Comparison of equations for dosing of medications in renal impairment

Khanal, A.; Peterson, GM; Jose, Matthew; Castelino, Ronald L.

doi:10.1111/nep.12834

Cited by 11 publications

(15 citation statements)

References 43 publications

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“…The eGFR presents in the unit of mL/min/1.73 m 2 whereas the CrCl presents in mL/min. The use of the adjusted CKD-EPI equation for body surface area (change to mL/min) had a great rate of concordance with the CG equation reported by Khanal et al23 and Okparavero et al24 Similar to our study, the CKD-EPI with adjustment for body surface area was appropriate for prediction of SDC, especially in the patients with serum creatinine ≥0.9 mg/dL.…”

Section: Discussionsupporting

confidence: 88%

<p>Prediction Of Serum Digoxin Concentration Using Estimated Glomerular Filtration Rate In Thai Population</p>

Sae-Lim¹,

Doungngern²,

Jaisue³

et al. 2019

IJGM

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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.

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Section: Discussionsupporting

confidence: 88%

<p>Prediction Of Serum Digoxin Concentration Using Estimated Glomerular Filtration Rate In Thai Population</p>

Sae-Lim¹,

Doungngern²,

Jaisue³

et al. 2019

IJGM

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“…Most of the included studies have used either the CG or MDRD formula to evaluate IP. Although both MDRD and CG equations were highly advocated for clinical practice, the accuracy of these equations in different patient groups is still disputable . Despite the lack of gold‐standard validated equation, these formulas have discordantly estimated GFR in obese and multi‐ethnic elderly patients .…”

Section: Discussionmentioning

confidence: 99%

Inappropriate prescribing in chronic kidney disease: A systematic review of prevalence, associated clinical outcomes and impact of interventions

et al. 2017

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“…Chronic kidney disease (CKD) alters the concentrations and effects of various drugs and, as a result, may increase the risks of toxicity as well as treatment failure. 1–6 In response, clinicians use multiple equations to estimate renal function and select doses of medications in such patients. 2–4 Previous studies, however, show that up to 77% of medicines prescribed in this population were unadjusted.…”

Section: Introductionmentioning

confidence: 99%

“…1–6 In response, clinicians use multiple equations to estimate renal function and select doses of medications in such patients. 2–4 Previous studies, however, show that up to 77% of medicines prescribed in this population were unadjusted. 7–16 Almost two–thirds of these improper amounts potentially bear adverse consequences and about 3% may, in fact, result in severe outcomes and even death.…”

Section: Introductionmentioning

confidence: 99%

“…i:Ab ( (Pharmacy) OR (Pharmacist) ) AND Ab ( (Recommendation) OR (intervention) OR (Suggestion) OR (Prescribing) ) AND Ab ( (Renal) OR (Kidney) OR (Nephrology) OR (Dialysis) )[6] [4] ii: Ab ( (Pharmacy) OR (Pharmacist) ) AND Ab ( (Recommendation) OR (intervention) OR (Suggestion) OR (Prescribing) ) AND Ti ((Renal) OR (Kidney) OR (Nephrology) OR (Dialysis) ) [18] [5] iii: Ab ( (Pharmacy) OR (Pharmacist) ) AND Ti ( (Recommendation) OR (intervention) OR (Suggestion) OR (Prescribing) ) AND Ab ( (Renal) OR (Kidney) OR (Nephrology) OR (Dialysis) ) [22] [5] iV: Ab ( (Pharmacy) OR (Pharmacist) ) AND Ti ( (Recommendation) OR (intervention) OR (Suggestion) OR (Prescribing) ) AND Ti ( (Renal) OR (Kidney) OR (Nephrology) OR (Dialysis) ) [63] [10] V: Ti ( (Pharmacy) OR (Pharmacist) ) AND Ab ( (Recommendation) OR (intervention) OR (Suggestion) OR (Prescribing) ) AND Ab ( (Renal) OR (Kidney) OR (Nephrology) OR (Dialysis) ) [13] [5] Vi: Ti ( (Pharmacy) OR (Pharmacist) ) AND Ab ( (Recommendation) OR (intervention) OR (Suggestion) OR (Prescribing) ) AND Ti ( (Renal) OR (Kidney) OR (Nephrology) OR (Dialysis) ) [37] [8] Vii: Ti ( (Pharmacy) OR (Pharmacist) ) AND Ti ( (Recommendation) OR (intervention) OR (Suggestion) OR (Prescribing) ) AND Ab ( (Renal) OR (Kidney) OR (Nephrology) OR (Dialysis) ) [58] [7] Viii: Ti ( (Pharmacy) OR (Pharmacist) ) AND Ti ( (Recommendation) OR (intervention) OR (Suggestion) OR (Prescribing) ) AND Ti ( (Renal) OR (Kidney) OR (Nephrology) OR (Dialysis) ) [173] [12]…”

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confidence: 99%

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Concordance Among Methods for Empiric Renal Drug Dosing: Meropenem as a Role Model for Clinical Superiority of Cockroft–Gault or Modification of Diet in Renal Disease

Jasmi

Khan

Saadah

et al. 2018

Clinical Medicine Insights: Therapeutics

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The objectives of this study are, first, to measure concordance between 5 different renal function estimates (methods) in terms of recommended drug doses, and, subsequently, to establish the potential for significant clinical differences between Cockroft-Gault (CG) and Modification of Diet in Renal Disease (MDRD) equations in dosing a specific medication, namely, meropenem. DESiGn AnD SETTinG: This study used a Monte Carlo simulation, and this is a computer-based study with no actual patient data. PATiEnTS: A total of 1200 and 8701 simulated cases to study the concordance for the 5 methods and the potential clinical significance of discordance between CG and MDRD, respectively, were chosen for the study. METhODS: Simulated factors were age, sex, height, weight, serum creatinine, ethnicity, and albumin. We estimated the renal function using 5 formulas (ie, 10 combinations) including CG, MDRD, and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI). Next, the team evaluated concordance for each combination in dosing 22 drugs. Finally, our researchers reviewed and simulated data from the literature to show how CG versus MDRD use can result in clinically significant differences for meropenem. RESulTS: Pairwise combinations yielded statistically significant differences (P < .0001) except for CG and MDRD (P < .5147). In addition, the highest concordance was for MDRD and CKD-EPI. Average discordance is in the range of 25% to 30% with the lowest being between CG and albumin-based estimates. Both CG and MDRD were largely discordant which can reach up to 40% with a drug like meropenem and may be associated with significant adverse outcomes. COnCluSiOnS: Both CG and MDRD in our simulation are statistically comparable. Clinically, nonetheless, they are significantly inconsistent in terms of recommended drug dosing. We encourage practical comparisons of outcomes for individual or groups of medications (eg, meropenem and antibiotics) empirically dosed in renal patients on the basis of equations used in distinct populations.

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Comparison of equations for dosing of medications in renal impairment

Cited by 11 publications

References 43 publications

<p>Prediction Of Serum Digoxin Concentration Using Estimated Glomerular Filtration Rate In Thai Population</p>

<p>Prediction Of Serum Digoxin Concentration Using Estimated Glomerular Filtration Rate In Thai Population</p>

Inappropriate prescribing in chronic kidney disease: A systematic review of prevalence, associated clinical outcomes and impact of interventions

Concordance Among Methods for Empiric Renal Drug Dosing: Meropenem as a Role Model for Clinical Superiority of Cockroft–Gault or Modification of Diet in Renal Disease

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