Key result
Causal modeling links regional wall motion abnormalities, fasting blood sugar, and ejection fraction to AMI.
Why the study?
Most machine learning studies evaluating laboratory detection, echocardiography, and acute myocardial infarction focus on data correlation while ignoring causal logic.
What are the causal relationships between laboratory/echocardiogram features and the onset of acute myocardial infarction using a Structural Causal Model?
Observational
What are the causal relationships between laboratory/echocardiogram features and the onset of acute myocardial infarction using a Structural Causal Model?
A Structural Causal Model identified specific echocardiographic and laboratory parameters, including regional wall motion abnormality and fasting blood sugar, as having significant causal effects on the onset of acute myocardial infarction.
Should not yet guide AMI risk assessment; leaves open causal roles pending prospective validation.
Acute Myocardial Infarction (AMI) is a serve disease with rapid onset, high mortality and complex symptoms. Therefore, the accurate diagnosis and effective treatment of AMI are important but difficult in clinic. In addition to symptoms and electrocardiogram (ECG) features, laboratory detection and echocardiogram (ECHO) provide significant help for physicians. With the support of big data, quite studies utilized machine learning to investigate the association between laboratory detection, ECHO and the onset of AMI. However, most researches just focused on the correlation in data while ignore the causal logic. Therefore in this paper, based on the electronic healthcare record data, the Structural Causal Model is adopted to discover the causal relationships and construct the causal graph. Furthermore, the effective information is utilized to quantify the causal effects between the features and AMI. The results show that regional wall motion abnormality, fasting blood sugar, ejection fraction, erythrocyte sedimentation rate, white blood cell and creatine have significant causal effects with AMI. The proposed method and analysis results can provide guidance for clinical diagnosis and treatment of AMI.
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Li et al. (2022) conducted an observational in Acute Myocardial Infarction. Structural Causal Model analysis was evaluated on Causal effects between clinical features and AMI. Structural Causal Model analysis of electronic healthcare records identified significant causal effects of regional wall motion abnormality, fasting blood sugar, and ejection fraction with AMI.
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