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. 2025 Jan 23;25(1):36.
doi: 10.1186/s12872-025-04480-7.

Prognosis modelling of adverse events for post-PCI treated AMI patients based on inflammation and nutrition indexes

Liu Yang  1 , Li Du  2 , Yuanyuan Ge  2 , Muhui Ou  1 , Wanyan Huang  1 , Xianmei Wang  3
Affiliations

Prognosis modelling of adverse events for post-PCI treated AMI patients based on inflammation and nutrition indexes

Liu Yang et al. BMC Cardiovasc Disord. .

Abstract

Objective: This study aimed to evaluate the predictive performance of inflammatory and nutritional indices for adverse cardiovascular events (ACE) in patients with acute myocardial infarction (AMI) after percutaneous coronary intervention (PCI) using a machine learning (ML) algorithm.

Methods: AMI patients who underwent PCI were recruited and randomly divided into non/ACE groups. Inflammatory and nutritional indices were graded according to the laboratory examination reports. Logistic Regression was used to screen for factors that were significant for ML model establishment. The performances of the algorithms were evaluated in terms of accuracy, kappa, F1, receiver operating characteristic, precision recall curve, etc. RESULTS: Age, LVEF%, Killip Grade, heart rate, creatinine, albumin, neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), and prognostic nutritional index (PNI) were significantly correlated with ACE by Logistic regression analysis (P < 0.05). These nine factors were employed to establish stepwise regression (SR), random forest (RF), naïve Bayes (NB), decision trees (DT), and artificial neutron network (ANN), whose performances were evaluated in terms of accuracy, kappa, F1, receiver operating characteristic, precision recall curve, etc. The accuracy of the decision tree was greater than that of other trees. The area under the curves was the highest in the ANN model compared with the other models.

Conclusion: ANN predictive performance had an advantage over other ML algorithms based on age, LVEF%, Killip Grade, heart rate, creatinine, albumin, NLR, PLR, and PNI.

Keywords: Acute myocardial infarction; Inflammatory index; Major adverse cardiovascular event; Nutritional status.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: The use of human samples was approved by the Ethics Committee of the 920th Hospital of the Joint Logistics Support Force of the Chinese People’s Liberation Army (NO 920IEC/AF/61/2021 − 01.1). All recruited participants provided written informed consent. All sample handling and data processing steps followed the principles of the Declaration of Helsinki. Consent for publication: All authors are aware of and have consented to publication. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Different disease composition of ACE based on cardiogenic death, cardiogenic shock, malignant arrhythmias, acute left heart failure, nonfatal cerebral stroke, myocardial re-infarction, and cardiac arrest
Fig. 2
Fig. 2
Curves of ROC/PRC and visualization of machine learning model parameters. A. ROC and PRC of inflammatory and nutritional indexes. The area under curve of ROC and PRC in PNI were less than 0.5 and 0.1, respectively. B. ROC and PRC of SR, RF and NB algorithm with three significant factors. C. ROC and PRC of SR, RF, NB, DT and ANN algorithm with nine significant factors. D. Decision tree classification. E. Importance of nine factors in ANN algorithm. F. visualization of ANN
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