Impact of the Choice of Risk Model for Identifying Low-risk Patients Using the 2014 American College of Cardiology/American Heart Association Perioperative Guidelines

Background: The 2014 American College of Cardiology Perioperative Guideline recommends risk stratifying patients scheduled to undergo noncardiac surgery using either: (1) the Revised Cardiac Index; (2) the American College of Surgeons National Surgical Quality Improvement Program Surgical Risk Calculator; or (3) the Myocardial Infarction or Cardiac Arrest calculator. The aim of this study is to determine how often these three risk-prediction tools agree on the classification of patients as low risk (less than 1%) of major adverse cardiac event. Methods: This is a retrospective observational study using a sample of 10,000 patient records. The risk of cardiac complications was calculated for the Revised Cardiac Index and the Myocardial Infarction or Cardiac Arrest models using published coefficients, and for the American College of Surgeons National Surgical Quality Improvement Program Surgical Risk Calculator using the publicly available website. The authors used the intraclass correlation coefficient and kappa analysis to quantify the degree of agreement between these three risk-prediction tools. Results: There is good agreement between the American College of Surgeons National Surgical Quality Improvement Program and Myocardial Infarction or Cardiac Arrest estimates of major adverse cardiac events (intraclass correlation coefficient = 0.68, 95% CI: 0.66 to 0.70), while only poor agreement between (1) American College of Surgeons National Surgical Quality Improvement Program Surgical Risk Calculator and the Revised Cardiac Index (intraclass correlation coefficient = 0.37; 95% CI: 0.34 to 0.40), and (2) Myocardial Infarction or Cardiac Arrest and Revised Cardiac Index (intraclass correlation coefficient = 0.26; 95% CI: 0.23 to 0.30). The three prediction models disagreed 29% of the time on which patients were low risk. Conclusions: There is wide variability in the predicted risk of cardiac complications using different risk-prediction tools. Including more than one prediction tool in clinical guidelines could lead to differences in decision-making for some patients depending on which risk calculator is used.