Prediction Of Serum Digoxin Concentration Using Estimated Glomerular Filtration Rate In Thai Population
Received 5 June 2019
Accepted for publication 5 November 2019
Published 2 December 2019 Volume 2019:12 Pages 455—463
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 2
Editor who approved publication: Dr Scott Fraser
Orawan Sae-lim,1 Thitima Doungngern,1 Siriluk Jaisue,2 Sirichai Cheewatanakornkul,3 Poukwan Arunmanakul,4 Sirirat Anutrakulchai,5 Rungsrit Kanjanavanit,6 Wibul Wongpoowarak7
1Department of Clinical Pharmacy, Faculty of Pharmaceutical Sciences, Prince of Songkla University, Songkhla, Thailand; 2Division of Clinical Pharmacy, Faculty of Pharmaceutical Sciences, Khon Kaen University, Khon Kaen, Thailand; 3Division of Cardiology, Department of Internal Medicine, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand; 4Department of Pharmaceutical Care, Faculty of Pharmacy, Chiang Mai University, Chiang Mai, Thailand; 5Division of Nephrology, Department of Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand; 6Division of Cardiology, Department of Internal Medicine, Faculty of Medicine, Chaing Mai University, Chiang Mai, Thailand; 7Department of Pharmaceutical Technology, Faculty of Pharmaceutical Sciences, Prince of Songkla University, Songkhla, Thailand
Correspondence: Thitima Doungngern
Department of Clinical Pharmacy, Faculty of Pharmaceutical Sciences, Prince of Songkla University Songkhla, 90112, Thailand
Purpose: Serum digoxin concentration (SDC) monitoring may be unavailable in some healthcare settings. Predicted SDC comes into play in the efficacy and toxicity monitoring of digoxin. Renal function is the important parameter for predicting SDC. This study was conducted to compare measured and predicted SDC when using creatinine clearance (CrCl) from Cockcroft–Gault (CG) equation and estimated glomerular filtration rate (eGFR) calculated from CKD-Epidemiology Collaboration (CKD-EPI), re-expressed Modification of Diet in Renal Disease (Re-MDRD4), Thai-MDRD4, and Thai-eGFR equations in Sheiner’s and Konishi’s pharmacokinetic models.
Patients and methods: In this retrospective study, patients with cardiovascular disease with a steady-state of SDC within 0.5–2.0 mcg/L were enrolled. CrCl and studied eGFR adjusted for body surface area (BSA) were used in the models to determine the predicted SDC. The discrepancies of the measured and the predicted SDC were analyzed and compared.
Results: One hundred and twenty-four patients ranging in age from 22 to 88 years (median 60 years, IQR 50.2, 69.2) were studied. Their serum creatinine ranged from 0.40 to 1.80 mg/dL (median 0.90 mg/dL, IQR 0.79, 1.10). The mean±SD of measured SDC was 1.12±0.34 mcg/L. In the Sheiner’s model, the mean predicted SDC was calculated by using the CG and the BSA adjusted CKD-EPI equations and was not different when compared with the measured levels (1.10±0.36 mcg/L (p=0.669) and 1.08±0.42 mcg/L (p=0.374), respectively). The CG, CKD-EPI, and Re-MDRD4 equations were a better fit for patients with creatinine ≥0.9 mg/dL for prediction with minimal errors. In the Konishi’s model, the predicted SDC using the CG and the studied eGFR equation was lower than the measured SDC (p<0.05).
Conclusion: In Sheiner’s model, the CG and the BSA adjusted CKD-EPI equations should be used for predicting SDC, especially in patients with serum creatinine ≥0.9 mg/dL. The other studied eGFRs underestimated SDC in both Sheiner’s and Konishi’s model.
Keywords: predicted concentration, digoxin, creatinine clearance, estimated glomerular filtration rate equation, Cockcroft–Gault equation
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