Machine learning prediction of no reflow in patients with ST-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention.

IF 2.1 3区 医学 Q3 CARDIAC & CARDIOVASCULAR SYSTEMS
Cardiovascular diagnosis and therapy Pub Date : 2024-08-31 Epub Date: 2024-08-08 DOI:10.21037/cdt-24-83
Lin Wang, Pei Bao, Xiaochen Wang, Banglong Xu, Zeyan Liu, Guangquan Hu
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引用次数: 0

Abstract

Background: No-reflow (NRF) phenomenon is a significant challenge in patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (pPCI). Accurate prediction of NRF may help improve clinical outcomes of patients. This retrospective study aimed at creating an optimal model based on machine learning (ML) to predict NRF in these patients, with the additional objective of guiding pre- and intra-operative decision-making to reduce NRF incidence.

Methods: Data were collected from 321 STEMI patients undergoing pPCI between January 2022 and May 2023, with the dataset being randomly divided into training and internal validation sets in a 7:3 ratio. Selected features included pre- and intra-operative demographic data, laboratory parameters, electrocardiogram, comorbidities, patients' clinical status, coronary angiographic data, and intraoperative interventions. Post comprehensive feature cleaning and engineering, three logistic regression (LR) models [LR-classic, LR-random forest (LR-RF), and LR-eXtreme Gradient Boosting (LR-XGB)], a RF model and an eXtreme Gradient Boosting (XGBoost) model were developed within the training set, followed by performance evaluation on the internal validation sets.

Results: Among the 261 patients who met the inclusion criteria, 212 were allocated to the normal flow group and 49 to the NRF group. The training group consisted of 183 patients, while the internal validation group included 78 patients. The LR-XGB model, with an area under the curve (AUC) of 0.829 [95% confidence interval (CI): 0.779-0.880], was selected as the representative model for logistic regression analyses. The LR model had an AUC slightly lower than XGBoost model (AUC 0.835, 95% CI: 0.781-0.889) but significantly higher than RF model (AUC 0.731, 95% CI: 0.660-0.802). Internal validation underscored the unique advantages of each model, with the LR model demonstrating the highest clinical net benefit at relevant thresholds, as determined by decision curve analysis. The LR model encompassed seven meaningful features, and notably, thrombolysis in myocardial infarction flow after initial balloon dilation (TFAID) was the most impactful predictor in all models. A web-based application based on the LR model, hosting these predictive models, is available at https://l7173o-wang-lyn.shinyapps.io/shiny-1/.

Conclusions: A LR model was successfully developed through ML to forecast NRF phenomena in STEMI patients undergoing pPCI. A web-based application derived from the LR model facilitates clinical implementation.

机器学习对接受经皮冠状动脉介入治疗的 ST 段抬高型心肌梗死患者无回流的预测。
背景:无复流(NRF)现象是接受经皮冠状动脉介入治疗(pPCI)的 ST 段抬高型心肌梗死(STEMI)患者面临的重大挑战。准确预测 NRF 有助于改善患者的临床预后。这项回顾性研究旨在创建一个基于机器学习(ML)的最佳模型来预测这些患者的 NRF,其额外目标是指导术前和术中决策,以降低 NRF 的发生率:数据收集自2022年1月至2023年5月期间接受pPCI手术的321名STEMI患者,数据集按7:3的比例随机分为训练集和内部验证集。所选特征包括术前和术中人口统计学数据、实验室参数、心电图、合并症、患者临床状态、冠状动脉造影数据和术中干预措施。经过全面的特征清理和工程设计,在训练集中开发了三个逻辑回归(LR)模型[LR-classic、LR-random forest(LR-RF)和LR-eXtreme Gradient Boosting(LR-XGB)]、一个RF模型和一个eXtreme Gradient Boosting(XGBoost)模型,然后在内部验证集中进行了性能评估:在符合纳入标准的 261 名患者中,212 人被分配到正常血流组,49 人被分配到 NRF 组。训练组包括 183 名患者,内部验证组包括 78 名患者。LR-XGB 模型的曲线下面积(AUC)为 0.829 [95% 置信区间 (CI):0.779-0.880],被选为逻辑回归分析的代表模型。LR 模型的 AUC 略低于 XGBoost 模型(AUC 0.835,95% 置信区间:0.781-0.889),但明显高于 RF 模型(AUC 0.731,95% 置信区间:0.660-0.802)。内部验证强调了每种模型的独特优势,根据决策曲线分析,LR 模型在相关阈值下显示出最高的临床净效益。LR 模型包含七个有意义的特征,值得注意的是,初始球囊扩张后心肌梗死血流溶栓(TFAID)是所有模型中影响最大的预测因子。基于 LR 模型的网络应用程序包含这些预测模型,可在 https://l7173o-wang-lyn.shinyapps.io/shiny-1/.Conclusions 网站上下载:通过 ML 成功开发了一个 LR 模型,用于预测接受 pPCI 的 STEMI 患者的 NRF 现象。基于 LR 模型的网络应用程序为临床实施提供了便利。
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来源期刊
Cardiovascular diagnosis and therapy
Cardiovascular diagnosis and therapy Medicine-Cardiology and Cardiovascular Medicine
CiteScore
4.90
自引率
4.20%
发文量
45
期刊介绍: The journal ''Cardiovascular Diagnosis and Therapy'' (Print ISSN: 2223-3652; Online ISSN: 2223-3660) accepts basic and clinical science submissions related to Cardiovascular Medicine and Surgery. The mission of the journal is the rapid exchange of scientific information between clinicians and scientists worldwide. To reach this goal, the journal will focus on novel media, using a web-based, digital format in addition to traditional print-version. This includes on-line submission, review, publication, and distribution. The digital format will also allow submission of extensive supporting visual material, both images and video. The website www.thecdt.org will serve as the central hub and also allow posting of comments and on-line discussion. The web-site of the journal will be linked to a number of international web-sites (e.g. www.dxy.cn), which will significantly expand the distribution of its contents.
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