Department of Thoracic Surgery, ZhongNan Hospital of Wuhan University, 169 Donghu Road, Wuhan, 430071, Hubei, China.
Hubei Key Laboratory of Tumor Biological Behaviors, Hubei Cancer Clinical Study Center, Wuhan, China.
J Cancer Res Clin Oncol. 2021 Apr;147(4):1247-1257. doi: 10.1007/s00432-020-03420-6. Epub 2020 Oct 11.
During the 2019 coronavirus disease (COVID-19) pandemic, oncologists face new challenges, and they need to adjust their cancer management strategies as soon as possible to reduce the risk of SARS-CoV-2 infection and tumor recurrence. However, data on cancer patients with SARS-CoV-2 infection remains scarce.
We conducted a retrospective study on 223 cancer patients with SARS-CoV-2 from 26 hospitals in Hubei, China. An individualized nomogram was constructed based on multivariate Cox analysis. Considering the convenience of the nomogram application, an online tool was also created. The predictive performance and clinical application of nomogram were verified by C-index, calibration curve and decision curve analysis (DCA).
Among cancer patients with SARS-CoV-2, there were significant differences in clinical characteristics between survivors and non-survivors, and compared with patients with solid tumors including lung cancer, patients with hematological malignancies had a worse prognosis. Male, dyspnea, elevated PCT, increased heart rate, elevated D-dimers, and decreased platelets were risk factors for these patients. Furthermore, a good prediction performance of the online tool (dynamic nomogram: https://covid-19-prediction-tool.shinyapps.io/DynNomapp/ ) was also fully demonstrated with the C-indexes of 0.841 (95% CI 0.782-0.900) in the development cohort and 0.780 (95% CI 0.678-0.882) in the validation cohort.
Overall, cancer patients with SARS-CoV-2 had unique clinical features, and the established online tool could guide clinicians to predict the prognosis of patients during the COVID-19 epidemic and to develop more rational treatment strategies for cancer patients.
在 2019 年冠状病毒病（COVID-19）大流行期间，肿瘤学家面临新的挑战，他们需要尽快调整癌症管理策略，以降低 SARS-CoV-2 感染和肿瘤复发的风险。然而，关于 COVID-19 感染的癌症患者的数据仍然很少。
我们对来自中国湖北 26 家医院的 223 例 COVID-19 癌症患者进行了回顾性研究。基于多变量 Cox 分析构建了个体化列线图。考虑到列线图应用的便利性，还创建了一个在线工具。通过 C 指数、校准曲线和决策曲线分析（DCA）验证了列线图的预测性能和临床应用。
在 COVID-19 癌症患者中，幸存者和非幸存者之间的临床特征存在显著差异，与包括肺癌在内的实体瘤患者相比，血液系统恶性肿瘤患者的预后更差。男性、呼吸困难、PCT 升高、心率加快、D-二聚体升高和血小板减少是这些患者的危险因素。此外，在线工具（动态列线图：https://covid-19-prediction-tool.shinyapps.io/DynNomapp/）也具有良好的预测性能，其开发队列中的 C 指数为 0.841（95%CI 0.782-0.900），验证队列中的 C 指数为 0.780（95%CI 0.678-0.882）。
总体而言，COVID-19 感染的癌症患者具有独特的临床特征，建立的在线工具可以指导临床医生预测 COVID-19 流行期间患者的预后，并为癌症患者制定更合理的治疗策略。
