Development and validation of a risk prediction model for tigecycline-induced hypofibrinogenemia in septic patients: a retrospective cohort study.

Background: Tigecycline is widely used in China to treat multidrug-resistant bacterial infections, with hypofibrinogenemia being the most common adverse effect due to its impact on coagulation. Although a predictive model for tigecycline-induced hypofibrinogenemia has been developed, it lacks external validation. This study aims to construct a predictive model for the risk of tigecycline-induced hypofibrinogenemia in sepsis patients.

Methods: This retrospective cohort study analyzed data from sepsis patients treated with tigecycline in the intensive care unit (ICU) of the People's Hospital of Inner Mongolia Autonomous Region between January 2018 and June 2024. Risk factors for tigecycline-induced hypofibrinogenemia were identified through univariate and multivariate logistic regression analyses. A nomogram prediction model was developed and externally validated using the MIMIC-IV database.

Results: A total of 465 patients participated, with 411 in the training set and 54 in the external validation set. Independent risk factors for hypofibrinogenemia included age (OR: 1.02, p = 0.009), duration of tigecycline treatment (OR: 1.33, p < 0.001), baseline fibrinogen level (OR: 0.65, p < 0.001), baseline platelet count (OR: 0.99, p = 0.025), and the presence of tumors (OR: 2.17, p = 0.021). The model demonstrated an AUC of 0.85 (95% CI: 0.81-0.89) in the training cohort and 0.83 (95% CI: 0.71-0.95) in the validation cohort. Calibration curves for both cohorts showed strong agreement between predicted and observed hypofibrinogenemia. Decision curve analysis (DCA) indicated good clinical applicability of the model.

Conclusion: The developed predictive model effectively predicts the risk of tigecycline-induced hypofibrinogenemia in sepsis patients, providing valuable information for clinical decision-making.