基于AFS策略导向的危重症患者抗真菌药物合理使用的决策模型构建与实证评价

现有抗真菌药物虽能治疗侵袭性念珠菌病（IC），但频发的不合理用药问题使IC在重症监护室（ICU）患者中仍具有较高的发病率和死亡率。抗真菌药物管理（AFS）在改善不合理用药上发挥了重要作用。如何基于AFS策略制定可量化的决策模型指导ICU合理规范使用抗真菌药物仍待研究。本项目拟采用时间序列分析模型（ARIMA及传递函数模型）和Logistic、COX回归模型建立耐药菌及不良反应高风险ICU患者预警模型，对患者进行用药前危险分级从而保障用药有效安全；采用指南评价、网状Meta分析以及药物经济学分析等循证医学手段，结合多中心大样本患者数据构建ICU患者抗真菌药物合理使用的列线图决策模型；最后通过间断性时间序列分析验证决策模型在医疗机构中的实际获益。本项目可为指导临床ICU合理使用抗真菌药物提供证据支持和决策方法，以期促进我国AFS管理工作。

Although existing antifungal agents can treat invasive candidiasis (IC), frequent irrational antifungal drug use is still associated with considerable morbidity and with mortality in intensive care unit (ICU) patients. Antifungal stewardship (AFS) program plays an important role in improving irrational drug use. It remains to be explored how to develop a quantifiable a decision-making model based on AFS strategy to guide the rational and standardized use of antifungal agents in ICU. In this project, time-series analysis model (ARIMA and transfer function model), Logistic and COX regression models were used to establish the early warning model for high-risk ICU patients with drug-resistant fungi and adverse events, grading the pre-medication risk of patients thereby ensure the efficacy and safety of antifungals. By means of evidence-based medicine methods such as guideline evaluation, network meta-analysis and pharmacoeconomics, combining multi-center and large-sample real world patient data, a nomogram decision-making model for rational use of antifungal agents in critically ill patients was established. interrupted time-series analysis is used to analyze the real data of medical institutions so as to verify the decision-making model. This project can provide evidence support and decision-making methods for guiding the rational use of antifungal agents in ICU patients in order to promote the AFS in China.