Rcri risk
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Summary
10 papers analyzed
These studies suggest the Revised Cardiac Risk Index (RCRI) moderately predicts cardiac risk in noncardiac surgeries, with potential improvements from biomarkers and specific adjustments, but is less effective for vascular surgeries and requires updates for better accuracy.
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Evaluating the Revised Cardiac Risk Index (RCRI) for Predicting Perioperative Cardiac Complications
Introduction to RCRI and Its Importance in Noncardiac Surgery
The Revised Cardiac Risk Index (RCRI) is a widely recognized tool used to predict the likelihood of perioperative cardiac complications in patients undergoing noncardiac surgery. Developed to aid in preoperative risk stratification, the RCRI assesses several risk factors to estimate the probability of major adverse cardiac events (MACE) such as myocardial infarction, cardiac arrest, and cardiac death .
Predictive Performance of RCRI in Various Surgical Contexts
General Noncardiac Surgery
The RCRI has been shown to moderately discriminate between patients at low and high risk for cardiac events in mixed noncardiac surgeries. Studies indicate an area under the receiver-operating characteristic curve (AUC) of 0.75, suggesting moderate accuracy in predicting cardiac complications. However, its performance is less reliable in predicting cardiac events following vascular surgeries, with an AUC of 0.64.
Vascular Surgery
In vascular surgery patients, the RCRI accurately predicts the risk of death and nonfatal myocardial infarction, but preoperative coronary artery revascularization does not reduce the incidence of these complications in high-risk subsets. Additionally, the Vascular Study Group of New England Cardiac Risk Index (VSG-CRI) has been found to predict cardiac complications more accurately than the RCRI in vascular surgery patients.
Specific Surgical Procedures
For lung resection candidates, a recalibrated version of the RCRI, which includes specific variables such as cerebrovascular disease and pneumonectomy, has shown higher discrimination (c-index of 0.72) compared to the traditional RCRI (c-index of 0.62). Similarly, in posterior lumbar decompression surgeries, the RCRI demonstrated good discriminative ability to predict myocardial infarction and cardiac arrest, outperforming the American Society of Anesthesiologists (ASA) classification.
Enhancing RCRI with Biomarkers and Other Models
Addition of Biomarkers
Research has explored the addition of biomarkers like NT-proBNP and troponin to the RCRI, which has improved its predictive performance for MACE. The combination of these biomarkers with the RCRI has shown a significant increase in the c-statistics, indicating better discrimination. For instance, the addition of NT-proBNP and troponin improved the median delta c-statistics by 0.08 and 0.14, respectively.
Comparison with Other Models
Other prediction models, such as the ACS-NSQIP-MICA and ACS-NSQIP-SRS, have been compared to the RCRI. These models have shown better performance in predicting myocardial infarction, cardiac arrest, and all-cause mortality in certain contexts. However, the RCRI remains a robust tool, especially when enhanced with additional biomarkers.
Age-Specific Performance and Renal Function Updates
Age-Specific Analysis
The performance of the RCRI varies across different age groups. It has been found to have the highest C-statistic among patients aged 56 to 65 years and the lowest for those over 85 years. Despite these variations, the RCRI maintains a high negative predictive value across all age groups, making it a reliable tool for excluding high-risk patients.
Updating Renal Function Component
The traditional RCRI includes serum creatinine as a measure of renal function. However, updating this component to use the estimated glomerular filtration rate (eGFR) has been proposed to improve accuracy. Studies are underway to validate this updated model in large cohorts.
Conclusion
The RCRI is a valuable tool for predicting perioperative cardiac complications in noncardiac surgery. While it performs moderately well in general noncardiac surgeries, its accuracy can be enhanced by incorporating biomarkers and recalibrating for specific surgical contexts. Future research should focus on refining the RCRI with updated renal function measures and exploring its integration with other predictive models to further improve its utility in clinical practice.
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