Constructing risk adjustment models for percutaneous coronary intervention: implications for quality assessment
Paulo Sousa1, António Sousa Uva, Adriana Belo
1Registo Nacional de Cardiologia de Intervenção da Sociedade Portuguesa de Cardiologia, Lisboa, Portugal. paulo.sousa@ensp.unl.pt
Insights
A new risk adjustment model was developed for percutaneous coronary intervention (PCI) outcomes, including major adverse cardiac and cerebrovascular events (MACCE) and mortality. This tool enables reliable quality assessment and provider comparison in interventional cardiology.
Area of Science:
- Cardiology
- Health Services Research
- Medical Informatics
Background:
- Assessing healthcare quality through patient outcomes requires risk adjustment due to variations in patient baseline clinical risk.
- Percutaneous coronary intervention (PCI) has seen increased volume and complexity, necessitating robust outcome evaluation.
- Risk adjustment models are crucial for reliable performance comparisons and quality improvement in interventional cardiology.
Purpose of the Study:
- To develop a risk adjustment model for in-hospital major adverse cardiac and cerebrovascular events (MACCE).
- To develop a risk adjustment model for in-hospital mortality following PCI.
- To utilize data from a national multicenter registry for model development.
Main Methods:
- A cohort study was conducted using data from the National Registry of Interventional Cardiology.
- The study included 10,399 procedures performed between June 30, 2003, and June 30, 2006.
- Multivariate prediction models were developed for in-hospital MACCE and mortality.
Main Results:
- Factors associated with in-hospital MACCE and mortality included advanced age, female gender, acute myocardial infarction, cardiogenic shock, renal failure, reduced ejection fraction, multi-vessel disease, and urgent PCI.
- The developed models demonstrated good discrimination (ROC AUC 0.83 for MACCE, 0.93 for mortality) and calibration.
- The models possess good discrimination and clinical value, indicating effective risk prediction.
Conclusions:
- A validated risk adjustment model for in-hospital MACCE and mortality after PCI was successfully developed.
- This model serves as a valuable tool for healthcare quality assessment.
- The model facilitates credible and reliable comparisons of outcomes among healthcare providers.
Introduction:
Quality standards, and subsequently benchmarking, based on patient outcome data are a rational means of assessing the quality of health care. However, variation in patients' baseline clinical risk precludes direct comparison of outcomes across operators, institutions and health care plans. In the years since the advent of interventional cardiology, there has been an enormous increase in the volume of activity and number of operators and centers performing percutaneous coronary intervention (PCI), together with considerable developments in the techniques, materials and adjunctive therapy associated with PCI. PCI outcomes depend on various factors, particularly patient characteristics and disease severity. The use of risk adjustment models to quantify differences in patient outcomes in interventional cardiology has been shown to provide a reliable and balanced comparison of performance and to lead to improvements in quality and safety in this area.
Objectives:
The aim of this study was to develop a risk adjustment model for in-hospital major adverse cardiac and cerebrovascular events (MACCE) and for a single adverse event (in-hospital mortality) following PCI, using data from a national multicenter registry.
Methods:
This was a cohort study of all patients who underwent PCI in the centers that participate in the National Registry of Interventional Cardiology of the Portuguese Society of Cardiology between June 30, 2003 and June 30, 2006, in a total of 10,399 procedures.
Results:
Factors associated with in-hospital MACCE included: age > 80 years; female gender; acute myocardial infarction; cardiogenic shock; renal failure; severely reduced ejection fraction; three or more diseased vessels; use of intra-aortic balloon pump; no stenting; and urgent/emergent PCI. The same variables were associated with the adverse event of in-hospital mortality. The area under the receiver operating characteristics (ROC) curve and the Hosmer-Lemeshow goodness-of-fit statistic, for both multivariate prediction models, were 0.83 and 0.69 (in-hospital MACCE) and 0.93 and 0.53 (in-hospital mortality), respectively, which indicates that these models have good discrimination and real clinical value and were well calibrated.
Conclusions:
A risk adjustment model for in-hospital MACCE and for in-hospital mortality after PCI was successfully developed using a large national multicenter registry. This is a powerful tool for quality assessment and represents a significant step towards credible and reliable comparison of results between providers.
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