背景:严重发热伴血小板减少综合征(SFTS)病毒载量与疾病严重程度之间存在相关性;然而,病毒载量的测量在一般实验室是困难的,它需要时间来获得病毒载量值。这里,确定了预测SFTS病毒载量动态变化的实验室参数.此外,我们试图通过实验室参数的动态变化来评估早期确定临床恶化的特定时间点.
方法:这项观察性研究包括韩国的SFTS患者(2013-2020年)。使用滞后值的互相关分析来确定SFTS病毒载量与时间序列变量之间的时间相关性。非重度组(NSG)包括58例SFTS患者,重度组(SG)包括11例。
结果:在横截面分析中,10个参数-白细胞,绝对中性粒细胞,淋巴细胞,血小板,活化部分凝血活酶时间(aPTT),C反应蛋白,天冬氨酸转氨酶(AST),丙氨酸转氨酶(ALT),乳酸脱氢酶(LDH),和肌酸磷酸激酶(CPK)-在症状发作后30天内进行评估;它们表现出三种不同的相关模式:(1)阳性,(2)具有时滞的正,(3)阴性。开发了一种预测评分系统,用于根据年龄和六个实验室变量-血小板,aPTT,AST,ALT,LDH,和CPK-在症状发作后5天;该评分系统的敏感性为87.5%,特异性为86.0%(95%置信区间:0.831-1.00,P<0.001)。
结论:确定了SFTS病毒载量动态变化与实验室参数之间的三种类型的相关模式。病毒载量的动态变化可以使用这些变量的动态变化来预测,这在无法进行病毒载量测试的临床环境中特别有用。此外,拟议的评分系统可以通过快速评估危重患者的临床病程为他们提供及时的治疗。
BACKGROUND: There is a correlation between the severe fever with thrombocytopenia syndrome (SFTS) viral load and disease severity; however, measurement of viral load is difficult in general laboratory and it takes time to obtain a viral load value. Here, the laboratory parameters for predicting the dynamic changes in SFTS viral load were identified. In addition, we tried to evaluate a specific time point for the early determination of clinical deterioration using dynamic change of laboratory parameters.
METHODS: This observational study included SFTS patients in Korea (2013 - 2020). Cross-correlation analysis at lagged values was used to determine the temporal correlation between the SFTS viral loads and time-series variables. Fifty-eight SFTS patients were included in the non-severe group (NSG) and 11 in the severe group (SG).
RESULTS: In the cross-sectional analyses, 10 parameters -white blood cell, absolute neutrophil cell, lymphocyte, platelet, activated partial thromboplastin time (aPTT), C-reactive protein, aspartate aminotransferase (AST), alanine transaminase (ALT), lactate dehydrogenase (LDH), and creatine phosphokinase (CPK)- were assessed within 30 days from the onset of symptoms; they exhibited three different correlation patterns: (1) positive, (2) positive with a time lag, and (3) negative. A prediction score system was developed for predicting SFTS fatality based on age and six laboratory variables -platelet, aPTT, AST, ALT, LDH, and CPK- in 5 days after the onset of symptoms; this scoring system had 87.5% sensitivity and 86.0% specificity (95% confidence interval: 0.831 - 1.00, P <0.001).
CONCLUSIONS: Three types of correlation patterns between the dynamic changes in SFTS viral load and laboratory parameters were identified. The dynamic changes in the viral load could be predicted using the dynamic changes in these variables, which can be particularly helpful in clinical settings where viral load tests cannot be performed. Also, the proposed scoring system could provide timely treatment to critical patients by rapidly assessing their clinical course.