Maximization of the usage of coronary CTA derived plaque information using a machine learning based algorithm to improve risk stratification; insights from the CONFIRM registry.
J Cardiovasc Comput Tomogr. 2018 May - Jun;12(3):204-209. doi: 10.1016/j.jcct.2018.04.011. Epub 2018 Apr 30. van Rosendael AR1, Maliakal G1, Kolli KK1, Beecy A1, Al'Aref SJ1, Dwivedi A1, Singh G1, Panday M1, Kumar A1, Ma X1, Achenbach S2, Al-Mallah MH3, Andreini D4, Bax JJ5, Berman DS6, Budoff MJ7, Cademartiri F8, Callister TQ9, Chang HJ10, Chinnaiyan K11, Chow BJW12, Cury RC13, DeLago A14, Feuchtner G15, Hadamitzky M16, Hausleiter J17, Kaufmann PA18, Kim YJ19, Leipsic JA20, Maffei E21, Marques H22, Pontone G4, Raff GL11, Rubinshtein R23, Shaw LJ24, Villines TC25, Gransar H26, Lu Y27, Jones EC1, Peña JM1, Lin FY1, Min JK28.
INTRODUCTION: Machine learning (ML) is a field in computer science that demonstrated to effectively integrate clinical and imaging data for the creation of prognostic scores. The current study investigated whether a ML score, incorporating only the 16 segment coronary tree information derived from coronary computed tomography angiography (CCTA), provides enhanced risk stratification compared with current CCTA based risk scores.
METHODS: From the multi-center CONFIRM registry, patients were included with complete CCTA risk score information and ≥3 year follow-up for myocardial infarction and death (primary endpoint). Patients with prior coronary artery disease were excluded. Conventional CCTA risk scores (conventional CCTA approach, segment involvement score, duke prognostic index, segment stenosis score, and the Leaman risk score) and a score created using ML were compared for the area under the receiver operating characteristic curve (AUC). Only 16 segment based coronary stenosis (0%, 1-24%, 25-49%, 50-69%, 70-99% and 100%) and composition (calcified, mixed and non-calcified plaque) were provided to the ML model. A boosted ensemble algorithm (extreme gradient boosting; XGBoost) was used and the entire data was randomly split into a training set (80%) and testing set (20%). First, tuned hyperparameters were used to generate a trained model from the training data set (80% of data). Second, the performance of this trained model was independently tested on the unseen test set (20% of data).
RESULTS: In total, 8844 patients (mean age 58.0 ± 11.5 years, 57.7% male) were included. During a mean follow-up time of 4.6 ± 1.5 years, 609 events occurred (6.9%). No CAD was observed in 48.7% (3.5% event), non-obstructive CAD in 31.8% (6.8% event), and obstructive CAD in 19.5% (15.6% event). Discrimination of events as expressed by AUC was significantly better for the ML based approach (0.771) vs the other scores (ranging from 0.685 to 0.701), P < 0.001. Net reclassification improvement analysis showed that the improved risk stratification was the result of down-classification of risk among patients that did not experience events (non-events).
CONCLUSION: A risk score created by a ML based algorithm, that utilizes standard 16 coronary segment stenosis and composition information derived from detailed CCTA reading, has greater prognostic accuracy than current CCTA integrated risk scores. These findings indicate that a ML based algorithm can improve the integration of CCTA derived plaque information to improve risk stratification.