Predicting target lesion failure following percutaneous coronary intervention through machine learning risk assessment models.

IF 3.9 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS European heart journal. Digital health Pub Date : 2023-08-31 eCollection Date: 2023-12-01 DOI:10.1093/ehjdh/ztad051
Mamas A Mamas, Marco Roffi, Ole Fröbert, Alaide Chieffo, Alessandro Beneduce, Andrija Matetic, Pim A L Tonino, Dragica Paunovic, Lotte Jacobs, Roxane Debrus, Jérémy El Aissaoui, Frank van Leeuwen, Evangelos Kontopantelis
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Abstract

Aims: Central to the practice of precision medicine in percutaneous coronary intervention (PCI) is a risk-stratification tool to predict outcomes following the procedure. This study is intended to assess machine learning (ML)-based risk models to predict clinically relevant outcomes in PCI and to support individualized clinical decision-making in this setting.

Methods and results: Five different ML models [gradient boosting classifier (GBC), linear discrimination analysis, Naïve Bayes, logistic regression, and K-nearest neighbours algorithm) for the prediction of 1-year target lesion failure (TLF) were trained on an extensive data set of 35 389 patients undergoing PCI and enrolled in the global, all-comers e-ULTIMASTER registry. The data set was split into a training (80%) and a test set (20%). Twenty-three patient and procedural characteristics were used as predictive variables. The models were compared for discrimination according to the area under the receiver operating characteristic curve (AUC) and for calibration. The GBC model showed the best discriminative ability with an AUC of 0.72 (95% confidence interval 0.69-0.75) for 1-year TLF on the test set. The discriminative ability of the GBC model for the components of TLF was highest for cardiac death with an AUC of 0.82, followed by target vessel myocardial infarction with an AUC of 0.75 and clinically driven target lesion revascularization with an AUC of 0.68. The calibration was fair until the highest risk deciles showed an underestimation of the risk.

Conclusion: Machine learning-derived predictive models provide a reasonably accurate prediction of 1-year TLF in patients undergoing PCI. A prospective evaluation of the predictive score is warranted.

Registration: Clinicaltrial.gov identifier is NCT02188355.

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通过机器学习风险评估模型预测经皮冠状动脉介入治疗后的靶病变失败。
精确医学在经皮冠状动脉介入治疗(PCI)中的核心是预测手术后结果的风险分层工具。本研究旨在评估基于机器学习(ML)的风险模型,以预测PCI的临床相关结果,并支持这种情况下的个性化临床决策。在35389名接受PCI的患者的广泛数据集上训练了五种不同的ML模型(梯度增强分类器、线性判别分析、Naive Bayes、Logistic回归和K-最近邻算法),用于预测1年靶病变失败(TLF),并在全球所有参与者的e-ULTIMATER注册中注册。数据集分为训练集(80%)和测试集(20%)。23名患者和手术特点被用作预测变量。根据受试者工作特性曲线下面积(AUC)对模型进行区分和校准比较。梯度提升分类器模型在测试集上显示出最佳的判别能力,1年TLF的AUC为0.72(95%CI 0.69-0.75)。梯度增强分类器模型对TLF成分的辨别能力在心脏性死亡中最高,AUC为0.82,其次是靶血管心肌梗死,AUC值为0.75,临床驱动的靶病变血运重建,AUC系数为0.68。校准是公平的,直到最高风险十分位数显示出对风险的低估。ML衍生的预测模型对接受PCI的患者的1年TLF提供了相当准确的预测。有必要对预测得分进行前瞻性评估。Clinicaltrial.gov标识符为NCT02188355。
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