# Physiological-immune resilience risk assessment model for predicting adverse cardiac outcomes in patients with acute myocardial infarction

## TL;DR

This study develops a model to predict adverse cardiac outcomes in acute myocardial infarction patients using physiological and immune resilience factors.

## Contribution

The novel contribution is a physio-immune resilience risk model that combines clinical and immune markers to predict adverse cardiac events in AMI patients.

## Key findings

- The model incorporating physiological resilience score, hs-CRP, SII, and SIRI showed higher predictive accuracy than individual variables.
- Bootstrap validation confirmed strong explanatory power and good calibration of the model.
- Early interventions targeting these risk factors may reduce adverse outcomes in AMI patients.

## Abstract

Adverse cardiac events have been identified as a major determinant of poor prognosis in patients with acute myocardial infarction (AMI), directly increasing mortality risk. Therefore, this study aimed to establish a physio-immune resilience risk assessment model to identify, at an early stage, the influencing factors of adverse cardiac outcomes in AMI patients, thereby providing clinical guidance for subsequent interventions.

A total of 345 patients diagnosed with AMI between August 2022 and March 2024 were prospectively enrolled. The occurrence of 30-day major adverse cardiac events (MACE) was independently assessed by two cardiology specialists, and participants were categorized into a MACE group and a non-MACE group accordingly. Clinical data and laboratory findings were compared between groups. Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate logistic regression were applied to determine the influencing factors of adverse cardiac outcomes. Furthermore, Receiver Operating Characteristic (ROC) curve analysis was performed to evaluate the predictive value of the physio-immune resilience model for MACE in AMI patients.

The left ventricular ejection fraction (LVEF) in the MACE group was significantly lower than that in the non-MACE group, whereas high-sensitivity C-reactive protein (hs-CRP) levels were markedly higher (P < 0.05). The physiological resilience score of the MACE group was lower than that of the non-MACE group, while the European Quality of Life Five-Dimension Scale (EQ-5D) score was significantly higher (P < 0.05). Moreover, the lymphocyte count was lower in the MACE group, but both the systemic immune-inflammation index (SII) and the systemic inflammatory response index (SIRI) were higher than those in the non-MACE group (P < 0.05). Results from LASSO and multivariate logistic regression indicated that the physiological resilience score (OR = 0.812) served as an independent protective factor for adverse cardiac events in AMI patients, whereas hs-CRP (OR = 1.622), SII (OR = 1.054), and SIRI (OR = 25.905) were independent risk factors. ROC analysis revealed that the combined predictive model incorporating the physiological resilience score, hs-CRP, SII, and SIRI yielded a higher area under the curve (AUC) than any single variable (P < 0.05). The model was validated using bootstrap resampling (1,000 iterations), with a Nagelkerke R2 = 0.543, suggesting a strong explanatory power for the dependent variable and good calibration performance. The Decision Curve Analysis (DCA) curve was consistently higher than the two extreme curves, indicating greater net clinical benefit of the model-derived predictors.

Patients with AMI undergoing percutaneous coronary intervention (PCI) remain at risk of adverse cardiac outcomes, which may be associated with the physiological resilience score, hs-CRP, SII, and SIRI. These indicators demonstrate substantial predictive value for 30-day MACE in AMI patients. Accordingly, early clinical interventions targeting these risk factors are recommended to reduce the incidence of adverse cardiac outcomes and improve patient prognosis.