Lower serum LDL-C levels are associated with poor prognosis in severe fever with thrombocytopenia syndrome: a single-center retrospective cohort study.

Background: Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease triggered by a novel bunyavirus (SFTSV). Characterized by fever, thrombocytopenia, leukocytopenia, and multiple organ dysfunction manifestations, its primary mode of transmission is through tick bites. Despite the critical role of lipid metabolism in viral infections, the role of lipids in SFTS remains unclear. Methods: This retrospective study analyzed 602 patients with SFTS treated at the Shandong Public Health Clinical Center from January 2021 to December 2023. Based on the endpoint events, patients were classified into survival (S) and death (D) groups. The S group was further classified into non-critical (non-C) and critical (C) groups based on symptoms. All patients were followed up for at least 28 days after admission. Propensity score matching, multivariable logistic regression, survival analysis, time trend analysis, and mediation analysis were conducted to assess the association between LDL-C levels and prognosis in SFTS. Results: The serum LDL-C levels on admission were significantly lower in the D and C groups than in the S and non-C groups. The logistic regression models indicated a potential association between LDL-C levels and a poor prognosis in SFTS. The restricted cubic spline showed a unidirectional trend between LDL-C levels and mortality, with a cutoff value of 1.59 mmol/L. The survival analysis revealed higher and earlier mortality in the low-LDL-C group than in the high-LDL-C group. The trends over 28 days post-admission showed that the serum LDL-C levels gradually increased in SFTS, with a favorable prognosis. Finally, the mediation analysis indicated that low LDL-C levels are associated with mortality through poor hepatic, cardiac, and coagulation functions. Conclusion: Low LDL-C levels are potentially associated with a poor prognosis in SFTS.

Scoring Model for Predicting the Occurrence of Severe Illness in Hospitalized Patients with Severe Fever with Thrombocytopenia Syndrome.

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging hemorrhagic fever with high mortality. Severe cases progressed rapidly, with deaths occurring within 2 weeks. Therefore, constructing a model to predict disease progression among hospitalized patients plays an important role in clinical practice. The development cohort included 121 patients with SFTS, 25 with severe SFTS, and 96 with mild SFTS. Two of the 64 variables were independent risk factors, including neurological symptoms (odds ratio [OR], 12.915; 95% confidence interval [CI], 3.342-49.916; P < 0.001) and aspartate aminotransferase/alanine aminotransferase levels (OR, 1.891; 95% CI, 1.272-2.813; P = 0.002). The model's area under the curve (AUC) was 0.882 (95% CI: 0.808-0.956). The mean AUC value obtained from the internal validation was 0.883 (95% CI: 0.809-0.957). The AUC in the external validation cohort was 0.873 (95% CI: 0.775-0.972). This model can be used to identify severely ill patients as early as possible with high predictive value, stability, and repeatability. This model can help clinicians with their treatment plans.