Biomarkers for Early Predicting In-Hospital Mortality in Severe Fever with Thrombocytopenia Syndrome and Differentiating It from Hemorrhagic Fever with Renal Syndrome
Chaochao Chen,1,* Yuwei Zheng,1,* Xuefen Li,2 Bo Shen,1 Xiaojie Bi1 1Department of Laboratory Medicine, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, Taizhou, 317000, People’s Republic of China; 2Department of Laboratory Medicine, The First A...
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Dove Medical Press
2025-03-01
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| author | Chen C Zheng Y Li X Shen B Bi X |
| author_facet | Chen C Zheng Y Li X Shen B Bi X |
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| description | Chaochao Chen,1,* Yuwei Zheng,1,* Xuefen Li,2 Bo Shen,1 Xiaojie Bi1 1Department of Laboratory Medicine, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, Taizhou, 317000, People’s Republic of China; 2Department of Laboratory Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiaojie Bi; Bo Shen, Department of Laboratory Medicine, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, No. 150, Ximen Street, Linhai, Taizhou, 317000, People’s Republic of China, Tel +86 13757693182, Email bixj@enzemed.com; shenb@enzemed.comPurpose: Severe fever with thrombocytopenia syndrome (SFTS) has a high mortality rate and is easily misdiagnosed as hemorrhagic fever with renal syndrome (HFRS), particularly in resource-limited rural areas where early diagnosis remains challenging. This study used routine laboratory parameters, epidemiology and clinical manifestations to develop a model for the early diagnosis of SFTS and identify fatal risk factors, ultimately reducing mortality of SFTS.Patients and Methods: This retrospective cohort study included 141 SFTS and 141 HFRS patients. Of these, 94 patients with SFTS were allocated to the model cohort for mortality risk identification by using multivariable Cox regression analysis. Sensitivity, specificity, and predictive values were calculated from validation cohort to assess the clinical values. Then, we analyzed 62 SFTS and 113 HFRS using multivariable logistic regression to identify SFTS. Receiver operating characteristic (ROC) curve analysis was used to evaluate their diagnostic value.Results: Multivariate Cox regression analysis showed that blood urea nitrogen (BUN) ≥ 10.22mmol/L activated partial thromboplastin time (APTT) ≥ 58.05s and D-dimer ≥ 4.68mg/L were the risk factors for death in SFTS. This combined indicators had an area under the curve (AUC) of 0.91 (95% CI: 0.847– 0.973), with a sensitivity and specificity of 86%, respectively. Any indicator was achieved the cutoff, and sensitivity and specificity in the validation group were 93% and 54%. Multivariable logistic regression showed that age (OR: 1.10) and initial laboratory indicators including WBC (OR: 0.48), Cr (OR: 0.86), CK (OR: 1.01), and APTT (OR: 1.09) can be used to identify SFTS from HFRS. This model achieved an AUC value of 0.97 (95% CI: 0.977– 0.999) and 0.98 (95% CI: 0.958– 1.000) in validation cohort.Conclusion: In resource-limited rural hospitals, the integration of routine laboratory parameters with epidemiology and clinical manifestations demonstrates enhanced sensitivity for early SFTS identification and mortality risk stratification to reduce mortality rate.Keywords: differential diagnosis, dynamic change, hemorrhagic fever with renal syndrome, risk factors, severe fever with thrombocytopenia syndrome |
| format | Article |
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| spelling | doaj-art-a6b8e8712eeb41b584ea480461e93f512025-08-20T02:57:25ZengDove Medical PressInfection and Drug Resistance1178-69732025-03-01Volume 1813551366101018Biomarkers for Early Predicting In-Hospital Mortality in Severe Fever with Thrombocytopenia Syndrome and Differentiating It from Hemorrhagic Fever with Renal SyndromeChen CZheng YLi XShen BBi XChaochao Chen,1,* Yuwei Zheng,1,* Xuefen Li,2 Bo Shen,1 Xiaojie Bi1 1Department of Laboratory Medicine, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, Taizhou, 317000, People’s Republic of China; 2Department of Laboratory Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiaojie Bi; Bo Shen, Department of Laboratory Medicine, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, No. 150, Ximen Street, Linhai, Taizhou, 317000, People’s Republic of China, Tel +86 13757693182, Email bixj@enzemed.com; shenb@enzemed.comPurpose: Severe fever with thrombocytopenia syndrome (SFTS) has a high mortality rate and is easily misdiagnosed as hemorrhagic fever with renal syndrome (HFRS), particularly in resource-limited rural areas where early diagnosis remains challenging. This study used routine laboratory parameters, epidemiology and clinical manifestations to develop a model for the early diagnosis of SFTS and identify fatal risk factors, ultimately reducing mortality of SFTS.Patients and Methods: This retrospective cohort study included 141 SFTS and 141 HFRS patients. Of these, 94 patients with SFTS were allocated to the model cohort for mortality risk identification by using multivariable Cox regression analysis. Sensitivity, specificity, and predictive values were calculated from validation cohort to assess the clinical values. Then, we analyzed 62 SFTS and 113 HFRS using multivariable logistic regression to identify SFTS. Receiver operating characteristic (ROC) curve analysis was used to evaluate their diagnostic value.Results: Multivariate Cox regression analysis showed that blood urea nitrogen (BUN) ≥ 10.22mmol/L activated partial thromboplastin time (APTT) ≥ 58.05s and D-dimer ≥ 4.68mg/L were the risk factors for death in SFTS. This combined indicators had an area under the curve (AUC) of 0.91 (95% CI: 0.847– 0.973), with a sensitivity and specificity of 86%, respectively. Any indicator was achieved the cutoff, and sensitivity and specificity in the validation group were 93% and 54%. Multivariable logistic regression showed that age (OR: 1.10) and initial laboratory indicators including WBC (OR: 0.48), Cr (OR: 0.86), CK (OR: 1.01), and APTT (OR: 1.09) can be used to identify SFTS from HFRS. This model achieved an AUC value of 0.97 (95% CI: 0.977– 0.999) and 0.98 (95% CI: 0.958– 1.000) in validation cohort.Conclusion: In resource-limited rural hospitals, the integration of routine laboratory parameters with epidemiology and clinical manifestations demonstrates enhanced sensitivity for early SFTS identification and mortality risk stratification to reduce mortality rate.Keywords: differential diagnosis, dynamic change, hemorrhagic fever with renal syndrome, risk factors, severe fever with thrombocytopenia syndromehttps://www.dovepress.com/biomarkers-for-early-predicting-in-hospital-mortality-in-severe-fever--peer-reviewed-fulltext-article-IDRdifferential diagnosisdynamic changehemorrhagic fever with renal syndromerisk factorssevere fever with thrombocytopenia syndrome |
| spellingShingle | Chen C Zheng Y Li X Shen B Bi X Biomarkers for Early Predicting In-Hospital Mortality in Severe Fever with Thrombocytopenia Syndrome and Differentiating It from Hemorrhagic Fever with Renal Syndrome Infection and Drug Resistance differential diagnosis dynamic change hemorrhagic fever with renal syndrome risk factors severe fever with thrombocytopenia syndrome |
| title | Biomarkers for Early Predicting In-Hospital Mortality in Severe Fever with Thrombocytopenia Syndrome and Differentiating It from Hemorrhagic Fever with Renal Syndrome |
| title_full | Biomarkers for Early Predicting In-Hospital Mortality in Severe Fever with Thrombocytopenia Syndrome and Differentiating It from Hemorrhagic Fever with Renal Syndrome |
| title_fullStr | Biomarkers for Early Predicting In-Hospital Mortality in Severe Fever with Thrombocytopenia Syndrome and Differentiating It from Hemorrhagic Fever with Renal Syndrome |
| title_full_unstemmed | Biomarkers for Early Predicting In-Hospital Mortality in Severe Fever with Thrombocytopenia Syndrome and Differentiating It from Hemorrhagic Fever with Renal Syndrome |
| title_short | Biomarkers for Early Predicting In-Hospital Mortality in Severe Fever with Thrombocytopenia Syndrome and Differentiating It from Hemorrhagic Fever with Renal Syndrome |
| title_sort | biomarkers for early predicting in hospital mortality in severe fever with thrombocytopenia syndrome and differentiating it from hemorrhagic fever with renal syndrome |
| topic | differential diagnosis dynamic change hemorrhagic fever with renal syndrome risk factors severe fever with thrombocytopenia syndrome |
| url | https://www.dovepress.com/biomarkers-for-early-predicting-in-hospital-mortality-in-severe-fever--peer-reviewed-fulltext-article-IDR |
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