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Clinical Trial
. 2018 Aug 8;13(8):e0198603.
doi: 10.1371/journal.pone.0198603. eCollection 2018.

Cardiac Phase Space Tomography: A novel method of assessing coronary artery disease utilizing machine learning

Thomas D Stuckey  1 , Roger S Gammon  2 , Robi Goswami  3 , Jeremiah P Depta  4 , John A Steuter  5 , Frederick J Meine 3rd  6 , Michael C Roberts  7 , Narendra Singh  8 , Shyam Ramchandani  9 , Tim Burton  9 , Paul Grouchy  9 , Ali Khosousi  9 , Ian Shadforth  10 , William E Sanders Jr  10
Affiliations
Clinical Trial

Cardiac Phase Space Tomography: A novel method of assessing coronary artery disease utilizing machine learning

Thomas D Stuckey et al. PLoS One. .

Abstract

Background: Artificial intelligence (AI) techniques are increasingly applied to cardiovascular (CV) medicine in arenas ranging from genomics to cardiac imaging analysis. Cardiac Phase Space Tomography Analysis (cPSTA), employing machine-learned linear models from an elastic net method optimized by a genetic algorithm, analyzes thoracic phase signals to identify unique mathematical and tomographic features associated with the presence of flow-limiting coronary artery disease (CAD). This novel approach does not require radiation, contrast media, exercise, or pharmacological stress. The objective of this trial was to determine the diagnostic performance of cPSTA in assessing CAD in patients presenting with chest pain who had been referred by their physician for coronary angiography.

Methods: This prospective, multicenter, non-significant risk study was designed to: 1) develop machine-learned algorithms to assess the presence of CAD (defined as one or more ≥ 70% stenosis, or fractional flow reserve ≤ 0.80) and 2) test the accuracy of these algorithms prospectively in a naïve verification cohort. This report is an analysis of phase signals acquired from 606 subjects at rest just prior to angiography. From the collective phase signal data, features were extracted and paired with the known angiographic results. A development set, consisting of signals from 512 subjects, was used for machine learning to determine an algorithm that correlated with significant CAD. Verification testing of the algorithm was performed utilizing previously untested phase signals from 94 subjects.

Results: The machine-learned algorithm had a sensitivity of 92% (95% CI: 74%-100%) and specificity of 62% (95% CI: 51%-74%) on blind testing in the verification cohort. The negative predictive value (NPV) was 96% (95% CI: 85%-100%).

Conclusions: These initial multicenter results suggest that resting cPSTA may have comparable diagnostic utility to functional tests currently used to assess CAD without requiring cardiac stress (exercise or pharmacological) or exposure of the patient to radioactivity.

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Conflict of interest statement

Authors SR, TB, PG, AK, IS, WES are employees of A4L or Analytics For Life (https://www.analytics4life.com/) and receive salary for that employment. This does not alter our adherence to all the PLOS ONE policies on sharing data and materials. There are no patents, products in development or marketed products to declare. All other authors have no competing interests to declare.

Figures

Fig 1
Fig 1. Utilization of the Cardiac Phase Space Tomography Analysis (cPSTA) System.
Phase signal data are collected and transferred to cloud. The generated models and analysis are available for physician assessment. cPSTA System = Cardiac Phase Space Tomography Analysis System, CAD = coronary artery disease. Reprinted from presentation materials of A4L under a license, with permission from A4L and W20, original production 2016.
Fig 2
Fig 2. Development and verification of machine-learned predictor.
The learning phase pairs “gold standard” results with phase signals for machine learning to develop algorithms. The verification phase tests the performance of the final algorithms on naïve signal data. cPSTA System = Cardiac Phase Space Tomography Analysis System. Reprinted from presentation materials of A4L under a license, with permission from A4L and W20, original production 2016.
See this image and copyright information in PMC

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