Key result
Proteomics machine learning outperforms clinical features in predicting CCTA-defined high-risk plaques and CAD absence.
Why the study?
Risk stratification is crucial to improve tailored therapy in patients with suspected CAD, prompting investigation into whether targeted proteomics could predict high-risk plaque presence or atherosclerosis absence.
Do machine learning models trained on targeted proteomics improve the prediction of high-risk coronary plaque or absence of CAD compared to conventional clinical characteristics in patients with suspected CAD?
Observational (n=196)
Do machine learning models trained on targeted proteomics improve the prediction of high-risk coronary plaque or absence of CAD compared to conventional clinical characteristics in patients with suspected CAD?
Effect estimate: AUC 0.79 ± 0.01
p-value: p=<0.05
Machine learning models using targeted proteomics outperform conventional clinical characteristics in predicting the presence of high-risk coronary plaques or the absence of CAD on CCTA.
May aid CCTA risk stratification in suspected CAD; hypothesis-generating and requires prospective validation before practice change.
BACKGROUND: Risk stratification is crucial to improve tailored therapy in patients with suspected coronary artery disease (CAD). This study investigated the ability of targeted proteomics to predict presence of high-risk plaque or absence of coronary atherosclerosis in patients with suspected CAD, defined by coronary computed tomography angiography (CCTA). METHODS: Patients with suspected CAD (n = 203) underwent CCTA. Plasma levels of 358 proteins were used to generate machine learning models for the presence of CCTA-defined high-risk plaques or complete absence of coronary atherosclerosis. Performance was tested against a clinical model containing generally available clinical characteristics and conventional biomarkers. FINDINGS: A total of 196 patients with analyzable protein levels (n = 332) was included for analysis. A subset of 35 proteins was identified predicting the presence of high-risk plaques. The developed machine learning model had fair diagnostic performance with an area under the curve (AUC) of 0·79 ± 0·01, outperforming prediction with generally available clinical characteristics (AUC = 0·65 ± 0·04, p < 0·05). Conversely, a different subset of 34 proteins was predictive for the absence of CAD (AUC = 0·85 ± 0·05), again outperforming prediction with generally available characteristics (AUC = 0·70 ± 0·04, p < 0·05). INTERPRETATION: Using machine learning models, trained on targeted proteomics, we defined two complementary protein signatures: one for identification of patients with high-risk plaques and one for identification of patients with absence of CAD. Both biomarker subsets were superior to generally available clinical characteristics and conventional biomarkers in predicting presence of high-risk plaque or absence of coronary atherosclerosis. These promising findings warrant external validation of the value of targeted proteomics to identify cardiovascular risk in outcome studies. FUND: This study was supported by an unrestricted research grant from HeartFlow Inc. and partly supported by a European Research Area Network on Cardiovascular Diseases (ERA-CVD) grant (ERA CVD JTC2017, OPERATION). Funders had no influence on trial design, data evaluation, and interpretation.
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Bom et al. (2018) conducted an observational in Suspected coronary artery disease (n=196). Targeted proteomics machine learning model vs. Generally available clinical characteristics and conventional biomarkers was evaluated on Presence of CCTA-defined high-risk plaques (AUC 0.79 ± 0.01, p=<0.05). A targeted proteomics machine learning model outperformed clinical characteristics in predicting CCTA-defined high-risk plaques (AUC 0.79 vs 0.65, p<0.05) and absence of CAD (AUC 0.85 vs 0.70, p<0.05).