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Methods: A retrospective cohort study enrolled 41 laboratory-confirmed patients from Chizhou People’s Hospital (May 2020–September 2024), stratified into severe (n=21) and non-severe (n=20) groups. Demographics, epidemiologic history, baseline laboratory parameters, and serial measurements of 16 indicators were analyzed using nonparametric tests and multivariate logistic regression with ROC evaluation. Results: AST peaked at day 5 and stayed elevated in severe cases; PLT progressively declined to low nadirs, while WBC, PLT, diarrhea, and neurological symptoms independently predicted severity, with ROC-derived cutoffs. Conclusions: Dynamic deterioration of AST, PLT, and RBC/Hb associates with poor outcomes, and combined admission markers support early warning for severe disease.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/dynamic-evolution-of-key-laboratory-parameters-predicts-clinical-outcomes-in-severe-fever-with-thrombocytopenia-syndrome-sfts-patients-a-cohort-study/451827/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/dynamic-evolution-of-key-laboratory-parameters-predicts-clinical-outcomes-in-severe-fever-with-thrombocytopenia-syndrome-sfts-patients-a-cohort-study/451827.png","ImageObject",300,407,{"name":92,"@type":93},"Aran","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How was the study designed and how were patients grouped?","Question",{"text":112,"@type":113},"The study was a retrospective cohort including 41 laboratory-confirmed SFTS patients from Chizhou People’s Hospital (May 2020–September 2024). Patients were stratified into severe (n=21) and non-severe (n=20) groups based on the 2023 Diagnosis and Treatment Protocol for SFTS.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which laboratory markers showed distinct dynamic changes in severe patients?",{"text":117,"@type":113},"In severe cases, AST peaked on day 5 post-admission and remained significantly elevated during hospitalization. PLT counts progressively declined to a minimum around day 5 and were significantly lower than in the non-severe group; RBC and Hb also showed continuous decline in the severe group.",{"name":119,"@type":110,"acceptedAnswer":120},"What factors independently predicted disease severity and how was prediction performance assessed?",{"text":121,"@type":113},"Multivariate logistic regression identified admission WBC, PLT, diarrhea, and neurological symptoms as independent severity predictors. ROC curve analysis provided diagnostic cutoff values and evaluated predictive efficacy using combined models.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},451827,1790764550,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},137455076865,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Ji et al. Virology Journal (2026) 23:3 [https://doi.org/10.1186/s12985-025-03040-x](https://doi.org/10.1186/s12985-025-03040-x)  \nVirology Journal  \nRESEARCH Open Access  \nDynamic evolution of key laboratory parameters predicts clinical outcomesin severe fever with thrombocytopenia syndrome (SFTS) patients: a cohort study  \nShanshan Ji 1,2†, Hua Fang2†, Xiangyu Zhao2, Bo Han2, Quan Sun2, Silian Chen2, Lie Wang2, Yizhong Hu2*, Jinbao Huang2* and Zhong-Xin Wang 1*  \nAbstract  \nBackground We explored laboratory marker patterns in patients diagnosed with severe fever with thrombocytopenia syndrome (SFTS) and evaluated their relationships with disease severity and clinical outcomes. Methods This retrospective cohort study included 41 patients with laboratory-confirmed SFTS at Chizhou People’s Hospital (May 2020–September 2024) . Stratification into severe (n = 21) and non-severe (n = 20) groups followed the 2023 Diagnosis and Treatment Protocol for Fever with Thrombocytopenia Syndrome. Demographic data, epidemiological history, baseline laboratory parameters, and serial measurements of 16 key indicators were extracted from electronic medical records. Mann–Whitney U test, chi-squared test, and Fisher’s exact test were used for intergroup comparisons. Multivariate logistic regression was used to identify independent severity predictors, whereas receiver operating characteristic curves were used to evaluate the predictive efficacy.  \nResults AST peaked on day 5 post-admission (> 10 × upper limit of normal), remaining significantly elevated throughout hospitalization in the severe group (P \u003C 0. 001) . PLT counts in the severe group declined progressively (minimum: day 5, \u003C 50 × 10⁹/L) and was significantly lower than that in the non-severe group (P \u003C 0. 0001) . RBC and Hb showed continuous decline in the severe group while gradually recovering in the non-severe group after initial decrease, although no significant differences were observed between groups. Multivariate analysis identified admission WBC (adjusted OR [aOR] = 0 . 021, 95% CI: 0 .001–0. 707, P = 0 . 031), PLT (aOR = 0 . 880, 95% CI: 0 .777–0. 997, P = 0 . 045), diarrhea (aOR = 12 . 929, 95% CI: 1.281–130. 500, P = 0 . 030), and neurological symptoms (aOR = 31 . 237, 95% CI: 1.817–536. 978, P = 0 . 018) as independent severity predictors. ROC curve analysis revealed optimal diagnostic  \n†Shanshan Ji and Hua Fang contributed equally to this manuscript.  \n*Correspondence: Yizhong Hu [13305669609@163.com](13305669609@163.com)[ ](13305669609@163.com)Jinbao Huang [hjb20240810@163.com](hjb20240810@163.com)[ ](hjb20240810@163.com)Zhong-Xin Wang[aywzhx87@163.com](aywzhx87@163.com)  \nFull list of author information is available at the end of the article  \n© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creati](http://creati)[vecommons.org/l](vecommons.org/l)icenses/by-nc-nd/4.0/.  \nJi et al. Virology Journal (2026) 23:3 Page 2 of 13  \ncutoff values for identifying severe patients: WBC \u003C 2.4 × 10⁹/L and PLT \u003C 48.5 × 10⁹/L. The combined model (WBC an","cbCaik0DyWwIYtIl","https://ap.wps.com/l/cbCaik0DyWwIYtIl","pdf",2861792,13,"English","# Abstract\n# Keywords\n# Introduction","[{\"question\":\"How was the study designed and how were patients grouped?\",\"answer\":\"The study was a retrospective cohort including 41 laboratory-confirmed SFTS patients from Chizhou People’s Hospital (May 2020–September 2024). Patients were stratified into severe (n=21) and non-severe (n=20) groups based on the 2023 Diagnosis and Treatment Protocol for SFTS.\"},{\"question\":\"Which laboratory markers showed distinct dynamic changes in severe patients?\",\"answer\":\"In severe cases, AST peaked on day 5 post-admission and remained significantly elevated during hospitalization. PLT counts progressively declined to a minimum around day 5 and were significantly lower than in the non-severe group; RBC and Hb also showed continuous decline in the severe group.\"},{\"question\":\"What factors independently predicted disease severity and how was prediction performance assessed?\",\"answer\":\"Multivariate logistic regression identified admission WBC, PLT, diarrhea, and neurological symptoms as independent severity predictors. ROC curve analysis provided diagnostic cutoff values and evaluated predictive efficacy using combined models.\"}]","Dynamic evolution of key laboratory parameters predicts clinical outcomes in severe fever with thrombocytopenia syndrome (SFTS) patients - a cohort study | PDF",1790735997,33]