Juan-Salvadores Pablo, Veiga Cesar, Jiménez Díaz Víctor Alfonso, Guitián González Alba, Iglesia Carreño Cristina, Martínez Reglero Cristina, Baz Alonso José Antonio, Caamaño Isorna Francisco, Romo Andrés Iñiguez
Cardiovascular Research Unit, Cardiology Department, Hospital Alvaro Cunqueiro, University Hospital of Vigo, 36213 Vigo, Spain.
Cardiovascular Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain.
Diagnostics (Basel). 2022 Feb 6;12(2):422. doi: 10.3390/diagnostics12020422.
Coronary artery disease is a chronic disease with an increased expression in the elderly. However, different studies have shown an increased incidence in young subjects over the last decades. The prediction of major adverse cardiac events (MACE) in very young patients has a significant impact on medical decision-making following coronary angiography and the selection of treatment. Different approaches have been developed to identify patients at a higher risk of adverse outcomes after their coronary anatomy is known. This is a prognostic study of combined data from patients ≤40 years old undergoing coronary angiography (n = 492). We evaluated whether different machine learning (ML) approaches could predict MACE more effectively than traditional statistical methods using logistic regression (LR). Our most effective model for long-term follow-up (60 ± 27 months) was random forest (RF), obtaining an area under the curve (AUC) = 0.79 (95%CI 0.69-0.88), in contrast with LR, obtaining AUC = 0.66 (95%CI 0.53-0.78, = 0.021). At 1-year follow-up, the RF test found AUC 0.80 (95%CI 0.71-0.89) vs. LR 0.50 (95%CI 0.33-0.66, < 0.001). The results of our study support the hypothesis that ML methods can improve both the identification of MACE risk patients and the prediction vs. traditional statistical techniques even in a small sample size. The application of ML techniques to focus the efforts on the detection of MACE in very young patients after coronary angiography could help tailor upfront follow-up strategies in such young patients according to their risk of MACE and to be used for proper assignment of health resources.
冠状动脉疾病是一种在老年人中表达增加的慢性疾病。然而，不同的研究表明，在过去几十年中，年轻受试者的发病率有所上升。预测非常年轻患者的主要不良心脏事件（MACE）对冠状动脉造影后的医疗决策和治疗选择具有重大影响。在已知冠状动脉解剖结构后，已经开发出不同的方法来识别不良结局风险较高的患者。这是一项对年龄≤40岁接受冠状动脉造影的患者（n = 492）的合并数据进行的预后研究。我们评估了与使用逻辑回归（LR）的传统统计方法相比，不同的机器学习（ML）方法是否能更有效地预测MACE。我们用于长期随访（60±27个月）的最有效模型是随机森林（RF），曲线下面积（AUC）= 0.79（95%CI 0.69 - 0.88），相比之下，LR的AUC = 0.66（95%CI 0.53 - 0.78，P = 0.021）。在1年随访时，RF测试的AUC为0.80（95%CI 0.71 - 0.89），而LR为0.50（95%CI 0.33 - 0.66，P < 0.001）。我们的研究结果支持这样的假设，即即使在小样本量的情况下，ML方法在识别MACE风险患者和预测方面都能优于传统统计技术。将ML技术应用于聚焦冠状动脉造影后非常年轻患者的MACE检测，有助于根据他们的MACE风险为这些年轻患者量身定制前期随访策略，并用于合理分配卫生资源。
