Bouzid Zeineb, Sejdic Ervin, Martin-Gill Christian, Faramand Ziad, Frisch Stephanie, Alrawashdeh Mohammad, Helman Stephanie, Gokhale Tanmay A, Riek Nathan T, Kraevsky-Phillips Karina, Gregg Richard E, Sereika Susan M, Clermont Gilles, Akcakaya Murat, Zègre-Hemsey Jessica K, Saba Samir, Callaway Clifton W, Al-Zaiti Salah S
University of Pittsburgh, Pittsburgh, PA, USA.
University of Toronto and North York General Hospital, Toronto, Ontario, Canada.
Eur Heart J. 2025 Mar 7;46(10):943-954. doi: 10.1093/eurheartj/ehae880.
The importance of risk stratification in patients with chest pain extends beyond diagnosis and immediate treatment. This study sought to evaluate the prognostic value of electrocardiogram feature-based machine learning models to risk-stratify all-cause mortality in those with chest pain.
This was a prospective observational cohort study of consecutive, non-traumatic patients with chest pain. All-cause death was ascertained from multiple sources, including the CDC National Death Index registry. Six machine learning models were trained for survival analysis using 73 morphological electrocardiogram features (80% training with 10-fold cross-validation and 20% testing), followed by a variational Bayesian Gaussian mixture model to define distinct risk groups. The resulting classification performance was compared against the HEART score.
The derivation cohort included 4015 patients (age 59 ± 16 years, 47% women). The mortality rate was 20.3% after a median follow-up period of 3.05 years (interquartile range 1.75-5.32). Extra Survival Trees outperformed other forecasting models, and the derived risk groups successfully classified patients into low-, moderate-, and high-risk groups (log-rank test statistic = 121.14, P < .001). This model outperformed the HEART score, reducing the rate of missed events by >90% with a negative predictive value and sensitivity of 93.4% and 85.9%, compared to 89.0% and 75.0%, respectively. In an independent external testing cohort (N = 3095, age 59 ± 15 years, 44% women, 30-day mortality 3.5%), patients in the moderate [odds ratio 3.62 (1.35-9.74)] and high [odds ratio 6.12 (2.38-15.75)] risk groups had significantly higher odds of mortality compared to those in the low-risk group.
The externally validated machine learning-based model, exclusively utilizing features from the 12-lead electrocardiogram, outperformed the HEART score in stratifying the mortality risk of patients with acute chest pain. This may have the potential to impact the precision of care delivery and the allocation of resources to those at highest risk of adverse events.
胸痛患者风险分层的重要性不仅限于诊断和即时治疗。本研究旨在评估基于心电图特征的机器学习模型对胸痛患者全因死亡率进行风险分层的预后价值。
这是一项对连续性非创伤性胸痛患者进行的前瞻性观察队列研究。通过多种来源确定全因死亡情况，包括美国疾病控制与预防中心国家死亡指数登记处。使用73种形态学心电图特征训练6种机器学习模型用于生存分析（80%用于训练并进行10倍交叉验证，20%用于测试），随后采用变分贝叶斯高斯混合模型定义不同的风险组。将所得分类性能与HEART评分进行比较。
推导队列包括4015例患者（年龄59±16岁，47%为女性）。中位随访期3.05年（四分位间距1.75 - 5.32年）后的死亡率为20.3%。Extra Survival Trees模型优于其他预测模型，所推导的风险组成功将患者分为低、中、高风险组（对数秩检验统计量 = 121.14，P <.001）。该模型优于HEART评分，将漏诊事件发生率降低了90%以上，阴性预测值和灵敏度分别为93.4%和85.9%，而HEART评分的阴性预测值和灵敏度分别为89.0%和75.0%。在一个独立的外部测试队列（N = 3095，年龄59±15岁，44%为女性，30天死亡率3.5%）中，中风险组[比值比3.62（1.35 - 9.74）]和高风险组[比值比6.12（2.38 - 15.75）]的患者死亡几率显著高于低风险组患者。
经外部验证的基于机器学习的模型仅利用12导联心电图特征，在对急性胸痛患者的死亡风险进行分层方面优于HEART评分。这可能有潜力影响医疗服务的精准度以及对不良事件风险最高者的资源分配。
