Diagnostic Model for Obstructive Coronary Artery Disease with CHG and DELC

A cross-sectional study of 1,645 patients from an unspecified location, conducted by Wenxin Lin and last updated in April 2026. The dataset was used to construct and validate a diagnostic model for angiographic obstructive coronary artery disease, incorporating the CHG index and diagonal earlobe crease alongside traditional risk factors.

Use Cases

• Training a binary classification model to predict obstructive CAD based on risk factors like sex, hypertension, and age.
• Evaluating the incremental diagnostic value of the CHG index and DELC using metrics like AUC, IDI, and NRI.
• Developing a clinical nomogram for visualizing the probability of obstructive CAD based on the six identified risk factors.
• Performing internal validation of a diagnostic model using bootstrap resampling methods.

Strengths

• Model was constructed and internally validated using a dataset of 1,645 patient records.
• Validation was performed using 500 bootstrap resamples, providing an optimistically corrected AUC of 0.683.
• The study employed multiple advanced statistical methods, including LASSO regression, restricted cubic splines, and decision curve analysis.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The geographic origin of the patient cohort is not specified, which may limit generalizability.

Provenance

Source

Wenxin Lin via figshare

Collection Method

Cross-sectional study of patients undergoing coronary angiography.

Freshness

Last updated 2026-04-17 10:56:39; freshness should be verified.