A new prognostic model for predicting 30-day mortality in acute oncology patients

Acute oncology services (AOS) provide rapid review and expedited pathways for referral to specialist care for cancer patients. Blood tests may support AOS in providing estimates of prognosis. We aimed to develop and validate a prognostic model of 30-day mortality based on routine blood markers to inform an AOS decision to actively treat or palliate patients. Using clinical data from 752 AOS referrals, multivariable logistic regression analysis was conducted to develop a 30-day mortality prognostic model. Internal validation and then internal–external cross-validation were used to examine overfitting and generalizability of the model’s predictive performance. Urea, alkaline phosphatase, albumin and neutrophils were the strongest predictors of outcome. The model separated patients into distinct prognostic groups from the cross-validation (C Statistic: 0.70; 95% CI: 0.64–0.76). Admission year was included as a predictor in the model to improve the model calibration. The developed prediction model was able to classify patients into distinct prognostic risk groups, which is clinically useful for delivering an evidence-based AOS. Collation of data from other AOS centers would allow for the development of a more generalizable prognostic model.

急性肿瘤专科服务（Acute Oncology Services, AOS）为癌症患者提供快速评估及专科诊疗的快捷转诊通道。血液检测可辅助AOS开展预后评估工作。本研究旨在基于常规血液标志物构建并验证一款30天死亡率预后模型，以辅助AOS决策患者的主动治疗或姑息治疗方案。本研究纳入752例AOS转诊患者的临床数据，通过多变量logistic回归分析构建30天死亡率预后模型。先后采用内部验证与内外交叉验证，评估模型的过拟合风险及预测性能的泛化能力。尿素、碱性磷酸酶、白蛋白与中性粒细胞是影响结局的最强预测因子。交叉验证结果显示，该模型可将患者划分为不同的预后风险组别，其C统计量为0.70，95%置信区间为0.64~0.76。为优化模型校准性能，本研究将入院年份作为预测因子纳入模型。本研究构建的预测模型可将患者划分为不同的预后风险层级，对开展循证医学导向的AOS临床实践具有重要应用价值。若能整合其他AOS中心的临床数据，可进一步构建泛化能力更强的预后模型。