Machine learning for dynamic and early prediction of acute kidney injury after cardiac surgery.
Machine learning for dynamic and early prediction of acute kidney injury after cardiac surgery.
复制标题
机器学习用于心脏手术后急性肾损伤的动态和早期预测。
DOI：
10.1016/j.jtcvs.2022.09.045
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Ghanta,RaviK
中科院分区：
文献类型：
--
作者：
Ryan,ChristopherT;Zeng,Zijian;Chatterjee,Subhasis;Wall,MatthewJ;Moon,MarcR;Coselli,JosephS;Rosengart,ToddK;Li,Meng;Ghanta,RaviK
心脏手术后急性肾损伤增加发病率和死亡率。诊断依赖于损伤后48至72小时出现的少尿或血清肌酐升高。我们假设机器学习结合术前，手术和重症监护室的数据可以动态预测急性肾损伤之前，传统的identifiation.MethodsCardiac手术患者在三级医院（2008-2019），确定使用电子病历中的医疗信息集市重症监护IV数据库。术前和术中参数包括人口统计学、Charlson合并症亚类和手术详情。重症监护室数据包括血流动力学、药物、液体摄入/输出和实验室结果。肾脏疾病：使用改善全球结局肌酐标准进行急性肾损伤诊断。训练集成机器学习模型，以在48小时内每小时预测未来的急性肾损伤。性能进行了评估，根据接收器工作特征曲线和平衡accuracy.ResultsWithin队列（n = 4267），有大约7万个数据点。中位基线肌酐为1.0 g/dL（四分位距，0.8-1.2），17%（735/4267）的患者患有慢性肾脏疾病。术后1期急性肾损伤发生率为50%（2129/4267），2期发生率为8%（324/4267），3期发生率为4%（183/4267）。对于未来48小时内任何急性肾损伤的每小时预测，受试者工作特征曲线下面积为0.82，平衡准确度为75%。对于在接下来的48小时内每小时预测2期或更高的急性肾损伤，受试者工作特征曲线下面积为0.95，平衡准确度为86%。该模型预测急性肾损伤的临床检测之前，在89%的cases. ConclusionsEnseminated机器学习模型使用电子病历数据可以动态预测心脏手术后急性肾损伤的风险。持续的术后风险评估有助于限制或预防肾损伤的干预措施。
ObjectiveAcute kidney injury after cardiac surgery increases morbidity and mortality. Diagnosis relies on oliguria or increased serum creatinine, which develop 48 to 72 hours after injury. We hypothesized machine learning incorporating preoperative, operative, and intensive care unit data could dynamically predict acute kidney injury before conventional identification.MethodsCardiac surgery patients at a tertiary hospital (2008-2019) were identified using electronic medical records in the Medical Information Mart for Intensive Care IV database. Preoperative and intraoperative parameters included demographics, Charlson Comorbidity subcategories, and operative details. Intensive care unit data included hemodynamics, medications, fluid intake/output, and laboratory results. Kidney Disease: Improving Global Outcomes creatinine criteria were used for acute kidney injury diagnosis. An ensemble machine learning model was trained for hourly predictions of future acute kidney injury within 48 hours. Performance was evaluated by area under the receiver operating characteristic curve and balanced accuracy.ResultsWithin the cohort (n = 4267), there were approximately 7 million data points. Median baseline creatinine was 1.0 g/dL (interquartile range, 0.8-1.2), with 17% (735/4267) of patients having chronic kidney disease. Postoperative stage 1 acute kidney injury occurred in 50% (2129/4267), stage 2 occurred in 8% (324/4267), and stage 3 occurred in 4% (183/4267). For hourly prediction of any acute kidney injury over the next 48 hours, area under the receiver operating characteristic curve was 0.82, and balanced accuracy was 75%. For hourly prediction of stage 2 or greater acute kidney injury over the next 48 hours, area under the receiver operating characteristic curve was 0.95 and balanced accuracy was 86%. The model predicted acute kidney injury before clinical detection in 89% of cases.ConclusionsEnsemble machine learning models using electronic medical records data can dynamically predict acute kidney injury risk after cardiac surgery. Continuous postoperative risk assessment could facilitate interventions to limit or prevent renal injury.
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影响因子：
3.7
作者：
S. Fienberg;O. D. Duncan
通讯作者：
O. D. Duncan
DOI：
10.1016/0013-4694(81)90221-2
发表时间：
1981-01-01
期刊：
ELECTROENCEPHALOGRAPHY AND CLINICAL NEUROPHYSIOLOGY
影响因子：
--
作者：
DUFFY, FH;BARTELS, PH;BURCHFIEL, JL
通讯作者：
BURCHFIEL, JL
临床和实验用途的谚语测试
影响因子：
2.3
作者：
D. R. Gorham
通讯作者：
D. R. Gorham
计算机断层扫描测量中与年龄相关的差异。
影响因子：
--
作者：
Stafford,JL;Albert,MS;Naeser,MA;Sandor,T;Garvey,AJ
通讯作者：
Garvey,AJ