Ma Hongling, Gong Zhaotang, Sun Jia, Chen LiNa, SiRi GuLeng
Department of Pharmacy, Inner Mongolia People's Hospital, Hohhot, 010000, Inner Mongolia, China.
Department of Pharmacy, Inner Mongolia Medical University, Hohhot, Inner Mongolia, China.
BMC Infect Dis. 2025 May 8;25(1):683. doi: 10.1186/s12879-025-11019-w.
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.
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.
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.
The developed predictive model effectively predicts the risk of tigecycline-induced hypofibrinogenemia in sepsis patients, providing valuable information for clinical decision-making.
替加环素在中国被广泛用于治疗多重耐药细菌感染，低纤维蛋白原血症是其因影响凝血而最常见的不良反应。尽管已开发出替加环素诱导低纤维蛋白原血症的预测模型，但缺乏外部验证。本研究旨在构建脓毒症患者中替加环素诱导低纤维蛋白原血症风险的预测模型。
这项回顾性队列研究分析了2018年1月至2024年6月期间在内蒙古自治区人民医院重症监护病房（ICU）接受替加环素治疗的脓毒症患者的数据。通过单因素和多因素逻辑回归分析确定替加环素诱导低纤维蛋白原血症的危险因素。开发了列线图预测模型，并使用MIMIC-IV数据库进行外部验证。
共有465名患者参与，其中411名在训练集，54名在外部验证集。低纤维蛋白原血症的独立危险因素包括年龄（OR：1.02，p = 0.009）、替加环素治疗持续时间（OR：1.33，p < 0.001）、基线纤维蛋白原水平（OR：0.65，p < 0.001）、基线血小板计数（OR：0.99，p = 0.025）和肿瘤存在情况（OR：2.17，p = 0.021）。该模型在训练队列中的AUC为0.85（95%CI：0.81 - 0.89），在验证队列中的AUC为0.83（95%CI：0.71 - 0.95）。两个队列的校准曲线显示预测的和观察到的低纤维蛋白原血症之间有很强的一致性。决策曲线分析（DCA）表明该模型具有良好的临床适用性。
所开发的预测模型能有效预测脓毒症患者中替加环素诱导低纤维蛋白原血症的风险，为临床决策提供有价值的信息。
