Le Li, Jingyuan Guan, Xi Peng, Likun Zhou, Zhuxin Zhang, Ligang Ding, Lihui Zheng, Lingmin Wu, Zhicheng Hu, Limin Liu, Yan Yao
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引用次数: 0
摘要
简介败血症相关急性肾损伤(SA-AKI)与预后不良密切相关。我们旨在建立一个基于机器学习(ML)的临床模型,以预测SA-AKI患者的1年死亡率:方法:采用六种 ML 算法进行模型拟合。特征选择基于SHapley Additive exPlanations(SHAP)值评估的特征重要性。接收者操作特征曲线下面积(AUROC)用于评估预测模型的判别能力。校准曲线和布赖尔评分用于评估校准能力。我们对基于 ML 的预测模型进行了内部和外部验证:共纳入了 12,750 例 SA-AKI 患者和 55 个特征来建立预测模型。根据特征的重要性,我们确定了前 10 个预测因子,包括年龄、重症监护室住院时间和 GCS 评分。在六种 ML 算法中,CatBoost 的预测效果最好,AUROC 为 0.813,Brier 得分为 0.119。在外部验证集中,预测值仍然良好(AUROC = 0.784):在这项研究中,我们开发并验证了一个基于 10 个常用临床特征的多模型预测模型,该模型可以准确、早期地识别 SA-AKI 患者中的长期死亡率高危人群。
Machine learning for the prediction of 1-year mortality in patients with sepsis-associated acute kidney injury.
Introduction: Sepsis-associated acute kidney injury (SA-AKI) is strongly associated with poor prognosis. We aimed to build a machine learning (ML)-based clinical model to predict 1-year mortality in patients with SA-AKI.
Methods: Six ML algorithms were included to perform model fitting. Feature selection was based on the feature importance evaluated by the SHapley Additive exPlanations (SHAP) values. Area under the receiver operating characteristic curve (AUROC) was used to evaluate the discriminatory ability of the prediction model. Calibration curve and Brier score were employed to assess the calibrated ability. Our ML-based prediction models were validated both internally and externally.
Results: A total of 12,750 patients with SA-AKI and 55 features were included to build the prediction models. We identified the top 10 predictors including age, ICU stay and GCS score based on the feature importance. Among the six ML algorithms, the CatBoost showed the best prediction performance with an AUROC of 0.813 and Brier score of 0.119. In the external validation set, the predictive value remained favorable (AUROC = 0.784).
Conclusion: In this study, we developed and validated a ML-based prediction model based on 10 commonly used clinical features which could accurately and early identify the individuals at high-risk of long-term mortality in patients with SA-AKI.
期刊介绍:
BMC Medical Informatics and Decision Making is an open access journal publishing original peer-reviewed research articles in relation to the design, development, implementation, use, and evaluation of health information technologies and decision-making for human health.