Arab journal of gastroenterology : the official publication of the Pan-Arab Association of Gastroenterology · 2025
A novel nomogram for predicting the risk of coronary atherosclerosis in patients with gastroesophageal reflux disease.
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Wu Ziyang, Wang Dong, Tang Chengchun
Аннотация
BACKGROUND AND STUDY AIMS: This study developed a novel nomogram to predict the incidence of coronary atherosclerosis (CA) in patients with gastroesophageal reflux disease (GERD) and evaluated its predictive value.
PATIENTS AND METHODS: In total, 13,658 patients with GERD from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database were analyzed. The patients were randomly divided into a training cohort (n = 9,560) and a validation cohort (n = 4,098) at a 7:3 ratio. Least absolute shrinkage and selection operator (LASSO) regression analyses were performed to identify associated risk variables. Then a nomogram was developed to predict the rate of CA in patients with GERD. The model's performance was evaluated using the concordance index, area under the receiver operating characteristic curve, calibration curve, and decision curve analysis.
RESULTS: LASSO regression identified nine potential predictors of CA. Multivariate logistic regression was used to evaluate these predictors and establish the final model. The concordance index was 0.750. The areas under the curve for the training and validation cohorts were 0.7500 and 0.7297, respectively.
CONCLUSION: Age, white blood cell count, hemoglobin, mean corpuscular hemoglobin, mean corpuscular volume, sodium, bicarbonate, creatinine, and chloride were identified as predictors of CA. The developed nomogram provides a reliable and convenient tool for predicting CA in patients with GERD.
PATIENTS AND METHODS: In total, 13,658 patients with GERD from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database were analyzed. The patients were randomly divided into a training cohort (n = 9,560) and a validation cohort (n = 4,098) at a 7:3 ratio. Least absolute shrinkage and selection operator (LASSO) regression analyses were performed to identify associated risk variables. Then a nomogram was developed to predict the rate of CA in patients with GERD. The model's performance was evaluated using the concordance index, area under the receiver operating characteristic curve, calibration curve, and decision curve analysis.
RESULTS: LASSO regression identified nine potential predictors of CA. Multivariate logistic regression was used to evaluate these predictors and establish the final model. The concordance index was 0.750. The areas under the curve for the training and validation cohorts were 0.7500 and 0.7297, respectively.
CONCLUSION: Age, white blood cell count, hemoglobin, mean corpuscular hemoglobin, mean corpuscular volume, sodium, bicarbonate, creatinine, and chloride were identified as predictors of CA. The developed nomogram provides a reliable and convenient tool for predicting CA in patients with GERD.