A Nomogram Incorporating Inflammation and Nutrition Indexes for Predicting Outcomes in Patients with Acute Coronary Syndrome and Chronic Kidney Disease.

Abstract

Background: Inflammation, immunity, and nutriture are associated with prognosis in cardiovascular disease. We aimed to devise a novel nomogram model based on inflammation and nutrition indexes that accurately predicts Major adverse renal and cardiovascular events (MARCE) in patients diagnosed with acute coronary syndrome (ACS) and coexisting chronic kidney disease (CKD).

Methods: We enrolled 685 individuals with ACS and CKD between January 2013 and August 2021. All patients were randomized into the training (70%) and validation (30%) cohorts. Univariable and multivariable Cox regression analyses were used to identify independent predictors for MARCE. The performance of the nomogram model was evaluated using receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA). The performance of the nomogram and GRACE score were compared.

Results: The nomogram included six variables: age, left ventricular ejection fraction, systemic immune-inflammatory index (SII), controlling nutritional status (CONUT) score, use of beta-blockers, and use of statins. The constructed nomogram demonstrated robust predictive performance, achieving ROC ranging from 0.830 to 0.935 in the training set and 0.793 to 0.889 in the validation set, respectively. Furthermore, the calibration curves exhibited excellent agreement between the predicted probabilities and the observed outcomes, indicating the reliability of the nomogram's predictions. Finally, the DCA confirmed the clinical value of the nomogram by demonstrating its potential to improve decision-making processes in the context of managing the condition under study. Compared with the GRACE score, the nomogram was superior in terms of both discrimination and reclassification ability.

Conclusion: Our novel nomogram, which incorporates the CONUT score and SII, shows promising utility for predicting MARCE in patients with ACS and CKD. The identification of patients at heightened risk through our nomogram model is paramount as it serves as a cornerstone for the implementation of targeted interventions aimed at modifiable variables.