Association of NT-proBNP/LVEF with angiographic coronary disease burden in acute myocardial infarction: a retrospective study with machine learning analyses
- 1Department of Cardiology, Linyi People’s Hospital Affiliated to Shandong Second Medical University
Abstract
Background: N-terminal pro-B-type natriuretic peptide (NT-proBNP) and left ventricular ejection fraction (LVEF) reflect complementary dimensions of ventricular stress and systolic function. Whether the NT-proBNP/LVEF ratio is consistently associated with angiographic coronary disease burden in patients with acute myocardial infarction (AMI), and whether its predictive contribution is robust across conventional and machine-learning approaches, remains uncertain.
Methods: This single-center retrospective study included 375 patients with AMI who underwent coronary angiography. The continuous Gensini score was the primary outcome, while clinically anchored anatomical outcomes, including multivessel disease and high-burden coronary anatomy, served as sensitivity outcomes. Associations between log-transformed NT-proBNP/LVEF and Gensini score were assessed using multivariable median regression. Direct discrimination of NT-proBNP/LVEF versus NT-proBNP alone was evaluated using the area under the receiver operating characteristic curve (AUC) and paired DeLong testing; the incremental information provided by adding LVEF to NT-proBNP was additionally assessed using continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Six multivariable models—logistic regression, least absolute shrinkage and selection operator (LASSO) logistic regression, random forest, support vector machine (SVM), extreme gradient boosting (XGBoost), and CatBoost—were internally validated using leakage-controlled repeated fivefold cross-validation with five repeats. Calibration, decision curve analysis, and Shapley additive explanations (SHAP)-based model interpretation were also evaluated.
Results: Higher NT-proBNP/LVEF was consistently associated with greater angiographic coronary disease burden. In the fully adjusted model, each doubling of NT-proBNP/LVEF was associated with a 12.20-point increase in median Gensini score (95% confidence interval [CI], 9.33–15.07; P < 0.001). Comparable associations were observed for high-burden anatomy (odds ratio [OR] per doubling, 1.69; 95% CI, 1.44–1.97) and multivessel disease (OR per doubling, 1.58; 95% CI, 1.36–1.84). For the secondary high-Gensini-burden outcome, NT-proBNP/LVEF and NT-proBNP showed comparable discrimination (AUC, 0.783 vs. 0.780; ΔAUC = 0.004; P = 0.362). Adding LVEF to NT-proBNP did not show clear incremental improvement by NRI or IDI. Among the six multivariable models, SVM yielded the numerically highest AUC (0.763; 95% CI, 0.715–0.811), but no pairwise AUC difference remained significant after correction for multiple comparisons. XGBoost and CatBoost showed highly concordant SHAP importance rankings (Spearman ρ = 0.978), with NT-proBNP/LVEF ranked first in both models.
Conclusions: Higher NT-proBNP/LVEF was consistently associated with greater angiographic coronary disease burden across the continuous Gensini score and clinically anchored anatomical outcomes in patients with AMI. The ratio provides a parsimonious representation of ventricular stress and systolic function and showed stable predictive importance across the two boosting algorithms examined. Its discriminative performance was comparable, rather than superior, to that of NT-proBNP alone, supporting its potential role as a readily available adjunctive marker of coronary disease burden rather than a replacement for established clinical assessment.
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BibTeX
@article{li2026association,
title = {{Association of NT-proBNP/LVEF with angiographic coronary disease burden in acute myocardial infarction: a retrospective study with machine learning analyses}},
author = {Zhongxin Li and Jiantong Hou and Linqing Li and Xianzhao Zhang and Qingrong Ji},
journal = {Frontiers in Cardiovascular Medicine},
year = {2026},
month = oct,
doi = {10.3389/fcvm.2026.1964997},
url = {https://www.frontiersin.org/articles/10.3389/fcvm.2026.1964997/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Association of NT-proBNP/LVEF with angiographic coronary disease burden in acute myocardial infarction: a retrospective study with machine learning analyses
AU - Zhongxin Li
AU - Jiantong Hou
AU - Linqing Li
AU - Xianzhao Zhang
AU - Qingrong Ji
PY - 2026
DA - 2026/10/08
JO - Frontiers in Cardiovascular Medicine
DO - 10.3389/fcvm.2026.1964997
UR - https://www.frontiersin.org/articles/10.3389/fcvm.2026.1964997/full
AB - Background: N-terminal pro-B-type natriuretic peptide (NT-proBNP) and left ventricular ejection fraction (LVEF) reflect complementary dimensions of ventricular stress and systolic function. Whether the NT-proBNP/LVEF ratio is consistently associated with angiographic coronary disease burden in patients with acute myocardial infarction (AMI), and whether its predictive contribution is robust across conventional and machine-learning approaches, remains uncertain. Methods: This single-center retrospective study included 375 patients with AMI who underwent coronary angiography. The continuous Gensini score was the primary outcome, while clinically anchored anatomical outcomes, including multivessel disease and high-burden coronary anatomy, served as sensitivity outcomes. Associations between log-transformed NT-proBNP/LVEF and Gensini score were assessed using multivariable median regression. Direct discrimination of NT-proBNP/LVEF versus NT-proBNP alone was evaluated using the area under the receiver operating characteristic curve (AUC) and paired DeLong testing; the incremental information provided by adding LVEF to NT-proBNP was additionally assessed using continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Six multivariable models—logistic regression, least absolute shrinkage and selection operator (LASSO) logistic regression, random forest, support vector machine (SVM), extreme gradient boosting (XGBoost), and CatBoost—were internally validated using leakage-controlled repeated fivefold cross-validation with five repeats. Calibration, decision curve analysis, and Shapley additive explanations (SHAP)-based model interpretation were also evaluated. Results: Higher NT-proBNP/LVEF was consistently associated with greater angiographic coronary disease burden. In the fully adjusted model, each doubling of NT-proBNP/LVEF was associated with a 12.20-point increase in median Gensini score (95% confidence interval [CI], 9.33–15.07; P < 0.001). Comparable associations were observed for high-burden anatomy (odds ratio [OR] per doubling, 1.69; 95% CI, 1.44–1.97) and multivessel disease (OR per doubling, 1.58; 95% CI, 1.36–1.84). For the secondary high-Gensini-burden outcome, NT-proBNP/LVEF and NT-proBNP showed comparable discrimination (AUC, 0.783 vs. 0.780; ΔAUC = 0.004; P = 0.362). Adding LVEF to NT-proBNP did not show clear incremental improvement by NRI or IDI. Among the six multivariable models, SVM yielded the numerically highest AUC (0.763; 95% CI, 0.715–0.811), but no pairwise AUC difference remained significant after correction for multiple comparisons. XGBoost and CatBoost showed highly concordant SHAP importance rankings (Spearman ρ = 0.978), with NT-proBNP/LVEF ranked first in both models. Conclusions: Higher NT-proBNP/LVEF was consistently associated with greater angiographic coronary disease burden across the continuous Gensini score and clinically anchored anatomical outcomes in patients with AMI. The ratio provides a parsimonious representation of ventricular stress and systolic function and showed stable predictive importance across the two boosting algorithms examined. Its discriminative performance was comparable, rather than superior, to that of NT-proBNP alone, supporting its potential role as a readily available adjunctive marker of coronary disease burden rather than a replacement for established clinical assessment.
SN - 2297-055X
ER -