Prediction of First-Onset Cerebral Infarction Risk in Patients with Acute Myocardial Infarction: A Retrospective Cohort Study

Abstract

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; first-onset; model; nomogram.

Figure 1

Feature selection using the least absolute shrinkage and selection operator (LASSO) analysis with 10-fold cross-validation. Lambda (tuning parameter) selection of deviance in the LASSO regression based on the one standard error criteria (right dotted line) and the minimum criteria (left dotted line) (A). LASSO coefficient profiles of the candidate features. The intersecting curves represent the number of features retained at that log (lambda) value, and six predictors with nonzero coefficients were selected according to the one standard error criteria (B).

Figure 2

Nomogram for predicting the risk of first-onset CI in patients with AMI.

Figure 3

Evaluation of the nomogram. The ROC curve (A), the calibration plot (B), and the decision curve analysis (C). In calibration plots, the calibration curve is expected to fall along the ideal line corresponding to a perfectly calibrated nomogram; the red line represents the apparent accuracy of the nomogram without correction for overfitting, while the blue line represents the bootstrap-corrected nomogram. In DCA plots, “all” refers to the assumption that all patients experienced first-onset CI, while “none” assumes that no patient experienced first-onset CI.