Cardiovascular diseases (CVDs) present multi-factorial pathophysiology and produce immense health and economic burdens globally. The most common type, coronary artery disease (CAD), shows a complex etiology with multiple genetic variants to interplay with various clinical features and demographic traits affecting CAD risk and severity. The development and clinical validation of machine learning (ML) algorithms that integrate genetic biomarkers and clinical features can improve diagnostic accuracy for CAD avoiding, thereby, unnecessary invasive procedures. To this end, we present, here, the development of a data-driven ML approach able to predict the existence and severity of CAD based on the analysis of 228 single nucleotide polymorphisms (SNPs) and clinical and demographic data of 953 patients enrolled in the Genetic Syntax Score (GESS) trial (NCT03150680). Two competing ensemble models (one with clinical predictors and another with clinical plus genetic predictors) were built and evaluated to infer their prediction capabilities. The ensemble model with both clinical and genetic predictors exhibited superior diagnostic performance compared to the competing model with only clinical predictors. The proposed ML framework identified a total of eight contributing SNPs as predictors for the existence of obstructive CAD and seven significant SNPs for the severity of CAD. Such algorithms positively contributes to global efforts aiming to predict the risk and severity of CAD in early stages, thus lowering the cost as well as achieving prognostic, diagnostic, and therapeutic benefits in healthcare and improving patient outcomes in a non-invasive way. Overall, the design and execution of this trial reinforces clinical decision-making and facilitate the harmonization in digitized healthcare within the concept of precision medicine.

Fani Chatzopoulou is employed by Labnet Laboratories. Dimitrios Chatzidimitriou is CEO of Labnet Laboratories. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. SNPs included in the ML model are covered by the international patent application No PCT/GR2022/000067: Development of ?GESScore Calculator? as predictive risk tool of cardiovascular events by the implementation of an algorithm using genetic factors and the complexity of coronary disease (International filing date: 30 November 2022) and the Greek patent Hellenic Industrial Property Organisation (OBI) - efiling number: 2410-0004615550 -Ref. Num. 24652/2022 (efiling date: 30 November 2022). The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

This study was funded by the European Regional Development Fund of the European Union and Greek national funds through the Operational Programme Competitiveness, Entrepreneurship, and Innovation, under the call RESEARCH CREATE INNOVATE (project code: T1EDK 02354).

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The ethical approval for the GESS trial was granted from the Scientific Committee of the AHEPA University Hospital of Thessaloniki, Greece (reference number 309/11 05 2017). Moreover, this study was registered in ClinicalTrials.gov with Identifier NCT03150680.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

All data produced in the present study are available upon reasonable request to the authors.