Cancer Center, Renmin Hospital of Wuhan University, Wuhan, China.
Department of Orthopaedic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Cancer Sci. 2021 Jun;112(6):2522-2532. doi: 10.1111/cas.14882. Epub 2021 May 1.
The 2019 novel coronavirus has spread rapidly around the world. Cancer patients seem to be more susceptible to infection and disease deterioration, but the factors affecting the deterioration remain unclear. We aimed to develop an individualized model for prediction of coronavirus disease (COVID-19) deterioration in cancer patients. The clinical data of 276 cancer patients diagnosed with COVID-19 in 33 designated hospitals of Hubei, China from December 21, 2019 to March 18, 2020, were collected and randomly divided into a training and a validation cohort by a ratio of 2:1. Cox stepwise regression analysis was carried out to select prognostic factors. The prediction model was developed in the training cohort. The predictive accuracy of the model was quantified by C-index and time-dependent area under the receiver operating characteristic curve (t-AUC). Internal validation was assessed by the validation cohort. Risk stratification based on the model was carried out. Decision curve analysis (DCA) were used to evaluate the clinical usefulness of the model. We found age, cancer type, computed tomography baseline image features (ground glass opacity and consolidation), laboratory findings (lymphocyte count, serum levels of C-reactive protein, aspartate aminotransferase, direct bilirubin, urea, and d-dimer) were significantly associated with symptomatic deterioration. The C-index of the model was 0.755 in the training cohort and 0.779 in the validation cohort. The t-AUC values were above 0.7 within 8 weeks both in the training and validation cohorts. Patients were divided into two risk groups based on the nomogram: low-risk (total points ≤ 9.98) and high-risk (total points > 9.98) group. The Kaplan-Meier deterioration-free survival of COVID-19 curves presented significant discrimination between the two risk groups in both training and validation cohorts. The model indicated good clinical applicability by DCA curves. This study presents an individualized nomogram model to individually predict the possibility of symptomatic deterioration of COVID-19 in patients with cancer.
2019 年新型冠状病毒在全球迅速传播。癌症患者似乎更容易感染和病情恶化，但影响恶化的因素尚不清楚。我们旨在为癌症患者开发一种预测冠状病毒病（COVID-19）恶化的个体化模型。收集了 2019 年 12 月 21 日至 2020 年 3 月 18 日期间中国湖北省 33 家指定医院确诊的 276 例 COVID-19 癌症患者的临床数据，并按 2:1 的比例随机分为训练和验证队列。采用 Cox 逐步回归分析筛选预后因素。在训练队列中建立预测模型。通过 C 指数和时间依赖性接受者操作特征曲线下面积（t-AUC）定量评估模型的预测准确性。通过验证队列评估内部验证。基于模型进行风险分层。决策曲线分析（DCA）用于评估模型的临床实用性。我们发现年龄、癌症类型、计算机断层扫描基线图像特征（磨玻璃影和实变）、实验室检查结果（淋巴细胞计数、血清 C 反应蛋白、天冬氨酸氨基转移酶、直接胆红素、尿素和 D-二聚体）与症状恶化显著相关。模型在训练队列中的 C 指数为 0.755，在验证队列中的 C 指数为 0.779。在训练和验证队列中，t-AUC 值在 8 周内均高于 0.7。根据列线图将患者分为两个风险组：低危（总分≤9.98）和高危（总分>9.98）组。COVID-19 恶化无生存曲线的 Kaplan-Meier 分析在训练和验证队列中均显示出两个风险组之间的显著差异。通过 DCA 曲线，该模型表明具有良好的临床适用性。本研究提出了一种个体化列线图模型，可单独预测癌症患者 COVID-19 症状恶化的可能性。
