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Deep learning model to predict exercise stress test results: Optimizing the diagnostic test selection strategy and
Juan Lu1, Jonathon Stewart2, Mohammed Bennamoun3
1Department of Computer Science and Software Engineering, The University of Western Australia, Australia; Medical School, Faculty of Health and Medical Sciences, University of Western Australia, Perth, Australia; Harry Perkins Institute of Medical Research, Perth, Australia.
Insights
Machine learning models can predict inconclusive cardiac exercise stress testing (EST) results using patient data. This approach helps identify patients likely to have non-diagnostic tests, optimizing diagnostic strategies for coronary artery disease (CAD).
Area of Science:
- Cardiology
- Medical Informatics
- Machine Learning
Background:
- Cardiac exercise stress testing (EST) is crucial for managing suspected coronary artery disease (CAD).
- A significant proportion (up to 30%) of EST results are inconclusive or non-diagnostic, leading to wasted healthcare resources.
- Predicting these non-diagnostic results beforehand is essential for efficient patient management.
Purpose of the Study:
- To develop machine learning (ML) models capable of predicting EST results, including inconclusive or non-diagnostic outcomes.
- To utilize readily available patient demographic and pre-test clinical information for prediction.
- To identify patients likely to have inconclusive tests prior to the procedure.
Main Methods:
- A large cohort of 30,710 patients was analyzed.
- Patient demographic data (age, sex) and pre-test clinical information were used as input variables.
- Various ML models were constructed and compared using the area under the receiver operating characteristic curve (AUC) for discriminant power.
Main Results:
- A network of Oblivious Decision Trees model achieved the highest discriminant power with an AUC of 0.83.
- The best model demonstrated a sensitivity of 69% and a specificity of 0.78% for predicting inconclusive EST results.
- The model correctly identified 2010 inconclusive ESTs within the testing set.
Conclusions:
- ML models utilizing demographic and pre-test clinical data can accurately predict EST outcomes.
- The developed system can identify patients at high risk for inconclusive or non-diagnostic ESTs.
- This predictive capability can serve as a personalized decision support tool for clinicians, optimizing test selection and reducing healthcare costs.
Background:
Cardiac exercise stress testing (EST) offers a non-invasive way in the management of patients with suspected coronary artery disease (CAD). However, up to 30% EST results are either inconclusive or non-diagnostic, which results in significant resource wastage. Our aim was to build machine learning (ML) based models, using patients demographic (age, sex) and pre-test clinical information (reason for performing test, medications, blood pressure, heart rate, and resting electrocardiogram), capable of predicting EST results beforehand including those with inconclusive or non-diagnostic results.
Methods:
A total of 30,710 patients (mean age 54.0 years, 69% male) were included in the study with 25% randomly sampled in the test set, and the remaining samples were split into a train and validation set with a ratio of 9:1. We constructed different ML models from pre-test variables and compared their discriminant power using the area under the receiver operating characteristic curve (AUC).
Results:
A network of Oblivious Decision Trees provided the best discriminant power (AUC=0.83, sensitivity=69%, specificity=0.78%) for predicting inconclusive EST results. A total of 2010 inconclusive ESTs were correctly identified in the testing set.
Conclusions:
Our ML model, developed using demographic and pre-test clinical information, can accurately predict EST results and could be used to identify patients with inconclusive or non-diagnostic results beforehand. Our system could thus be used as a personalised decision support tool by clinicians for optimizing the diagnostic test selection strategy for CAD patients and to reduce healthcare expenditure by reducing nondiagnostic or inconclusive ESTs.
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