Risk stratification applied to CAST registry data: combining 9 predictors. Cardiac Arrhythmia Suppression Trial
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Timothy R Church1, Morrison Hodges, James J Bailey
1Division of Environmental & Occupational Health, University of Minnesota School of Public Health, Minneapolis, MN 55455, USA. trc@cccs.umn.edu
Insights
Developing better risk assessment for sudden cardiac death (SCD) can help identify more high-risk individuals for life-saving implantable cardioverter-defibrillators (ICDs). Multivariate analysis of patient data significantly improves prediction accuracy over individual factors.
Area of Science:
- Cardiology
- Biostatistics
- Preventive Medicine
Background:
- Sudden cardiac death (SCD) claims over 200,000 lives annually in the U.S.
- Current criteria for implantable cardioverter-defibrillators (ICDs) miss many at-risk patients.
- Improved risk stratification is crucial for preventing SCD.
Purpose of the Study:
- To develop and validate a multivariate statistical method for individual patient risk assessment for SCD.
- To identify a higher proportion of patients who would benefit from ICD therapy.
Main Methods:
- Utilized Cox regression survival analysis on data from the Cardiac Arrhythmia Suppression Trial (CAST).
- Combined multiple predictors including patient demographics, clinical history, and electrocardiogram (ECG) findings.
- Developed a multivariate predictor integrating age, sex, ejection fraction, smoking, prior myocardial infarction, premature beats, and ventricular tachycardia runs.
Main Results:
- The multivariate predictor significantly outperformed individual risk factors.
- A proposed test identified 7% of the CAST registry as high-risk, with an average 47% risk of death.
- The model correctly identified 20% of patients who died within 2 years.
Conclusions:
- Multivariate statistical methods can effectively combine diverse patient data for accurate SCD risk prediction.
- This approach has the potential to significantly improve risk stratification and guide ICD therapy selection.
- Further refinement of component measurements could enhance the prevention of sudden cardiac death.
Abstract:
Over 200,000 people in the United States die of sudden cardiac death (SCD) every year. Although many of these deaths occur in asymptomatic individuals, the vast majority of deaths occur in people who are under care for existing coronary heart disease. Implantable cardioverter/defibrillators (ICDs) have been shown in several randomized trials to be effective in prolonging lives of those at high risk for sudden cardiac death, but the criteria used in these trials and the ACC/AHA consensus guidelines would cover only a minority of patients. Developing methods to assign risk to individual patients without prior SCD events could promote the use of this life-saving therapy in those with especially high risk. Given sufficient physiologically relevant measurements from electrocardiogram analysis, clinical assessment, and demographic status, multivariate statistical methods for predicting survival can be used to combine many predictors of risk and calculate the risk for an individual patient. A survival analysis using Cox regression on data from the Cardiac Arrhythmia Suppression Trial (CAST) illustrates this concept. Patient age, sex, ejection fraction, smoking history, and prior myocardial infarction history, along with the frequency of premature beats and the presence of runs of ventricular tachycardia on Holter monitoring and the time from the index myocardial infarction to the baseline Holter and to recruitment into CAST were combined in a multivariate predictor derived from the Cox regression; this predictor significantly outperforms the individual predictors. A proposed test based on this predictor would identify as positive 7% of the CAST registry, with an average risk of death among the positives of 47%; 20% of those dead at 2 years would be positive. With improved component measurements, this approach has the potential for significantly improving risk stratification for the prevention of SCD.