Department of Cardiac Surgery, Klinikum Nuremberg, Paracelsus Medical University, Nuremberg, Germany.
Medical School, University of Ljubljana, Ljubljana, Slovenia.
Eur J Cardiothorac Surg. 2022 Oct 4;62(5). doi: 10.1093/ejcts/ezac289.
This study aims to improve the early detection of cardiac surgery-associated acute kidney injury using artificial intelligence-based algorithms.
Data from consecutive patients undergoing cardiac surgery between 2008 and 2018 in our institution served as the source for artificial intelligence-based modelling. Cardiac surgery-associated acute kidney injury was defined according to the Kidney Disease Improving Global Outcomes criteria. Different machine learning algorithms were trained and validated to detect cardiac surgery-associated acute kidney injury within 12 h after surgery. Demographic characteristics, comorbidities, preoperative cardiac status and intra- and postoperative variables including creatinine and haemoglobin values were retrieved for analysis.
From 7507 patients analysed, 1699 patients (22.6%) developed cardiac surgery-associated acute kidney injury. The ultimate detection model, 'Detect-A(K)I', recognizes cardiac surgery-associated acute kidney injury within 12 h with an area under the curve of 88.0%, sensitivity of 78.0%, specificity of 78.9% and accuracy of 82.1%. The optimal parameter set includes serial changes of creatinine and haemoglobin, operative emergency, bleeding-associated variables, cardiac ischaemic time and cardiac function-associated variables, age, diuretics and active infection, chronic obstructive lung and peripheral vascular disease.
The 'Detect-A(K)I' model successfully detects cardiac surgery-associated acute kidney injury within 12 h after surgery with the best discriminatory characteristics reported so far.
本研究旨在利用基于人工智能的算法提高心脏手术后急性肾损伤的早期检测。
本研究使用我院 2008 年至 2018 年间连续接受心脏手术的患者数据进行基于人工智能的建模。根据肾脏病改善全球结局标准定义心脏手术后急性肾损伤。训练和验证了不同的机器学习算法，以在手术后 12 小时内检测心脏手术后急性肾损伤。检索了人口统计学特征、合并症、术前心脏状况以及包括肌酐和血红蛋白值在内的术中及术后变量进行分析。
在分析的 7507 名患者中，1699 名（22.6%）患者发生了心脏手术后急性肾损伤。最终的检测模型“Detect-A(K)I”在 12 小时内识别出心脏手术后急性肾损伤，曲线下面积为 88.0%，敏感性为 78.0%，特异性为 78.9%，准确性为 82.1%。最佳参数集包括肌酐和血红蛋白的连续变化、手术紧急情况、与出血相关的变量、心脏缺血时间和与心脏功能相关的变量、年龄、利尿剂和活动性感染、慢性阻塞性肺病和外周血管疾病。
“Detect-A(K)I”模型成功地在手术后 12 小时内检测到心脏手术后急性肾损伤，具有迄今为止报道的最佳鉴别特征。
