Data Sheet 1_Diagnostic model for angiographic obstructive coronary artery disease combining CHG, DELC, and traditional risk factors: a bootstrap validation study.docx

ObjectiveTo 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.
MethodsThe 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).
ResultsLasso 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.
ConclusionCHG 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.

研究目的：本研究旨在构建并内部验证一项针对血管造影阻塞性冠状动脉疾病（obstructive CAD，定义为冠状动脉造影显示狭窄程度≥50%的冠心病）的诊断模型，该模型纳入胆固醇、高密度脂蛋白与葡萄糖（CHG）指数、对角耳垂褶皱（DELC），以及其他传统心血管危险因素。
研究方法：本研究采用横断面设计，共纳入1645例患者，按诊断结果分为阻塞性CAD组（n=1298）与非阻塞性CAD组（n=347）。首先通过最小绝对收缩和选择算子（LASSO）回归筛选独立危险因素，随后将其纳入二元logistic回归模型以构建诊断模型。采用限制性立方样条分析（RCS）探究CHG指数与阻塞性CAD发病风险之间的剂量-反应关系。通过DeLong检验比较受试者工作特征曲线下面积（AUC）以评估增量诊断价值，并采用综合判别改善指数（IDI）与净重新分类改善指数（NRI）分析风险重新分类与判别改善情况。采用Bootstrap法（重采样次数B=500）进行内部验证。通过列线图、AUC、校准曲线、决策曲线分析（DCA）以及临床影响曲线（CIC）全面评估模型的判别能力、校准度与临床实用性。
研究结果：LASSO回归最终筛选出6项独立危险因素：男性性别、高血压、年龄、血清肌酐（Scr）、CHG指数与DELC。RCS分析显示，CHG指数与阻塞性CAD发病风险呈线性正相关（非线性检验P=0.865）。所构建的模型在全数据集上的表观AUC为0.692（95%CI：0.661~0.724），经Bootstrap内部验证校正后的乐观校正AUC为0.683（95%CI：0.650~0.715）。
研究结论：CHG指数与DELC均为阻塞性CAD的独立危险因素，且CHG水平与阻塞性CAD发病风险呈线性相关。基于上述因素构建的诊断模型可辅助指导高危人群的后续诊疗工作。