Department of Pharmacy, Nanjing Drum Tower Hospital, School of Pharmacy, Nanjing Medical University, Nanjing, Jiangsu, China.
School of Pharmacy, Nanjing Medical University, Nanjing, Jiangsu, China.
BMC Infect Dis. 2024 Sep 18;24(1):996. doi: 10.1186/s12879-024-09867-z.
Severe fever with thrombocytopenia syndrome (SFTS) is a highly fatal infectious disease caused by the SFTS virus (SFTSV), posing a significant public health threat. This study aimed to construct a dynamic model for the early identification of SFTS patients at high risk of disease progression.
All eligible patients enrolled between April 2014 and July 2023 were divided into training and validation sets. Thirty-four clinical variables in the training set underwent analysis using least absolute shrinkage and selection operator (LASSO) logistic regression. Selected variables were then input into the multivariate logistic regression model to construct a dynamic nomogram. The model's performance was assessed using the area under the receiver operating characteristic curve (AUC-ROC), concordance index (C-index), calibration curve, and decision curve analysis (DCA) in both training and validation sets. Kaplan-Meier survival analysis was utilized to evaluate prognostic performance.
299 SFTS patients entered the final investigation, with 208 patients in the training set and 90 patients in the validation set. LASSO and the multivariate logistic regression identified six significant prediction factors: age (OR, 1.060; 95% CI, 1.017-1.109; P = 0.007), CREA (OR, 1.017; 95% CI, 1.003-1.031; P = 0.019), PT (OR, 1.765; 95% CI, 1.175-2.752; P = 0.008), D-dimer (OR, 1.039; 95% CI, 1.005-1.078; P = 0.032), nervous system symptoms (OR, 8.244; 95% CI, 3.035-26.858; P < 0.001) and hemorrhage symptoms (OR, 3.414; 95% CI, 1.096-10.974; P = 0.035). The AUC-ROC, C-index, calibration plots, and DCA demonstrated the robust performance of the nomogram in predicting severity at admission, and Kaplan-Meier survival analysis indicated its utility in predicting 28-day mortality among SFTS patients. The dynamic nomogram is accessible at https://sfts.shinyapps.io/SFTS_severity_nomogram/ .
This study provided a practical and readily applicable tool for the early identification of high-risk SFTS patients, enabling the timely initiation of intensified treatments and protocol adjustments to mitigate disease progression.
严重发热伴血小板减少综合征（SFTS）是一种由 SFTS 病毒（SFTSV）引起的高致命性传染病，对公共卫生构成重大威胁。本研究旨在构建一个用于早期识别 SFTS 患者疾病进展高危风险的动态模型。
将 2014 年 4 月至 2023 年 7 月期间纳入的所有合格患者分为训练集和验证集。在训练集中，使用最小绝对收缩和选择算子（LASSO）逻辑回归分析 34 项临床变量。选择的变量随后被输入多变量逻辑回归模型中，以构建动态列线图。使用受试者工作特征曲线（ROC）下面积（AUC-ROC）、一致性指数（C-index）、校准曲线和决策曲线分析（DCA）在训练集和验证集中评估模型性能。Kaplan-Meier 生存分析用于评估预后性能。
共有 299 例 SFTS 患者进入最终研究，其中 208 例患者进入训练集，90 例患者进入验证集。LASSO 和多变量逻辑回归确定了 6 个有意义的预测因素：年龄（OR，1.060；95%CI，1.017-1.109；P=0.007）、CREA（OR，1.017；95%CI，1.003-1.031；P=0.019）、PT（OR，1.765；95%CI，1.175-2.752；P=0.008）、D-二聚体（OR，1.039；95%CI，1.005-1.078；P=0.032）、神经系统症状（OR，8.244；95%CI，3.035-26.858；P<0.001）和出血症状（OR，3.414；95%CI，1.096-10.974；P=0.035）。AUC-ROC、C-index、校准图和 DCA 表明，列线图在预测入院时严重程度方面具有稳健的性能，Kaplan-Meier 生存分析表明，它可用于预测 SFTS 患者 28 天死亡率。动态列线图可在 https://sfts.shinyapps.io/SFTS_severity_nomogram/ 上获得。
本研究提供了一种实用且易于应用的工具，用于早期识别 SFTS 高危患者，从而能够及时开始强化治疗和调整方案以减轻疾病进展。
