Cancer Center, Renmin Hospital of Wuhan University, Wuhan, China.
Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
J Immunother Cancer. 2020 Sep;8(2). doi: 10.1136/jitc-2020-001314.
Individualized prediction of mortality risk can inform the treatment strategy for patients with COVID-19 and solid tumors and potentially improve patient outcomes. We aimed to develop a nomogram for predicting in-hospital mortality of patients with COVID-19 with solid tumors.
We enrolled patients with COVID-19 with solid tumors admitted to 32 hospitals in China between December 17, 2020, and March 18, 2020. A multivariate logistic regression model was constructed via stepwise regression analysis, and a nomogram was subsequently developed based on the fitted multivariate logistic regression model. Discrimination and calibration of the nomogram were evaluated by estimating the area under the receiver operator characteristic curve (AUC) for the model and by bootstrap resampling, a Hosmer-Lemeshow test, and visual inspection of the calibration curve.
There were 216 patients with COVID-19 with solid tumors included in the present study, of whom 37 (17%) died and the other 179 all recovered from COVID-19 and were discharged. The median age of the enrolled patients was 63.0 years and 113 (52.3%) were men. Multivariate logistic regression revealed that increasing age (OR=1.08, 95% CI 1.00 to 1.16), receipt of antitumor treatment within 3 months before COVID-19 (OR=28.65, 95% CI 3.54 to 231.97), peripheral white blood cell (WBC) count ≥6.93 ×10/L (OR=14.52, 95% CI 2.45 to 86.14), derived neutrophil-to-lymphocyte ratio (dNLR; neutrophil count/(WBC count minus neutrophil count)) ≥4.19 (OR=18.99, 95% CI 3.58 to 100.65), and dyspnea on admission (OR=20.38, 95% CI 3.55 to 117.02) were associated with elevated mortality risk. The performance of the established nomogram was satisfactory, with an AUC of 0.953 (95% CI 0.908 to 0.997) for the model, non-significant findings on the Hosmer-Lemeshow test, and rough agreement between predicted and observed probabilities as suggested in calibration curves. The sensitivity and specificity of the model were 86.4% and 92.5%.
Increasing age, receipt of antitumor treatment within 3 months before COVID-19 diagnosis, elevated WBC count and dNLR, and having dyspnea on admission were independent risk factors for mortality among patients with COVID-19 and solid tumors. The nomogram based on these factors accurately predicted mortality risk for individual patients.
对死亡率风险进行个体化预测，可以为 COVID-19 合并实体瘤患者的治疗策略提供信息，并可能改善患者预后。我们旨在开发一种列线图来预测 COVID-19 合并实体瘤患者的住院死亡率。
我们纳入了 2020 年 12 月 17 日至 2020 年 3 月 18 日期间在中国 32 家医院住院的 COVID-19 合并实体瘤患者。通过逐步回归分析构建多变量逻辑回归模型，随后基于拟合的多变量逻辑回归模型开发了一个列线图。通过估计模型的受试者工作特征曲线（ROC）下面积（AUC）、bootstrap 重采样、Hosmer-Lemeshow 检验和校准曲线的直观检查来评估列线图的判别和校准。
本研究共纳入 216 例 COVID-19 合并实体瘤患者，其中 37 例（17%）死亡，其余 179 例均从 COVID-19 中康复并出院。纳入患者的中位年龄为 63.0 岁，113 例（52.3%）为男性。多变量逻辑回归显示，年龄增长（OR=1.08，95%CI 1.00 至 1.16）、COVID-19 诊断前 3 个月内接受抗肿瘤治疗（OR=28.65，95%CI 3.54 至 231.97）、外周血白细胞（WBC）计数≥6.93×10/L（OR=14.52，95%CI 2.45 至 86.14）、衍生中性粒细胞与淋巴细胞比值（dNLR；中性粒细胞计数/（WBC 计数减去中性粒细胞计数））≥4.19（OR=18.99，95%CI 3.58 至 100.65）和入院时呼吸困难（OR=20.38，95%CI 3.55 至 117.02）与死亡率升高相关。建立的列线图性能良好，模型的 AUC 为 0.953（95%CI 0.908 至 0.997），Hosmer-Lemeshow 检验无显著差异，校准曲线表明预测概率与观察概率大致吻合。该模型的灵敏度和特异性分别为 86.4%和 92.5%。
年龄增长、COVID-19 诊断前 3 个月内接受抗肿瘤治疗、WBC 计数和 dNLR 升高以及入院时呼吸困难是 COVID-19 合并实体瘤患者死亡的独立危险因素。基于这些因素的列线图可以准确预测个体患者的死亡率风险。
