{"title":"预测冠心病危重患者急性肾损伤的机器学习:算法开发和验证。","authors":"Yike Li, Mingyang Xiao, Yaqian Li, Lulu Lv, Shanshan Zhang, Yuhui Liu, Juan Zhang","doi":"10.2196/72349","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Acute kidney injury (AKI) frequently occurs in critically ill patients with coronary heart disease (CHD), and its development markedly elevates mortality rates and prolongs hospitalization duration. Early AKI prediction is crucial for timely intervention and amelioration of patient outcomes.</p><p><strong>Objective: </strong>This study aimed to develop and verify a clinical prediction model for the occurrence of AKI upon admission in the critically ill population with CHD through machine learning (ML).</p><p><strong>Methods: </strong>Data from the MIMIC-IV (Medical Information Mart for Intensive Care IV) version 2.2 database were gathered and included information about critically ill individuals with CHD in the intensive care unit (ICU). The dataset was randomized into a training set (70%) and a testing set (30%). Least absolute shrinkage and selection operator (LASSO) regression was used for feature variable selection. ML models, including logistic regression (LR), decision tree (DT), naive Bayes (NB), random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM), were constructed using 13 variables in the training set. The 6 models were compared in the testing set to identify the best-performing model. Subsequently, the model was assessed using calibration curve analysis and decision curve analysis (DCA). External validation was conducted using data from the Second Affiliated Hospital of Zhengzhou University. Ultimately, the predictive model was interpreted via Shapley Additive Explanation (SHAP) values.</p><p><strong>Results: </strong>In total, 2711 patients with CHD admitted to the ICU were selected, with 1809 (66.7%) having AKI. XGBoost exhibited the best performance regarding discrimination (area under the receiver operating characteristic curve [AUROC]=0.765, 95% CI 0.731-0.800), accuracy (0.725), and sensitivity (0.759). External validation using a cohort of 226 patients confirmed the strong generalizability of the XGBoost model (AUROC=0.835, 95% CI 0.782-0.887). Feature importance analyses derived from SHAP values, DT, RF, and XGBoost consistently identified 5 key predictors associated with the development of AKI: mechanical ventilation, use of antiplatelet agents, age, N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels, and acute physiology score III (APSIII).</p><p><strong>Conclusions: </strong>ML models can serve as reliable tools for forecasting AKI in the critically ill population with CHD. 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Early AKI prediction is crucial for timely intervention and amelioration of patient outcomes.</p><p><strong>Objective: </strong>This study aimed to develop and verify a clinical prediction model for the occurrence of AKI upon admission in the critically ill population with CHD through machine learning (ML).</p><p><strong>Methods: </strong>Data from the MIMIC-IV (Medical Information Mart for Intensive Care IV) version 2.2 database were gathered and included information about critically ill individuals with CHD in the intensive care unit (ICU). The dataset was randomized into a training set (70%) and a testing set (30%). Least absolute shrinkage and selection operator (LASSO) regression was used for feature variable selection. ML models, including logistic regression (LR), decision tree (DT), naive Bayes (NB), random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM), were constructed using 13 variables in the training set. The 6 models were compared in the testing set to identify the best-performing model. Subsequently, the model was assessed using calibration curve analysis and decision curve analysis (DCA). External validation was conducted using data from the Second Affiliated Hospital of Zhengzhou University. Ultimately, the predictive model was interpreted via Shapley Additive Explanation (SHAP) values.</p><p><strong>Results: </strong>In total, 2711 patients with CHD admitted to the ICU were selected, with 1809 (66.7%) having AKI. XGBoost exhibited the best performance regarding discrimination (area under the receiver operating characteristic curve [AUROC]=0.765, 95% CI 0.731-0.800), accuracy (0.725), and sensitivity (0.759). External validation using a cohort of 226 patients confirmed the strong generalizability of the XGBoost model (AUROC=0.835, 95% CI 0.782-0.887). Feature importance analyses derived from SHAP values, DT, RF, and XGBoost consistently identified 5 key predictors associated with the development of AKI: mechanical ventilation, use of antiplatelet agents, age, N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels, and acute physiology score III (APSIII).</p><p><strong>Conclusions: </strong>ML models can serve as reliable tools for forecasting AKI in the critically ill population with CHD. 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引用次数: 0
摘要
背景:急性肾损伤(Acute kidney injury, AKI)多发于危重冠心病(CHD)患者,其发展可显著提高死亡率并延长住院时间。早期AKI预测对于及时干预和改善患者预后至关重要。目的:本研究旨在通过机器学习(ML)建立并验证危重冠心病患者入院时AKI发生的临床预测模型。方法:从MIMIC-IV(版本2.2)数据库中收集数据,包括重症监护病房(ICU)危重冠心病患者的信息。数据集随机分为训练集(70%)和测试集(30%)。特征变量选择采用LASSO回归。利用训练集构建了逻辑回归(LR)、决策树(DT)、朴素贝叶斯(NB)、随机森林(RF)、极端梯度增强(XGBoost)和支持向量机(SVM)等机器学习模型。在测试集中对六个模型进行比较,以确定性能最好的模型。通过标定曲线和决策曲线分析(DCA)对模型进行评价。采用郑州大学第二附属医院的数据进行外部验证。最后,通过SHapley加性解释(SHAP)值对预测模型进行解释。结果:入选2711例icu住院冠心病患者,其中1809例(66.7%)有AKI。选取13个变量构建6个ML模型。XGBoost在鉴别(AUC =0.765, 95% CI 0.731-0.800)、准确度(0.725)和灵敏度(0.759)方面表现最佳。226例患者队列的外部验证证实了XGBoost模型的强泛化性(AUC = 0.835, 95% CI 0.782-0.887)。从SHAP值、DT、RF和XGBoost得出的特征重要性分析一致确定了与AKI发展相关的五个关键预测因素:机械通气、抗血小板药物的使用、年龄、n端前b型利钠肽(NT-proBNP)水平和急性生理评分III (APSIII)。结论:ML模型可作为预测冠心病危重患者AKI的可靠工具。XGBoost模型非常准确,可以帮助医生识别高危人群,进行早期干预,以降低死亡率。临床试验:
Machine Learning for the Prediction of Acute Kidney Injury in Critically Ill Patients With Coronary Heart Disease: Algorithm Development and Validation.
Background: Acute kidney injury (AKI) frequently occurs in critically ill patients with coronary heart disease (CHD), and its development markedly elevates mortality rates and prolongs hospitalization duration. Early AKI prediction is crucial for timely intervention and amelioration of patient outcomes.
Objective: This study aimed to develop and verify a clinical prediction model for the occurrence of AKI upon admission in the critically ill population with CHD through machine learning (ML).
Methods: Data from the MIMIC-IV (Medical Information Mart for Intensive Care IV) version 2.2 database were gathered and included information about critically ill individuals with CHD in the intensive care unit (ICU). The dataset was randomized into a training set (70%) and a testing set (30%). Least absolute shrinkage and selection operator (LASSO) regression was used for feature variable selection. ML models, including logistic regression (LR), decision tree (DT), naive Bayes (NB), random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM), were constructed using 13 variables in the training set. The 6 models were compared in the testing set to identify the best-performing model. Subsequently, the model was assessed using calibration curve analysis and decision curve analysis (DCA). External validation was conducted using data from the Second Affiliated Hospital of Zhengzhou University. Ultimately, the predictive model was interpreted via Shapley Additive Explanation (SHAP) values.
Results: In total, 2711 patients with CHD admitted to the ICU were selected, with 1809 (66.7%) having AKI. XGBoost exhibited the best performance regarding discrimination (area under the receiver operating characteristic curve [AUROC]=0.765, 95% CI 0.731-0.800), accuracy (0.725), and sensitivity (0.759). External validation using a cohort of 226 patients confirmed the strong generalizability of the XGBoost model (AUROC=0.835, 95% CI 0.782-0.887). Feature importance analyses derived from SHAP values, DT, RF, and XGBoost consistently identified 5 key predictors associated with the development of AKI: mechanical ventilation, use of antiplatelet agents, age, N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels, and acute physiology score III (APSIII).
Conclusions: ML models can serve as reliable tools for forecasting AKI in the critically ill population with CHD. The XGBoost model is highly accurate and may aid doctors in identifying high-risk individuals for early intervention to lower mortality.
期刊介绍:
JMIR Medical Informatics (JMI, ISSN 2291-9694) is a top-rated, tier A journal which focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals.
Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2016: 5.175), JMIR Med Inform has a slightly different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.