Key result
CG and CG-BSA formulas best predict major adverse events in AF patients.
Why the study?
CKD and AF often coexist, making accurate renal function estimation crucial, but the concordance and predictive performance of different estimation equations in European AF patients needed comparison.
Which renal function estimation formula best predicts adverse outcomes in patients with atrial fibrillation?
Observational (n=8,506)
Yes
Which renal function estimation formula best predicts adverse outcomes in patients with atrial fibrillation?
The Cockcroft-Gault (CG) and CG-BSA formulas demonstrated superior discriminative ability for predicting adverse outcomes in patients with atrial fibrillation compared to other renal function estimation equations.
Answers are grounded in this paper and cite their sources.
May enhance AF risk stratification with CG formulas; hypothesis-generating and requires prospective validation before practice change.
BACKGROUND: Chronic kidney disease (CKD) and atrial fibrillation (AF) often coexist, making accurate renal function estimation crucial, typically through equations calculating estimated glomerular filtration rate (eGFR) or creatinine clearance (CrCl). OBJECTIVE: To compare the concordance and predictive performance of different renal function estimation equations in a European cohort of AF patients. METHODS: We analyzed data from AF patients enrolled in a prospective observational European registry. Renal function was estimated using eight formulas: BIS-1, CG, CG-BSA, CKD-EPI, EKFC, FAS, LMR and MDRD. Concordance between formulas was assessed using weighted Cohen's Kappa, while Cox regression and receiver operating characteristic (ROC) curves evaluated their association with outcomes (composite of all-cause death, any coronary revascularization and any thromboembolism). RESULTS: We included 8,506 patients. CKD-EPI demonstrated good to excellent concordance with other formulas, with the lowest concordance with CG (K = 0.607; 95% CI, 0.595-0.618) and the highest with MDRD (K = 0.880; 95% CI, 0.873-0.887). The risk of adverse outcomes increased sharply when renal function dropped below 60 ml/min across all formulas. CG-BSA and CG formulas showed the best discriminative ability for predicting composite outcomes (AUC 0.660, 95% CI 0.644-0.677, and 0.661, 95% CI 0.644-0.678, respectively). Based on integrated discrimination improvement (IDI) analysis, compared to the CKD-EPI equation, the CG and CG-BSA formulas showed significant improvements in sensitivity of 0.9% and 1.1%, respectively CONCLUSION: Equations for estimating renal function vary in concordance, with potential implications for drug prescription and predicting adverse events. CG and CG-BSA formulas showed superior performance in identifying patients at risk for adverse outcomes.
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Boriani et al. (2025) conducted an observational in Atrial fibrillation (n=8,506). Renal function estimation equations (BIS-1, CG, CG-BSA, CKD-EPI, EKFC, FAS, LMR, MDRD) vs. Comparison among formulas was evaluated on Composite of all-cause death, any coronary revascularization and any thromboembolism (AUC 0.661 (CG) and 0.660 (CG-BSA), 95% CI 0.644-0.678 (CG) and 0.644-0.677 (CG-BSA)). The CG and CG-BSA formulas showed the best discriminative ability for predicting adverse outcomes in AF patients (AUC 0.661 [95% CI 0.644-0.678] and 0.660 [95% CI 0.644-0.677], respectively).
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