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基于机器学习的心脏手术后急性肾损伤预测

Prediction of acute kidney injury following cardiac surgery by machine learning

    摘要
  • 摘要:
      背景  心脏手术相关的急性肾损伤(cardiac surgery-associated acute kidney injury，CSA-AKI)是心脏手术后的主要并发症之一，其对患者的近期和远期生存率都有负面影响。
      目的  开发基于机器学习技术的预测模型，以此识别心脏手术中CSA-AKI的高危患者。
      方法  选取2017年1月1日- 2018年6月1日于解放军总医院第一医学中心心血管外科行心脏手术的638例患者的临床资料，包括人口学特征、合并症、术前用药、实验室检查结果和手术相关数据等78个变量。采用支持向量机(support vector machine，SVM)、决策树(decision tree，DT)和随机森林(random foresst，RF)这3个机器学习算法构建CSA-AKI的预测模型。通过受试者工作特征(receiver operating characteristic，ROC)曲线的曲线下面积(area under the curve，AUC)和决策曲线分析(decision curve analysis，DCA)评估以上预测模型的性能。沙普利可加性特征解释方法(shapley additive explanation，SHAP)用于预测模型的可视化。
      结果  在纳入分析的638例患者中，188例(29.5%)在术后第1周发生CSA-AKI。在3种机器学习算法中，RF模型在性能指标AUC和DCA方面表现最好，其敏感度为0.784，特异性为0.934，准确率为0.927，AUC为0.890(95% CI：0.762 ~ 1.000)，高于DT模型和SVM模型。SHAP图可视化了RF模型在个体水平推断CSA-AKI的风险。在RF变量重要性矩阵图中，排名前10的变量依次为肌酐清除率、血红蛋白、手术时间、射血分数、术中尿量、左心房直径、手术权重、血清肌酐、术中失血量和体外循环时间。
      结论  本研究成功建立了用于预测CSA-AKI高危患者的机器学习方法，可供临床医生参考并优化治疗策略以减少术后并发症。

     

    Abstract:
      Background  Cardiac surgery-associated acute kidney injury (CSA-AKI) is one of the major complications after cardiac surgery, and its course has a negative impact on the short- and long-term survival of patients.
      Objective  To develop predictive models based on machine learning techniques, so as to identify patients at high risk for CSA-AKI after cardiac surgery.
      Methods  A total of 638 patients who underwent cardiac surgery in the First Medical Center of Chinese PLA General Hospital from January 1, 2017 to June 1, 2018 were enrolled in the study. Totally, 78 variables including demographic characteristics, complications, preoperative medication, laboratory test results, and operation-related data were included in the analysis for modelling. In this study, the support vector machine (SVM), decision tree (DT) and random forest (RF) were used to develop the prediction model of CSA-AKI. The performance of the above predictive models was evaluated by area under the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Shapley additive exPlanation (SHAP) was used for model visualization.
      Results  Of the 638 patients included in the analysis, 188 cases (29.5%) developed CSA-AKI in the first week after surgery. Among the three machine learning algorithms, RF model achieved the best performance in AUC and DCA, with sensitivity of 0.784, specificity of 0.934, accuracy of 0.927 and AUC value of 0.890 (95% CI: 0.762-1.000), which were higher than those of DT model and SVM model. The SHAP plots visualized the risk of developing CSA-AKI at the individual level. In the importance matrix of RF model, the top 10 variables were as follows: creatinine clearance, hemoglobin, operation time, ejection fraction, intraoperative urine output, left atrial diameter, surgical weight of the intervention, serum creatinine, intraoperative blood loss, and cardiopulmonary bypass time.
      Conclusion  This study successfully establishes machine learning models for predicting patients at high-risk for CSA-AKI, thus enabling clinicians to optimize treatment strategies and minimize postoperative complications.

     

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