Data_Sheet_1_Identifying the risk factors of ICU-acquired fungal infections: clinical evidence from using machine learning.docx

BackgroundFungal 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.ObjectivesThis study aimed to provide evidence for the early warning and management of fungal infections.MethodsWe 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.ResultsA 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.ConclusionThe 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.

背景真菌感染与重症监护病房（ICU）中高发病率和高死亡率密切相关，但其诊断较为困难。本研究通过应用机器学习技术，设计并构建了基于随机森林算法的ICU获得性真菌感染（ICU-AF）早期预测模型。目标旨在为真菌感染的早期预警和管理提供证据。方法上，我们分析了重庆医科大学附属第一医院七家ICU自2015年1月1日至2019年12月31日期间住院患者的真菌培养阳性数据。将ICU入院后首次真菌培养结果阳性且持续时间超过48小时的病例纳入ICU-AF队列。采用最小绝对收缩和选择算子与机器学习相结合的方法构建了ICU-AF预测模型，并分析了模型内部特征与患者疾病严重程度及死亡率之间的关系。最后，对ICU-AF模型、抗真菌治疗以及经验性抗真菌治疗之间的关系进行了分析。结果最终纳入病例共1,434例。我们对所有特征进行了lasso降维处理，并在最优模型中选取了重要性≥0.05的六个特征，即动脉导管置入次数、肠内营养、皮质类固醇、广谱抗生素、导尿管和有创机械通气。预测ICU-AF的模型在测试集上的曲线下面积为0.981，敏感性为0.960，特异性为0.990。动脉导管置入次数（p=0.011，OR=1.057，95%CI=1.053–1.104）和有创机械通气（p=0.007，OR=1.056，95%CI=1.015–1.098）是ICU-AF抗真菌治疗的独立风险因素。动脉导管置入次数（p=0.004，OR=1.098，95%CI=0.855–0.970）是经验性抗真菌治疗的独立风险因素。结论ICU-AF最重要的风险因素是六个与时间相关的临床参数特征（动脉导管、肠内营养、皮质类固醇、广谱抗生素、导尿管和有创机械通气），这些特征为真菌感染的发生提供了早期预警。此外，该模型有助于ICU医师评估是否应对易感于真菌感染的ICU患者实施经验性抗真菌治疗。