<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Yang H</submitter><funding>Medical Science Research Project of Hebei Province</funding><funding>Hebei Province Master's Graduate Innovation Ability Training Funding Project</funding><funding>S&amp;T Program of Chengde</funding><pagination>72</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC11852830</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>24(1)</volume><pubmed_abstract>&lt;h4>Background&lt;/h4>Coronary heart disease (CHD) represents a severe form of ischemic cardiac condition that necessitates timely and accurate diagnosis. Although coronary angiography (CAG) remains widely used to detect CHD, healthcare facilities, medical expenses, and equipment technology often limit its availability. Therefore, it is imperative to identify a non-invasive diagnostic approach with high accuracy for CHD.&lt;h4>Methods&lt;/h4>This cross-sectional research included patients with chest pain (≥ 18 years) hospitalized at Chengde Central Hospital between September 2020 and March 2024. Among the participants, 70% were split into the training, and 30% were randomly entered into the validation sets. In the training dataset, univariate and multivariate logistic regression analyses were rigor</pubmed_abstract><journal>Lipids in health and disease</journal><pubmed_title>Establishing and internally validating a predictive model for coronary heart disease incorporating triglyceride-glucose index, monocyte-to-high-density lipoprotein cholesterol ratio and conventional risk factors in patients experiencing chest pain and referred for invasive coronary angiography.</pubmed_title><pmcid>PMC11852830</pmcid><funding_grant_id>20200348</funding_grant_id><funding_grant_id>202109A019</funding_grant_id><funding_grant_id>CXZZSS2023147</funding_grant_id><pubmed_authors>Yang H</pubmed_authors><pubmed_authors>Sun M</pubmed_authors><pubmed_authors>Shu HJ</pubmed_authors><pubmed_authors>Dou J</pubmed_authors><pubmed_authors>Gao J</pubmed_authors><pubmed_authors>Wang X</pubmed_authors><pubmed_authors>Guo R</pubmed_authors><pubmed_authors>Sun H</pubmed_authors><pubmed_authors>Hou Y</pubmed_authors><pubmed_authors>Zhao X</pubmed_authors><pubmed_authors>Song Y</pubmed_authors><pubmed_authors>Luo D</pubmed_authors></additional><is_claimable>false</is_claimable><name>Establishing and internally validating a predictive model for coronary heart disease incorporating triglyceride-glucose index, monocyte-to-high-density lipoprotein cholesterol ratio and conventional risk factors in patients experiencing chest pain and referred for invasive coronary angiography.</name><description>&lt;h4>Background&lt;/h4>Coronary heart disease (CHD) represents a severe form of ischemic cardiac condition that necessitates timely and accurate diagnosis. Although coronary angiography (CAG) remains widely used to detect CHD, healthcare facilities, medical expenses, and equipment technology often limit its availability. Therefore, it is imperative to identify a non-invasive diagnostic approach with high accuracy for CHD.&lt;h4>Methods&lt;/h4>This cross-sectional research included patients with chest pain (≥ 18 years) hospitalized at Chengde Central Hospital between September 2020 and March 2024. Among the participants, 70% were split into the training, and 30% were randomly entered into the validation sets. In the training dataset, univariate and multivariate logistic regression analyses were rigor</description><dates><release>2025-01-01T00:00:00Z</release><publication>2025 Feb</publication><modification>2025-04-03T23:32:46.04Z</modification><creation>2025-04-03T23:32:46.04Z</creation></dates><accession>S-EPMC11852830</accession><cross_references><pubmed>40001112</pubmed><doi>10.1186/s12944-025-02486-w</doi></cross_references></HashMap>