Predictive value of PHR and FHR for in-hospital mortality risk in patients with acute myocardial infarction.

Abstract

Background: Inflammation is closely associated with various diseases. The platelet-to-high-density lipoprotein cholesterol (HDL-C) ratio (PHR) and the fibrinogen-to-HDL‑C ratio (FHR) are considered important biomarkers for assessing the level of inflammation. This study aimed to investigate the relationship between PHR, FHR, and in-hospital mortality risk in patients with acute myocardial infarction (AMI).

Methods: This retrospective study included patients with first-time AMI at Cangzhou People's Hospital, China, from 2020 to 2021. Multivariable logistic regression analysis was performed to evaluate the association between PHR, FHR, and in-hospital mortality in patients with AMI. Restricted cubic spline (RCS) was used to visualize the dose-response relationship between PHR, FHR, and in-hospital mortality. Receiver operating characteristic (ROC) curve analysis was conducted to determine the predictive value of PHR and FHR for in-hospital mortality. Additionally, subgroup analyses were performed.

Results: In the study, 170 out of 2398 patients with AMI (7.09%) died. In the multivariable logistic regression model, both PHR and FHR were identified as independent predictors of in-hospital mortality in patients with AMI. The adjusted RCS regression analysis indicated that there is no significant nonlinear association between PHR, FHR, and in-hospital mortality. The ROC curve analysis revealed that the area under the curve (AUC) for PHR and FHR was 0.718 (95% CI: 0.700-0.736, p < 0.001) and 0.717 (95% CI: 0.699-0.735, p < 0.001), respectively. In the subgroup analysis, we found that admission route, AMI type, congestive heart failure, and cardiac arrest significantly influenced the relationship between PHR, FHR, and in-hospital mortality (p < 0.05 for interaction).

Conclusion: Both PHR and FHR are independent prognostic factors for in-hospital mortality in patients with AMI. The clinical utility of these inflammatory biomarkers needs to be further validated in studies with larger sample sizes and diverse populations.