Cardiac Risk Index

Cardiac Risk Index: Evaluating Predictive Models for Perioperative Cardiac Complications

Introduction to Cardiac Risk Indices

Cardiac risk indices are essential tools used to predict the likelihood of cardiac complications in patients undergoing noncardiac surgery. These indices help clinicians identify high-risk patients and make informed decisions about perioperative care. Among these indices, the Revised Cardiac Risk Index (RCRI) is one of the most widely used models.

Revised Cardiac Risk Index (RCRI) Performance

General Predictive Ability

The RCRI is designed to predict major cardiac complications such as cardiac death, myocardial infarction, and nonfatal cardiac arrest. It has been shown to discriminate moderately well between patients at low and high risk for cardiac events after mixed noncardiac surgery, with an area under the receiver-operating characteristic curve (AUC) of 0.75. However, its performance is less accurate for predicting cardiac events after vascular noncardiac surgery, with an AUC of 0.64.

Age-Specific Performance

The performance of the RCRI varies across different age groups. In a study involving a nationwide cohort, the RCRI showed the highest predictive accuracy in patients aged 56 to 65 years (C statistic of 0.772) and the lowest in those aged over 85 years (C statistic of 0.683). Despite these variations, the RCRI maintained a high negative predictive value across all age groups, indicating its reliability in ruling out major adverse cardiovascular events in patients without risk factors.

Comparison with Other Risk Indices

Vascular Surgery-Specific Models

The Vascular Study Group of New England Cardiac Risk Index (VSG-CRI) has been developed specifically for vascular surgery patients and has shown to predict cardiac complications more accurately than the RCRI. The VSG-CRI demonstrated better calibration and discrimination, with ROC curves ranging from 0.68 to 0.74 for different procedures, compared to the RCRI's underestimation of risk by 1.7- to 7.4-fold.

Other Predictive Models

Several other models have been compared to the RCRI, including the American Society of Anesthesiologists (ASA) classification, Goldman index, and Detsky index. However, none of these models have consistently outperformed the RCRI in predicting major adverse cardiac events (MACE). The ACS-NSQIP-MICA and ACS-NSQIP-SRS models have shown better performance in predicting myocardial infarction, cardiac arrest, and all-cause mortality, respectively, but the evidence is not conclusive due to high risks of bias in the studies.

Enhancing Predictive Accuracy with Biomarkers

Addition of Biomarkers

Studies have investigated the added predictive value of biomarkers such as NT-proBNP and troponin to the RCRI. The addition of these biomarkers has improved the RCRI's predictive accuracy for MACE, with median delta c-statistics of 0.08 for NT-proBNP and 0.14 for troponin. These findings suggest that incorporating biomarkers into the RCRI could enhance its predictive performance.

Biomarkers Alone

When used in isolation, biomarkers like BNP and NT-proBNP have shown higher discriminative performance than the RCRI for predicting MACE, with median delta c-statistics of 0.15 and 0.12, respectively. This indicates that biomarkers could potentially serve as standalone predictors for perioperative cardiac risk.

Conclusion

The Revised Cardiac Risk Index remains a valuable tool for predicting perioperative cardiac complications in noncardiac surgery. While it performs moderately well in general, its accuracy varies across different surgical populations and age groups. The development of surgery-specific models like the VSG-CRI and the incorporation of biomarkers such as NT-proBNP and troponin can enhance the predictive accuracy of cardiac risk indices. Future research should focus on refining these models and exploring new biomarkers to improve perioperative cardiac risk stratification.

Summary

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