Abstract：
BACKGROUND: Although several biomarkers exist for patients with heart failure (HF), their use in routine clinical practice is often constrained by high costs and limited availability.
OBJECTIVE: We examined the utility of an artificial intelligence (AI) algorithm that analyzes printed electrocardiograms (ECGs) for outcome prediction in patients with acute HF.
METHODS: We retrospectively analyzed prospectively collected data of patients with acute HF at two tertiary centers in Korea. Baseline ECGs were analyzed using a deep-learning system called Quantitative ECG (QCG), which was trained to detect several urgent clinical conditions, including shock, cardiac arrest, and reduced left ventricular ejection fraction (LVEF).
RESULTS: Among the 1254 patients enrolled, in-hospital cardiac death occurred in 53 (4.2%) patients, and the QCG score for critical events (QCG-Critical) was significantly higher in these patients than in survivors (mean 0.57, SD 0.23 vs mean 0.29, SD 0.20; P<.001). The QCG-Critical score was an independent predictor of in-hospital cardiac death after adjustment for age, sex, comorbidities, HF etiology/type, atrial fibrillation, and QRS widening (adjusted odds ratio [OR] 1.68, 95% CI 1.47-1.92 per 0.1 increase; P<.001), and remained a significant predictor after additional adjustments for echocardiographic LVEF and N-terminal prohormone of brain natriuretic peptide level (adjusted OR 1.59, 95% CI 1.36-1.87 per 0.1 increase; P<.001). During long-term follow-up, patients with higher QCG-Critical scores (>0.5) had higher mortality rates than those with low QCG-Critical scores (<0.25) (adjusted hazard ratio 2.69, 95% CI 2.14-3.38; P<.001).
CONCLUSIONS: Predicting outcomes in patients with acute HF using the QCG-Critical score is feasible, indicating that this AI-based ECG score may be a novel biomarker for these patients.
BACKGROUND: ClinicalTrials.gov NCT01389843; https://clinicaltrials.gov/study/NCT01389843.
OBJECTIVE: We examined the utility of an artificial intelligence (AI) algorithm that analyzes printed electrocardiograms (ECGs) for outcome prediction in patients with acute HF.
METHODS: We retrospectively analyzed prospectively collected data of patients with acute HF at two tertiary centers in Korea. Baseline ECGs were analyzed using a deep-learning system called Quantitative ECG (QCG), which was trained to detect several urgent clinical conditions, including shock, cardiac arrest, and reduced left ventricular ejection fraction (LVEF).
RESULTS: Among the 1254 patients enrolled, in-hospital cardiac death occurred in 53 (4.2%) patients, and the QCG score for critical events (QCG-Critical) was significantly higher in these patients than in survivors (mean 0.57, SD 0.23 vs mean 0.29, SD 0.20; P<.001). The QCG-Critical score was an independent predictor of in-hospital cardiac death after adjustment for age, sex, comorbidities, HF etiology/type, atrial fibrillation, and QRS widening (adjusted odds ratio [OR] 1.68, 95% CI 1.47-1.92 per 0.1 increase; P<.001), and remained a significant predictor after additional adjustments for echocardiographic LVEF and N-terminal prohormone of brain natriuretic peptide level (adjusted OR 1.59, 95% CI 1.36-1.87 per 0.1 increase; P<.001). During long-term follow-up, patients with higher QCG-Critical scores (>0.5) had higher mortality rates than those with low QCG-Critical scores (<0.25) (adjusted hazard ratio 2.69, 95% CI 2.14-3.38; P<.001).
CONCLUSIONS: Predicting outcomes in patients with acute HF using the QCG-Critical score is feasible, indicating that this AI-based ECG score may be a novel biomarker for these patients.
BACKGROUND: ClinicalTrials.gov NCT01389843; https://clinicaltrials.gov/study/NCT01389843.
摘要：
背景：尽管心力衰竭（HF）患者存在几种生物标志物，它们在常规临床实践中的使用通常受到高成本和有限可用性的限制。
目的：我们研究了一种人工智能（AI）算法的实用性，该算法分析了打印心电图（ECG），以预测急性HF患者的预后。
方法：我们回顾性分析了韩国两个三级中心的急性HF患者的前瞻性数据。使用称为定量心电图（QCG）的深度学习系统分析基线心电图，它被训练来检测几种紧急的临床状况，包括休克,心脏骤停,左心室射血分数（LVEF）降低。
结果：在1254名患者中，53例(4.2%)患者发生院内心脏死亡，这些患者的关键事件QCG评分（QCG-Critical）明显高于幸存者（平均0.57，SD0.23与平均0.29，SD0.20；P<.001）。QCG-Critical评分是调整年龄后院内心源性死亡的独立预测因子,性别,合并症,HF病因/类型，心房颤动,和QRS扩大(调整后优势比[OR]1.68，95%CI1.47-1.92每0.1增加；P<.001)，在对超声心动图LVEF和N末端脑钠肽激素原水平进行额外调整后，仍然是一个显著的预测因子（校正OR1.59，95%CI1.36-1.87每0.1增加；P<.001）。在长期随访中，QCG-Critical评分较高(>0.5)的患者死亡率高于QCG-Critical评分较低(<0.25)的患者(校正风险比2.69,95%CI2.14-3.38;P<.001).
结论：使用QCG-Critical评分预测急性HF患者的预后是可行的，表明这种基于AI的ECG评分可能是这些患者的新生物标志物。
背景：ClinicalTrials.govNCT01389843;https://clinicaltrials.gov/study/NCT01389843。
目的：我们研究了一种人工智能（AI）算法的实用性，该算法分析了打印心电图（ECG），以预测急性HF患者的预后。
方法：我们回顾性分析了韩国两个三级中心的急性HF患者的前瞻性数据。使用称为定量心电图（QCG）的深度学习系统分析基线心电图，它被训练来检测几种紧急的临床状况，包括休克,心脏骤停,左心室射血分数（LVEF）降低。
结果：在1254名患者中，53例(4.2%)患者发生院内心脏死亡，这些患者的关键事件QCG评分（QCG-Critical）明显高于幸存者（平均0.57，SD0.23与平均0.29，SD0.20；P<.001）。QCG-Critical评分是调整年龄后院内心源性死亡的独立预测因子,性别,合并症,HF病因/类型，心房颤动,和QRS扩大(调整后优势比[OR]1.68，95%CI1.47-1.92每0.1增加；P<.001)，在对超声心动图LVEF和N末端脑钠肽激素原水平进行额外调整后，仍然是一个显著的预测因子（校正OR1.59，95%CI1.36-1.87每0.1增加；P<.001）。在长期随访中，QCG-Critical评分较高(>0.5)的患者死亡率高于QCG-Critical评分较低(<0.25)的患者(校正风险比2.69,95%CI2.14-3.38;P<.001).
结论：使用QCG-Critical评分预测急性HF患者的预后是可行的，表明这种基于AI的ECG评分可能是这些患者的新生物标志物。
背景：ClinicalTrials.govNCT01389843;https://clinicaltrials.gov/study/NCT01389843。
