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Published on: May 28, 2019
Development and Validation of a Novel Risk Score for Primary Percutaneous Coronary Intervention for ST-Elevation
Michael Andrews1, Javaid Iqbal2, Joshua J Wall1
1Department of Cardiovascular Science, University of Sheffield, UK.
Insights
A new risk model predicts 30-day mortality after primary percutaneous coronary intervention (PPCI) for ST-elevation myocardial infarction (STEMI) using four simple variables: age, call-to-balloon time, congestive heart failure, and shock.
Area of Science:
- Cardiology
- Interventional Cardiology
- Clinical Risk Prediction
Background:
- Primary percutaneous coronary intervention (PPCI) is standard for ST-elevation myocardial infarction (STEMI).
- PPCI carries higher risks than elective or urgent PCI.
- Existing risk scores may not accurately reflect PPCI risks due to limited STEMI patient data.
Purpose of the Study:
- To develop a simple, practical risk model for risk stratification in PPCI.
- To improve risk assessment for patients undergoing PPCI for STEMI.
Main Methods:
- Data from 2870 patients undergoing PPCI (2009-2013) were analyzed.
- Multiple regression identified independent predictors of 30-day mortality.
- The model was validated internally (693 patients) and externally (660 patients).
Main Results:
- Four variables predicted 30-day mortality: age, call-to-balloon (CTB) time, cardiogenic shock, and congestive heart failure.
- The model demonstrated excellent discrimination (C-Stat 0.87 internal, 0.86 external) and calibration.
- 30-day mortality in the derivation cohort was 5.1%.
Conclusions:
- A bedside risk model was developed to predict 30-day mortality after PPCI.
- The model utilizes only four key variables: age, CTB time, congestive heart failure, and shock.
- This simple model offers practical risk stratification for PPCI patients.
Background:
Primary percutaneous coronary intervention (PPCI) is the default treatment for patients with ST elevation myocardial infarction (STEMI) and carries a higher risk of adverse outcomes when compared with elective and urgent PCI. Conventional PCI risk scores tend to be complex and may underestimate the risk associated with PPCI due to under-representation of patients with STEMI in their datasets. This study aimed to develop a simple, practical and contemporary risk model to provide risk stratification in PPCI.
Methods:
Demographic, clinical and outcome data were collected for all patients who underwent PPCI between January 2009 and October 2013 at the Northern General Hospital, Sheffield. Multiple regression analysis was used to identify independent predictors of mortality and to construct a risk model. This model was then separately validated on an internal and external dataset.
Results:
The derivation cohort included 2870 patients with a 30-day mortality of 5.1% (145 patients). Only four variables were required to predict 30-day mortality: age [OR:1.047, 95% CI:1.031-1.063], call-to-balloon (CTB) time [OR:1.829, 95% CI:1.198-2.791], cardiogenic shock [OR:13.886, 95% CI:8.284-23.275] and congestive heart failure [OR:3.169, 95% CI:1.420-7.072]. Internal validation was performed in 693 patients and external validation in 660 patients undergoing PPCI. Our model showed excellent discrimination on ROC-curve analysis (C-Stat = 0.87 internal and 0.86, external), and excellent calibration on Hosmer-Lemeshow testing (p = 0.37 internal, 0.55 external).
Conclusions:
We have developed a bedside risk model which can predict 30-day mortality after PPCI using only four variables: age, CTB time, congestive heart failure and shock.
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