基于荟萃分析的模型用于评估严重发热伴血小板减少综合征患者的不良预后。
A model based on meta-analysis to evaluate poor prognosis of patients with severe fever with thrombocytopenia syndrome.
作者信息
Liu Zishuai, Jiang Zhouling, Zhang Ligang, Xue Xiaoyu, Zhao Chenxi, Xu Yanli, Zhang Wei, Lin Ling, Chen Zhihai
机构信息
Department of Infectious Disease, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
Department of Infectious Diseases, Yantai Qishan Hospital, Yantai, China.
出版信息
Front Microbiol. 2024 Jan 8;14:1307960. doi: 10.3389/fmicb.2023.1307960. eCollection 2023.
BACKGROUND
Early identification of risk factors associated with poor prognosis in Severe fever with thrombocytopenia syndrome (SFTS) patients is crucial to improving patient survival.
METHOD
Retrieve literature related to fatal risk factors in SFTS patients in the database, extract the risk factors and corresponding RRs and 95% CIs, and merge them. Statistically significant factors were included in the model, and stratified and assigned a corresponding score. Finally, a validation cohort from Yantai Qishan Hospital in 2021 was used to verify its predictive ability.
RESULT
A total of 24 articles were included in the meta-analysis. The model includes six risk factors: age, hemorrhagic manifestations, encephalopathy, Scr and BUN. The analysis of lasso regression and multivariate logistic regression shows that model score is an independent risk factor (OR = 1.032, 95% CI 1.002-1.063, = 0.034). The model had an area under the curve (AUC) of 0.779 (95% CI 0.669-0.889, <0.001). The validation cohort was divided into four risk groups with cut-off values. Compared with the low-medium risk group, the mortality rate of high-risk and very high-risk patients was more significant (RR =5.677, 95% CI 4.961-6.496, <0.001).
CONCLUSION
The prediction model for the fatal outcome of SFTS patients has shown positive outcomes.https://www.crd.york.ac.uk/prospero/ (CRD42023453157).
背景
早期识别与严重发热伴血小板减少综合征（SFTS）患者预后不良相关的危险因素对于提高患者生存率至关重要。
方法
检索数据库中与SFTS患者死亡危险因素相关的文献，提取危险因素及相应的RRs和95% CIs，并进行合并。将具有统计学意义的因素纳入模型，并进行分层和赋予相应分数。最后，使用烟台奇山医院2021年的验证队列来验证其预测能力。
结果
荟萃分析共纳入24篇文章。该模型包括六个危险因素：年龄、出血表现、脑病、Scr和BUN。套索回归和多变量逻辑回归分析表明，模型评分是一个独立危险因素（OR = 1.032，95% CI 1.002 - 1.063，P = 0.034）。该模型的曲线下面积（AUC）为0.779（95% CI 0.669 - 0.889，P < 0.001）。验证队列根据截断值分为四个风险组。与低 - 中风险组相比，高风险和极高风险患者的死亡率更高（RR = 5.677，95% CI 4.961 - 6.496，P < 0.001）。
结论
SFTS患者死亡结局的预测模型已显示出阳性结果。https://www.crd.york.ac.uk/prospero/（CRD42023453157）
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