Lihua Huang,1,* Jun Wu,2,* Jiao Luo,1 Wei Gu1 

1Department of Infection Disease, The First Affiliated Hospital of Dali University, Dali, Yunnan, People’s Republic of China; 2Department of Ophthalmology, Dali Bai Autonomous Prefecture People’s Hospital, Dali, Yunnan, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Wei Gu, Department of Infection Disease, The First Affiliated Hospital of Dali University, 32 Jia Shi Bo Road, Dali, Yunnan, 671000, People’s Republic of China, Email gw777@163.com

Objective: To explore the risk factors for the severity of hemorrhagic fever with renal syndrome (HFRS) and construct a nomogram model.
Methods: A retrospective analysis was conducted on the data of 191 patients diagnosed with HFRS at the First Affiliated Hospital of Dali University between January 1, 2013, and September 30, 2024. Based on whether severe disease occurred, the patients were divided into a severe HFRS group (n=42) and a mild HFRS group (n=149). The clinical data of the two groups were compared, and after eliminating the influence of collinearity, LASSO-Logistic regression analysis was used to screen for factors influencing the severity of HFRS. Additionally, a nomogram model was constructed to predict the severity of HFRS.
Results: Compared with the mild HFRS group, patients in the severe HFRS group had a prolonged length of stay, increased usage rates of Continuous Renal Replacement Therapy (CRRT) and ventilators, and an elevated 30-day mortality rate (P< 0.001). Procalcitonin (PCT, OR= 0.86), Albumin (ALB, OR: 0.86), Platelet count-to-Albumin ratio (PAR, OR: 0.64), and pleural effusion (OR: 4.49) were identified as independent risk factors for severe HFRS. The Area Under Curve (AUC) of the nomogram model was 0.890. The Hosmer-Lemeshow test result was χ²=2.92, P=0.94, and in combination with the Calibration curve, it indicated a good fit between the calibration curve and the ideal curve. Most of the Decision Curve Analysis (DCA) curves of the nomogram model were above the two extreme lines, suggesting that using this model to predict severe HFRS patients could clinically benefit those with severe HFRS, demonstrating the clinical practicality of the nomogram model.
Conclusion: PCT, ALB, PAR, and pleural effusion are risk factors for the severity of HFRS. The constructed nomogram model exhibits good discriminatory power, fit, and clinical practicality, enabling early identification of patients with severe HFRS in southwestern China.

Keywords: hemorrhagic fever with renal syndrome, severity, risk factors, nomogram