Güldener Ulrich, Kessler Thorsten, von Scheidt Moritz, Hawe Johann S, Gerhard Beatrix, Maier Dieter, Lachmann Mark, Laugwitz Karl-Ludwig, Cassese Salvatore, Schömig Albert W, Kastrati Adnan, Schunkert Heribert
Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, 80636 Munich, Germany.
DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, 80802 Munich, Germany.
J Clin Med. 2023 Apr 18;12(8):2941. doi: 10.3390/jcm12082941.
Machine learning (ML) approaches have the potential to uncover regular patterns in multi-layered data. Here we applied self-organizing maps (SOMs) to detect such patterns with the aim to better predict in-stent restenosis (ISR) at surveillance angiography 6 to 8 months after percutaneous coronary intervention with stenting.
In prospectively collected data from 10,004 patients receiving percutaneous coronary intervention (PCI) for 15,004 lesions, we applied SOMs to predict ISR angiographically 6-8 months after index procedure. SOM findings were compared with results of conventional uni- and multivariate analyses. The predictive value of both approaches was assessed after random splitting of patients into training and test sets (50:50).
Conventional multivariate analyses revealed 10, mostly known, predictors for restenosis after coronary stenting: balloon-to-vessel ratio, complex lesion morphology, diabetes mellitus, left main stenting, stent type (bare metal vs. first vs. second generation drug eluting stent), stent length, stenosis severity, vessel size reduction, and prior bypass surgery. The SOM approach identified all these and nine further predictors, including chronic vessel occlusion, lesion length, and prior PCI. Moreover, the SOM-based model performed well in predicting ISR (AUC under ROC: 0.728); however, there was no meaningful advantage in predicting ISR at surveillance angiography in comparison with the conventional multivariable model (0.726, = 0.3).
The agnostic SOM-based approach identified-without clinical knowledge-even more contributors to restenosis risk. In fact, SOMs applied to a large prospectively sampled cohort identified several novel predictors of restenosis after PCI. However, as compared with established covariates, ML technologies did not improve identification of patients at high risk for restenosis after PCI in a clinically relevant fashion.
机器学习（ML）方法有潜力揭示多层数据中的规律模式。在此，我们应用自组织映射（SOM）来检测此类模式，旨在更好地预测经皮冠状动脉介入治疗并植入支架后6至8个月的随访血管造影时的支架内再狭窄（ISR）。
在对10004例接受经皮冠状动脉介入治疗（PCI）的15004个病变进行前瞻性收集的数据中，我们应用SOM预测首次手术后6 - 8个月血管造影显示的ISR。将SOM的结果与传统单变量和多变量分析的结果进行比较。在将患者随机分为训练集和测试集（50:50）后，评估两种方法的预测价值。
传统多变量分析揭示了冠状动脉支架置入术后再狭窄的10个主要已知预测因素：球囊与血管比率、复杂病变形态、糖尿病、左主干支架置入、支架类型（裸金属支架与第一代或第二代药物洗脱支架）、支架长度、狭窄严重程度、血管尺寸减小以及既往搭桥手术。SOM方法识别出了所有这些因素以及另外9个预测因素，包括慢性血管闭塞、病变长度和既往PCI。此外，基于SOM的模型在预测ISR方面表现良好（ROC曲线下面积：0.728）；然而，与传统多变量模型相比，在随访血管造影时预测ISR并无显著优势（0.726，P = 0.3）。
基于SOM的不可知方法在没有临床知识的情况下识别出了更多再狭窄风险因素。实际上，应用于大量前瞻性抽样队列的SOM识别出了PCI术后再狭窄的几个新预测因素。然而，与既定的协变量相比，ML技术在以临床相关方式识别PCI术后再狭窄高危患者方面并未有所改进。
