Hyperkalemia risk with finerenone in diabetic kidney disease: a real-world analysis from the FINE-TURK cohort.
Source: PubMed, NCBI / U.S. National Library of Medicine
Finerenone is associated with a lower, yet clinically relevant, risk of hyperkalemia compared with steroidal mineralocorticoid receptor antagonists in diabetic kidney disease (DKD) trials. However, real-world data on hyperkalemia and its associated factors are scarce. FINE-TURK is a national, observational cohort of DKD patients who were initiated on finerenone. Eligible adults were included; demographic, clinical, and laboratory data were evaluated. The primary outcome was hyperkalemia risk signal (potassium ≥5.0 mEq/l), and the secondary outcome was clinically meaningful hyperkalemia (potassium ≥5.5 mEq/l) within 3 months. Multivariate logistic regression (LR) was used to define features associated with the both outcomes. Machine learning methods of LR, random forest (RF), extreme gradient boosting, and categorical boosting classifiers were used to define important features associated with the primary outcome. Total 699 patients were included. Of them, 259 (37.1%) reached the primary outcome, and 51 (7.3%) reached the secondary outcome. Baseline serum potassium, estimated glomerular filtration rate (eGFR), and finerenone dose were associated with the both outcomes in multivariate LR. Machine learning analyses consistently identified baseline serum potassium and eGFR as the most influential factors associated with primary outcome, with thiazide use being associated with lower risk and 20 mg (compared to 10 mg) finerenone initiation being associated wi
Abstract
Finerenone is associated with a lower, yet clinically relevant, risk of hyperkalemia compared with steroidal mineralocorticoid receptor antagonists in diabetic kidney disease (DKD) trials. However, real-world data on hyperkalemia and its associated factors are scarce. FINE-TURK is a national, observational cohort of DKD patients who were initiated on finerenone. Eligible adults were included; demographic, clinical, and laboratory data were evaluated. The primary outcome was hyperkalemia risk signal (potassium ≥5.0 mEq/l), and the secondary outcome was clinically meaningful hyperkalemia (potassium ≥5.5 mEq/l) within 3 months. Multivariate logistic regression (LR) was used to define features associated with the both outcomes. Machine learning methods of LR, random forest (RF), extreme gradient boosting, and categorical boosting classifiers were used to define important features associated with the primary outcome. Total 699 patients were included. Of them, 259 (37.1%) reached the primary outcome, and 51 (7.3%) reached the secondary outcome. Baseline serum potassium, estimated glomerular filtration rate (eGFR), and finerenone dose were associated with the both outcomes in multivariate LR. Machine learning analyses consistently identified baseline serum potassium and eGFR as the most influential factors associated with primary outcome, with thiazide use being associated with lower risk and 20 mg (compared to 10 mg) finerenone initiation being associated with higher risk. LR demonstrated the highest recall; RF achieved the highest precision in performance. Real-world data suggest that the risk of clinically meaningful hyperkalemia is similar to that in the clinical trials. In parallel with the safety analysis of clinical trials, baseline potassium and eGFR were consistently the most important factors associated with hyperkalemia risk.