First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China.
Eur J Gastroenterol Hepatol. 2019 Jul;31(7):749-755. doi: 10.1097/MEG.0000000000001350.
Currently, there are no competing risk analyses of cause-specific mortality in patients with pancreatic neuroendocrine tumors.
We estimated a cumulative incidence function for cause-specific mortality. The first nomogram for predicting cause-specific mortality was constructed using a proportional subdistribution hazard model, validated using bootstrap cross-validation, and evaluated with decision curve analysis.
Sex, age, positive lymph node status, metastasis, surveillance, epidemiology, and end results historic stage, grade, and surgery strongly predicted cause-specific mortality. The discrimination performance of Fine-Gray models was evaluated using the c-index, which was 0.864. In addition, the calibration plot of the developed nomogram demonstrated good concordance between the predicted and actual outcomes. Decision curve analysis yielded a range of threshold probabilities (0.014-0.779) at which the clinical net benefit of the risk model was greater than that in hypothetical all-screening or no-screening scenarios.
Our nomogram allows selection of a patient population at high risk for cancer-specific mortality and thus facilitates the design of prevention trials for the affected population.
目前，尚无针对胰腺神经内分泌肿瘤患者的特定原因死亡率的竞争风险分析。
我们估计了特定原因死亡率的累积发生率函数。使用比例子分布风险模型构建了第一个用于预测特定原因死亡率的列线图，该模型使用 bootstrap 交叉验证进行了验证，并使用决策曲线分析进行了评估。
性别、年龄、阳性淋巴结状态、转移、监测、流行病学和结果历史分期、分级和手术强烈预测了特定原因的死亡率。Fine-Gray 模型的判别性能通过 c 指数进行评估，c 指数为 0.864。此外，开发的列线图的校准图表明预测结果与实际结果之间具有良好的一致性。决策曲线分析在 0.014-0.779 的阈值概率范围内得出了风险模型的临床净获益大于假设的全筛查或无筛查情况下的临床净获益的范围。
我们的列线图可以选择癌症特异性死亡率高的患者人群，从而有助于为受影响人群设计预防试验。
