# Development and external validation of a nomogram prediction model based on quantitative coronary angiography for predicting ischemic lesions: a multi-centre study

## TL;DR

This study creates and validates a model using coronary angiography data to predict heart artery blockages that cause reduced blood flow.

## Contribution

A novel nomogram model using QCA and LASSO regression is developed and externally validated for predicting ischemic lesions.

## Key findings

- The nomogram achieved high AUC values (0.922 in development, 0.915 in validation) for predicting ischemic lesions.
- Predictors included lesion length, minimal lumen diameter, and stenosis flow reserve with strong accuracy and sensitivity.
- The model demonstrated consistent performance across per-vessel and per-patient analyses in both development and validation cohorts.

## Abstract

Quantitative coronary angiography (QCA) has significantly contributed to the diagnosis of coronary artery disease. This study aimed to construct and validate a QCA-based prediction model, represented as a nomogram, for predicting ischemic lesions defined by invasive fractional flow reserve (FFR) ≤ 0.80.

In this multi-centre study, we enrolled 220 patients with 303 interrogated vessels who underwent FFR measurements during clinically indicated invasive coronary angiography. QCA predictors for ischemic lesions were extracted to construct a nomogram model using Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis of the development set (n = 113 patients). An external validation (n = 107 patients) was performed to assess the nomogram model's discrimination and consistency.

Lesion length, minimal lumen diameter, stenosis flow reserve, percent diameter stenosis by visual estimation, and weight were included as predictors in the nomogram. The nomogram yielded an area under the curve (AUC) of 0.922 and 0.912 at per-vessel and per-patient levels, respectively, in the development set. In the validation set, it achieved an AUC of 0.915 and 0.912 at per-vessel and per-patient levels, respectively. Per-vessel accuracy, sensitivity, and specificity derived from the nomogram were 86.5%, 88.2%, 86.2% in the development cohort and 84.2%, 85.5%, and 83.1% in the validation cohort. For per-patient analysis, the corresponding values were 85.8%, 85.7%, 86.0% in the development cohort and 82.2%, 83.3%, 81.1% in the validation cohort.

The nomogram may be useful for predicting ischemic lesions using QCA measurements and the LASSO regression algorithm, with external validation indicating potential predictive value in cardiology care settings.

Development and external validation of a nomogram prediction model based on quantitative coronary angiography for predicting ischemic lesions: A multi-centre study.Diagram displaying a study on a nomogram prediction model for coronary ischemia. The dataset comprises 220 patients with 303 vessels. It is divided into a development set with 113 patients and an external validation set with 107 patients. Predictors include visual diameter stenosis, stenotic flow reserve, weight, minimal lumen diameter, and lesion length. ROC curves show the development cohort with an AUC of 0.922 and the validation cohort with an AUC of 0.915. The title is "Development and External Validation of a Nomogram Prediction Model Based on Quantitative Coronary Angiography for Predicting Ischemic Lesions: A Multi-centre Study.

Development and external validation of a nomogram prediction model based on quantitative coronary angiography for predicting ischemic lesions: A multi-centre study.