{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T15:58:48Z","timestamp":1784303928115,"version":"3.55.0"},"reference-count":25,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T00:00:00Z","timestamp":1754870400000},"content-version":"vor","delay-in-days":10,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Medical Virology"],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:label\/>\n                    <jats:p>\n                      Few prediction models exist for the prognosis of patients with severe fever with thrombocytopenia syndrome (SFTS). This study analyzed the risk factors for poor prognosis in patients with SFTS and established a prognostic risk prediction model based on these factors. A total of 194 patients with SFTS admitted to Tongji Hospital from April 1, 2023, to July 18, 2024, were enrolled. The patients were divided into the survival (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009127) and death groups (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200967). Baseline information and initial laboratory indices obtained within 24\u2009h after admission were retrospectively analyzed. Least absolute shrinkage and selection operator regression analysis was used to screen variables. Univariate and multivariate logistic regression analyses were used to determine the independent risk factors affecting patient prognosis, and a prognostic risk prediction model was established. Viral load, age, myoglobin, blood urea nitrogen, and pancreatic amylase were independent risk factors affecting prognosis. A nomogram model was constructed based on these five independent risk factors, which showed excellent prediction performance. This study developed a prognostic risk prediction model for patients with SFTS with a strong predictive performance, which can be used as a tool for early clinical prognostic prediction.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jmv.70554","type":"journal-article","created":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T13:29:11Z","timestamp":1754918951000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Development and Validation of a Prognostic Risk Model for Severe Fever With Thrombocytopenia Syndrome Patients"],"prefix":"10.1002","volume":"97","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6211-1759","authenticated-orcid":false,"given":"Xu","family":"Xiang","sequence":"first","affiliation":[{"name":"Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College Huazhong University of Science and Technology Wuhan 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