Geltser B I, Shahgeldyan K J, Rublev V Y, Kotelnikov V N, Krieger A B, Shirobokov V G
Far Eastern federal university. School of biomedicine. Vladivostok.
Institute of Information Technologies, Vladivostok State University of Economics and Service, Vladivostok.
Kardiologiia. 2020 Nov 12;60(10):38-46. doi: 10.18087/cardio.2020.10.n1170.
Aim To compare the accuracy of predicting an in-hospital fatal outcome for models based on current machine-learning technologies in patients with ischemic heart disease (IHD) after coronary bypass (CB) surgery.Material and methods A retrospective analysis of 866 electronic medical records was performed for patients (685 men and 181 women) who have had a CB surgery for IHD in 2008-2018. Results of clinical, laboratory, and instrumental evaluations obtained prior to the CB surgery were analyzed. Patients were divided into two groups: group 1 included 35 (4 %) patients who died within the first 20 days of CB, and group 2 consisted of 831 (96 %) patients with a beneficial outcome of the surgery. Predictors of the in-hospital fatal outcome were identified by a multistep selection procedure with analysis of statistical hypotheses and calculation of weight coefficients. For construction of models and verification of predictors, machine-learning methods were used, including the multifactorial logistic regression (LR), random forest (RF), and artificial neural networks (ANN). Model accuracy was evaluated by three metrics: area under the ROC curve (AUC), sensitivity, and specificity. Cross validation of the models was performed on test samples, and the control validation was performed on a cohort of patients with IHD after CB, whose data were not used in development of the models.Results The following 7 risk factors for in-hospital fatal outcome with the greatest predictive potential were isolated from the EuroSCORE II scale: ejection fraction (EF) <30 %, EF 30-50 %, age of patients with recent MI, damage of peripheral arterial circulation, urgency of CB, functional class III-IV chronic heart failure, and 5 additional predictors, including heart rate, systolic blood pressure, presence of aortic stenosis, posterior left ventricular (LV) wall relative thickness index (RTI), and LV relative mass index (LVRMI). The models developed by the authors using LR, RF and ANN methods had higher AUC values and sensitivity compared to the classical EuroSCORE II scale. The ANN models including the RTI and LVRMI predictors demonstrated a maximum level of prognostic accuracy, which was illustrated by values of the quality metrics, AUC 93 %, sensitivity 90 %, and specificity 96 %. The predictive robustness of the models was confirmed by results of the control validation.Conclusion The use of current machine-learning technologies allowed developing a novel algorithm for selection of predictors and highly accurate models for predicting an in-hospital fatal outcome after CB.
目的 比较基于当前机器学习技术的模型对冠心病（IHD）患者冠状动脉搭桥（CB）手术后院内死亡结局的预测准确性。
材料与方法 对2008 - 2018年因IHD接受CB手术的患者（685名男性和181名女性）的866份电子病历进行回顾性分析。分析CB手术前获得的临床、实验室和器械评估结果。患者分为两组：第1组包括35名（4%）在CB术后前20天内死亡的患者，第2组由831名（96%）手术结局良好的患者组成。通过多步骤选择程序，分析统计假设并计算权重系数，确定院内死亡结局的预测因素。为构建模型和验证预测因素，使用了机器学习方法，包括多因素逻辑回归（LR）、随机森林（RF）和人工神经网络（ANN）。通过三个指标评估模型准确性：ROC曲线下面积（AUC）、敏感性和特异性。在测试样本上进行模型的交叉验证，并在CB术后IHD患者队列上进行对照验证，该队列患者的数据未用于模型开发。
结果 从欧洲心脏手术风险评估系统（EuroSCORE）II量表中分离出以下7个具有最大预测潜力的院内死亡结局风险因素：射血分数（EF）<30%、EF 30 - 50%、近期心肌梗死患者年龄、外周动脉循环损伤、CB的紧急程度、III - IV级慢性心力衰竭功能分级，以及另外5个预测因素，包括心率、收缩压、主动脉瓣狭窄的存在、左心室后壁相对厚度指数（RTI）和左心室相对质量指数（LVRMI）。作者使用LR、RF和ANN方法开发的模型与经典的EuroSCORE II量表相比，具有更高的AUC值和敏感性。包括RTI和LVRMI预测因素的ANN模型显示出最高水平的预后准确性，质量指标值表明：AUC为93%，敏感性为90%，特异性为96%。对照验证结果证实了模型的预测稳健性。
结论 使用当前机器学习技术能够开发一种用于选择预测因素的新算法以及用于预测CB术后院内死亡结局的高精度模型。
