Literature DB >> 33481401

Free Hemoglobin Ratio as a Novel Biomarker of Acute Kidney Injury After On-Pump Cardiac Surgery: Secondary Analysis of a Randomized Controlled Trial.

Jie Hu1,2, Emanuele Rezoagli3, Francesco Zadek4, Edward A Bittner2, Chong Lei5, Lorenzo Berra2.   

Abstract

BACKGROUND: Cardiac surgery with cardiopulmonary bypass (CPB) is associated with a high risk of postoperative acute kidney injury (AKI). Due to limitations of current diagnostic strategies, we sought to determine whether free hemoglobin (fHb) ratio (ie, levels of fHb at the end of CPB divided by baseline fHb) could predict AKI after on-pump cardiac surgery.
METHODS: This is a secondary analysis of a randomized controlled trial comparing the effect of nitric oxide (intervention) versus nitrogen (control) on AKI after cardiac surgery (NCT01802619). A total of 110 adult patients in the control arm were included. First, we determined whether fHb ratio was associated with AKI via multivariable analysis. Second, we verified whether fHb ratio could predict AKI and incorporation of fHb ratio could improve predictive performance at an early stage, compared with prediction using urinary biomarkers alone. We conducted restricted cubic spline in logistic regression for model development. We determined the predictive performance, including area under the receiver-operating-characteristics curve (AUC) and calibration (calibration plot and accuracy, ie, number of correct predictions divided by total number of predictions). We also used AUC test, likelihood ratio test, and net reclassification index (NRI) to compare the predictive performance between competing models (ie, fHb ratio versus neutrophil gelatinase-associated lipocalin [NGAL], N-acetyl-β-d-glucosaminidase [NAG], and kidney injury molecule-1 [KIM-1], respectively, and incorporation of fHb ratio with NGAL, NAG, and KIM-1 versus urinary biomarkers alone), if applicable.
RESULTS: Data stratified by median fHb ratio showed that subjects with an fHb ratio >2.23 presented higher incidence of AKI (80.0% vs 49.1%; P = .001), more need of renal replacement therapy (10.9% vs 0%; P = .036), and higher in-hospital mortality (10.9% vs 0%; P = .036) than subjects with an fHb ratio ≤2.23. fHb ratio was associated with AKI after adjustment for preestablished factors. fHb ratio outperformed urinary biomarkers with the highest AUC of 0.704 (95% confidence interval [CI], 0.592-0.804) and accuracy of 0.714 (95% CI, 0.579-0.804). Incorporation of fHb ratio achieved better discrimination (AUC test, P = .012), calibration (likelihood ratio test, P < .001; accuracy, 0.740 [95% CI, 0.617-0.832] vs 0.632 [95% CI, 0.477-0.748]), and significant prediction increment (NRI, 0.638; 95% CI, 0.269-1.008; P < .001) at an early stage, compared with prediction using urinary biomarkers alone.
CONCLUSIONS: Results from this exploratory, hypothesis-generating retrospective, observational study shows that fHb ratio at the end of CPB might be used as a novel, widely applicable biomarker for AKI. The use of fHb ratio might help for an early detection of AKI, compared with prediction based only on urinary biomarkers.
Copyright © 2021 International Anesthesia Research Society.

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Year:  2021        PMID: 33481401      PMCID: PMC8154645          DOI: 10.1213/ANE.0000000000005381

Source DB:  PubMed          Journal:  Anesth Analg        ISSN: 0003-2999            Impact factor:   6.627


  38 in total

1.  Urinary biomarkers for sensitive and specific detection of acute kidney injury in humans.

Authors:  Vishal S Vaidya; Sushrut S Waikar; Michael A Ferguson; Fitz B Collings; Kelsey Sunderland; Costas Gioules; Gary Bradwin; Roland Matsouaka; Rebecca A Betensky; Gary C Curhan; Joseph V Bonventre
Journal:  Clin Transl Sci       Date:  2008-12       Impact factor: 4.689

2.  Vasoactive-inotropic score and the prediction of morbidity and mortality after cardiac surgery.

Authors:  Timo Koponen; Johanna Karttunen; Tadeusz Musialowicz; Laura Pietiläinen; Ari Uusaro; Pasi Lahtinen
Journal:  Br J Anaesth       Date:  2019-02-18       Impact factor: 9.166

3.  Haptoglobin and free haemoglobin during cardiac surgery-is there a link to acute kidney injury?

Authors:  A J Wetz; E M Richardt; H Schotola; M Bauer; A Bräuer
Journal:  Anaesth Intensive Care       Date:  2017-01       Impact factor: 1.669

4.  Association between postoperative fluid balance and acute kidney injury in patients after cardiac surgery: A retrospective cohort study.

Authors:  Yanfei Shen; Weimin Zhang; Xuping Cheng; Manzhen Ying
Journal:  J Crit Care       Date:  2017-12-01       Impact factor: 3.425

5.  Acute Kidney Injury in Patients Undergoing the Extracardiac Fontan Operation With and Without the Use of Cardiopulmonary Bypass.

Authors:  Claudia A Algaze; Andrew M Koth; Lisa W Faberowski; Frank L Hanley; Catherine D Krawczeski; David M Axelrod
Journal:  Pediatr Crit Care Med       Date:  2017-01       Impact factor: 3.624

6.  Urinary L-FABP and its combination with urinary NGAL in early diagnosis of acute kidney injury after cardiac surgery in adult patients.

Authors:  Shang Liu; Miaolin Che; Song Xue; Bo Xie; Mingli Zhu; Renhua Lu; Weimin Zhang; Jiaqi Qian; Yucheng Yan
Journal:  Biomarkers       Date:  2012-11-21       Impact factor: 2.658

7.  Epidemiology of Acute Kidney Injury in Critically Ill Children and Young Adults.

Authors:  Ahmad Kaddourah; Rajit K Basu; Sean M Bagshaw; Stuart L Goldstein
Journal:  N Engl J Med       Date:  2016-11-18       Impact factor: 91.245

8.  Plasma Free Hemoglobin Is an Independent Predictor of Mortality among Patients on Extracorporeal Membrane Oxygenation Support.

Authors:  Hesham R Omar; Mehdi Mirsaeidi; Stephanie Socias; Collin Sprenker; Christiano Caldeira; Enrico M Camporesi; Devanand Mangar
Journal:  PLoS One       Date:  2015-04-22       Impact factor: 3.240

9.  Hemolysis during cardiac surgery is associated with increased intravascular nitric oxide consumption and perioperative kidney and intestinal tissue damage.

Authors:  Iris C Vermeulen Windsant; Norbert C J de Wit; Jonas T C Sertorio; Annemarie A van Bijnen; Yuri M Ganushchak; John H Heijmans; Jose E Tanus-Santos; Michael J Jacobs; Jos G Maessen; Wim A Buurman
Journal:  Front Physiol       Date:  2014-09-08       Impact factor: 4.566

10.  Correcting for optimistic prediction in small data sets.

Authors:  Gordon C S Smith; Shaun R Seaman; Angela M Wood; Patrick Royston; Ian R White
Journal:  Am J Epidemiol       Date:  2014-06-24       Impact factor: 4.897

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