Identifying the risk factors of ICU-acquired fungal infections: clinical evidence from using machine learning

作者全名：Zhao, Yi-si; Lai, Qing-pei; Tang, Hong; Luo, Ren-jie; He, Zhi-wei; Huang, Wei; Wang, Liu-yang; Zhang, Zheng-tao; Lin, Shi-hui; Qin, Wen-jian; Xu, Fang

作者地址：[Zhao, Yi-si; Tang, Hong; Luo, Ren-jie; He, Zhi-wei; Huang, Wei; Wang, Liu-yang; Zhang, Zheng-tao; Lin, Shi-hui; Xu, Fang] Chongqing Med Univ, Affiliated Hosp 1, Dept Crit Care Med, Chongqing, Peoples R China; [Zhao, Yi-si] Chongqing Med Univ, Med Data Sci Acad, Chongqing, Peoples R China; [Lai, Qing-pei] Univ Chinese Acad Sci, Shenzhen Coll Adv Technol, Shenzhen, Peoples R China; [Lai, Qing-pei; Qin, Wen-jian] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China

通信作者：Xu, F (通讯作者)，Chongqing Med Univ, Affiliated Hosp 1, Dept Crit Care Med, Chongqing, Peoples R China.; Qin, WJ (通讯作者)，Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China.

来源：FRONTIERS IN MEDICINE

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001230369800001

JCR分区：Q1

影响因子：3.1

年份：2024

卷号：11

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：fungal infection; ICU-acquired fungi; machine learning; empiric antifungal therapy; risk factors

摘要：Background Fungal infections are associated with high morbidity and mortality in the intensive care unit (ICU), but their diagnosis is difficult. In this study, machine learning was applied to design and define the predictive model of ICU-acquired fungi (ICU-AF) in the early stage of fungal infections using Random Forest.Objectives This study aimed to provide evidence for the early warning and management of fungal infections.Methods We analyzed the data of patients with culture-positive fungi during their admission to seven ICUs of the First Affiliated Hospital of Chongqing Medical University from January 1, 2015, to December 31, 2019. Patients whose first culture was positive for fungi longer than 48 h after ICU admission were included in the ICU-AF cohort. A predictive model of ICU-AF was obtained using the Least Absolute Shrinkage and Selection Operator and machine learning, and the relationship between the features within the model and the disease severity and mortality of patients was analyzed. Finally, the relationships between the ICU-AF model, antifungal therapy and empirical antifungal therapy were analyzed.Results A total of 1,434 cases were included finally. We used lasso dimensionality reduction for all features and selected six features with importance >= 0.05 in the optimal model, namely, times of arterial catheter, enteral nutrition, corticosteroids, broadspectrum antibiotics, urinary catheter, and invasive mechanical ventilation. The area under the curve of the model for predicting ICU-AF was 0.981 in the test set, with a sensitivity of 0.960 and specificity of 0.990. The times of arterial catheter (p = 0.011, OR = 1.057, 95% CI = 1.053-1.104) and invasive mechanical ventilation (p = 0.007, OR = 1.056, 95%CI = 1.015-1.098) were independent risk factors for antifungal therapy in ICU-AF. The times of arterial catheter (p = 0.004, OR = 1.098, 95%CI = 0.855-0.970) were an independent risk factor for empirical antifungal therapy.Conclusion The most important risk factors for ICU-AF are the six time-related features of clinical parameters (arterial catheter, enteral nutrition, corticosteroids, broadspectrum antibiotics, urinary catheter, and invasive mechanical ventilation), which provide early warning for the occurrence of fungal infection. Furthermore, this model can help ICU physicians to assess whether empiric antifungal therapy should be administered to ICU patients who are susceptible to fungal infections.

基金机构：Chongqing Medical University [YJSZHYX202222]; Innovation Project for Doctoral Students at the First Affiliated Hospital of Chongqing Medical University; Chongqing medical scientific research project (Joint project of Chongqing Health Commission and Science and Technology Bureau) [lpjd202001]; Clinical Medicine Postgraduate Joint Training Base of Chongqing Medical University-the First Affiliated Hospital of Chongqing Medical University [cstc2022ycjh-bgzxm0131]; Project of Chongqing talents [CYYY-BSYJSCXXM-202209]; [2023ZDXM004]

基金资助正文：The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was funded by 2022 Chongqing Medical University Graduate Smart Medicine Special Research and Development Plan (Project Number: YJSZHYX202222 to Y-sZ), Innovation Project for Doctoral Students at the First Affiliated Hospital of Chongqing Medical University (CYYY-BSYJSCXXM-202209 to Y-sZ), Chongqing medical scientific research project (Joint project of Chongqing Health Commission and Science and Technology Bureau, 2023ZDXM004 to FX), Clinical Medicine Postgraduate Joint Training Base of Chongqing Medical University-the First Affiliated Hospital of Chongqing Medical University (lpjd202001 to FX), The project of Chongqing talents (cstc2022ycjh-bgzxm0131 to FX).