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dc.contributor.authorOrawan Sae-Limen_US
dc.contributor.authorThitima Doungngernen_US
dc.contributor.authorSiriluk Jaisueen_US
dc.contributor.authorSirichai Cheewatanakornkulen_US
dc.contributor.authorPoukwan Arunmanakulen_US
dc.contributor.authorSirirat Anutrakulchaien_US
dc.contributor.authorRungsrit Kanjanavaniten_US
dc.contributor.authorWibul Wongpoowaraken_US
dc.date.accessioned2020-04-02T15:15:53Z-
dc.date.available2020-04-02T15:15:53Z-
dc.date.issued2019-01-01en_US
dc.identifier.issn11787074en_US
dc.identifier.other2-s2.0-85076224031en_US
dc.identifier.other10.2147/IJGM.S218393en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85076224031&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/68014-
dc.description.abstract© 2019 Sae-lim et al. 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.en_US
dc.subjectMedicineen_US
dc.titlePrediction of serum digoxin concentration using estimated glomerular filtration rate in Thai populationen_US
dc.typeJournalen_US
article.title.sourcetitleInternational Journal of General Medicineen_US
article.volume12en_US
article.stream.affiliationsFaculty of Medicine, Khon Kaen Universityen_US
article.stream.affiliationsFaculty of Medicine, Prince of Songkia Universityen_US
article.stream.affiliationsKhon Kaen Universityen_US
article.stream.affiliationsPrince of Songkla Universityen_US
article.stream.affiliationsChiang Mai Universityen_US
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