Dataset Information

A machine-learning based bio-psycho-social model for the prediction of non-obstructive and obstructive coronary artery disease.

ABSTRACT:

Background

Mechanisms of myocardial ischemia in obstructive and non-obstructive coronary artery disease (CAD), and the interplay between clinical, functional, biological and psycho-social features, are still far to be fully elucidated.

Objectives

To develop a machine-learning (ML) model for the supervised prediction of obstructive versus non-obstructive CAD.

Methods

From the EVA study, we analysed adults hospitalized for IHD undergoing conventional coronary angiography (CCA). Non-obstructive CAD was defined by a stenosis < 50% in one or more vessels. Baseline clinical and psycho-socio-cultural characteristics were used for computing a Rockwood and Mitnitski frailty index, and a gender score according to GENESIS-PRAXY methodology. Serum concentration of inflammatory cytokines was measured with a multiplex flow cytometry assay. Through an XGBoost classifier combined with an explainable artificial intelligence tool (SHAP), we identified the most influential features in discriminating obstructive versus non-obstructive CAD.

Results

Among the overall EVA cohort (n = 509), 311 individuals (mean age 67 ± 11 years, 38% females; 67% obstructive CAD) with complete data were analysed. The ML-based model (83% accuracy and 87% precision) showed that while obstructive CAD was associated with higher frailty index, older age and a cytokine signature characterized by IL-1β, IL-12p70 and IL-33, non-obstructive CAD was associated with a higher gender score (i.e., social characteristics traditionally ascribed to women) and with a cytokine signature characterized by IL-18, IL-8, IL-23.

Conclusions

Integrating clinical, biological, and psycho-social features, we have optimized a sex- and gender-unbiased model that discriminates obstructive and non-obstructive CAD. Further mechanistic studies will shed light on the biological plausibility of these associations.

Clinical trial registration

NCT02737982.

SUBMITTER: Raparelli V

PROVIDER: S-EPMC10449670 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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Publications

A machine-learning based bio-psycho-social model for the prediction of non-obstructive and obstructive coronary artery disease.

Raparelli Valeria V Romiti Giulio Francesco GF Di Teodoro Giulia G Seccia Ruggiero R Tanzilli Gaetano G Viceconte Nicola N Marrapodi Ramona R Flego Davide D Corica Bernadette B Cangemi Roberto R Pilote Louise L Basili Stefania S Proietti Marco M Palagi Laura L Stefanini Lucia L

Clinical research in cardiology : official journal of the German Cardiac Society 20230401 9

<h4>Background</h4>Mechanisms of myocardial ischemia in obstructive and non-obstructive coronary artery disease (CAD), and the interplay between clinical, functional, biological and psycho-social features, are still far to be fully elucidated.<h4>Objectives</h4>To develop a machine-learning (ML) model for the supervised prediction of obstructive versus non-obstructive CAD.<h4>Methods</h4>From the EVA study, we analysed adults hospitalized for IHD undergoing conventional coronary angiography (CCA ...[more]

PMID: 37004526

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Project description:Coronary artery calcium score (CACS) is a reliable predictor for future cardiovascular disease risk. Although deep learning studies using computed tomography (CT) images to predict CACS have been reported, no study has assessed the feasibility of machine learning (ML) algorithms to predict the CACS using clinical variables in a healthy general population. Therefore, we aimed to assess whether ML algorithms other than binary logistic regression (BLR) could predict high CACS in a healthy population with general health examination data. This retrospective observational study included participants who had regular health screening including coronary CT angiography. High CACS was defined by the Agatston score ≥ 100. Univariable and multivariable BLR was performed to assess predictors for high CACS in the entire dataset. When performing ML prediction for high CACS, the dataset was randomly divided into a training and test dataset with a 7:3 ratio. BLR, catboost, and xgboost algorithms with 5-fold cross-validation and grid search technique were used to find the best performing classifier. Performance comparison of each ML algorithm was evaluated with the area under the receiver operating characteristic (AUROC) curve. A total of 2133 participants were included in the final analysis. Mean age and proportion of male sex were 55.4 ± 11.3 years and 1483 (69.5%), respectively. In multivariable BLR analysis, age (odds ratio [OR], 1.12; 95% confidence interval [CI], 1.10-1.15, p < 0.001), male sex (OR, 2.91; 95% CI, 1.57-5.38, p < 0.001), systolic blood pressure (OR, 1.02; 95% CI, 1.00-1.03, p = 0.019), and low-density lipoprotein cholesterol (OR, 1.00; 95% CI, 0.99-1.00, p = 0.047) were significant predictors for high CACS. Performance in predicting high CACS of xgboost was AUROC of 0.823, followed by catboost (0.750) and BLR (0.585). The comparison of AUROC between xgboost and BLR was significant (p for AUROC comparison < 0.001). Xgboost ML algorithm was found to be a more reliable predictor of CACS in healthy participants compared to the BLR algorithm. ML algorithms may be useful for predicting CACS with only laboratory data in healthy participants.

Dataset Information

A machine-learning based bio-psycho-social model for the prediction of non-obstructive and obstructive coronary artery disease.

Background

Objectives

Methods

Results

Conclusions

Clinical trial registration

Publications

A machine-learning based bio-psycho-social model for the prediction of non-obstructive and obstructive coronary artery disease.

Similar Datasets

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