Evidence map›Paper›PMID 37004526›Full record

ArticleClinical research in cardiology : official journal of the German Cardiac Society2023

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

Valeria Raparelli, Giulio Francesco Romiti, Giulia Di Teodoro, Ruggiero Seccia, Gaetano Tanzilli, Nicola Viceconte, Ramona Marrapodi, Davide Flego, Bernadette Corica, Roberto Cangemi and 6 more

Registry-linked trialOpen access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical research in cardiology : official journal of the German Cardiac Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02737982 (Sex and Gender Differences in Ischemic Heart Disease), which is not on this map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
2.0field-weighted citation impact, top 12% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT02737982 completednot on this map

Sex and Gender Differences in Ischemic Heart Disease: From Bench to Bedside EVA (Endocrine Vascular Disease Approach)

Typeobservational_patient_registrySponsorUniversity of Roma La SapienzaRan2016 to 2020Enrolled509ConditionsMyocardial IschemiaArmspercutaneous coronary intervention
3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Performance of risk models to predict mortality risk for patients with heart failure: evaluation in an integrated health system.Clinical research in cardiology : official journal of the German Cardiac Society · 2024
    Article
  6. Review
  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors at 5 institutions in 3 countries.

Valeria RaparelliDepartment of Experimental Medicine, Sapienza University of Rome, Rome, Italy. valeria.raparelli@unife.it.ORCID http://orcid.org/0000-0002-2100-5682
Giulio Francesco RomitiDepartment of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy.
Giulia Di TeodoroDepartment of Computer Control and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy.
Ruggiero SecciaDepartment of Computer Control and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy.
Gaetano TanzilliDepartment of Clinical, Internal, Anesthesiology and Cardiovascular Sciences, Umberto I Hospital, Sapienza University of Rome, Rome, Italy.
Nicola ViceconteDepartment of Clinical, Internal, Anesthesiology and Cardiovascular Sciences, Umberto I Hospital, Sapienza University of Rome, Rome, Italy.
Ramona MarrapodiDepartment of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy.
Davide FlegoDepartment of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy.
Bernadette CoricaDepartment of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy.
Roberto CangemiDepartment of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy.
Louise PiloteCentre for Outcomes Research and Evaluation, McGill University Health Centre Research Institute, Montreal, QC, Canada.
Stefania BasiliDepartment of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy.
Marco ProiettiLiverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart and Chest Hospital, Liverpool, UK.
Laura PalagiDepartment of Computer Control and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy.
Lucia StefaniniDepartment of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy.
EVA Investigators
Sapienza University of Rome · ITUniversity of Liverpool · GBPoliclinico Umberto I · ITMcGill University Health Centre · CAUniversity of Alberta · CA

Funding

Ministero dell'Istruzione, dell'Università e della Ricerca PRIN- 2017ATZ2YKMinistero dell'Istruzione, dell'Università e della Ricerca PRIN-2017WJBKKWMinistero dell'Istruzione, dell'Università e della Ricerca RBSI14HNVT
6 · The paper itself

Abstract

backgroundMechanisms 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.

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

methodsFrom 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.

resultsAmong 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.

conclusionsIntegrating 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 registrationNCT02737982.

Indexed as

Coronary Artery DiseaseFrailtyMyocardial IschemiaAdultAgedArtificial IntelligenceCoronary AngiographyCytokinesFemaleHumansMachine LearningMaleMiddle AgedPredictive Value of TestsRisk FactorsCytokinesCytokinesFrailtyGenderInflammationIschemic heart diseaseMachine learningNon-obstructive coronary artery disease

Identifiers

PMID37004526
PMCPMC10449670
OpenAlexW4362507127

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.