Development and validation of a predictive models for predicting the cardiac events within one year for patients underwent percutaneous coronary intervention procedure at IJN
Abstract Purpose Percutaneous coronary intervention (PCI) is a common treatment modality for coronary artery disease. Accurate prediction of patients at risk for complications and hospital readmission after PCI could improve the overall clinical management. We aimed to develop and validate predictiv...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-11-01
|
| Series: | BMC Cardiovascular Disorders |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12872-023-03536-w |
| _version_ | 1797630470309019648 |
|---|---|
| author | Kok Yew Ngew Hao Zhe Tay Ahmad K. M. Yusof |
| author_facet | Kok Yew Ngew Hao Zhe Tay Ahmad K. M. Yusof |
| author_sort | Kok Yew Ngew |
| collection | DOAJ |
| description | Abstract Purpose Percutaneous coronary intervention (PCI) is a common treatment modality for coronary artery disease. Accurate prediction of patients at risk for complications and hospital readmission after PCI could improve the overall clinical management. We aimed to develop and validate predictive models to predict any cardiac event within a year post PCI procedure. Methods This is a retrospective cohort study utilizing data from the National Cardiovascular Disease (NCVD)-PCI registry. The data collected (N = 28,007) were split into training set (n = 24,409) and testing set (n = 3598). Four predictive models (logistic regression [LR], random forest method, support vector machine [SVM], and artificial neural network) were developed and validated. The outcome on risk prediction were compared. Results The demographic and clinical features of patients in the training and testing cohorts were similar. Patients had mean age ± standard deviation of 58.15 ± 10.13 years at admission with a male majority (82.66%). In over half of the procedures (50.61%), patients had chronic stable angina. Within 1 year of follow up mortality, target vessel revascularization (TVR), and composite event of mortality and TVR were 3.92%, 9.48%, and 12.98% respectively. LR was the best model in predicting mortality event within 1-year post-PCI (AUC: 0.820). SVM had the highest discrimination power for both TVR event (AUC: 0.720) and composite event of mortality and TVR (AUC: 0.720). Conclusions This study successfully identified optimal prediction models with the good discriminatory ability for mortality outcome and good discrimination ability for TVR and composite event of mortality and TVR with a simple machine learning framework. |
| first_indexed | 2024-03-11T11:08:33Z |
| format | Article |
| id | doaj.art-8c07cd5da4674f3d9674e68f4e1aaf12 |
| institution | Directory Open Access Journal |
| issn | 1471-2261 |
| language | English |
| last_indexed | 2024-03-11T11:08:33Z |
| publishDate | 2023-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cardiovascular Disorders |
| spelling | doaj.art-8c07cd5da4674f3d9674e68f4e1aaf122023-11-12T12:06:04ZengBMCBMC Cardiovascular Disorders1471-22612023-11-012311910.1186/s12872-023-03536-wDevelopment and validation of a predictive models for predicting the cardiac events within one year for patients underwent percutaneous coronary intervention procedure at IJNKok Yew Ngew0Hao Zhe Tay1Ahmad K. M. Yusof2Novartis Corporation (Malaysia) Sdn BhdNovartis Corporation (Malaysia) Sdn BhdDepartment of Imaging Centre, National Heart InstituteAbstract Purpose Percutaneous coronary intervention (PCI) is a common treatment modality for coronary artery disease. Accurate prediction of patients at risk for complications and hospital readmission after PCI could improve the overall clinical management. We aimed to develop and validate predictive models to predict any cardiac event within a year post PCI procedure. Methods This is a retrospective cohort study utilizing data from the National Cardiovascular Disease (NCVD)-PCI registry. The data collected (N = 28,007) were split into training set (n = 24,409) and testing set (n = 3598). Four predictive models (logistic regression [LR], random forest method, support vector machine [SVM], and artificial neural network) were developed and validated. The outcome on risk prediction were compared. Results The demographic and clinical features of patients in the training and testing cohorts were similar. Patients had mean age ± standard deviation of 58.15 ± 10.13 years at admission with a male majority (82.66%). In over half of the procedures (50.61%), patients had chronic stable angina. Within 1 year of follow up mortality, target vessel revascularization (TVR), and composite event of mortality and TVR were 3.92%, 9.48%, and 12.98% respectively. LR was the best model in predicting mortality event within 1-year post-PCI (AUC: 0.820). SVM had the highest discrimination power for both TVR event (AUC: 0.720) and composite event of mortality and TVR (AUC: 0.720). Conclusions This study successfully identified optimal prediction models with the good discriminatory ability for mortality outcome and good discrimination ability for TVR and composite event of mortality and TVR with a simple machine learning framework.https://doi.org/10.1186/s12872-023-03536-wPercutaneous coronary interventionPredictive modelsMachine learningMalaysiaCoronary artery disease |
| spellingShingle | Kok Yew Ngew Hao Zhe Tay Ahmad K. M. Yusof Development and validation of a predictive models for predicting the cardiac events within one year for patients underwent percutaneous coronary intervention procedure at IJN BMC Cardiovascular Disorders Percutaneous coronary intervention Predictive models Machine learning Malaysia Coronary artery disease |
| title | Development and validation of a predictive models for predicting the cardiac events within one year for patients underwent percutaneous coronary intervention procedure at IJN |
| title_full | Development and validation of a predictive models for predicting the cardiac events within one year for patients underwent percutaneous coronary intervention procedure at IJN |
| title_fullStr | Development and validation of a predictive models for predicting the cardiac events within one year for patients underwent percutaneous coronary intervention procedure at IJN |
| title_full_unstemmed | Development and validation of a predictive models for predicting the cardiac events within one year for patients underwent percutaneous coronary intervention procedure at IJN |
| title_short | Development and validation of a predictive models for predicting the cardiac events within one year for patients underwent percutaneous coronary intervention procedure at IJN |
| title_sort | development and validation of a predictive models for predicting the cardiac events within one year for patients underwent percutaneous coronary intervention procedure at ijn |
| topic | Percutaneous coronary intervention Predictive models Machine learning Malaysia Coronary artery disease |
| url | https://doi.org/10.1186/s12872-023-03536-w |
| work_keys_str_mv | AT kokyewngew developmentandvalidationofapredictivemodelsforpredictingthecardiaceventswithinoneyearforpatientsunderwentpercutaneouscoronaryinterventionprocedureatijn AT haozhetay developmentandvalidationofapredictivemodelsforpredictingthecardiaceventswithinoneyearforpatientsunderwentpercutaneouscoronaryinterventionprocedureatijn AT ahmadkmyusof developmentandvalidationofapredictivemodelsforpredictingthecardiaceventswithinoneyearforpatientsunderwentpercutaneouscoronaryinterventionprocedureatijn |