ArticleFrontiers in endocrinology2026
Diagnostic model for angiographic obstructive coronary artery disease combining CHG, DELC, and traditional risk factors: a bootstrap validation study.
Article in Frontiers in endocrinology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To construct and internally validate a diagnostic model for angiographic obstructive coronary artery disease (obstructive CAD) (defined as ≥50% stenosis on coronary angiography) incorporating the cholesterol, high-density lipoprotein, and glucose (CHG) index and diagonal earlobe crease (DELC) alongside other traditional risk factors. Methods: The study employed a cross-sectional design, involving a total of 1,645 patients, who were divided into two groups: those diagnosed with obstructive CAD (n = 1,298) and those without (n = 347). Independent risk factors were screened using least absolute shrinkage and selection operator (LASSO) regression and subsequently incorporated into a binary logistic regression model to construct the diagnostic model. The dose-response relationship between CHG and obstructive CAD risk was examined using restricted cubic spline analysis (RCS). The incremental diagnostic value was examined through DeLong's test for area under the receiver operating characteristic curve (AUC) comparisons and through integrated discrimination improvement (IDI) and net reclassification improvement (NRI) for risk reclassification and discrimination improvement. Internal validation was performed using the Bootstrap method (B = 500 resamples). The model's discriminative ability, calibration, and clinical utility were comprehensively assessed through a nomogram, AUC, calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC). Results: Lasso regression ultimately identified six independent risk factors: male sex, hypertension, age, serum creatinine (Scr), CHG, and DELC. RCS revealed a linear positive correlation between the CHG index and obstructive CAD risk (P for nonlinear = 0.865). The constructed model yielded an apparent AUC of 0.692 (95% CI: 0.661-0.724) on the full dataset, with an optimistically corrected AUC of 0.683 (95% CI: 0.650-0.715) following internal validation via bootstrapping. Conclusion: CHG and DELC represent independent risk factors for obstructive CAD, with CHG levels exhibiting a linear relationship with obstructive CAD risk. The diagnostic model constructed based on these factors could assist in guiding subsequent diagnosis and treatment in high-risk populations.
Indexed as
Identifiers
What OpenQuestion holds
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.