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Published on: January 28, 2020
Development and validation of a Bayesian index for predicting major adverse cardiac events with percutaneous
A J de Belder1, D E Jewitt, R J Wainwright
1Department of Cardiology, Royal Sussex County Hospital, Eastern Road, Brighton BN2 5BE, UK.
Insights
This study developed a risk model to predict major adverse complicating events (MACE) during percutaneous transluminal coronary angioplasty (PTCA). The model demonstrated moderate predictive accuracy in a prospective patient cohort.
Area of Science:
- Cardiology
- Interventional Cardiology
- Medical Informatics
Background:
- Percutaneous transluminal coronary angioplasty (PTCA) is a common procedure for coronary artery disease.
- Predicting major adverse complicating events (MACE) such as death, emergency coronary artery bypass grafting (CABG), or Q wave infarct is crucial for patient management and informed consent.
- Existing predictive tools require validation on specific patient populations.
Purpose of the Study:
- To develop and validate a risk prediction model for MACE in patients undergoing PTCA.
- To assess the accuracy of the developed model using a prospective cohort.
Main Methods:
- A Bayesian risk model was created using data from 1500 PTCA patients (development group).
- Predictive factors included age, sex, left ventricular function, lesion morphology, cardiogenic shock, prior CABG, diabetes, hypertension, and multivessel PTCA.
- Model performance was evaluated on a subsequent cohort of 1000 patients (evaluation group) using receiver operating characteristic (ROC) curves and calibration plots.
Main Results:
- The developed Bayesian model showed a moderately predictive area under the ROC curve of 0.76 (SEM 0.07) for MACE in the evaluation group.
- Calibration plots confirmed the predictive accuracy of the model.
- The model effectively identified risk factors associated with MACE in the studied PTCA population.
Conclusions:
- Accurate prediction of MACE is achievable for patients undergoing percutaneous coronary interventions.
- The developed risk model allows operators to utilize their own patient data for informed consent and risk assessment.
- Establishing national PTCA databases could facilitate benchmarking for individual operators and centers.
Objective:
To create a risk model for predicting major adverse complicating events of percutaneous transluminal coronary angioplasty (PTCA), and to test the accuracy of the model on a prospective cohort of patients
Setting:
Tertiary cardiac centre
Methods:
Available software can predict probabilities of events using Bayes's theorem. To establish the accuracy of these predictive tools, a Bayes table was created to evaluate major adverse complicating events (MACE)-death, emergency coronary artery bypass grafting (CABG), or Q wave infarct occurring during the in-patient episode-on the first 1500 patients in the department PTCA database (development group); the predictive value of this model was then tested with the subsequent 1000 patients (evaluation group). The following probabilities were assessed to determine their association with MACE: age, sex, left ventricular function, American Heart Association lesion morphology classification, cardiogenic shock, previous CABG, diabetes, hypertension, multivessel PTCA.
Main Outcome Measures:
To establish the discriminatory ability of the predictive index, calibration plots and receiver operating characteristic (ROC) curves were obtained to compare the development and evaluation groups.
Results:
The ROC curve plotted to determine the discriminatory value of the Bayesian table created from the development group (n = 1500) in predicting MACE in the evaluation group (n = 1000) showed a moderately predictive area under the curve of 0.76 (SEM 0.07). This predictive accuracy was confirmed with separately constructed calibration plots.
Conclusions:
Accurate predictions of MACE can be identified in populations undergoing percutaneous intervention. The database used allows operators to obtain consent from patients appropriately from their own experience rather than from other published data. If a national PTCA database existed along similar lines, individual operators and interventional centres could compare themselves with nationally available data.
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