Characteristics and risk factors for mortality in patients with acute coronary syndrome concomitant sepsis: a...

The purpose of this research was to examine the risk factors associated with in-hospital mortality in patients with acute coronary syndrome (ACS) concomitant sepsis, and to develop and verify a nomogram model for predicting mortality risk.

This multicenter retrospective analysis examined clinical data from patients with ACS concomitant sepsis who were hospitalized in the intensive care units of tertiary hospitals in Southern China between January 2013 and December 2023. In-hospital mortality functioned as the principal outcome. Univariate and multivariate logistic regression analysis, together with LASSO regression, were used to ascertain independent risk factors for the outcome. The evaluation of model performance was conducted by receiver operating characteristic (ROC) curves, area under the curve (AUC), and calibration plots.

This study comprised a total of 200 patients. During hospitalization, 122 people (61.0%) succumbed. Multivariate logistic regression analysis indicated that the diagnosis of ST-segment elevation myocardial infarction (STEMI) at admission (OR = 2.081, 95% CI: 1.120-3.866, P = 0.0206), an elevated initial neutrophil count (OR = 1.05, 95% CI: 1.000-1.102, P = 0.0495), and a history of coronary artery disease (OR = 2.953, 95% CI: 1.173-7.436, P = 0.0215) were independent risk factors for in-hospital mortality. The nomogram model that includes these parameters exhibited an AUC of 0.641 (95% CI: 0.564-0.718), with a sensitivity of 0.656 and a specificity of 0.603. Calibration curves demonstrated strong concordance between expected and observed results (Hosmer-Lemeshow test P > 0.05).

Patients with ACS concomitant sepsis experience heightened in-hospital mortality, which is substantially correlated with a diagnosis of STEMI at admission, increased initial neutrophil count, and pre-existing coronary artery disease. While the discriminative capacity (AUC = 0.641) of this three-factor nomogram necessitates additional enhancement, its commendable calibration provides a first instrument for early risk categorization, illustrating practical applicability for swift evaluation. Extensive investigations are necessary to improve model efficacy.