Establishment of an Early Prediction Model for Severe Fever With Thrombocytopenia Syndrome‐Associated Encephalitis
ABSTRACT Background Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease primarily transmitted by ticks. The development of encephalitis in SFTS patients significantly increases the risk of adverse outcomes. However, the understanding of SFTS‐associated encephalitis (...
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| Format: | Article |
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Wiley
2024-12-01
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| Series: | Immunity, Inflammation and Disease |
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| Online Access: | https://doi.org/10.1002/iid3.70096 |
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| author | Yijiang Liu Naisheng Zhu Zimeng Qin Chenzhe He Jiaqi Li Hongbo Zhang Ke Cao Wenkui Yu |
| author_facet | Yijiang Liu Naisheng Zhu Zimeng Qin Chenzhe He Jiaqi Li Hongbo Zhang Ke Cao Wenkui Yu |
| author_sort | Yijiang Liu |
| collection | DOAJ |
| description | ABSTRACT Background Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease primarily transmitted by ticks. The development of encephalitis in SFTS patients significantly increases the risk of adverse outcomes. However, the understanding of SFTS‐associated encephalitis (SFTSAE) is still limited. This study aimed to identify the clinical characteristics of SFTSAE and develop a predictive model for early detection. Methods We retrospectively collected data from 220 SFTS patients admitted to Nanjing Drum Tower Hospital between May 2019 and January 2024. The patients were first randomly divided into a training set (154 people, 70%) and a validation set (66 people, 30%). The patients in the training set were divided into SFTSAE and non‐SFTSAE groups according to the presence of encephalitis. A prediction model was constructed using the training set: important clinical parameters were selected using univariate logistic regression, and then multivariate logistic regression was performed to determine the independent risk factors for SFTSAE. A prediction model was constructed using these independent risk factors. Finally, the validation set was used to verify the predictive ability of the model. Results Age, C‐reactive protein, d‐dimer, and viral load were independent risk factors for SFTSAE (p < 0.05). A nomogram containing these four indicators was constructed, and the predictive performance of the nomogram was evaluated using the ROC curve. The AUC of the model was 0.846 (95% confidence interval [CI]: 0.770–0.921), which had good predictive ability for SFTSAE. Conclusion Conclusion: The overall mortality rate of SFTS patients was 17.53%, and the mortality rate of encephalitis patients was 50%. Old age, high C‐reactive protein, elevated d‐dimer, and high viral load were independent risk factors for SFTSAE. The nomogram constructed based on these four indicators had good predictive ability and could be used as an evaluation tool for clinical treatment. |
| format | Article |
| id | doaj-art-84beae474a9244a3819a4f80df49b553 |
| institution | DOAJ |
| issn | 2050-4527 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Immunity, Inflammation and Disease |
| spelling | doaj-art-84beae474a9244a3819a4f80df49b5532025-08-20T02:43:38ZengWileyImmunity, Inflammation and Disease2050-45272024-12-011212n/an/a10.1002/iid3.70096Establishment of an Early Prediction Model for Severe Fever With Thrombocytopenia Syndrome‐Associated EncephalitisYijiang Liu0Naisheng Zhu1Zimeng Qin2Chenzhe He3Jiaqi Li4Hongbo Zhang5Ke Cao6Wenkui Yu7Department of Critical Care Medicine Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University Nanjing ChinaDepartment of Emergency Medicine, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School Nanjing University Nanjing ChinaDepartment of Critical Care Medicine Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University Nanjing ChinaDepartment of Critical Care Medicine Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University Nanjing ChinaDepartment of Critical Care Medicine Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University Nanjing ChinaSouthwest Medical University Luzhou ChinaDepartment of Critical Care Medicine Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University Nanjing ChinaDepartment of Critical Care Medicine Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University Nanjing ChinaABSTRACT Background Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease primarily transmitted by ticks. The development of encephalitis in SFTS patients significantly increases the risk of adverse outcomes. However, the understanding of SFTS‐associated encephalitis (SFTSAE) is still limited. This study aimed to identify the clinical characteristics of SFTSAE and develop a predictive model for early detection. Methods We retrospectively collected data from 220 SFTS patients admitted to Nanjing Drum Tower Hospital between May 2019 and January 2024. The patients were first randomly divided into a training set (154 people, 70%) and a validation set (66 people, 30%). The patients in the training set were divided into SFTSAE and non‐SFTSAE groups according to the presence of encephalitis. A prediction model was constructed using the training set: important clinical parameters were selected using univariate logistic regression, and then multivariate logistic regression was performed to determine the independent risk factors for SFTSAE. A prediction model was constructed using these independent risk factors. Finally, the validation set was used to verify the predictive ability of the model. Results Age, C‐reactive protein, d‐dimer, and viral load were independent risk factors for SFTSAE (p < 0.05). A nomogram containing these four indicators was constructed, and the predictive performance of the nomogram was evaluated using the ROC curve. The AUC of the model was 0.846 (95% confidence interval [CI]: 0.770–0.921), which had good predictive ability for SFTSAE. Conclusion Conclusion: The overall mortality rate of SFTS patients was 17.53%, and the mortality rate of encephalitis patients was 50%. Old age, high C‐reactive protein, elevated d‐dimer, and high viral load were independent risk factors for SFTSAE. The nomogram constructed based on these four indicators had good predictive ability and could be used as an evaluation tool for clinical treatment.https://doi.org/10.1002/iid3.70096encephalopathyrisk factorscore modelsevere fever with thrombocytopenia syndrome |
| spellingShingle | Yijiang Liu Naisheng Zhu Zimeng Qin Chenzhe He Jiaqi Li Hongbo Zhang Ke Cao Wenkui Yu Establishment of an Early Prediction Model for Severe Fever With Thrombocytopenia Syndrome‐Associated Encephalitis Immunity, Inflammation and Disease encephalopathy risk factor score model severe fever with thrombocytopenia syndrome |
| title | Establishment of an Early Prediction Model for Severe Fever With Thrombocytopenia Syndrome‐Associated Encephalitis |
| title_full | Establishment of an Early Prediction Model for Severe Fever With Thrombocytopenia Syndrome‐Associated Encephalitis |
| title_fullStr | Establishment of an Early Prediction Model for Severe Fever With Thrombocytopenia Syndrome‐Associated Encephalitis |
| title_full_unstemmed | Establishment of an Early Prediction Model for Severe Fever With Thrombocytopenia Syndrome‐Associated Encephalitis |
| title_short | Establishment of an Early Prediction Model for Severe Fever With Thrombocytopenia Syndrome‐Associated Encephalitis |
| title_sort | establishment of an early prediction model for severe fever with thrombocytopenia syndrome associated encephalitis |
| topic | encephalopathy risk factor score model severe fever with thrombocytopenia syndrome |
| url | https://doi.org/10.1002/iid3.70096 |
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