# Nomogram for predicting the severity of high-risk plaques in acute coronary syndrome

## TL;DR

This study developed a nomogram to predict high-risk coronary plaques in patients with acute coronary syndrome, using clinical and OCT data to improve diagnosis and prevention.

## Contribution

The novel contribution is a validated nomogram model for predicting high-risk plaques in acute coronary syndrome patients.

## Key findings

- The nomogram includes five predictors: age, BMI ≥25, triglycerides, LDL cholesterol, and Log NT-proBNP.
- The model showed good discrimination with an AUC of 0.780 and was internally validated using bootstrap methods.
- Lipid arc >180° was the most common high-risk plaque characteristic observed in the study population.

## Abstract

The CLIMA study [Relationship between Optical Coherence Tomography (OCT) Coronary Plaque Morphology and Clinical Outcome; NCT02883088] introduced the concept of high-risk plaque (HRP) and demonstrated that HRP was associated with a high risk of major coronary events. HRP is defined by four simultaneous characteristics: minimum lumen area (MLA) <3.5 mm2, fibrous cap thickness (FCT) <75 μm, lipid arc circumferential extension >180°, and macrophage infiltration. Early prediction of HRP formation is critical for preventing and treating acute coronary syndrome (ACS), but no studies have been conducted on this topic.

To identify the risk factors associated with OCT HRP in ACS and develop a risk prediction model for HRPs in ACS.

A prospective observational study was conducted on patients with ACS between September 2019 and August 2022. A total of 169 patients were divided into two groups: OCT HRP (n = 55) and OCT non-HRP (n = 114) groups. Clinical data, laboratory results, and OCT characteristics of the patients were collected. Least absolute shrinkage and selection operator (LASSO) regression was used to screen variables, while multivariate logistic regression was used to create a risk prediction model. A nomogram was created, and the receiver operating characteristic curve was used to assess the model's discrimination, as well as the bootstrap method to internally validate it.

The most commonly observed HRP characteristic was lipid plague >180° (147 patients), followed by MLA < 3.5 mm2 (141 patients), macrophages (127 patients), and FCT < 75 μm (64 patients). The LASSO regression model was used to screen variables and develop an HRP risk factor model. The nomogram includes five predictors: age, BMI ≥ 25 kg/m2, triglycerides, low-density lipoprotein cholesterol, and Log N-terminal brain natriuretic peptide precursor. The model is highly differentiated (area under the curve 0.780, 95% confidence interval 0.705–855) and calibrated. The calibration curve and decision curve analysis demonstrated the model's clinical usefulness.

A simple and practical nomogram for predicting HRPs accurately in patients with ACS was developed and validated, and is expected to help clinicians diagnose and prevent plaque stability.