Khan Ehsan, Lambrakis Kristina, Liao Zhibin, Gerlach Joey, Briffa Tom, Cullen Louise, Nelson Adam J, Verjans Johan, Chew Derek P
College of Medicine & Public Health, Flinders University of South Australia, Adelaide, Australia.
Department of Cardiology, Southern Adelaide Local Health Network, Adelaide, Australia.
JACC Adv. 2024 Jun 19;3(9):101011. doi: 10.1016/j.jacadv.2024.101011. eCollection 2024 Sep.
Clinical work-up for suspected acute coronary syndrome (ACS) is resource intensive.
This study aimed to develop a machine learning model for digitally phenotyping myocardial injury and infarction and predict 30-day events in suspected ACS patients.
Training and testing data sets, predominantly derived from electronic health records, included suspected ACS patients presenting to 6 and 26 South Australian hospitals, respectively. All index presentations and 30-day death and myocardial infarction (MI) were adjudicated using the Fourth Universal Definition of MI. We developed 2 diagnostic prediction models which phenotype myocardial injury and infarction according to the Fourth UDMI (chronic myocardial injury vs acute myocardial injury patterns, the latter further differentiated into acute non-ischaemic myocardial injury, Types 1 and 2 MI) using eXtreme Gradient Boosting (XGB) and deep-learning (DL). We also developed an event prediction model for risk prediction of 30-day death or MI using XGB. Analyses were performed in Python 3.6.
The training and testing data sets had 6,722 and 8,869 participants, respectively. The diagnostic prediction XGB and deep learning models achieved an area under the curve of 99.2% ± 0.1% and 98.8% ± 0.2%, respectively, for differentiating an acute myocardial injury from no injury or chronic myocardial injury and achieved 95.5% ± 0.2% and 94.6% ± 0.9%, respectively, for differentiating type 1 MI from type 2 MI or acute nonischemic myocardial injury. The 30-day death/MI event prediction model achieved an area under the curve of 88.5% ± 0.5%.
Machine learning models can digitally phenotype suspected ACS patients at index presentation and predict subsequent events within 30 days. These models require external validation in a randomized clinical trial to evaluate their impact in clinical practice.
对疑似急性冠状动脉综合征（ACS）的临床检查需要大量资源。
本研究旨在开发一种机器学习模型，用于对心肌损伤和梗死进行数字表型分析，并预测疑似ACS患者的30天事件。
训练和测试数据集主要来自电子健康记录，分别包括在南澳大利亚6家和26家医院就诊的疑似ACS患者。所有索引病例以及30天内的死亡和心肌梗死（MI）均根据心肌梗死的第四次通用定义进行判定。我们开发了2种诊断预测模型，使用极端梯度提升（XGB）和深度学习（DL），根据第四次心肌梗死通用定义（慢性心肌损伤与急性心肌损伤模式，后者进一步分为急性非缺血性心肌损伤、1型和2型心肌梗死）对心肌损伤和梗死进行表型分析。我们还使用XGB开发了一个事件预测模型，用于预测30天内死亡或心肌梗死的风险。分析在Python 3.6中进行。
训练和测试数据集分别有6722名和8869名参与者。诊断预测XGB模型和深度学习模型在区分急性心肌损伤与无损伤或慢性心肌损伤方面的曲线下面积分别为99.2%±0.1%和98.8%±0.2%，在区分1型心肌梗死与2型心肌梗死或急性非缺血性心肌损伤方面的曲线下面积分别为95.5%±0.2%和94.6%±0.9%。30天死亡/心肌梗死事件预测模型的曲线下面积为88.5%±0.5%。
机器学习模型可以在索引病例时对疑似ACS患者进行数字表型分析，并预测30天内的后续事件。这些模型需要在随机临床试验中进行外部验证，以评估它们在临床实践中的影响。
