A Novel Nomogram for Predicting the Risk of Coronary Atherosclerosis in Patients with Gastroesophageal Reflux Disease A Nomogram Predicting Coronary Atherosclerosis

Abstract

Aims This study developed a novel nomogram to predict the incidence of coronary atherosclerosis (CA) in patients with gastroesophageal reflux disease (GERD) and evaluated the predictive value of the nomogram. Methods 13658 patients of gastroesophageal reflux disease from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database were analysed. The patients were randomly divided into two groups in a seven-to-three ratio to form a training cohort (n = 9560) and a validation cohort (n = 4098). Least absolute shrinkage and selection operator (LASSO) regression analyses were used to identify associated risk variables. A nomogram was established to predict the rate of coronary atherosclerosis in patients with gastroesophageal reflux disease. The new model was assessed in terms of the concordance index (C-index), the area under the curve (AUC) of receiver operating characteristic (ROC) analysis, calibration curve, and decision curve analysis (DCA). Results Least absolute shrinkage and selection operator regression analysis identified nine potential predictors of coronary atherosclerosis. Multivariate logistic regression analysis was used to evaluate the effects of these predictors and create a final model. The concordance index values were 0.750. The areas under the curves for the training and validation sets were 0.7500 and 0.7297, respectively. Conclusion The age, white blood cells (WBC), hemoglobin, mean corpsular hemoglobin (MCH), mean corpuscular volume (MCV), sodium, bicarbonate, creatinine and chloride were identified as predictors. Our nomogram is a reliable convenient approach for predicting coronary atherosclerosis in patients with gastroesophageal reflux disease.