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Prediction Of Serum Digoxin Concentration Using Estimated Glomerular Filtration Rate In Thai Population
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 predi...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6896919/ https://www.ncbi.nlm.nih.gov/pubmed/31819596 http://dx.doi.org/10.2147/IJGM.S218393 |
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author | Sae-lim, Orawan Doungngern, Thitima Jaisue, Siriluk Cheewatanakornkul, Sirichai Arunmanakul, Poukwan Anutrakulchai, Sirirat Kanjanavanit, Rungsrit Wongpoowarak, Wibul |
author_facet | Sae-lim, Orawan Doungngern, Thitima Jaisue, Siriluk Cheewatanakornkul, Sirichai Arunmanakul, Poukwan Anutrakulchai, Sirirat Kanjanavanit, Rungsrit Wongpoowarak, Wibul |
author_sort | Sae-lim, Orawan |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6896919 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Dove |
record_format | MEDLINE/PubMed |
spelling | pubmed-68969192019-12-09 Prediction Of Serum Digoxin Concentration Using Estimated Glomerular Filtration Rate In Thai Population Sae-lim, Orawan Doungngern, Thitima Jaisue, Siriluk Cheewatanakornkul, Sirichai Arunmanakul, Poukwan Anutrakulchai, Sirirat Kanjanavanit, Rungsrit Wongpoowarak, Wibul Int J Gen Med Original Research 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. Dove 2019-12-02 /pmc/articles/PMC6896919/ /pubmed/31819596 http://dx.doi.org/10.2147/IJGM.S218393 Text en © 2019 Sae-lim et al. http://creativecommons.org/licenses/by-nc/3.0/ This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). |
spellingShingle | Original Research Sae-lim, Orawan Doungngern, Thitima Jaisue, Siriluk Cheewatanakornkul, Sirichai Arunmanakul, Poukwan Anutrakulchai, Sirirat Kanjanavanit, Rungsrit Wongpoowarak, Wibul Prediction Of Serum Digoxin Concentration Using Estimated Glomerular Filtration Rate In Thai Population |
title | Prediction Of Serum Digoxin Concentration Using Estimated Glomerular Filtration Rate In Thai Population |
title_full | Prediction Of Serum Digoxin Concentration Using Estimated Glomerular Filtration Rate In Thai Population |
title_fullStr | Prediction Of Serum Digoxin Concentration Using Estimated Glomerular Filtration Rate In Thai Population |
title_full_unstemmed | Prediction Of Serum Digoxin Concentration Using Estimated Glomerular Filtration Rate In Thai Population |
title_short | Prediction Of Serum Digoxin Concentration Using Estimated Glomerular Filtration Rate In Thai Population |
title_sort | prediction of serum digoxin concentration using estimated glomerular filtration rate in thai population |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6896919/ https://www.ncbi.nlm.nih.gov/pubmed/31819596 http://dx.doi.org/10.2147/IJGM.S218393 |
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