Department of Cardiology, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA.
Computational Health Informatics Program, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA.
J Am Coll Cardiol. 2024 Aug 27;84(9):815-828. doi: 10.1016/j.jacc.2024.05.062.
Artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis shows promise to detect biventricular pathophysiology. However, AI-ECG analysis remains underexplored in congenital heart disease (CHD).
The purpose of this study was to develop and externally validate an AI-ECG model to predict cardiovascular magnetic resonance (CMR)-defined biventricular dysfunction/dilation in patients with CHD.
We trained (80%) and tested (20%) a convolutional neural network on paired ECG-CMRs (≤30 days apart) from patients with and without CHD to detect left ventricular (LV) dysfunction (ejection fraction ≤40%), RV dysfunction (ejection fraction ≤35%), and LV and RV dilation (end-diastolic volume z-score ≥4). Performance was assessed during internal testing and external validation on an outside health care system using area under receiver-operating curve (AUROC) and area under precision recall curve.
The internal and external cohorts comprised 8,584 ECG-CMR pairs (n = 4,941; median CMR age 20.7 years) and 909 ECG-CMR pairs (n = 746; median CMR age 25.4 years), respectively. Model performance was similar for internal testing (AUROC: LV dysfunction 0.87; LV dilation 0.86; RV dysfunction 0.88; RV dilation 0.81) and external validation (AUROC: LV dysfunction 0.89; LV dilation 0.83; RV dysfunction 0.82; RV dilation 0.80). Model performance was lowest in functionally single ventricle patients. Tetralogy of Fallot patients predicted to be at high risk of ventricular dysfunction had lower survival (P < 0.001). Model explainability via saliency mapping revealed that lateral precordial leads influence all outcome predictions, with high-risk features including QRS widening and T-wave inversions for RV dysfunction/dilation.
AI-ECG shows promise to predict biventricular dysfunction/dilation, which may help inform CMR timing in CHD.
人工智能增强心电图（AI-ECG）分析有望检测双心室病理生理学。然而，AI-ECG 分析在先天性心脏病（CHD）中仍未得到充分探索。
本研究旨在开发和外部验证一种 AI-ECG 模型，以预测 CHD 患者心血管磁共振（CMR）定义的双心室功能障碍/扩张。
我们使用来自有和无 CHD 患者的配对 ECG-CMR（相隔≤30 天）对卷积神经网络进行训练（80%）和测试（20%），以检测左心室（LV）功能障碍（射血分数≤40%）、右心室（RV）功能障碍（射血分数≤35%）和 LV 和 RV 扩张（舒张末期容积 z 分数≥4）。使用接收器操作特征曲线下面积（AUROC）和精度召回曲线下面积来评估内部测试和外部验证在外部医疗保健系统中的性能。
内部和外部队列分别包括 8584 对 ECG-CMR（n=4941；CMR 年龄中位数为 20.7 岁）和 909 对 ECG-CMR（n=746；CMR 年龄中位数为 25.4 岁）。内部测试的模型性能相似（LV 功能障碍 AUROC：0.87；LV 扩张 AUROC：0.86；RV 功能障碍 AUROC：0.88；RV 扩张 AUROC：0.81）和外部验证（LV 功能障碍 AUROC：0.89；LV 扩张 AUROC：0.83；RV 功能障碍 AUROC：0.82；RV 扩张 AUROC：0.80）。在功能单一心室患者中，模型性能最低。预测为 RV 功能障碍高风险的法洛四联症患者生存率较低（P<0.001）。通过显着性映射进行的模型可解释性表明，外侧前胸部导联影响所有结果预测，高危特征包括 RV 功能障碍/扩张的 QRS 增宽和 T 波倒置。
AI-ECG 有望预测双心室功能障碍/扩张，这可能有助于在 CHD 中告知 CMR 时机。
