Emergency Department, The Second Hospital of Hebei Medical University, Shijiazhuang, China.
Department of Critical Care Medicine, the First Medical Centre, Chinese PLA General Hospital, Beijing, China.
Shock. 2024 Jun 1;61(6):817-827. doi: 10.1097/SHK.0000000000002312. Epub 2024 Feb 20.
The delayed diagnosis of invasive fungal infection (IFI) is highly correlated with poor prognosis in patients. Early identification of high-risk patients with invasive fungal infections and timely implementation of targeted measures is beneficial for patients. The objective of this study was to develop a machine learning-based predictive model for invasive fungal infection in patients during their intensive care unit (ICU) stay. Retrospective data was extracted from adult patients in the MIMIC-IV database who spent a minimum of 48 h in the ICU. Feature selection was performed using LASSO regression, and the dataset was balanced using the BL-SMOTE approach. Predictive models were built using six machine learning algorithms. The Shapley additive explanation algorithm was used to assess the impact of various clinical features in the optimal model, enhancing interpretability. The study included 26,346 ICU patients, of whom 379 (1.44%) were diagnosed with invasive fungal infection. The predictive model was developed using 20 risk factors, and the dataset was balanced using the borderline-SMOTE (BL-SMOTE) algorithm. The BL-SMOTE random forest model demonstrated the highest predictive performance (area under curve = 0.88, 95% CI = 0.84-0.91). Shapley additive explanation analysis revealed that the three most influential clinical features in the BL-SMOTE random forest model were dialysis treatment, APSIII scores, and liver disease. The machine learning model provides a reliable tool for predicting the occurrence of IFI in ICU patients. The BL-SMOTE random forest model, based on 20 risk factors, exhibited superior predictive performance and can assist clinicians in early assessment of IFI occurrence in ICU patients. Importance: Invasive fungal infections are characterized by high incidence and high mortality rates characteristics. In this study, we developed a clinical prediction model for invasive fungal infections in critically ill patients based on machine learning algorithms. The results show that the machine learning model based on 20 clinical features has good predictive value.
侵袭性真菌感染（IFI）的延迟诊断与患者预后不良高度相关。早期识别有侵袭性真菌感染风险的高危患者，并及时实施针对性措施，有利于患者。本研究旨在建立一种基于机器学习的重症监护病房（ICU）住院患者侵袭性真菌感染预测模型。从 MIMIC-IV 数据库中提取至少在 ICU 住院 48 小时的成年患者的回顾性数据。使用 LASSO 回归进行特征选择，并使用 BL-SMOTE 方法平衡数据集。使用六种机器学习算法构建预测模型。使用 Shapley 加法解释算法评估最佳模型中各种临床特征的影响，增强可解释性。该研究纳入了 26346 例 ICU 患者，其中 379 例（1.44%）被诊断为侵袭性真菌感染。预测模型使用 20 个危险因素构建，使用边界-SMOTE（BL-SMOTE）算法平衡数据集。BL-SMOTE 随机森林模型表现出最高的预测性能（曲线下面积=0.88，95%置信区间=0.84-0.91）。Shapley 加法解释分析显示，BL-SMOTE 随机森林模型中三个最具影响力的临床特征是透析治疗、APSIII 评分和肝病。机器学习模型为预测 ICU 患者IFI 的发生提供了可靠的工具。基于 20 个危险因素的 BL-SMOTE 随机森林模型具有优越的预测性能，可帮助临床医生早期评估 ICU 患者IFI 的发生。重要性：侵袭性真菌感染具有发病率高和死亡率高的特点。在这项研究中，我们基于机器学习算法开发了一种用于重症监护病房患者侵袭性真菌感染的临床预测模型。结果表明，基于 20 个临床特征的机器学习模型具有良好的预测价值。
