PDF(Pubmed)
Abstract：
UNASSIGNED: To derive and validate a machine learning (ML) prediction model of acute kidney injury (AKI) that could be used for AKI surveillance and management to improve clinical outcomes.
UNASSIGNED: This retrospective cohort study was conducted in Fuwai Hospital, including patients aged 18 years and above undergoing cardiac surgery admitted between January 1, 2017, and December 31, 2018. Seventy percent of the observations were randomly selected for training and the remaining 30% for testing. The demographics, comorbidities, laboratory examination parameters, and operation details were used to construct a prediction model for AKI by logistic regression and eXtreme gradient boosting (Xgboost). The discrimination of each model was assessed on the test cohort by the area under the receiver operator characteristic (AUROC) curve, while calibration was performed by the calibration plot.
UNASSIGNED: A total of 15,880 patients were enrolled in this study, and 4845 (30.5%) had developed AKI. Xgboost model had the higher discriminative ability compared with logistic regression (AUROC, 0.849 [95% CI, 0.837-0.861] vs 0.803[95% CI 0.790-0.817], P<0.001) in the test dataset. The estimated glomerular filtration (eGFR) and creatine on intensive care unit (ICU) arrival are the two most important prediction parameters. A SHAP summary plot was used to illustrate the effects of the top 15 features attributed to the Xgboost model.
UNASSIGNED: ML models can provide clinical decision support to determine which patients should focus on perioperative preventive treatment to preemptively reduce acute kidney injury by predicting which patients are not at risk.
UNASSIGNED: This retrospective cohort study was conducted in Fuwai Hospital, including patients aged 18 years and above undergoing cardiac surgery admitted between January 1, 2017, and December 31, 2018. Seventy percent of the observations were randomly selected for training and the remaining 30% for testing. The demographics, comorbidities, laboratory examination parameters, and operation details were used to construct a prediction model for AKI by logistic regression and eXtreme gradient boosting (Xgboost). The discrimination of each model was assessed on the test cohort by the area under the receiver operator characteristic (AUROC) curve, while calibration was performed by the calibration plot.
UNASSIGNED: A total of 15,880 patients were enrolled in this study, and 4845 (30.5%) had developed AKI. Xgboost model had the higher discriminative ability compared with logistic regression (AUROC, 0.849 [95% CI, 0.837-0.861] vs 0.803[95% CI 0.790-0.817], P<0.001) in the test dataset. The estimated glomerular filtration (eGFR) and creatine on intensive care unit (ICU) arrival are the two most important prediction parameters. A SHAP summary plot was used to illustrate the effects of the top 15 features attributed to the Xgboost model.
UNASSIGNED: ML models can provide clinical decision support to determine which patients should focus on perioperative preventive treatment to preemptively reduce acute kidney injury by predicting which patients are not at risk.
摘要：
■获取并验证可用于AKI监测和管理以改善临床结果的急性肾损伤（AKI）的机器学习（ML）预测模型。
■这项回顾性队列研究在阜外医院进行，包括2017年1月1日至2018年12月31日期间收治的18岁及以上接受心脏手术的患者.随机选择70％的观察结果进行训练，其余30％进行测试。人口统计，合并症,实验室检查参数，和操作细节用于通过逻辑回归和极限梯度增强（Xgboost）构建AKI的预测模型。通过受试者操作特征（AUROC）曲线下的面积在测试队列上评估每个模型的区别性，同时通过校准图进行校准。
■本研究共纳入15880名患者，4845例(30.5%)发生AKI。与逻辑回归相比，Xgboost模型具有更高的判别能力(AUROC，0.849[95%CI，0.837-0.861]vs0.803[95%CI0.790-0.817]，P<0.001)在测试数据集中。估计的肾小球滤过率(eGFR)和肌酸在重症监护病房(ICU)的到达是两个最重要的预测参数。SHAP摘要图用于说明归因于Xgboost模型的前15个特征的效果。
■ML模型可以提供临床决策支持，以确定哪些患者应专注于围手术期预防性治疗，以通过预测哪些患者没有风险来先发制人地减少急性肾损伤。
■这项回顾性队列研究在阜外医院进行，包括2017年1月1日至2018年12月31日期间收治的18岁及以上接受心脏手术的患者.随机选择70％的观察结果进行训练，其余30％进行测试。人口统计，合并症,实验室检查参数，和操作细节用于通过逻辑回归和极限梯度增强（Xgboost）构建AKI的预测模型。通过受试者操作特征（AUROC）曲线下的面积在测试队列上评估每个模型的区别性，同时通过校准图进行校准。
■本研究共纳入15880名患者，4845例(30.5%)发生AKI。与逻辑回归相比，Xgboost模型具有更高的判别能力(AUROC，0.849[95%CI，0.837-0.861]vs0.803[95%CI0.790-0.817]，P<0.001)在测试数据集中。估计的肾小球滤过率(eGFR)和肌酸在重症监护病房(ICU)的到达是两个最重要的预测参数。SHAP摘要图用于说明归因于Xgboost模型的前15个特征的效果。
■ML模型可以提供临床决策支持，以确定哪些患者应专注于围手术期预防性治疗，以通过预测哪些患者没有风险来先发制人地减少急性肾损伤。
