Value and validation of a nomogram model based on the Charlson comorbidity index for predicting in-hospital mortality in patients with acute myocardial infarction complicated by ventricular arrhythmias.

OBJECTIVES

The Charlson comorbidity index reflects overall comorbidity burden and has been applied in cardiovascular medicine. However, its role in predicting in-hospital mortality in patients with acute myocardial infarction (AMI) complicated by ventricular arrhythmias (VA) remains unclear. This study aims to evaluate the predictive value of the Charlson comorbidity index in this setting and to construct a nomogram model for early risk identification and individualized management to improve outcomes.

METHODS

Using the open-access critical care database MIMIC-IV (Medical Information Mart for Intensive Care IV), we identified intensive care unit (ICU) patients diagnosed with AMI complicated by VA. Patients were grouped according to in-hospital survival. The predictive performance of the Charlson comorbidity index and other clinical variables for in-hospital mortality was analyzed. Key predictors were selected using the least absolute shrinkage and selection operator (LASSO) regression, followed by multivariable Logistic regression. A nomogram model was constructed based on the regression results. Model performance was assessed using receiver operating characteristic (ROC) curves and calibration plots.

RESULTS

A total of 1 492 patients with AMI and VA were included, of whom 340 died and 1 152 survived during hospitalization. Significant differences were observed between survivors and non-survivors in sex distribution, vital signs, comorbidity burden, organ function, and laboratory parameters (all <0.05). The area under the curve (AUC) of the Charlson comorbidity index for predicting in-hospital mortality was 0.712 (95% 0.681 to 0.742), significantly higher than albumin, international normalized ratio (INR), hemoglobin, body temperature, and platelet count (all <0.001), but comparable to Sequential Organ Failure Assessment (SOFA) score (>0.05). LASSO regression identified seven key predictors: the Charlson comorbidity index (quartile groups: T1, <6; T2, ≥6-<7; T3, ≥7-<9; T4, ≥9), ventricular fibrillation, age, systolic blood pressure, respiratory rate, body temperature, and SOFA score. Multivariate Logistic regression showed that compared with T1, mortality risk increased significantly in T2 (=1.996, 95% 1.135 to 3.486, =0.016), T3 (=3.386, 95% 2.192 to 5.302, <0.001), and T4 (=5.679, 95% 3.711 to 8.842, <0.001). Age (=1.056, <0.001), respiratory rate (=1.069, <0.001), SOFA score (=1.223, <0.001), and ventricular fibrillation (=2.174, <0.001) were independent risk factors, while systolic blood pressure (=0.984, <0.001) and body temperature (=0.648, <0.001) were protective factors. The nomogram incorporating these predictors achieved an AUC of 0.849 (95% 0.826 to 0.871) with high discrimination and good calibration (mean absolute error=0.014).

CONCLUSIONS

The Charlson comorbidity index is an independent predictor of in-hospital mortality in AMI patients complicated by VA, with performance comparable to the SOFA score. The nomogram model based on the Charlson comorbidity index and additional clinical variables effectively estimates mortality risk and provides a valuable reference for clinical decision-making.