Zifeng Zeng,1,2 Rongtai Luo,1,2 Weiyong Xu,1,2 Huaqing Yao,1,2 Xinping Lan1,2 

1Center for Cardiovascular Diseases, Meizhou People’s Hospital, Meizhou, People’s Republic of China; 2Guangdong Provincial Engineering and Technology Research Center for Molecular Diagnostics of Cardiovascular Diseases, Meizhou People’s Hospital, Meizhou, People’s Republic of China

Correspondence: Xinping Lan, Center for Cardiovascular Diseases, Meizhou People’s Hospital, Meizhou Academy of Medical Sciences, Meizhou, People’s Republic of China, Email lan-xinping@163.com

Background: The occurrence of cerebral infarction significantly increases the risk of major adverse cardiovascular events in patients with acute myocardial infarction (AMI), highlighting the importance of early identification and intervention. Currently, no validated tools exist for individualized risk stratification of cerebral infarction (CI) in patients with AMI.
Objective: This study aimed to identify the most valuable predictors (MVPs) of in-hospital first-onset CI in AMI patients and construct a nomogram for risk stratification.
Methods: This retrospective cohort study enrolled 1,350 AMI patients admitted to the Cardiovascular Center of Meizhou People’s Hospital between January and December 2022. Clinical characteristics and laboratory parameters were analyzed. Least Absolute Shrinkage and Selection Operator regression (LASSO) was used to select MVPs. The nomogram was developed by integrating coefficients of MVPs from logistic regression, and its discrimination, calibration, and clinical utility were validated in the cohort. The optimal cutoff value of the nomogram probability was determined.
Results: CI occurred in 60 patients (4.44%). MVPs included Killip classification (OR = 1.42, 95% CI 1.05– 1.93), PCI therapy (OR = 0.29, 95% CI 0.16– 0.51), C-reactive protein (CRP: OR = 1.01, 95% CI 1.00– 1.01), blood urea nitrogen (BUN: OR = 1.03, 95% CI 0.99– 1.07), and neutrophil-to-lymphocyte ratio (NLR: OR = 1.02, 95% CI 0.99– 1.05). The discriminatory ability of the nomogram was up to 0.804( 95% CI 0.749– 0.859). Additionally, the nomogram showed good calibration and clinical utility in the cohort. Furthermore, the optimal cutoff value of the nomogram probability for distinguishing those who will experience in-hospital first-onset CI was 0.035 (sensitivity 78.3%, specificity 71.1%).
Conclusion: The first nomogram integrating multimodal predictors for discerning AMI patients who will experience in-hospital first-onset CI was developed and validated, which will aid clinicians in clinical decision-making.

Keywords: acute myocardial infarction, cerebral infarction, nomogram, model, first-onset