Qian Fang, Zhou Wei, Liu Yuanni, Ge Ziruo, Lai Jianming, Zhao Zhenghua, Feng Yang, Lin Ling, Shen Yi, Zhang Zhonglu, Zhang Wei, Fan Tianli, Zhao Yongxiang, Chen Zhihai
Center of Infectious Disease, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
Department of Infectious Diseases, Dalian Sixth People's Hospital, Dalian, China.
J Med Virol. 2023 Feb;95(2):e28546. doi: 10.1002/jmv.28546.
Severe fever with thrombocytopenia syndrome (SFTS) is a life-threatening infectious disease caused by the SFTS virus (SFTSV). This study aimed to evaluate the predictive power of C-reactive protein to lymphocyte ratio (CLR) and establish an early-warning model for SFTS mortality. We retrospectively analyzed hospitalized SFTS patients in six clinical centers from May 2011 to 2022. The efficacy of CLR prediction was evaluated by the receiver operating characteristic (ROC) analysis. A nomogram was established and validated. Eight hundred and eighty-two SFTS patients (median age 64 years, 48.5% male) were enrolled in this study, with a mortality rate of 17.8%. The area under the ROC curve (AUC) of CLR was 0.878 (95% confidence interval [CI]: 0.850-0.903, p < 0.001), which demonstrates high predictive strength. The least absolute shrinkage and selection operator regression selected seven potential predictors. Multivariate logistic regression analysis determined three independent risk factors, including CLR, to construct the nomogram. The performance of the nomogram displayed excellent discrimination and calibration, with significant net benefits in clinical uses. CLR is a brand-new predictor for SFTS mortality. The nomogram based on CLR can serve as a convenient tool for physicians to identify critical SFTS cases in clinical practice.
发热伴血小板减少综合征(SFTS)是一种由发热伴血小板减少综合征病毒(SFTSV)引起的危及生命的传染病。本研究旨在评估C反应蛋白与淋巴细胞比值(CLR)的预测能力,并建立SFTS死亡率的预警模型。我们回顾性分析了2011年5月至2022年期间六个临床中心收治的SFTS患者。通过受试者工作特征(ROC)分析评估CLR预测的有效性。建立并验证了列线图。本研究纳入了882例SFTS患者(中位年龄64岁,男性占48.5%),死亡率为17.8%。CLR的ROC曲线下面积(AUC)为0.878(95%置信区间[CI]:0.850 - 0.903,p < 0.001),显示出较高的预测强度。最小绝对收缩和选择算子回归选择了七个潜在预测因子。多因素逻辑回归分析确定了包括CLR在内的三个独立危险因素,以构建列线图。列线图的性能显示出优异的区分度和校准度,在临床应用中具有显著的净效益。CLR是SFTS死亡率的一个全新预测因子。基于CLR的列线图可作为医生在临床实践中识别SFTS重症病例的便捷工具。