Work

Development and Validation of a Novel Predictive Model and Simplified Clinical Indicator for Mortality in Severe Fever with Thrombocytopenia Syndrome Patients

Year: 2026

Type: article

Abstract: 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 × 10 9 /L, estimated glomerular filtration rate (eGFR) ≤ 53 mL/min/1.73m 2 , 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

Source: Infection and Drug Resistance

DOI: 10.2147/idr.s573018

Authors: Debao Shi, Yaping Pan

Institutions: Anhui Medical University, First Affiliated Hospital of Anhui Medical University

Language: English

Topic: Viral Infections and Vectors

Subfield: Infectious Diseases

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,836