Department of Clinical Oncology, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
Department of Clinical Oncology, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
Int J Infect Dis. 2023 Jul;132:40-49. doi: 10.1016/j.ijid.2023.04.397. Epub 2023 Apr 16.
We sought to identify the predictors of delayed viral clearance in patients with cancer with asymptomatic COVID-19 when the SARS-CoV-2 Omicron variants prevailed in Hong Kong.
All patients with cancer who were attending radiation therapy for head and neck malignancies or systemic anticancer therapy saved their deep throat saliva or nasopharyngeal swabs at least twice weekly for SARS-CoV-2 screening between January 1 and April 30, 2022. The multivariate analyses identified predictors of delayed viral clearance (or slow recovery), defined as >21 days for the cycle threshold values rising to ≥30 or undetectable in two consecutive samples saved within 72 hours. Three machine learning algorithms evaluated the prediction performance of the predictors.
A total of 200 (15%) of 1309 patients tested positive for SARS-CoV-2. Age >65 years (P = 0.036), male sex (P = 0.003), high Charlson comorbidity index (P = 0.042), lung cancer (P = 0.018), immune checkpoint inhibitor (P = 0.036), and receipt of one or no dose of COVID-19 vaccine (P = 0.003) were significant predictors. The three machine learning algorithms revealed that the mean ± SD area-under-the-curve values predicting delayed viral clearance with the cut-off cycle threshold value ≥30 was 0.72 ± 0.11.
We identified subgroups with delayed viral clearance that may benefit from targeted interventions.
我们旨在确定在 SARS-CoV-2 奥密克戎变异株在香港流行时,无症状 COVID-19 的癌症患者病毒清除延迟的预测因素。
所有接受头颈部恶性肿瘤放射治疗或全身抗癌治疗的癌症患者,在 2022 年 1 月 1 日至 4 月 30 日期间,每周至少两次保存深部咽唾液或鼻咽拭子,用于 SARS-CoV-2 筛查。多变量分析确定了病毒清除延迟(或恢复缓慢)的预测因素,定义为在 72 小时内连续两次保存的样本中,循环阈值值升高至≥30 或无法检测的时间超过 21 天。三种机器学习算法评估了预测因素的预测性能。
在 1309 名接受 SARS-CoV-2 检测的患者中,共有 200 名(15%)检测结果呈阳性。年龄>65 岁(P=0.036)、男性(P=0.003)、高 Charlson 合并症指数(P=0.042)、肺癌(P=0.018)、免疫检查点抑制剂(P=0.036)和接受 1 剂或 0 剂 COVID-19 疫苗(P=0.003)是显著的预测因素。三种机器学习算法显示,预测病毒清除延迟的截断循环阈值≥30 的曲线下面积的平均值±SD 值为 0.72±0.11。
我们确定了病毒清除延迟的亚组,这些亚组可能受益于针对性干预。