Estimated glomerular filtration rate is an early biomarker of
cardiac surgery-associated acute kidney injury
dc.contributor.author | Candela-Toha, Ángel | |
dc.contributor.author | Pardo, María Carmen | |
dc.contributor.author | Pérez, Teresa | |
dc.contributor.author | Muriel, Alfonso | |
dc.contributor.author | Zamora, Javier | |
dc.date.accessioned | 2023-06-17T12:45:41Z | |
dc.date.available | 2023-06-17T12:45:41Z | |
dc.date.issued | 2018 | |
dc.description.abstract | Background: Acute kidney injury (AKI) diagnosis is still based on serum creatinine and diuresis. However, increases in creatinine are typically delayed 48h or longer after injury. Our aim was to determine the utility of routine postoperative renal function blood tests, to predict AKI one or 2 days in advance in a cohort of cardiac surgery patients. Patients and methods: Using a prospective database, we selected a sample of patients who had undergone major cardiac surgery between January 2002 and December 2013. The ability of the parameters to predict AKI was based on Acute Kidney Injury Network serum creatinine criteria. A cohort of 3962 cases was divided into 2 groups of similar size, one being exploratory and the other a validation sample. The exploratory group was used to show primary objectives and the validation group to confirm results. The ability to predict AKI of several kidney function parameters measured in routine postoperative blood tests, was measured with time-dependent ROC curves. The primary endpoint was time from measurement to AKI diagnosis. Results: AKI developed in 610 (30.8%) and 623 (31.4%) patients in the exploratory and validation samples, respectively. Estimated glomerular filtration rate using the MDRD-4 equation showed the best AKI prediction capacity, with values for the AUC ROC curves between 0.700 and 0.946. We obtained different cut-off values for estimated glomerular filtration rate depending on the degree of AKI severity and on the time elapsed between surgery and parameter measurement. Results were confirmed in the validation sample. Conclusions: Postoperative estimated glomerular filtration rate using the MDRD-4 equation showed good ability to predict AKI following cardiac surgery one or 2 days in advance. | |
dc.description.abstract | Antecedentes y objetivo: El diagnóstico de insuficiencia renal aguda (IRA) todavía se basa en la creatinina sérica y la diuresis. Sin embargo, el incremento de la creatinina a menudo se retrasa 48h o más con respecto al momento de la lesión. El objetivo de este estudio es determinar la utilidad de las pruebas analíticas de función renal habituales en el postoperatorio, para predecir la IRA con uno o 2 días de antelación, en una cohorte de pacientes intervenidos mediante cirugía cardíaca. Pacientes y métodos: A partir de una base de datos prospectiva, se seleccionó una muestra de pacientes operados de cirugía cardíaca mayor, entre enero de 2002 y diciembre de 2013. La definición de IRA se basó en el criterio de la creatinina sérica utilizado por la Acute Kidney Injury Network. La cohorte de 3.962 casos se dividió en 2 grupos de tamaño similar, uno exploratorio y otro de validación. El grupo exploratorio se utilizó para demostrar los objetivos principales y el de validación para confirmar los resultados. La capacidad de predicción de la IRA, de varios parámetros de función renal medidos en la analítica postoperatoria habitual, se evaluó utilizando curvas ROC tiempo-dependientes. Como variable principal se consideró el tiempo transcurrido desde la medida del marcador hasta el diagnóstico de la IRA. Resultados: Se observaron 610 (30,8%) y 623 (31,4%) episodios de IRA en los grupos exploratorio y de validación, respectivamente. La tasa de filtrado glomerular estimada por la ecuación MDRD-4 demostró la mejor capacidad predictiva de IRA, con valores del área bajo la curva ROC entre 0,700 y 0,946. Se calcularon distintos puntos de corte para dicho parámetro, en función de la gravedad de la IRA y del tiempo transcurrido entre la cirugía y su medición. Los resultados obtenidos se confirmaron en el grupo de validación. Conclusión: La tasa de filtrado glomerular postoperatoria, estimada por la ecuación MDRD-4, mostró una alta capacidad de predicción de IRA con uno o 2 días de antelación, en pacientes operados de cirugía cardíaca. | |
dc.description.department | Depto. de Estadística e Investigación Operativa | |
dc.description.faculty | Fac. de Ciencias Matemáticas | |
dc.description.refereed | TRUE | |
dc.description.status | pub | |
dc.eprint.id | https://eprints.ucm.es/id/eprint/77296 | |
dc.identifier.doi | 10.1016/j.nefro.2018.01.002 | |
dc.identifier.issn | 1989-2284 | |
dc.identifier.officialurl | https://doi.org/10.1016/j.nefro.2018.01.002 | |
dc.identifier.uri | https://hdl.handle.net/20.500.14352/12908 | |
dc.issue.number | 6 | |
dc.journal.title | Nefrología | |
dc.language.iso | eng | |
dc.page.final | 605 | |
dc.page.initial | 596 | |
dc.publisher | Sociedad Española de Nefrología | |
dc.rights | Atribución-NoComercial-SinDerivadas 3.0 España | |
dc.rights.accessRights | open access | |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/3.0/es/ | |
dc.subject.cdu | 311 | |
dc.subject.cdu | 616.12 | |
dc.subject.keyword | Acute kidney injury | |
dc.subject.keyword | Cardiac surgery | |
dc.subject.keyword | MDRD-4 | |
dc.subject.keyword | Prediction | |
dc.subject.keyword | Serum creatinine | |
dc.subject.keyword | Time-dependent ROC curve | |
dc.subject.keyword | Insuficiencia renal aguda | |
dc.subject.keyword | Cirugía cardíaca | |
dc.subject.keyword | Predicción | |
dc.subject.keyword | Creatinina sérica | |
dc.subject.keyword | Curva ROC tiempo-dependiente | |
dc.subject.ucm | Estadística | |
dc.subject.ucm | Cardiología | |
dc.subject.unesco | 1209 Estadística | |
dc.subject.unesco | 3205.01 Cardiología | |
dc.title | Estimated glomerular filtration rate is an early biomarker of cardiac surgery-associated acute kidney injury | |
dc.type | journal article | |
dc.volume.number | 38 | |
dcterms.references | 1. Rosner MH, Okusa MD. Acute kidney injury associated with cardiac surgery. Clin J Am Soc Nephrol. 2006;1:19–32. 2. Mao H, Katz N, Ariyanon W, Blanca-Martos L, Adybellíz, Giuliani A, et al. Cardiac surgery-associated acute kidney injury. Cardiorenal Med. 2013;3:178–99. 3. Thakar CV, Arrigain S, Worley S, Yared JP, Paganini EP. A clinical score to predict acute renal failure after cardiac surgery. J Am Soc Nephrol. 2005;16:162–8. 4. Wijeysundera DN, Karkouti K, Dupuis JY, Rao V, Chan CT, Granton JT, et al. Derivation and validation of a simplified predictive index for renal replacement therapy after cardiac surgery. JAMA. 2007;297:1801–9. 5. Kashani K, Al-Khafaji A, Ardiles T, Artigas A, Bagshaw S, Bell M, et al. Discovery and validation of cell cycle arrest biomarkers in human acute kidney injury. Crit Care. 2013;17:R25. 6. Haase M, Bellomo R, Devarajan P, Schlattmann P, Haase A. Accuracy of neutrophil gelatinase-associated lipocalin (NGAL) in diagnosis and prognosis in acute kidney injury: a systematic review and meta-analysis. Am J Kidney Dis. 2009;54:1012–24. 7. Zhang Z, Lu B, Sheng X, Jin N. Cystatin C in prediction of acute kidney injury: a systemic review and meta-analysis. Am J Kidney Dis. 2011;58:356–65. 8. Liu Y, Guo W, Zhang J, Xu C, Yu S, Mao Z, et al. Urinary interleukin 18 for detection of acute kidney injury: a meta-analysis. Am J Kidney Dis. 2013;62:1058–67. 9. Coca SG, Parikh CR. Urinary biomarkers for acute kidney injury: perspectives on translation. Clin J Am Soc Nephrol. 2008;3:481–90. 10. Wyckoff T, Augoustides JG. Advances in acute kidney injury associated with cardiac surgery: the unfolding revolution in early detection. J Cardiothorac Vasc Anesth. 2012;26:340–5. 11. Wijeysundera DN. Improving the identification of patients at risk of postoperative renal failure after cardiac surgery. Anesthesiology. 2006;104:65–72. 12. Najafi M, Goodarzynejad H, Karimi A, Ghiasi A, Soltatninia H, Marzban M, et al. Is preoperative serum creatinine a reliable indicator of outcome in patients undergoing coronary artery bypass surgery? J Thorac Cardiovasc Surg. 2009;137: 304–8. 13. Atkinson AJ, Colburn WA, DeGruttola VG, DeMets DL, Downing GJ, Hoth DF, et al. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther. 2001;69:89–95. 14. Mehta R, Kellum J, Shah S, Molitoris B, Ronco C, Warnock D, et al. Acute Kidney Injury Network: report of an initiative to improve outcomes in acute kidney injury. Crit Care. 2007;11:R31. 15. Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Ann. Intern Med. 1999;130:461–70. 16. Candela-Toha A, Elias-Martin E, Abraira V, Tenorio MT, Parise D, de Pablo A, et al. Predicting acute renal failure after cardiac surgery: external validation of two new clinical scores. Clin J Am Soc Nephrol. 2008;3:1260–5. 17. Heagerty P, Lumley T, Pepe M. Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics. 2000;56:337–44. 18. Pepe MS. The receiver operating characteristic curve. In: Pepe MS, editor. The statistical evaluation of medical tests for classification and prediction. Oxford: Oxford University Press; 2003. p. 66–95. 19. Omar RZ, Ambler G, Royston P, Eliahoo J, Taylor KM. Cardiac surgery risk modeling for mortality: a review of current practice and suggestions for improvement. Ann Thorac Surg. 2004;77:2232–7. 20. Heagerty P, Zheng Y. Survival model predictive accuracy and ROC curves. Biometrics. 2005;61:92–105. 21. Schuirmann D. A comparison of the two one-sided tests procedure and the power approach for assessing the equivalence of average bioavailability. J Pharmacokinet Biopharm. 1987;15:657–80. 22. Nguyen MT, Maynard SE, Kimmel PL. Misapplications of commonly used kidney equations: renal physiology in practice. Clin J Am Soc Nephrol. 2009;4:528–34. 23. Mehta RH, Grab JD, O’Brien SM, Bridges CR, Gammie JS, Han CK, et al. Bedside tool for predicting the risk of postoperative dialysis in patients undergoing cardiac surgery. Circulation. 2006;114:2208–16. 24. Englberger L, Suri RM, Li Z, Dearani JA, Park SJ, Sundt TM, et al. Validation of clinical scores predicting severe acute kidney injury after cardiac surgery. Am J Kidney Dis. 2010;56: 623–31. 25. Mishra J, Dent C, Tarabishi R, Mitsnefes MM, Ma Q, Kelly C, et al. Neutrophil gelatinase-associated lipocalin (NGAL) as a biomarker for acute renal injury after cardiac surgery. Lancet. 2005;365:1231–8. 26. Pepe SM, Zheng Y, Jin Y, Huang Y, Parikh CR, Levy WC. Evaluating the ROC performance of markers for future events. Lifetime Data Anal. 2008;14:86–113. 27. Pipili C, Ioannidou S, Tripodaki ES, Parisi M, Douka E, Vasileiadis I, et al. Prediction of the renal replacement therapy requirement in mechanically ventilated critically ill patients by combining biomarkers for glomerular filtration and tubular damage. J Crit Care. 2014;29, 692.e7–692.e13. 28. McIlroy DR, Wagener GM, Lee HT. Biomarkers of acute kidney injury: an evolving domain. Anesthesiology. 2010;112:998–1004. 29. KDIGO Clinical practice guideline for acute kidney injury. Kidney Int Suppl. 2012;2:1–138 30. Meersch M, Schmidt C, Hoffmeier A, Van Aken H, Wempe C, Gerss J, et al. Prevention of cardiac surgery-associated AKI by implementing the KDIGO guidelines in high risk patients identified by biomarkers: the PrevAKI randomized controlled trial. Intensive Care Med. 2017;43:1551–61. 31. Lagny MG, Jouret F, Koch JN, Blaffart F, Donneau AF, Albert A, et al. Incidence and outcomes of acute kidney injury after cardiac surgery using either criteria of the RIFLE classification. BMC Nephrol. 2015;16:1–9. 32. Englberger L, Suri RM, Li Z, Casey ET, Daly RC, Dearani JA, et al. Clinical accuracy of RIFLE and Acute Kidney Injury Network (AKIN) criteria for acute kidney injury in patients undergoing cardiac surgery. Crit Care. 2011;15:R16. 33. McIlroy DR, Wagener G, Lee HT. Neutrophil gelatinase-associated lipocalin and acute kidney injury after cardiac surgery: the effect of baseline renal function on diagnostic performance. Clin J Am Soc Nephrol. 2010;5:211–9 | |
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