Prognostic Analysis of PCI-HBR Patients and Exploration of Appropriate DAPT Strategies

Coronary Artery Disease Clinical Trial

Official title: Prognostic Analysis of Patients With High Bleeding Risk in PCI and Exploration of Appropriate Antiplatelet Strategies

Status Not yet recruiting

Clinical Trial Summary

Percutaneous coronary intervention (PCI) is an important treatment strategy for patients with coronary artery disease. Combined bleeding after PCI significantly increases the risk of death in patients. The search for prognostic predictors and optimal antiplatelet therapy for patients with high bleeding risk (HBR) after PCI has been a hot topic in cardiovascular research. There is no accepted prognostic model or recommended antiplatelet therapy for patients with PCI-HBR. In this project, based on retrospective data extraction and prospective database building, we used artificial intelligence (AI) to analyze the adverse prognostic predictors of PCI-HBR patients, observe the types of antiplatelet drugs and duration of dual antiplatelet therapy in PCI-HBR patients, and compare the safety and feasibility of different antiplatelet regimens and treatment courses. The safety and feasibility of different antiplatelet regimens and regimens were compared.

Clinical Trial Description

This trail is a single center investigator-initiated prospective registry. PPP-PCI aims to observe the characteristics and prognosis of the PCI-HBR population and to explore appropriate antiplatelet therapy regimens to provide a basis for intervention guidance for patients with PCI-HBR. This project will help to further improve the existing bleeding prediction models and improve the efficiency of treating PCI-HBR patients. Patients' baseline information is based on the latest test before PCI procedure. Basic information include age, gender, systolic blood pressure, diastolic blood pressure, body mass index, smoking status, smoking volume, positive family history of cardiovascular disease, hyperlipidemia, hypertension, diabetes, stroke history, peripheral artery disease, etc. Real-time update features include DAPT sessions, MACE event records, symptom records, sign records, test results, diagnosis, medical advice, real-time sign monitoring equipment data, ECG abnormalities, etc. Tests include a full set of lipid levels (including triglycerides, cholesterol, HDL, LDL, etc.), biochemical parameters (including creatinine, glomerular filtration rate, uric acid, etc.), hemoglobin, glucose, glycated hemoglobin, homocysteine, and lipoprotein(a) level. Imaging and functional testing data include coronary angiography images, intervention-related parameters, and target vessel lesion characteristics. The patient data is correlated with the visit intensity. The imaging images are used for deep learning to build unstructured classification models. The non-imaging data are used for machine learning to build a structured classification model. Pre-processing of the data includes image normalization, correction and normalization of irregular values, detection and removal of outliers and anomalies, interpolation and rejection of null values, removal of multicollinearity, and data normalization. For the imaging images, a deep learning model was constructed using convolutional neural network to dichotomize the coronary vascular lesions and functional conditions contained in the coronary angiography images. For the non-imaging image data, Embedded method was used as the top-level method, and logistic regression, random forest, and gradient boosting tree were used as the bottom-level algorithms, and the key factors affecting the occurrence of MACE in the PCI-HBR population were extracted by fusing the feature weights through integrated learning. Based on the extracted key factors, a binary machine learning discriminative model was established, and SVM, XGBoost, random forest, and artificial neural network were used to complete the evaluation of multiple models, and the best model was selected as the machine learning classification model. The deep learning model and machine learning model structures are weighted and fused to output the final results. Then the data collected by the future model is passed back to the training dataset for incremental learning to correct the model. This trial will provide new insights and evidence on optimal antiplatelet therapy for a high bleeding risk patient cohort which is frequently encountered in real-world practice. ;

Related Conditions & MeSH terms

• Coronary Artery Disease

• NCT number: NCT05369442
• Study type: Observational [Patient Registry]
• Source: West China Hospital
• Status: Not yet recruiting
• Start date: May 1, 2022
• Completion date: August 1, 2024