School of Informatics, Communications and Media, University of Applied Sciences Upper Austria, Hagenberg, Austria.
Institute for Machine Learning, Johannes Kepler University, Linz, Austria.
Eur J Cardiothorac Surg. 2021 Dec 1;60(6):1378-1385. doi: 10.1093/ejcts/ezab219.
Machine learning methods potentially provide a highly accurate and detailed assessment of expected individual patient risk before elective cardiac surgery. Correct anticipation of this risk allows for the improved counselling of patients and avoidance of possible complications. We therefore investigated the benefit of modern machine learning methods in personalized risk prediction for patients undergoing elective heart valve surgery.
We performed a monocentric retrospective study in patients who underwent elective heart valve surgery between 1 January 2008 and 31 December 2014 at our centre. We used random forests, artificial neural networks and support vector machines to predict the 30-day mortality from a subset of 129 available demographic and preoperative parameters. Exclusion criteria were reoperation of the same patient, patients who needed anterograde cerebral perfusion due to aortic arch surgery and patients with grown-up congenital heart disease. Finally, the cohort consisted of 2229 patients with a 30-day mortality of 3.86% (86 of 2229 cases). This trial has been registered at clinicaltrials.gov (NCT03724123).
The final random forest model trained on the entire data set provided an out-of-bag area under the receiver operator characteristics curve (AUC) of 0.839, which significantly outperformed the European System for Cardiac Operative Risk Evaluation (EuroSCORE) (AUC = 0.704) and a model trained only on the subset of features EuroSCORE uses (AUC = 0.745).
Advanced machine learning methods can predict outcomes of valve surgery procedures with higher accuracy than established risk scores based on logistic regression on pre-selected parameters. This approach is generalizable to other elective high-risk interventions and allows for training models to the cohorts of specific institutions.
机器学习方法有可能在择期心脏手术前对患者的个体预期风险进行高度准确和详细的评估。正确预测这种风险可以改善对患者的咨询并避免可能的并发症。因此，我们研究了现代机器学习方法在预测择期心脏瓣膜手术患者风险方面的优势。
我们进行了一项单中心回顾性研究，纳入了 2008 年 1 月 1 日至 2014 年 12 月 31 日期间在我院接受择期心脏瓣膜手术的患者。我们使用随机森林、人工神经网络和支持向量机，根据 129 个可用的人口统计学和术前参数子集来预测 30 天死亡率。排除标准为同一患者再次手术、因主动脉弓手术需要顺行性脑灌注以及患有成人先天性心脏病的患者。最终，该队列包括 2229 名患者，其中 30 天死亡率为 3.86%（2229 例中有 86 例）。本试验已在 clinicaltrials.gov 注册（NCT03724123）。
最终在整个数据集上训练的随机森林模型提供了 0.839 的袋外接收者操作特征曲线下面积（AUC），明显优于欧洲心脏手术风险评估系统（EuroSCORE）（AUC=0.704）和仅基于 EuroSCORE 使用的特征子集训练的模型（AUC=0.745）。
与基于逻辑回归和预选择参数的既定风险评分相比，先进的机器学习方法可以更准确地预测瓣膜手术的结果。这种方法可以推广到其他择期高危干预措施，并允许针对特定机构的队列进行模型训练。
