Zhang Zhongwei, Ma Ke, Yang Zhongyuan, Cheng Qiuyu, Hu Xue, Liu Meiqi, Liu Yunhui, Liu Tingting, Zhang Meng, Luo Xiaoping, Chen Tao, Ning Qin
Department and Institute of Infectious Disease, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095, Jiefang Avenue, Wuhan, 430030, Hubei, China.
Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095, Jiefang Avenue, Wuhan, 430030, Hubei, China.
Infect Dis Ther. 2021 Sep;10(3):1347-1361. doi: 10.1007/s40121-021-00454-2. Epub 2021 May 15.
Bacterial infection is one of the most frequent complications in hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF), which leads to high mortality. However, a predictive model for bacterial infection in HBV-ACLF has not been well established. This study aimed to establish and validate a predictive model for bacterial infection in two independent patient cohorts.
Admission data from a prospective cohort of patients with HBV-ACLF without bacterial infection on admission was used for derivation. Bacterial infection development from day 3 to 7 of admission was captured. Independent predictors of bacterial infection development on multivariate logistic regression were used to develop the predictive model. External validation was performed on a separate retrospective cohort.
A total of 377 patients were enrolled into the derivation cohort, including 88 patients (23.3%) who developed bacterial infection from day 3 to 7 of admission. On multivariate regression analysis, admission serum globulin (OR 0.862, 95% CI 0.822-0.904; P < 0.001), interleukin-6 (OR 1.023, 95% CI 1.006-1.040; P = 0.009), and C-reactive protein (OR 1.123, 95% CI 1.081-1.166; P < 0.001) levels were independent predictors for the bacterial infection development, which were adopted as parameters of the predictive model (GIC). In the derivation cohort, the area under the curve (AUC) of GIC was 0.861 (95% CI 0.821-0.902). A total of 230 patients were enrolled into the validation cohort, including 57 patients (24.8%) who developed bacterial infection from day 3 to 7 of admission, and the AUC of GIC was 0.836 (95% CI 0.782-0.881). The Hosmer-Lemeshow test showed a good calibration performance of the predictive model in the two cohorts (P = 0.199, P = 0.746). Decision curve analysis confirmed the clinical utility of the predictive model.
GIC was established and validated for the prediction of bacterial infection development in HBV-ACLF, which may provide a potential auxiliary solution for the primary complication of HBV-ACLF.
细菌感染是乙型肝炎病毒相关慢加急性肝衰竭（HBV-ACLF）最常见的并发症之一，可导致高死亡率。然而，尚未建立完善的HBV-ACLF细菌感染预测模型。本研究旨在建立并验证两个独立患者队列中细菌感染的预测模型。
前瞻性队列中入院时无细菌感染的HBV-ACLF患者的入院数据用于模型推导。记录入院第3至7天细菌感染的发生情况。多因素逻辑回归分析中细菌感染发生的独立预测因素用于建立预测模型。在一个单独的回顾性队列中进行外部验证。
推导队列共纳入377例患者，其中88例（23.3%）在入院第3至7天发生细菌感染。多因素回归分析显示，入院时血清球蛋白（OR 0.862，95%CI 0.822-0.904；P<0.001）、白细胞介素-6（OR 1.023，95%CI 1.006-1.040；P=0.009）和C反应蛋白（OR 1.123，95%CI 1.081-1.166；P<0.001）水平是细菌感染发生的独立预测因素，这些因素被用作预测模型（GIC）的参数。在推导队列中，GIC的曲线下面积（AUC）为0.861（95%CI 0.821-0.902）。验证队列共纳入230例患者，其中57例（24.8%）在入院第3至7天发生细菌感染，GIC的AUC为0.836（95%CI 0.782-0.881）。Hosmer-Lemeshow检验显示预测模型在两个队列中具有良好的校准性能（P=0.199，P=0.746）。决策曲线分析证实了预测模型的临床实用性。
建立并验证了GIC用于预测HBV-ACLF患者细菌感染的发生，这可能为HBV-ACLF的主要并发症提供一种潜在的辅助解决方案。
