Glomerular filtration rate (GFR) during and after STEMI : A single-centre, methodological study comparing estimated and measured GFR
(2015) In BMJ Open 5(9).- Abstract
Objectives: To validate the performance of the most commonly used formulas for estimation of glomerular filtration rate (GFR) against measured GFR during the index hospitalisation for ST-elevation myocardial infarction (STEMI). Setting: Single centre, methodological study. Participants: 40 patients with percutaneous coronary intervention-treated STEMI were included between November 2011 and February 2013. Patients on dialysis, cardiogenic shock or known allergy to iodine were excluded. Outcome measures: Creatinine and cystatin C were determined at admission and before discharge in 40 patients with STEMI. Clearance of iohexol was measured (mGFR) before discharge. We evaluated and compared the Cockcroft-Gault (CG), the Modification of... (More)
Objectives: To validate the performance of the most commonly used formulas for estimation of glomerular filtration rate (GFR) against measured GFR during the index hospitalisation for ST-elevation myocardial infarction (STEMI). Setting: Single centre, methodological study. Participants: 40 patients with percutaneous coronary intervention-treated STEMI were included between November 2011 and February 2013. Patients on dialysis, cardiogenic shock or known allergy to iodine were excluded. Outcome measures: Creatinine and cystatin C were determined at admission and before discharge in 40 patients with STEMI. Clearance of iohexol was measured (mGFR) before discharge. We evaluated and compared the Cockcroft-Gault (CG), the Modification of Diet in Renal Disease (MDRD-IDMS), the Chronic Kidney Disease Epidemiology (CKD-EPI) and the Grubb relative cystatin C (rG-CystC) with GFR regarding correlation, bias, precision and accuracy (P30). Agreement between eGFR and mGFR to discriminate CKD was assessed by Cohen's κ statistics. Results: MDRD-IDMS and CKD-EPI demonstrated good performance to estimate GFR (correlation 0.78 vs 0.81%, bias.1.3% vs 1.5%, precision 17.9 vs 17.1 mL/min 1.73 m2 and P30 82.5% vs 82.5% for MDRD-IDMS vs CKD-EPI). CKD was best classified by CKD-EPI (κ 0.83). CG showed the worst performance (correlation 0.73%, bias.1% to 3%, precision 22.5 mL/min 1.73 m2 and P30 75%). The rG-CystC formula had a marked bias of.17.8% and significantly underestimated mGFR (p=0.03). At arrival, CKD-EPI and rG-CystC had almost perfect agreement in CKD classification (κ=0.87), whereas at discharge agreement was substantially lower (κ=0.59) and showed a significant discrepancy in CKD classification (p=0.02). Median cystatin C concentration increased by 19%. Conclusions: In acute STEMI, CKD-EPI showed the best CKD-classification ability followed by MDRDIDMS, whereas CG performed the worst. STEMI altered the performance of the cystatin C equation during the acute phase, suggesting that other factors might be involved in the rise of cystatin C.
(Less)
- author
- Venetsanos, Dimitrios ; Alfredsson, Joakim ; Segelmark, Mårten LU ; Swahn, Eva and Lawesson, Sofia Sederholm
- publishing date
- 2015
- type
- Contribution to journal
- publication status
- published
- in
- BMJ Open
- volume
- 5
- issue
- 9
- article number
- e007835
- publisher
- BMJ Publishing Group
- external identifiers
-
- pmid:26399570
- scopus:84956928527
- ISSN
- 2044-6055
- DOI
- 10.1136/bmjopen-2015-007835
- language
- English
- LU publication?
- no
- id
- 74bc9210-dda3-4368-8394-607dbcb00285
- date added to LUP
- 2020-06-17 16:40:11
- date last changed
- 2024-06-12 16:54:38
@article{74bc9210-dda3-4368-8394-607dbcb00285,
abstract = {{<p>Objectives: To validate the performance of the most commonly used formulas for estimation of glomerular filtration rate (GFR) against measured GFR during the index hospitalisation for ST-elevation myocardial infarction (STEMI). Setting: Single centre, methodological study. Participants: 40 patients with percutaneous coronary intervention-treated STEMI were included between November 2011 and February 2013. Patients on dialysis, cardiogenic shock or known allergy to iodine were excluded. Outcome measures: Creatinine and cystatin C were determined at admission and before discharge in 40 patients with STEMI. Clearance of iohexol was measured (mGFR) before discharge. We evaluated and compared the Cockcroft-Gault (CG), the Modification of Diet in Renal Disease (MDRD-IDMS), the Chronic Kidney Disease Epidemiology (CKD-EPI) and the Grubb relative cystatin C (rG-CystC) with GFR regarding correlation, bias, precision and accuracy (P30). Agreement between eGFR and mGFR to discriminate CKD was assessed by Cohen's κ statistics. Results: MDRD-IDMS and CKD-EPI demonstrated good performance to estimate GFR (correlation 0.78 vs 0.81%, bias.1.3% vs 1.5%, precision 17.9 vs 17.1 mL/min 1.73 m<sup>2</sup> and P30 82.5% vs 82.5% for MDRD-IDMS vs CKD-EPI). CKD was best classified by CKD-EPI (κ 0.83). CG showed the worst performance (correlation 0.73%, bias.1% to 3%, precision 22.5 mL/min 1.73 m<sup>2</sup> and P30 75%). The rG-CystC formula had a marked bias of.17.8% and significantly underestimated mGFR (p=0.03). At arrival, CKD-EPI and rG-CystC had almost perfect agreement in CKD classification (κ=0.87), whereas at discharge agreement was substantially lower (κ=0.59) and showed a significant discrepancy in CKD classification (p=0.02). Median cystatin C concentration increased by 19%. Conclusions: In acute STEMI, CKD-EPI showed the best CKD-classification ability followed by MDRDIDMS, whereas CG performed the worst. STEMI altered the performance of the cystatin C equation during the acute phase, suggesting that other factors might be involved in the rise of cystatin C.</p>}},
author = {{Venetsanos, Dimitrios and Alfredsson, Joakim and Segelmark, Mårten and Swahn, Eva and Lawesson, Sofia Sederholm}},
issn = {{2044-6055}},
language = {{eng}},
number = {{9}},
publisher = {{BMJ Publishing Group}},
series = {{BMJ Open}},
title = {{Glomerular filtration rate (GFR) during and after STEMI : A single-centre, methodological study comparing estimated and measured GFR}},
url = {{http://dx.doi.org/10.1136/bmjopen-2015-007835}},
doi = {{10.1136/bmjopen-2015-007835}},
volume = {{5}},
year = {{2015}},
}