Erdogan Aslan, Genc Omer, Inan Duygu, Yildirim Abdullah, Ibisoglu Ersin, Guler Yeliz, Genc Duygu, Guler Ahmet, Karagoz Ali, Kurt Ibrahim Halil, Kirma Cevat
Department of Cardiology, Basaksehir Cam and Sakura City Hospital, Istanbul, Türkiye.
Department of Cardiology, University of Health Sciences Türkiye, Adana City Training and Research Hospital, Adana, Türkiye.
Sisli Etfal Hastan Tip Bul. 2024 Jun 28;58(2):216-225. doi: 10.14744/SEMB.2024.00836. eCollection 2024.
Predictive risk scores have a significant impact on patient selection and assessing the likelihood of complications following interventions in patients with severe aortic stenosis (AS). This study aims to explore the utility of machine learning (ML) techniques in predicting 30-day major adverse cardiac events (MACE) by analyzing parameters, including the Global Registry of Acute Coronary Events (GRACE) score.
This retrospective, multi-center, observational study enrolled 453 consecutive patients diagnosed with severe AS who underwent transcatheter aortic valve implantation (TAVI) from April 2020 to January 2023. The primary outcome was defined as a composition of MACE comprising periprocedural myocardial infarction (MI), cerebrovascular events (CVE), and all-cause mortality during the 1-month follow-up period after the procedure. Conventional binomial logistic regression and ML models were utilized and compared for prediction purposes.
The study population had a mean age of 76.1, with 40.8% being male. The primary endpoint was observed in 7.5% of cases. Among the individual components of the primary endpoint, the rates of all-cause mortality, MI, and CVE were reported as 4.2%, 2.4%, and 1.9%, respectively. The ML-based Extreme Gradient Boosting (XGBoost) model with the GRACE score demonstrated superior discriminative performance in predicting the primary endpoint, compared to both the ML model without the GRACE score and the conventional regression model [Area Under the Curve (AUC)= 0.98 (0.91-0.99), AUC= 0,87 (0.80-0.98), AUC= 0.84 (0.79-0.96)].
ML techniques hold the potential to enhance outcomes in clinical practice, especially when utilized alongside established clinical tools such as the GRACE score.
预测风险评分对严重主动脉瓣狭窄（AS）患者的干预后并发症可能性评估及患者选择有重大影响。本研究旨在通过分析包括全球急性冠状动脉事件注册（GRACE）评分在内的参数，探索机器学习（ML）技术在预测30天主要不良心脏事件（MACE）中的效用。
这项回顾性、多中心、观察性研究纳入了2020年4月至2023年1月期间连续453例诊断为严重AS并接受经导管主动脉瓣植入术（TAVI）的患者。主要结局定义为MACE的组合，包括围手术期心肌梗死（MI）、脑血管事件（CVE）以及术后1个月随访期内的全因死亡率。为进行预测，采用并比较了传统二项逻辑回归和ML模型。
研究人群的平均年龄为76.1岁，男性占40.8%。7.5%的病例观察到主要终点。在主要终点的各个组成部分中，全因死亡率、MI和CVE的发生率分别报告为4.2%、2.4%和1.9%。与不包含GRACE评分的ML模型和传统回归模型相比，基于ML的带有GRACE评分的极端梯度提升（XGBoost）模型在预测主要终点方面表现出更好的数据区分性能[曲线下面积（AUC）=0.98（0.91 - 0.99），AUC = 0.87（0.80 - 0.98），AUC = 0.84（0.79 - 0.96）]。
ML技术有潜力改善临床实践中的治疗效果，特别是与GRACE评分等既定临床工具一起使用时。
