Identification of variables needed to risk adjust outcomes of coronary interventions: evidence-based guidelines for efficient data collection
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Published on: January 8, 2020
P C Block1, E D Peterson, R Krone
1Heart Institute, Providence St. Vincent Medical Center, Portland, Oregon 97225, USA. petervblockvmd@phsor.org
Insights
Researchers identified 29 key variables for predicting adverse outcomes in percutaneous coronary interventions. These objective measures, including hemodynamic instability and disease severity, will standardize data collection and improve outcome comparisons.
Area of Science:
- Cardiovascular Medicine
- Medical Informatics
- Health Services Research
Background:
- Outcomes of percutaneous coronary interventions (PCI) are influenced by patient risk, disease severity, and acuity.
- Inconsistent variable definitions and tracking across databases hinder meaningful comparison of PCI outcomes.
- Standardizing predictor variables is crucial for developing universal risk stratification tools.
Purpose of the Study:
- To identify and define a core set of variables with significant statistical power for predicting adverse outcomes in interventional cardiology.
- To ensure identified variables are objective, reproducible, and predictive in existing cardiac databases.
Main Methods:
- Empirically derived variables were tested across eight cardiac databases (158,273 cases).
- Analysis focused on three endpoints: in-hospital death, coronary artery bypass graft surgery, and Q wave myocardial infarction.
- Univariate and multivariate regression models quantified predictive value, followed by expert consensus for variable definition.
Main Results:
- Patient demographics were similar across databases, but disease severity varied significantly.
- Hemodynamic instability, disease severity, demographics, and comorbid conditions emerged as the most potent predictors of adverse outcomes.
- Both univariate and multivariate analyses confirmed these powerful predictors.
Conclusions:
- A set of 29 objectively defined variables strongly associated with adverse outcomes post-coronary intervention was identified.
- Implementing these standardized variables across cardiac datasets will ensure uniform data collection.
- This standardization will enable more meaningful comparisons of outcomes among healthcare providers, institutions, and databases.
Objectives:
Our objectives were to identify and define a minimum set of variables for interventional cardiology that carried the most statistical weight for predicting adverse outcomes. Though "gaming" cannot be completely avoided, variables were to be as objective as possible and reproducible and had to be predictive of outcome in current databases.
Background:
Outcomes of percutaneous coronary interventions depend on patient risk characteristics and disease severity and acuity. Comparing results of interventions has been difficult because definitions of similar variables differ in databases, and variables are not uniformly tracked. Identifying the best predictor variables and standardizing their definitions are a first step in developing a universal stratification instrument.
Methods:
A list of empirically derived variables was first tested in eight cardiac databases (158,273 cases). Three end points (in-hospital death, in-hospital coronary artery bypass graft surgery, Q wave myocardial infarction) were chosen for analysis. Univariate and multivariate regression models were used to quantify the predictive value of the variable in each database. The variables were then defined by consensus by a panel of experts.
Results:
In all databases patient demographics were similar, but disease severity varied greatly. The most powerful predictors of adverse outcome were measures of hemodynamic instability, disease severity, demographics and comorbid conditions in both univariate and multivariate analyses.
Conclusions:
Our analysis identified 29 variables that have the strongest statistical association with adverse outcomes after coronary interventions. These variables were also objectively defined. Incorporation of these variables into every cardiac dataset will provide uniform standards for data collected. Comparisons of outcomes among physicians, institutions and databases will therefore be more meaningful.
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