ArticleInfection & chemotherapy2022
The Evaluation of Surrogate Laboratory Parameters for Predicting the Trend of Viral Loads in Patients with Severe Fever with Thrombocytopenia Syndrome: Cross-Correlation Analysis of Time Series.
Article in Infection & chemotherapy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 10 citations in OpenAlex.
- Aspartate aminotransferase-to-lymphocyte ratio predicts in-hospital mortality in patients with severe fever with thrombocytopenia syndrome: a multicenter retrospective cohort study.BMC infectious diseases · 2026Observational
- Neurological Manifestations and High Viral Load as Independent Predictors of Mortality in Severe Fever With Thrombocytopenia Syndrome.Open forum infectious diseases · 2026Article
- Integration of SFTSV Viral Load, Age, and Double-Negative B-Cells as Prognostic Biomarkers for Severe Fever With Thrombocytopenia Syndrome Outcomes.Journal of immunology research · 2026Article
- Dynamic evolution of key laboratory parameters predicts clinical outcomes in severe fever with thrombocytopenia syndrome (SFTS) patients: a cohort study.Virology journal · 2025Article
- Hand, Foot, and Mouth Disease Risk Prediction in Southern China: Time Series Study Integrating Web-Based Search and Epidemiological Surveillance Data.JMIR infodemiology · 2025Article
- Dynamic biomarkers and Cox regression with time-dependent covariate for mortality prediction in severe fever with thrombocytopenia syndrome.Scientific reports · 2025Article
- Epidemiologic and Clinical Characteristics of Patients with Severe Fever with Thrombocytopenia Syndrome at Tertiary Hospital in Jeju for 10 years.Infection & chemotherapy · 2023Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundThere 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. MATERIALS AND
methodsThis 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).
resultsIn 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,
conclusionThree 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.
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