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Prediction of obstructive coronary artery disease using machine learning algorithms

Metselaar, Rutger J. (2020) Prediction of obstructive coronary artery disease using machine learning algorithms.

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Abstract:Introduction: Accurate risk stratification for patients with coronary artery disease(CAD) is essential for accurate treatment. The current diagnostic pathway comprises a number of medical examinations , including a computed tomography scan and positron emission tomography myocardial perfusion, which yield prognostic data that may be utilized for risk stratification purposes. The aim of this thesis was to develop a risk model for obstructive CAD with machine learning(ML) algorithms. This model may provide an individualized risk score based on a combination of clinical features and quantitative parameters derived from imaging.Methods: We retrospectively included 1007 patients with no prior cardiovascular history, who were referred for rest and regadenoson-induced stress Rubidium-82 positron emission tomograpy (PET)/computated tomography (CT). Presence of obstructive CAD was defined as a composite of a significant fractional flow rate measurement during invasive coronary angiography, percutaneous coronary intervention or a coronary artery bypass graft procedure, and was acquired via follow-up. Furthermore, each patient was characterized by a broad array of features, including cardiovascular risk factors (cigarette smoking, hypertension, hypercholesterolemia, diabetes, positive family history of CAD), prior medical history; current medication usage age; gender; body mass index (BMI); creatinine serum values; coronary artery calcification (CAC) score and PET/CT derived myocardial blood flows. Additionally, the visual interpretation by a team of two clinicians of the PET/CT scan was obtained. Two sets of input parameter were used to train the models. First, the entire set of features except the visual interpretation. Secondly, the entire set of features, including the visual interpretation. Four different ML algorithms were used, so in total, 8 different models were optimized. These models were developed using a subset of 805 cases of the dataset to identify obstructive CAD by using 5-fold cross validation in combination with a grid search, whilst their performance was measured using the F1-score. The optimized algorithms were validated on 202 cases of the dataset, never previously seen by the models. The performance on these unseen examples was compared with the current diagnostic performance by clinicians, as measured by the visual interpretation of the scan.Results: The best performing algorithm to predict obstructive CAD was XGBoost, an ensemble of gradient boosted decision trees. On the unseen dataset this algorithm reached an area under the curve of 0.93 while obtaining a sensitivity of 64% (95% CI: 41-83) and a specificity of 96% (95% CI: 91-98). The sensitivity by the clinicians on this same dataset was 77% (95% CI 55-93) and the specificity was 92% (95% CI (87-96). The low prevalence of obstructive events in evaluation dataset (11%) resulted in wide confidence intervals, making it so that no significant differences were found. Furthermore, we were able to make a ranking via the XGBoost model of important predictors for obstructive CAD. Summarized, CAC-scores and quantitative PET derived features were the most important predictors. Classical risk factors and medication however, could not be used in the current setup to distinguish obstructive CAD from non-obstructive CAD. We also conclude that the visual interpretation by the clinician added incremental prognostic information to the model. Conclusion: We used a set of clinical and quantitative features to develop a ML model. This model is able to provide individualized risk stratification by predicting the possibility of an obstructive cardiovascular event. Although validation with a larger dataset could result in a more well defined performance range, this model still shows potential to be implemented in the diagnostic workflow by providing a computer aided second opinion to the clinicians.
Item Type:Essay (Master)
Faculty:TNW: Science and Technology
Subject:44 medicine
Programme:Technical Medicine MSc (60033)
Link to this item:http://purl.utwente.nl/essays/85511
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