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Nomogram model for predicting multivessel disease in coronary artery disease using cardiac function parameters and
Aibao Huang1, Guangdong Yan1, Mengying Yu1
1Guangzhou Red Cross Hospital of Jinan University, Guangzhou, Guangdong, China.
Insights
This study developed a nomogram to predict multivessel disease (MVD), a severe form of coronary artery disease. The tool accurately identifies high-risk patients using clinical factors and cardiac function, aiding early intervention.
Area of Science:
- Cardiology
- Medical Diagnostics
- Predictive Modeling
Background:
- Multivessel disease (MVD) is a severe coronary artery disease phenotype linked to poor prognosis.
- Early, non-invasive identification of MVD is a significant clinical challenge.
- Existing methods for MVD detection lack sufficient accuracy and accessibility.
Purpose of the Study:
- To develop and validate a nomogram for the individualized, non-invasive prediction of MVD risk.
- To integrate cardiac function parameters and clinical characteristics into a predictive model.
- To improve early identification of patients with MVD for timely intervention.
Main Methods:
- Retrospective collection of clinical data from 353 patients with confirmed coronary artery disease.
- Utilized Least Absolute Shrinkage and Selection Operator (LASSO) regression for risk factor screening.
- Developed a predictive model using multivariate logistic regression and validated it with AUC, calibration curves, and decision curve analysis.
Main Results:
- A nomogram incorporating age, gender, prior stent, total cholesterol, LVEF, HDL, and albumin was created.
- The model demonstrated good discrimination with AUCs of 0.84 (training) and 0.79 (validation).
- The nomogram accurately stratified patients into high- and low-risk groups for MVD.
Conclusions:
- The developed nomogram offers an accurate, individualized, and non-invasive tool for predicting MVD risk.
- This tool can assist clinicians in identifying high-risk MVD patients who may benefit from intensified treatment.
- The nomogram enhances clinical decision-making for coronary artery disease management.
Background:
Multivessel disease (MVD) represents a severe phenotype of coronary artery disease and is associated with poor prognosis. Early, non-invasive identification of MVD remains a clinical challenge. This study aimed to develop and validate a nomogram integrating cardiac function parameters and clinical characteristics for individualized prediction of MVD risk.
Methods:
Clinical data were retrospectively collected from 353 patients with angiographically confirmed coronary artery disease at Guangzhou Red Cross Hospital between January 2023 and December 2024. Patients were randomly assigned to a training set (70%) and an internal validation set (30%). Least absolute shrinkage and selection operator (LASSO) regression was used to screen potential risk factors, followed by multivariate logistic regression to construct the predictive model and generate the nomogram. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA).
Results:
Seven predictors were ultimately included in the nomogram: age, gender, prior stent implantation, total cholesterol, left ventricular ejection fraction (LVEF), high-density lipoprotein, and albumin. The model exhibited good discrimination, with an AUC of 0.84 (95% CI [0.79-0.90]) in the training set and 0.79 (95% CI [0.68-0.89]) in the validation set. Accuracy was 0.81 in both datasets. Calibration curves and decision curve analysis demonstrated good predictive accuracy and clinical utility of the nomogram. Furthermore, the nomogram scores successfully stratified patients into high-risk (≥191) and low-risk (<191) groups, with significantly different score distributions between the MVD and non-MVD groups (P < 0.001). The developed nomogram provides an accurate and individualized tool for non-invasive prediction of MVD risk and may assist clinicians in identifying high-risk patients who could benefit from intensified intervention.
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