头孢呋辛致药物性肝损伤的自动监测与预测模型的建立及验证

摘要/Abstract

摘要： 目的探究头孢呋辛致药物性肝损伤(DILI)的危险因素,并构建相关预测模型。方法借助自动监测系统,回顾性筛选合肥京东方医院2019年10月至2020年10月使用注射用头孢呋辛钠治疗的271例患者,采用整群随机分组法将患者分为训练集(n=203)和验证集(n=68),采用单因素和Logistic回归多因素分析训练集头孢呋辛致DILI的危险因素,并建立相关列线图预测模型。结果年龄≥60岁(OR=3.497,95%CI:1.177～10.391)、BMI≥28 kg/m²(OR=3.333,95%CI:1.207～9.202)、酗酒(OR=3.399,95%CI:1.129～10.234)、低蛋白血症(OR=3.272,95%CI:1.088～9.837)和用药剂量＞2.25 g/d(OR=9.045,95%CI:3.397～24.083)是头孢呋辛致DILI的独立危险因素(P<0.05)。基于危险因素建立预测头孢呋辛致DILI的列线图模型,并对该模型进行验证,结果显示训练集和验证集的C-index分别为0.774和0.758,校正曲线均趋近于标准曲线,ROC曲线的AUC分别为0.785(95%CI:0.735~0.834)和0.765(95%CI:0.707~0.779),表明该模型具有良好的预测能力。结论头孢呋辛致DILI的危险因素较多,基于危险因素构建的列线图预警模型能够准确预测头孢呋辛致DILI的风险。

关键词: 头孢呋辛, 药物性肝损伤, 自动监测, 列线图模型, 模型验证

Abstract: Objective To investigate the risk factors of drug-induced liver injury (DILI) induced by cefuroxime, and to construct the related prediction model.Methods Two hundred and seventy-one patients treated with cefuroxime sodium injection in our hospital from October 2019 to October 2020 were retrospectively selected through automatic monitoring system. The patients were divided into training group (n=203) and validation group (n=68) by cluster random grouping method. The risk factors of DILI caused by cefuroxime in training group were analyzed by univariate analysis and multivariate logistic regression analysis. The related nomogram prediction model was also established.Results Age ≥ 60 years (OR=3.497, 95%CI: 1.177~10.391), BMI ≥ 28 kg/m² (OR=3.333, 95%CI: 1.207~9.202), alcoholism (OR=3.399, 95%CI: 1.129~10.234) ), hypoalbuminemia (OR=3.272, 95%CI: 1.088~9.837) and dosage > 2.25 g/d (OR=9.045, 95%CI: 3.397~24.083) were independent risk factors of DILI caused by cefuroxime (P<0.05). A nomogram model was established to predict the DILI induced by cefuroxime based on the risk factors, and the model was also validated. The results showed that the C-index of training group and validation group were 0.774 and 0.758, respectively. The calibration curve was close to the standard curve. The area under the curve (AUC) of training group and validation group were 0.785 (95% CI: 0.0735-0.834) and 0.765 (95% CI: 0.707-0.779), indicating that the model had a good prediction ability.Conclusion There are many risk factors for DILI caused by cefuroxime. The nomogram model based on risk factors can accurately predict the incidence of DILI induced by cefuroxime.

Key words: Cefuroxime, Drug-induced liver injury, Automatic monitoring, Nomogram model, Model validation

惠磊, 李樱宁, 罗晶晶, 吴新安. 头孢呋辛致药物性肝损伤的自动监测与预测模型的建立及验证[J]. 肝脏, 2022, 27(1): 28-32.

HUI Lei, LI Ying-ning, LUO Jing-jing, WU Xin-an. Establishment and validation of an automatic monitoring and prediction model for DILI induced by cefuroxime[J]. Chinese Hepatolgy, 2022, 27(1): 28-32.

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