Hu Yingying, Lin Kongying, Lin Kecan, Lin Haitao, Chen Ruijia, Li Shengcong, Wang Jinye, Zeng Yongyi, Liu Jingfeng
Department of Pharmacy, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China.
Department of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China.
Saudi J Gastroenterol. 2020 Aug 8;26(6):326-36. doi: 10.4103/sjg.SJG_128_20.
BACKGROUND/AIMS: The aim of this study was to develop a tool to predict multidrug-resistant bacteria infections among patients with biliary tract infection for targeted therapy.
We conducted a single-center retrospective descriptive study from January 2016 to December 2018. Univariate and multivariable logistic regression analysis were used to identify independent risk factors of multidrug-resistant bacterial infections. A nomogram was constructed according to multivariable regression model. Moreover, the clinical usefulness of the nomogram was estimated by decision curve analysis.
121 inpatients were randomly divided into a training cohort (n = 79) and validation cohort (n = 42). In multivariate analysis, 5 factors were associated with biliary tract infections caused by multidrug-resistant bacterial infections: aspartate aminotransferase (Odds ratio (OR), 13.771; 95% confidence interval (CI), 3.747-64.958; P < 0.001), previous antibiotic use within 90 days (OR, 4.130; 95% CI, 1.192-16.471; P = 0.032), absolute neutrophil count (OR, 3.491; 95% CI, 1.066-12.851; P = 0.046), previous biliary surgery (OR, 3.303; 95% CI, 0.910-13.614; P = 0.079), and hemoglobin (OR, 0.146; 95% CI, 0.030-0.576; P = 0.009). The nomogram model was constructed based on these variables, and showed good calibration and discrimination in the training set [area under the curve (AUC), 0.86] and in the validation set (AUC, 0.799). The decision curve analysis demonstrated the clinical usefulness of our nomogram. Using the nomogram score, high risk and low risk patients with multidrug-resistant bacterial infection could be differentiated.
This simple bedside prediction tool to predict multidrug-resistant bacterial infection can help clinicians identify low versus high risk patients as well as choose appropriate, timely initial empirical antibiotics therapy. This model should be validated before it is widely applied in clinical settings.
背景/目的：本研究旨在开发一种工具，用于预测胆道感染患者的多重耐药菌感染，以便进行针对性治疗。
我们进行了一项单中心回顾性描述性研究，时间跨度为2016年1月至2018年12月。采用单因素和多因素逻辑回归分析来确定多重耐药菌感染的独立危险因素。根据多因素回归模型构建了列线图。此外，通过决策曲线分析评估了列线图的临床实用性。
121例住院患者被随机分为训练队列（n = 79）和验证队列（n = 42）。在多因素分析中，5个因素与多重耐药菌引起的胆道感染相关：天冬氨酸转氨酶（比值比（OR），13.771；95%置信区间（CI），3.747 - 64.958；P < 0.001）、90天内既往使用抗生素（OR，4.130；95% CI，1.192 - 16.471；P = 0.032）、绝对中性粒细胞计数（OR，3.491；95% CI，1.066 - 12.851；P = 0.046）、既往胆道手术史（OR，3.303；95% CI，0.910 - 13.614；P = 0.079）以及血红蛋白（OR，0.146；95% CI，0.030 - 0.576；P = 0.009）。基于这些变量构建了列线图模型，该模型在训练集[曲线下面积（AUC），0.86]和验证集（AUC，0.799）中显示出良好的校准和区分能力。决策曲线分析证明了我们列线图的临床实用性。使用列线图评分，可以区分多重耐药菌感染的高风险和低风险患者。
这种用于预测多重耐药菌感染的简单床边预测工具可以帮助临床医生识别低风险和高风险患者，并选择合适、及时的初始经验性抗生素治疗。在广泛应用于临床之前，该模型应进行验证。
