Machine Learning for Maximizing Heart Attack Diagnosis Accuracy

Machine Learning for Maximizing Heart Attack Diagnosis Accuracy

Table of Contents

What is Cardiac Troponin Assays?

High-sensitivity cardiac troponin assays have become an integral part of the evaluation and management of patients with suspected acute myocardial infarction, more commonly known as a heart attack. These assays have significantly enhanced the ability to identify patients at risk of cardiac death and guide appropriate treatment decisions. 

Guidelines recommend fixed cardiac troponin thresholds for the diagnosis of myocardial infarction, but troponin concentrations are influenced by patient-specific factors, including age, sex, comorbidities, and time from symptom onset. A personalized medicine approach to cardiac troponin assays could improve their accuracy, thus increasing the number of correctly-diagnosed heart attacks and decreasing false alarm scares for patients.

Developing CoDE-ACS, a Clinical Decision Support System

In a study recently published in Nature Medicine, researchers aimed to improve the diagnosis of myocardial infarction using machine learning models that integrate cardiac troponin concentrations at presentation or on serial testing with clinical features known to influence the concentrations. Additionally, they developed a clinical decision support system called CoDE-ACS (Collaboration for the Diagnosis and Evaluation of Acute Coronary Syndrome) that uses machine learning models to calculate an individual’s probability of myocardial infarction. 

The researchers trained the machine learning models using data from 10,038 patients and conducted external validation on data from 10,286 patients from seven different cohorts. They then compared the diagnostic performance of CoDE-ACS with that of guideline-recommended pathways to demonstrate its potential use in clinical practice.

Machine Learning-Based System Successfully Implements Personalized Probability of Heart Attack

The CoDE-ACS clinical decision support system demonstrated excellent discrimination for myocardial infarction at presentation (area under the curve [AUC], 0.953) and with serial testing (AUC, 0.966). This is key – both overestimating and underestimating a person’s likelihood that they are experiencing heart attacks have dangerous consequences. 

It also performed well across patient subgroups, including men and women, older persons, those with renal impairment or those who present early following the onset of symptoms, and identified more patients at presentation as having a low probability of myocardial infarction compared to fixed cardiac troponin thresholds (61% versus 27%). 

The study demonstrates that the clinical decision support system has the potential to benefit both patients and healthcare providers by reducing time spent in emergency departments, preventing unnecessary hospital admission in patients unlikely to have myocardial infarction and at low risk of cardiac death, and improving the recognition and treatment of those with myocardial infarction. It also further validates the benefits of personalized medicine over static, outdated metrics applied to whole populations.

Outsourcing Bioinformatics Analysis: How Bridge Informatics Can Help

Groundbreaking studies like these are made possible by technological advances making biological data generation, storage, and analysis faster and more accessible than ever before. From pipeline development and software engineering to deploying existing bioinformatics tools, Bridge Informatics can help you on every step of your research journey.

As experts across data types from leading sequencing platforms, we can help you tackle the challenging computational tasks of storing, analyzing, and interpreting genomic and transcriptomic data. Bridge Informatics’ bioinformaticians are trained bench biologists, so they understand the biological questions driving your computational analysis. Click here to schedule a free introductory call with a member of our team.

Lauren Dembeck, Ph.D., Geneticist & Science Writer, Bridge Informatics

Lauren Dembeck, Ph.D., is an experienced science and medical writer. During her doctoral research at North Carolina State University, she conducted genome-wide association studies to identify genetic variants contributing to natural variation in complex traits and used a combination of classical and molecular genetics approaches in validation studies. Lauren was a postdoctoral fellow at the Okinawa Institute of Science and Technology in Japan. During her postdoc, she used fluorescence-activated cell sorting paired with high-throughput sequencing approaches to study the formation and regulation of neuronal circuits. 

She is part of our team of expert content writers at Bridge Informatics, bringing our readers and customers everything they need to know at the cutting edge of bioinformatics research. If you’re interested in reaching out, please email [email protected] or [email protected].

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