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. Graphical abstract

Predictive function of tumor burden-incorporated machine-learning algorithms for overall survival and their value in guiding management decisions in patients with locally advanced nasopharyngeal carcinoma

“…XGBoost has demonstrated exceptional performance in numerous predictive works and recently found applications in medical research. 11 , 20 , 21 It produces a sequence of tree models constructed iteratively, each built upon the previous one. The desired outcome of the XGBoost survival version includes the OS rate and the survival time.…”

Gender scores in epidemiological research: methods, advantages and implications

“…Ultimately, 24 articles that describe 26 gender indices are included in this review ( Table 1 , Appendix 2 ). 2 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 …”

“…(2024) 44 x Signature Biobank (Canada; 2012–2020; N = 1708; 39% female) Gender index score, ranging from 0 to 1, with higher values indicating masculinity Hostile behavior during childhood and adulthood Having at least a secondary school diploma or bachelor's degree Sleep satisfaction and efficacity Having private housing Experiences of sexual violence during childhood 21 f Raparelli et al. (2023) 45 x EVA Cohort (Italy; 2016–2020; N = 311; 38% female) EVA gender score, ranging from 0 to 100, with higher scores relating to characteristics traditionally ascribed to women. Engagement in social leisure activities Marital status Responsible for household tasks Time spend on household tasks Primary earnership Level of stress at home Level of received emotional support Availability of trust and confidence measures 22 g Teterina et al.…”

“…It should be noted that some indices are developed in patient cohorts, for example among patients with cardiovascular disease or in a subgroup of the general population that experienced traumatic brain injury. 35 , 45 , 46 Components of developed indices in these populations may therefore be disease-specific.…”