Prognosis modelling of adverse events for post-PCI treated AMI patients based on inflammation and nutrition indexes
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. Method...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-01-01
|
| Series: | BMC Cardiovascular Disorders |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12872-025-04480-7 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832586045275766784 |
|---|---|
| author | Liu Yang Li Du Yuanyuan Ge Muhui Ou Wanyan Huang Xianmei Wang |
| author_facet | Liu Yang Li Du Yuanyuan Ge Muhui Ou Wanyan Huang Xianmei Wang |
| author_sort | Liu Yang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-36c417db2404490b8065948225651b0d |
| institution | Kabale University |
| issn | 1471-2261 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cardiovascular Disorders |
| spelling | doaj-art-36c417db2404490b8065948225651b0d2025-01-26T12:14:15ZengBMCBMC Cardiovascular Disorders1471-22612025-01-0125111110.1186/s12872-025-04480-7Prognosis modelling of adverse events for post-PCI treated AMI patients based on inflammation and nutrition indexesLiu Yang0Li Du1Yuanyuan Ge2Muhui Ou3Wanyan Huang4Xianmei Wang5Department of Cardiology, The Affiliated 920th Hospital of Joint Logistics Support Force, Kunming Medical UniversityDepartment of Cardiology, 920th Hospital of Joint Logistics Support Force, People’s Liberation Army of China (PLA)Department of Cardiology, 920th Hospital of Joint Logistics Support Force, People’s Liberation Army of China (PLA)Department of Cardiology, The Affiliated 920th Hospital of Joint Logistics Support Force, Kunming Medical UniversityDepartment of Cardiology, The Affiliated 920th Hospital of Joint Logistics Support Force, Kunming Medical UniversityDepartment of Cardiology, 920th Hospital of Joint Logistics Support Force, People’s Liberation Army of China (PLA)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.https://doi.org/10.1186/s12872-025-04480-7Acute myocardial infarctionInflammatory indexNutritional statusMajor adverse cardiovascular event |
| spellingShingle | Liu Yang Li Du Yuanyuan Ge Muhui Ou Wanyan Huang Xianmei Wang Prognosis modelling of adverse events for post-PCI treated AMI patients based on inflammation and nutrition indexes BMC Cardiovascular Disorders Acute myocardial infarction Inflammatory index Nutritional status Major adverse cardiovascular event |
| title | Prognosis modelling of adverse events for post-PCI treated AMI patients based on inflammation and nutrition indexes |
| title_full | Prognosis modelling of adverse events for post-PCI treated AMI patients based on inflammation and nutrition indexes |
| title_fullStr | Prognosis modelling of adverse events for post-PCI treated AMI patients based on inflammation and nutrition indexes |
| title_full_unstemmed | Prognosis modelling of adverse events for post-PCI treated AMI patients based on inflammation and nutrition indexes |
| title_short | Prognosis modelling of adverse events for post-PCI treated AMI patients based on inflammation and nutrition indexes |
| title_sort | prognosis modelling of adverse events for post pci treated ami patients based on inflammation and nutrition indexes |
| topic | Acute myocardial infarction Inflammatory index Nutritional status Major adverse cardiovascular event |
| url | https://doi.org/10.1186/s12872-025-04480-7 |
| work_keys_str_mv | AT liuyang prognosismodellingofadverseeventsforpostpcitreatedamipatientsbasedoninflammationandnutritionindexes AT lidu prognosismodellingofadverseeventsforpostpcitreatedamipatientsbasedoninflammationandnutritionindexes AT yuanyuange prognosismodellingofadverseeventsforpostpcitreatedamipatientsbasedoninflammationandnutritionindexes AT muhuiou prognosismodellingofadverseeventsforpostpcitreatedamipatientsbasedoninflammationandnutritionindexes AT wanyanhuang prognosismodellingofadverseeventsforpostpcitreatedamipatientsbasedoninflammationandnutritionindexes AT xianmeiwang prognosismodellingofadverseeventsforpostpcitreatedamipatientsbasedoninflammationandnutritionindexes |