The development and validation of a prognostic prediction modeling study in acute myocardial infarction patients after percutaneous coronary intervention: hemorrhea and major cardiovascular adverse events

Abstract

Background: Percutaneous coronary intervention (PCI) is one of the most important diagnostic and therapeutic techniques in cardiology. At present, the traditional prediction models for postoperative events after PCI are ineffective, but machine learning has great potential in identification and prediction of risk. Machine learning can reduce overfitting through regularization techniques, cross-validation and ensemble learning, making the model more accurate in predicting large amounts of complex unknown data. This study sought to identify the risk of hemorrhea and major adverse cardiovascular events (MACEs) in patients after PCI through machine learning.

Methods: The entire study population consisted of 7,931 individual patients who underwent PCI at Jiangsu Provincial Hospital and The Affiliated Wuxi Second People's Hospital from January 2007 to January 2022. The risk of postoperative hemorrhea and MACE (including cardiac death and in-stent restenosis) was predicted by 53 clinical features after admission. The population was assigned to the training set and the validation set in a specific ratio by simple randomization. Different machine learning algorithms, including eXtreme Gradient Boosting (XGBoost), random forest (RF), and deep learning neural network (DNN), were trained to build prediction models. A 5-fold cross-validation was applied to correct errors. Several evaluation indexes, including the area under the receiver operating characteristic (ROC) curve (AUC), accuracy (Acc), sensitivity (Sens), specificity (Spec), and net reclassification improvement (NRI), were used to compare the predictive performance. To improve the interpretability of the model and identify risk factors individually, SHapley Additive exPlanation (SHAP) was introduced.

Results: In this study, 306 patients (3.9%) experienced hemorrhea, 107 patients (1.3%) experienced cardiac death, and 218 patients (2.7%) developed in-stent restenosis. In the training set and validation set, except for previous PCI and statins, there were no significant differences. XGBoost was observed to be the best predictor of every event, namely hemorrhea [AUC: 0.921, 95% confidence interval (CI): 0.864-0.978, Acc: 0.845, Sens: 0.851, Spec: 0.837 and NRI: 0.140], cardiac death (AUC: 0.939, 95% CI: 0.903-0.975, Acc: 0.914, Sens: 0.950, Spec: 0.800 and NRI: 0.148), and in-stent restenosis (AUC: 0.915; 95% CI: 0.863-0.967, Acc: 0.834, Sens: 0.778, Spec: 0.902 and NRI: 0.077). SHAP showed that the number of stents had the greatest influence on hemorrhea, while age and drug-coated balloon were the main factors in cardiogenic death and stent restenosis (all P<0.05).

Conclusions: The XGBoost model (machine learning) performed better than the traditional logistic regression model in identifying hemorrhea and MACE after PCI. Machine learning models can be used as a tool for risk prediction. The machine learning model described in this study can personalize the prediction of hemorrhea and MACE after PCI for specific patients, helping clinicians adjust intervenable features.

Keywords: Acute myocardial infarction (AMI); eXtreme Gradient Boosting (XGBoost); machine learning; major adverse cardiovascular events (MACEs); percutaneous coronary intervention (PCI).

Figure 1

Machine learning model construction and validation. AUC, area under the curve; SMOTE, synthetic minority oversampling technique.

Figure 2

Missing values. ALB, albumin; WBC, white blood cell; DCB, drug-coated balloon; LDL-C, low-density lipoprotein cholesterol; TC, total cholesterol; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; PT, prothrombin time; INR, international normalized ratio; APTT, activated partial thromboplastin time; FBG, fasting blood glucose; CCr, creatinine clearance rate; BMI, body mass index; hs-cTNT, high-sensitivity cardiac troponin T; HbA1c, hemoglobin A1c; CRP, C-reactive protein; cTNI, cardiac troponin I.

Figure 3

Correlation matrix of balanced data. BMI, body mass index; PCI, percutaneous coronary intervention; CABG, coronary artery bypass grafting; Hgb, hemoglobin; PLT, platelet; TC, total cholesterol; TG, triglyceride; LDL-C, low density lipoprotein cholesterol; HDL-C, high density lipoprotein cholesterol; Cr, creatinine; ALB, albumin; PT, prothrombin time; APTT, activated partial thromboplastin time; INR, international normalized ratio; ALT, alanine aminotransferase; AST, aspartate aminotransferase; CCr, endogenous creatinine clearance rate; FBG, fasting blood glucose; WBC, white blood cell; DCB, drug-coated balloon; ACEI, angiotensin-converting enzyme inhibitors; ARB, angiotensin receptor blocker; PPI, proton pump inhibitor.

Figure 4

Hemorrhea. (A) ROC curve of hemorrhea; (B) feature importance of hemorrhea; (C) model verification of hemorrhea. ROC, receiver operating characteristic; LR, logistic regression; AUC, area under the curve; DCB, drug-coated balloon; PLT, platelet; AST, aspartate aminotransferase; ALB, albumin; Cr, creatinine; ALT, alanine aminotransferase; CCr, creatinine clearance rate; APTT, activated partial thromboplastin time; TG, triglyceride; PPI, proton pump inhibitor; FBG, fasting blood glucose; BMI, body mass index; Hgb, hemoglobin; PT, prothrombin time; ACEI or ARB, angiotensin-converting enzyme inhibitor or angiotensin receptor blocker; XGBoost, eXtreme Gradient Boost.

Figure 5

Cardiac death. (A) ROC curve of cardiac death; (B) feature importance of cardiac death; (C) model verification of cardiac death. ROC, receiver operating characteristic; LR, logistic regression; AUC, area under the curve; DCB, drug-coated balloon; INR, international normalized ratio; PT, prothrombin time; PPI, proton pump inhibitor; ALB, albumin; BMI, body mass index; ACEI or ARB, angiotensin-converting enzyme inhibitor or angiotensin receptor blocker; Cr, creatinine; CCr, creatinine clearance rate; AST, aspartate aminotransferase; WBC, white blood cell; Hgb, hemoglobin; XGBoost, eXtreme Gradient Boost.

Figure 6

In-stent restenosis. (A) ROC curve of in-stent restenosis; (B) feature importance of in-stent restenosis; (C) model verification of in-stent restenosis. ROC, receiver operating characteristic; LR, logistic regression; AUC, area under the curve; DCB, drug-coated balloon; PLT, platelet; Hgb, hemoglobin; WBC, white blood cell; CCr, creatinine clearance rate; INR, international normalized ratio; PT, prothrombin time; Cr, creatinine; APTT, activated partial thromboplastin time; XGBoost, eXtreme Gradient Boost.