Abstract
BackgroundCoronary artery disease (CAD) costs healthcare billions of dollars annually and is the leading cause of death despite available noninvasive diagnostic tools.ObjectiveThis study aims to examine the usefulness of machine learning in predicting hemodynamically significant CAD using routine demographics, clinical factors, and laboratory data.MethodsConsecutive patients undergoing cardiac catheterization between March 17, 2015, and July 15, 2016, at UNC Chapel Hill were screened for comorbidities and CAD risk factors. In this pilot, single-center, prospective cohort study, patients were screened and selected for moderate CAD risk (n = 185). Invasive coronary angiography and CAD prediction with machine learning were independently performed. Results were blinded from operators and patients. Outcomes were followed up for up to 90 days for major adverse cardiovascular and renal events (MACREs). Greater than 70% stenosis or a fractional flow reserve less than or equal to 0.8 represented hemodynamically significant coronary disease. A random forest model using demographic, comorbidities, risk factors, and lab data was trained to predict CAD severity. The Random Forest Model predictive accuracy was assessed by area under the receiver operating characteristic curve with comparison to the final diagnoses made from coronary angiography.ResultsHemodynamically significant CAD was predicted by 18-point clinical data input with a sensitivity of 81% ± 7.8%, and specificity of 61% ± 14.4% by the established model. The best machine learning model predicted a 90-day MACRE with specificity of 44.61% ± 14.39%, and sensitivity of 57.13% ± 18.70%.ConclusionMachine learning models based on routine demographics, clinical factors, and lab data can be used to predict hemodynamically significant CAD with accuracy that approximates current noninvasive functional modalities.
Highlights
In the United States, coronary artery disease (CAD) is the leading cause of death, 1 in 6 yearly, for men and women, costing billions of dollars annually.[1]
Machine learning models based on routine demographics, clinical factors, and lab data can be used to predict hemodynamically significant CAD with accuracy that approximates current noninvasive functional modalities
As a proof of concept, we present the first prospective pilot cohort study assessing the ability of machine learning in predicting hemodynamically significant CAD
Summary
In the United States, coronary artery disease (CAD) is the leading cause of death, 1 in 6 yearly, for men and women, costing billions of dollars annually.[1]. Optimizing the diagnostic process of hemodynamically significant CAD presents great opportunities in improving population health outcomes, healthcare efficiency, and cost reduction. Significant limitations exist for noninvasive functional cardiac tests in terms of cost-effectiveness and accuracy.[4]. Noninvasive functional cardiac tests used to detect hemodynamically significant CAD for clinical decision-making include nuclear myocardial perfusion study, stress electrocardiography, stress echocardiogram, and stress magnetic resonance imaging. Coronary artery disease (CAD) costs healthcare billions of dollars annually and is the leading cause of death despite available noninvasive diagnostic tools
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
