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已发表论文
重症发热伴血小板减少综合征患者死亡率新型预测模型和简化临床指标的开发与验证
Received 23 October 2025
Accepted for publication 28 January 2026
Published 8 February 2026 Volume 2026:19 573018
DOI https://doi.org/10.2147/IDR.S573018
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Hemant Joshi
Debao Shi, Yaping Pan
Department of Clinical Laboratory, The First Affliated Hospital of Anhui Medical University, Hefei, Anhui, People’s Republic of China
Correspondence: Yaping Pan, Department of Clinical Laboratory, The First Affliated Hospital of Anhui Medical University, 218 Jixi road, Hefei, Anhui, People’s Republic of China, Tel +86 551 65908075, Fax +230022, Email panyaping117@163.com
Purpose: Severe fever with thrombocytopenia syndrome (SFTS) is a highly fatal infectious disease endemic in rural areas, underscoring the need for early prognostic risk stratification.
Patients and Methods: This study aimed to develop and validate a prognostic model using readily available clinical indicators. We conducted a retrospective analysis of 260 SFTS patients (mortality rate 19.6%), integrating epidemiological characteristics, clinical symptoms and laboratory variables through a hybrid multi-model feature screening approach incorporating univariate Cox, LASSO, Random Forest, and XGBoost algorithms.
Results: Four core risk factors for mortality were identified: age ≥ 56 years, platelet count ≤ 44 × 109/L, estimated glomerular filtration rate (eGFR) ≤ 53 mL/min/1.73m2, and activated partial thromboplastin time (APTT) ≥ 66.8 seconds. Based on these factors, we developed a comprehensive prognostic model and a simplified index termed AER (APTT-to-eGFR ratio). Internal validation demonstrated excellent discriminative ability, with a C-index of 0.831 (95% CI: 0.780– 0.882) for the model and an AUC of 0.838 (95% CI: 0.782– 0.893) for the AER index. External validation further confirmed the robustness of both tools, showing no significant difference in predictive performance (p = 0.614).
Conclusion: The developed model and AER index provide valuable tools for early identification of high-risk SFTS patients, facilitating improved clinical management and resource allocation.
Keywords: severe fever with thrombocytopenia syndrome, predictive models, machine learning algorithms, activated partial thromboplastin time-to-estimated glomerular filtration rate ratio, acute kidney injury
Department of Clinical Laboratory, The First Affliated Hospital of Anhui Medical University, Hefei, Anhui, People’s Republic of China
Correspondence: Yaping Pan, Department of Clinical Laboratory, The First Affliated Hospital of Anhui Medical University, 218 Jixi road, Hefei, Anhui, People’s Republic of China, Tel +86 551 65908075, Fax +230022, Email panyaping117@163.com
Purpose: Severe fever with thrombocytopenia syndrome (SFTS) is a highly fatal infectious disease endemic in rural areas, underscoring the need for early prognostic risk stratification.
Patients and Methods: This study aimed to develop and validate a prognostic model using readily available clinical indicators. We conducted a retrospective analysis of 260 SFTS patients (mortality rate 19.6%), integrating epidemiological characteristics, clinical symptoms and laboratory variables through a hybrid multi-model feature screening approach incorporating univariate Cox, LASSO, Random Forest, and XGBoost algorithms.
Results: Four core risk factors for mortality were identified: age ≥ 56 years, platelet count ≤ 44 × 109/L, estimated glomerular filtration rate (eGFR) ≤ 53 mL/min/1.73m2, and activated partial thromboplastin time (APTT) ≥ 66.8 seconds. Based on these factors, we developed a comprehensive prognostic model and a simplified index termed AER (APTT-to-eGFR ratio). Internal validation demonstrated excellent discriminative ability, with a C-index of 0.831 (95% CI: 0.780– 0.882) for the model and an AUC of 0.838 (95% CI: 0.782– 0.893) for the AER index. External validation further confirmed the robustness of both tools, showing no significant difference in predictive performance (p = 0.614).
Conclusion: The developed model and AER index provide valuable tools for early identification of high-risk SFTS patients, facilitating improved clinical management and resource allocation.
Keywords: severe fever with thrombocytopenia syndrome, predictive models, machine learning algorithms, activated partial thromboplastin time-to-estimated glomerular filtration rate ratio, acute kidney injury