危重症患者米卡芬净相关低镁血症的风险因素分析及预测模型构建

doi:10.5246/jcps.2026.06.041

摘要/Abstract

摘要：

本研究回顾性选取2020年1月至2023年12月南昌大学第一附属医院接受米卡芬净治疗的139例危重症患者, 根据血清镁水平将患者分为低镁血症组(血清镁 < 0.75 mmol/L)与非低镁血症组, 收集两组患者的人口统计学特征、实验室检验指标、合并用药等临床资料, 采用单因素及多因素Logistic回归分析确定米卡芬净相关低镁血症的独立危险因素, 运用R软件rms程序包建立列线图预测模型并进行内部验证。结果显示, 低镁血症发生率为33.8%(47/139)。单因素分析表明, 两组在SOFA评分、米卡芬净疗程、基线血肌酐、基线血清镁及治疗后血肌酐方面均有统计学差异(均P < 0.05)。多因素Logistic回归分析表明米卡芬净疗程(OR = 1.117, 95% CI: 1.056~1.181; P < 0.001)、治疗后白蛋白(OR = 0.889, 95% CI: 0.807~0.979; P = 0.017)及基线血肌酐(OR = 0.992, 95% CI: 0.987~0.997; P = 0.001)是独立危险因素。列线图模型预测效能良好(AUC = 0.807, 95% CI: 0.728~0.886), 敏感度为66.0%, 特异度为87.0%。校准曲线平均绝对误差为0.027, Hosmer-Lemeshow检验显示模型校准度良好(χ² = 13.12, P = 0.11)。本研究提示, 在危重症患者中, 米卡芬净相关低镁血症发生率显著较高, 米卡芬净疗程、治疗后白蛋白及基线血肌酐是米卡芬净相关低镁血症的独立危险因素, 所构建的列线图模型可为该不良事件的预测提供可靠工具。

关键词: 危重症患者, 米卡芬净, 低镁血症, 危险因素, 预测模型

Abstract:

This retrospective study included 139 critically ill patients who received micafungin therapy at the First Affiliated Hospital of Nanchang University between January 2020 and December 2023. Patients were classified into a hypomagnesemia group (serum magnesium < 0.75 mmol/L) and a non-hypomagnesemia group. Demographic characteristics, laboratory data, and concomitant medications were collected. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors. A predictive nomogram was subsequently developed using the rms package in R software and underwent internal validation. The incidence of hypomagnesemia in this cohort was 33.8% (47/139). Univariate analysis revealed significant between-group differences in SOFA score, duration of micafungin therapy, baseline serum creatinine, baseline serum magnesium, and post-treatment serum creatinine (all P < 0.05). Multivariate analysis further identified micafungin treatment duration (OR = 1.117, 95% CI: 1.056–1.181; P < 0.001), post-treatment albumin (OR = 0.889, 95% CI: 0.807–0.979; P = 0.017), and baseline serum creatinine (OR = 0.992, 95% CI: 0.987–0.997; P = 0.001) as independent predictors of hypomagnesemia. The nomogram demonstrated strong discriminatory ability (AUC = 0.807, 95% CI: 0.728–0.886), with corresponding sensitivity and specificity of 66.0% and 87.0%, respectively. The calibration curve showed a mean absolute error of 0.027, and the Hosmer-Lemeshow test confirmed satisfactory model calibration (χ² = 13.12, P = 0.11). Overall, this study revealed a notably high incidence of micafungin-induced hypomagnesemia among critically ill patients. Treatment duration, post-treatment albumin level, and baseline serum creatinine emerged as independent risk factors. The proposed nomogram offered a practical and reliable tool for predicting the risk of micafungin-related hypomagnesemia in the critically ill population.

Key words: Critically ill patients, Micafungin, Hypomagnesemia, Risk factors, Prediction model

Supporting:

张秀莹, 龚明, 钟海利, 杨志华, 宋小玲. 危重症患者米卡芬净相关低镁血症的风险因素分析及预测模型构建[J]. 中国药学(英文版), 2026, 35(6): 574-582.

Xiuying Zhang, Ming Gong, Haili Zhong, Zhihua Yang, Xiaoling Song. Risk factors analysis and prediction model construction of micafungin-induced hypomagnesemia in critically ill patients[J]. Journal of Chinese Pharmaceutical Sciences, 2026, 35(6): 574-582.

图/表 5

Table 1. Univariate analysis of micafungin-induced hypomagnesemia.

Table 2. Results of multivariate logistic regression analysis of micafungin-induced hypomagnesemia.

Figure 1. Nomogram model of micafungin-induced risk of hypomagnesemia.

Figure 2. ROC curve of the nomogram model.

Figure 3. Calibration curves of the nomogram model.

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