Nomogram for predicting the severity of high-risk plaques in acute coronary syndrome
Miao-Na Bai1,2,3, Ji-Xiang Wang2,3, Xiao-Wei Li1,2,3
1Graduate School, Clinical School of Thoracic, Tianjin Medical University, Tianjin, China.
Insights
This study developed a nomogram to predict high-risk plaque (HRP) in acute coronary syndrome (ACS) patients. The model accurately identifies individuals at risk, aiding in preventing major coronary events.
Area of Science:
- Cardiology
- Medical Imaging
- Biomedical Engineering
Background:
- The CLIMA study identified high-risk plaque (HRP) as a predictor of major coronary events.
- HRP is characterized by specific Optical Coherence Tomography (OCT) findings: small lumen area, thin fibrous cap, extensive lipid arc, and macrophage infiltration.
- Early prediction of HRP is crucial for preventing acute coronary syndrome (ACS), but lacks dedicated studies.
Purpose of the Study:
- To identify risk factors for OCT-defined HRP in ACS patients.
- To develop and validate a predictive model for HRP in ACS.
Main Methods:
- A prospective observational study included 169 ACS patients (September 2019 - August 2022).
- Patients were categorized into OCT HRP (n=55) and non-HRP (n=114) groups.
- Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate logistic regression were used to develop a risk prediction model and nomogram.
Main Results:
- The most frequent HRP characteristics were lipid arc >180° and minimum lumen area <3.5 mm².
- A validated nomogram identified five predictors: age, BMI ≥ 25 kg/m², triglycerides, LDL cholesterol, and Log NT-proBNP.
- The model demonstrated strong discrimination (AUC 0.780) and clinical usefulness.
Conclusions:
- A practical nomogram for predicting HRP in ACS patients was developed and validated.
- This tool can assist clinicians in diagnosing and preventing plaque instability.
- The nomogram aids in the early identification of patients at high risk for coronary events.
Background:
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.
Purpose:
To identify the risk factors associated with OCT HRP in ACS and develop a risk prediction model for HRPs in ACS.
Methods:
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.
Results:
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.
Conclusion:
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.
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