In interventional cardiology (IC) the radiation dose variation is very significant, and its estimation has been difficult due to the complexity of the treatments. In order to tackle this problem, the aim of this study was to identify the most important demographic and clinical features to estimate Kerma-Area Product (KAP) radiation dose in coronary angiographies (CA) and percutaneous coronary interventions (PCI). The study was retrospective using clinical patient data from 838 CA and PCI procedures. A total of 59 features were extracted from the patient data and 9 different filter-based feature selection methods were used to select the most informative features in terms of the KAP radiation dose from the treatments. The selected features were then used in a support vector regression (SVR) model to evaluate their performance in estimating the radiation dose. The ten highest-ranking features were: (1) FN1AC (CA), (2) FN2BA (PCI), (3) weight, (4) post-stenosis 0%, (5) multi-vessel disease, (6) number of procedures 3, (7) pre-stenosis 100%, (8) American Heart Association (AHA) score C, (9) pre-stenosis 85% and (10) gender. The performance of the SVR model increased (mean squared error ≈ 450) with the number of features approximately up to 30 features. The identification of the most informative features for CA and PCI KAP is an important step in determining suitable complexity models for clinical practice. The highest-ranking features can be used as individual predictors of IC procedure KAP or can be incorporated into combined complexity score or different estimation models in the future.

中文翻译：

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全功能选择，可估计冠状动脉造影和经皮冠状动脉介入治疗中的KAP辐射剂量。

在介入心脏病学（IC）中，辐射剂量变化非常显着，由于治疗的复杂性，很难对其进行估算。为了解决这个问题，本研究的目的是确定最重要的人口统计学和临床​​特征，以估计冠状动脉造影（CA）和经皮冠状动脉介入治疗（PCI）中的克马面积积（KAP）辐射剂量。这项研究使用了来自838 CA和PCI程序的临床患者数据进行回顾性研究。从患者数据中总共提取了59个特征，并且使用了9种不同的基于过滤器的特征选择方法，以根据治疗中的KAP辐射剂量选择信息最多的特征。然后将所选特征用于支持向量回归（SVR）模型中，以评估其在估算辐射剂量方面的性能。排名最高的十个特征是：（1）FN1AC（CA），（2）FN2BA（PCI），（3）体重，（4）狭窄后0％，（5）多支血管疾病，（6）数目程序3，（7）狭窄前100％，（8）美国心脏协会（AHA）得分C，（9）狭窄前85％和（10）性别。随着大约30个特征的特征数量增加，SVR模型的性能提高了（均方误差≈450）。确定CA和PCI KAP最有用的功能是确定适合临床实践的复杂性模型的重要步骤。