证 C 指数分别为 0.789 [0.714-0.864]、0.748 [0.685-0.811]和 0.757 [0.691-0.823]。列线图预测的生存情况具有良好的校准度，因为预测概率与预期概率非常接近。此外，DCA 曲线表明，在广泛的阈值下，列线图的标准化净获益更高。
本研究使用 LASSO 构建了一个带有重要预测因子的非实验室列线图，用于分析临床参数。它可能是一种有用的工具，可以帮助临床医生轻松估计临终关怀肺癌患者的预后。
