Abstract：
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.
摘要：
严重发热伴血小板减少综合征（SFTS）是由SFTS病毒（SFTSV）引起的一种危及生命的传染病。本研究旨在评估C反应蛋白与淋巴细胞比率（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5月在六个临床中心住院的SFTS患者。通过接收器工作特性（ROC）分析评估CLR预测的有效性。建立并验证了列线图。882例SFTS患者（中位年龄64岁，48.5%男性）参加了这项研究，死亡率为17.8%。CLR的ROC曲线下面积(AUC)为0.878(95%CI:0.850-0.903,P<0.001),这证明了很高的预测强度。最小绝对收缩和选择算子(LASSO)回归选择了七个潜在的预测因子。多因素logistic回归分析确定了三个独立的危险因素，包括CLR,构造列线图。列线图的性能显示出出色的辨别和校准，在临床应用中具有显著的净效益。CLR是SFTS死亡率的全新预测指标。基于CLR的列线图可以作为医生在临床实践中识别关键SFTS病例的方便工具。本文受版权保护。保留所有权利。
