Clinical Research Service Center, The Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524001, Guangdong Province, China.
Collaborative Innovation Engineering Technology Research Center of Clinical Medical Big Data Cloud Service in Medical Consortium of West Guangdong Province, The Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524001, Guangdong Province, China.
J Transl Med. 2022 May 13;20(1):215. doi: 10.1186/s12967-022-03364-0.
Acute kidney injury (AKI) is the most common and serious complication of sepsis, accompanied by high mortality and disease burden. The early prediction of AKI is critical for timely intervention and ultimately improves prognosis. This study aims to establish and validate predictive models based on novel machine learning (ML) algorithms for AKI in critically ill patients with sepsis.
Data of patients with sepsis were extracted from the Medical Information Mart for Intensive Care III (MIMIC- III) database. Feature selection was performed using a Boruta algorithm. ML algorithms such as logistic regression (LR), k-nearest neighbors (KNN), support vector machine (SVM), decision tree, random forest, Extreme Gradient Boosting (XGBoost), and artificial neural network (ANN) were applied for model construction by utilizing tenfold cross-validation. The performances of these models were assessed in terms of discrimination, calibration, and clinical application. Moreover, the discrimination of ML-based models was compared with those of Sequential Organ Failure Assessment (SOFA) and the customized Simplified Acute Physiology Score (SAPS) II model.
A total of 3176 critically ill patients with sepsis were included for analysis, of which 2397 cases (75.5%) developed AKI during hospitalization. A total of 36 variables were selected for model construction. The models of LR, KNN, SVM, decision tree, random forest, ANN, XGBoost, SOFA and SAPS II score were established and obtained area under the receiver operating characteristic curves of 0.7365, 0.6637, 0.7353, 0.7492, 0.7787, 0.7547, 0.821, 0.6457 and 0.7015, respectively. The XGBoost model had the best predictive performance in terms of discrimination, calibration, and clinical application among all models.
The ML models can be reliable tools for predicting AKI in septic patients. The XGBoost model has the best predictive performance, which can be used to assist clinicians in identifying high-risk patients and implementing early interventions to reduce mortality.
急性肾损伤（AKI）是脓毒症最常见和最严重的并发症，伴有高死亡率和疾病负担。AKI 的早期预测对于及时干预至关重要，最终改善预后。本研究旨在建立和验证基于新型机器学习（ML）算法的预测模型，用于预测脓毒症重症患者的 AKI。
从 Medical Information Mart for Intensive Care III（MIMIC-III）数据库中提取脓毒症患者的数据。使用 Boruta 算法进行特征选择。应用逻辑回归（LR）、k-最近邻（KNN）、支持向量机（SVM）、决策树、随机森林、极端梯度提升（XGBoost）和人工神经网络（ANN）等 ML 算法，通过十折交叉验证构建模型。通过判别、校准和临床应用评估这些模型的性能。此外，还比较了基于 ML 的模型与序贯器官衰竭评估（SOFA）和定制简化急性生理学评分（SAPS）II 模型的判别能力。
共纳入 3176 例脓毒症重症患者进行分析，其中 2397 例（75.5%）在住院期间发生 AKI。共选择了 36 个变量用于模型构建。建立了 LR、KNN、SVM、决策树、随机森林、ANN、XGBoost、SOFA 和 SAPS II 评分模型，获得了 0.7365、0.6637、0.7353、0.7492、0.7787、0.7547、0.821、0.6457 和 0.7015 的受试者工作特征曲线下面积。在所有模型中，XGBoost 模型在判别、校准和临床应用方面具有最佳的预测性能。
ML 模型可以成为预测脓毒症患者 AKI 的可靠工具。XGBoost 模型具有最佳的预测性能，可用于协助临床医生识别高危患者并实施早期干预，以降低死亡率。
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