Performance of Prediction Models for Contrast-Induced Acute Kidney Injury after Transcutaneous Aortic Valve...

Area of Science:

• Cardiology
• Nephrology
• Medical Devices

Background:

• Acute kidney injury (AKI) is a significant complication impacting outcomes in patients undergoing transcutaneous aortic valve replacement (TAVR).
• Accurate risk estimation of AKI is crucial for procedural planning and patient management strategies.

Purpose of the Study:

• To evaluate and compare the predictive performance of six established risk scores for AKI following TAVR.
• To identify the most accurate score for predicting AKI in this patient population.

Main Methods:

• Six risk scores were assessed: contrast material limit score, volume-to-creatinine clearance ratio, ACEF, CR4EATME3AD3, Mehran model A, and Mehran model B.
• Data from 559 consecutive patients in the Brazilian TAVR registry were analyzed.

Main Results:

• All tested scores demonstrated poor accuracy and calibration for predicting lower grades (1 or 2) of AKI.
• Predictive accuracy improved for moderate to severe AKI (grade 2/3 and grade 3).
• The CR4EATME3AD3 score was the best predictor for AKI stage 2/3 (AUC: 0.62) and stage 3 (AUC: 0.64), while Mehran models A and B also showed utility for stage 3 AKI.

Conclusions:

• Existing risk prediction models lack validity for detecting lower-grade AKI post-TAVR.
• The CR4EATME3AD3 score emerged as the most effective predictor for moderate to severe AKI after TAVR.
• These findings suggest multifactorial causes of kidney injury beyond contrast-induced AKI in TAVR patients.