Zhou Kaihuan, Qin Lian, Chen Yin, Gao Hanming, Ling Yicong, Qin Qianqian, Mou Chenglin, Qin Tao, Lu Junyu
Intensive Care Unit, The Second Affiliated Hospital of Guangxi Medical University, No 166 Daxuedong Road, Nanning, 530007, Guangxi, China.
BMC Infect Dis. 2025 Apr 21;25(1):568. doi: 10.1186/s12879-025-10974-8.
BACKGROUND: Acute respiratory distress syndrome (ARDS) is a severe complication associated with a high mortality rate in patients with sepsis. Early identification of patients with sepsis at high risk of developing ARDS is crucial for timely intervention, optimization of treatment strategies, and improvement of clinical outcomes. However, traditional risk prediction methods are often insufficient. This study aimed to develop a machine learning (ML) model to predict the risk of ARDS in patients with sepsis using circulating immune cell parameters and other physiological data. METHODS: Clinical data from 10,559 patients with sepsis were obtained from the MIMIC-IV database. Principal component analysis (PCA) was used for dimensionality reduction and to comprehensively evaluate the models' predictive capabilities, we used several ML algorithms, including decision trees, k-nearest neighbors (KNN), logistic regression, naive Bayes, random forests, neural networks, XGBoost, and support vector machines (SVM) to predict ARDS risk. The model performance was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and F1 score. Shapley additive explanations (SHAP) were used to interpret the contribution of individual features to model predictions. RESULTS: Among all models, XGBoost showed the best performance with an AUC of 0.764. Feature importance analysis revealed that mean arterial pressure, monocyte count, neutrophil count, pH, and platelet count were key predictors of ARDS risk in patients with sepsis. The SHAP analysis provided further information on how these features contributed to the model's predictions, aiding in interpretability and potential clinical applications. CONCLUSION: The XGBoost model using circulating immune cell parameters accurately predicted the risk of ARDS in patients with sepsis. This model could be a useful tool for the early identification of high-risk patients and timely intervention; however, further validation and integration into clinical practice are required.
背景：急性呼吸窘迫综合征（ARDS）是脓毒症患者的一种严重并发症，死亡率很高。早期识别有发生ARDS高风险的脓毒症患者对于及时干预、优化治疗策略和改善临床结局至关重要。然而，传统的风险预测方法往往不够充分。本研究旨在开发一种机器学习（ML）模型，利用循环免疫细胞参数和其他生理数据预测脓毒症患者发生ARDS的风险。 方法：从MIMIC-IV数据库中获取10559例脓毒症患者的临床数据。主成分分析（PCA）用于降维，为全面评估模型的预测能力，我们使用了几种ML算法，包括决策树、k近邻（KNN）、逻辑回归、朴素贝叶斯、随机森林、神经网络、XGBoost和支持向量机（SVM）来预测ARDS风险。使用受试者工作特征曲线下面积（AUC）、准确性、敏感性、特异性和F1分数评估模型性能。使用夏普利加性解释（SHAP）来解释个体特征对模型预测的贡献。 结果：在所有模型中，XGBoost表现最佳，AUC为0.764。特征重要性分析显示，平均动脉压、单核细胞计数、中性粒细胞计数、pH值和血小板计数是脓毒症患者发生ARDS风险的关键预测因素。SHAP分析提供了关于这些特征如何对模型预测做出贡献的进一步信息，有助于解释和潜在的临床应用。 结论：使用循环免疫细胞参数的XGBoost模型准确预测了脓毒症患者发生ARDS的风险。该模型可能是早期识别高危患者和及时干预的有用工具；然而，需要进一步验证并整合到临床实践中。
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