Assessing the validity of cardiac surgery risk stratification systems for CABG patients in a single center
Giedrius Vanagas, Sarunas Kinduris
Med Sci Monit 2005; 11(5): CR215-218
Available online: 2005-05-05
Background:Most cardiac surgery risk stratification systems were primarily designed using patient-related factors to predict mortality and postoperative morbidity. Relative mortality rates are higher at cardiac surgery centers which perform surgery on elderly patients. Our aim was to assess the validity of risk stratification systems for our regional population.Material/Methods:The study involved 1021 patients. Risk stratification was carried out using the EuroSCORE, Ontario, and QMMI scoring systems. Analysis comparing the scoring systems included sensitivity, specificity, predictive values, and receiver operating characteristics (ROC) curves. Accuracy was assessed using the systems’ ability to avoid Type I and Type II errors.Results:Sensitivity and specificity of the QMMI scoring system were 33.3% and 97.2%, of EuroSCORE 20.7% and 96.7%, and of Ontario 21.1% and 94.4%, respectively. The best positive predictive value was for QMMI and EuroSCORE with 75% versus Ontario’s 50%. The highest negative predictive value was QMMI’s 85.4% versus Ontario’s 78.9% and EuroSCORE’s 72.0%. The best accuracy showed QMMI scoring with 84.5% versus Ontario’s 78.9% and EuroSCORE’s 72.2%.Conclusions:All the investigated risk stratification systems were moderately predictive. The QMMI score showed the best predictive characteristics (sensitivity, specificity, and accuracy) for our patient population. The QMMI system had high specificity and accuracy. The EuroSCORE system showed mortality overprediction for our population, associated with high false negative test results and low accuracy. The Ontario risk stratification system often commits Type II errors, associated with a high rate of false positive test results and low accuracy.
Keywords: Age Factors, Coronary Artery Bypass - mortality, Coronary Artery Bypass - statistics & numerical data, Data Interpretation, Statistical, Lithuania - epidemiology, Risk Factors, Sensitivity and Specificity, Age Factors, Coronary Artery Bypass - statistics & numerical data, Data Interpretation, Statistical, Lithuania - epidemiology, Risk Factors, Sensitivity and Specificity