Accès gratuit
Numéro
Ann. Fr. Med. Urgence
Volume 12, Numéro 6, Novembre 2022
Page(s) 375 - 382
Section Mise au point / Update
DOI https://doi.org/10.3166/afmu-2022-0438
Publié en ligne 11 octobre 2022
  • Khwaja A (2012) KDIGO clinical practice guidelines for acute kidney injury. Nephron Clin Pract 120:c179–84 [CrossRef] [PubMed] [Google Scholar]
  • Hertzberg D, Rydén L, Pickering JW, et al (2017) Acute kidney injury-an overview of diagnostic methods and clinical management. Clin Kidney J 10:323–31 [CrossRef] [PubMed] [Google Scholar]
  • Murray PT, Devarajan P, Levey AS, et al (2008) A framework and key research questions in AKI diagnosis and staging in different environments. Clin J Am Soc Nephrol 3:864–8 [CrossRef] [PubMed] [Google Scholar]
  • Chawla LS, Bellomo R, Bihorac A, et al (2017) Acute kidney disease and renal recovery: consensus report of the acute disease quality initiative (ADQI) 16 Workgroup. Nat Rev Nephrol 13: 241–57 [CrossRef] [PubMed] [Google Scholar]
  • Ronco C, Bellomo R, Kellum JA (2019) Acute kidney injury. Lancet 394:1949–64 [CrossRef] [PubMed] [Google Scholar]
  • Mehta RL, Cerdá J, Burdmann EA, et al (2015) International society of nephrology’s 0by25 initiative for acute kidney injury (zero preventable deaths by 2025): a human rights case for nephrology. Lancet 385:2616–43 [CrossRef] [PubMed] [Google Scholar]
  • Hoste EAJ, Bagshaw SM, Bellomo R, et al (2015) Epidemiology of acute kidney injury in critically ill patients: the multinational AKI-EPI study. Intensive Care Med 41:1411–23 [CrossRef] [PubMed] [Google Scholar]
  • Foxwell DA, Pradhan S, Zouwail S, et al (2020) Epidemiology of emergency department acute kidney injury. Nephrology 25:457–66 [CrossRef] [PubMed] [Google Scholar]
  • Bellomo R, Ronco C, Kellum JA, et al (2004) Acute renal failure -definition, outcome measures, animal models, fluid therapy and information technology needs: the second international consensus conference of the acute dialysis quality initiative (ADQI) Group. Crit Care 8:R204–12 [CrossRef] [PubMed] [Google Scholar]
  • Mehta RL, Kellum JA, Shah SV, et al (2007) Acute kidney injury network: report of an initiative to improve outcomes in acute kidney injury. Crit Care 11:R31 [CrossRef] [PubMed] [Google Scholar]
  • Thongprayoon C, Hansrivijit P, Kovvuru K, et al (2020) Diagnostics, risk factors, treatment and outcomes of acute kidney injury in a new paradigm. J Clin Med 9:E1104 [CrossRef] [Google Scholar]
  • Zager RA (1987) Partial aortic ligation: a hypoperfusion model of ischemic acute renal failure and a comparison with renal artery occlusion. J Lab Clin Med 110:396–405 [PubMed] [Google Scholar]
  • Chua H-R, Glassford N, Bellomo R (2012) Acute kidney injury after cardiac arrest. Resuscitation 83:721–7 [CrossRef] [PubMed] [Google Scholar]
  • Langenberg C, Wan L, Egi M, et al (2006) Renal blood flow in experimental septic acute renal failure. Kidney Int 69:1996–2002 [CrossRef] [PubMed] [Google Scholar]
  • Peerapornratana S, Manrique-Caballero CL, Gómez H, Kellum JA (2019) Acute kidney injury from sepsis: current concepts, epidemiology, pathophysiology, prevention and treatment. Kidney Int 96:1083–99 [CrossRef] [PubMed] [Google Scholar]
  • Botev R, Mallié J-P, Couchoud C, et al (2009) Estimating glomerular filtration rate: Cockcroft-Gault and modification of diet in renal disease formulas compared to renal inulin clearance. Clin J Am Soc Nephrol 4:899–906 [CrossRef] [PubMed] [Google Scholar]
  • Sharma A, Mucino MJ, Ronco C (2014) Renal functional reserve and renal recovery after acute kidney injury. Nephron Clin Pract 127:94–100 [CrossRef] [PubMed] [Google Scholar]
  • Singh R, Dodkins J, Doyle JF, Forni LG (2018) Acute kidney injury biomarkers: what do they tell us? Contrib Nephrol 193:21–34 [CrossRef] [PubMed] [Google Scholar]
  • Lewington AJP, Cerdá J, Mehta RL (2013) Raising awareness of acute kidney injury: a global perspective of a silent killer. Kidney Int 84:457–67 [CrossRef] [PubMed] [Google Scholar]
  • Levey AS, Coresh J, Tighiouart H, et al (2020) Measured and estimated glomerular filtration rate: current status and future directions. Nat Rev Nephrol 16:51–64 [CrossRef] [PubMed] [Google Scholar]
  • Dharnidharka VR, Kwon C, Stevens G (2002) Serum cystatin C is superior to serum creatinine as a marker of kidney function: a meta-analysis. Am J Kidney Dis 40:221–6 [CrossRef] [PubMed] [Google Scholar]
  • Mehta RL (2010) Timed and targeted therapy for acute kidney injury: a glimpse of the future. Kidney Int 77:947–9 [CrossRef] [PubMed] [Google Scholar]
  • Nalesso F, Cattarin L, Gobbi L, et al (2020) Evaluating Nephrocheck® as a predictive tool for acute kidney injury. Int J Nephrol Renov Dis 13:85–96 [CrossRef] [Google Scholar]
  • Wang Y, Zou Z, Jin J, et al (2017) Urinary TIMP-2 and IGFBP7 for the prediction of acute kidney injury following cardiac surgery. BMC Nephrol 18:177 [CrossRef] [PubMed] [Google Scholar]
  • Yang HS, Hur M, Lee KR, et al (2022) Biomarker rule-in or rule-out in patients with acute diseases for validation of acute kidney injury in the emergency department (BRAVA): a multicenter study evaluating urinary TIMP-2/IGFBP7. Ann Lab Med 42:178–87 [CrossRef] [PubMed] [Google Scholar]
  • Fiorentino M, Xu Z, Smith A, et al (2020) Serial measurement of cell-cycle arrest biomarkers [TIMP-2] • [IGFBP7] and risk for progression to death, dialysis, or severe acute kidney injury in patients with septic shock. Am J Respir Crit Care Med 202:1262–70 [CrossRef] [PubMed] [Google Scholar]
  • Friedl A, Stoesz SP, Buckley P, Gould MN (1999) Neutrophil gelatinase-associated lipocalin in normal and neoplastic human tissues. Cell type-specific pattern of expression. Histochem J 31:433–41 [CrossRef] [PubMed] [Google Scholar]
  • Mishra J, Ma Q, Prada A, et al (2003) Identification of neutrophil gelatinase-associated lipocalin as a novel early urinary biomarker for ischemic renal injury. J Am Soc Nephrol 14:2534–43 [CrossRef] [PubMed] [Google Scholar]
  • Stevens JS, Xu K, Corker A, et al (2020) Rule out acute kidney injury in the emergency department with a urinary dipstick. Kidney Int Rep 5:1982–92 [CrossRef] [PubMed] [Google Scholar]
  • Teo KHB, Lim SH, Hao Y, et al (2022) Neutrophil gelatinaseassociated lipocalin: a biochemical marker for acute kidney injury and long-term outcomes in patients presenting to the emergency department. Singapore Med J 64:In press [Google Scholar]
  • Yasuda K, Nakanishi K, Tsutsui H (2019) Interleukin-18 in health and disease. Int J Mol Sci 20:E649 [CrossRef] [Google Scholar]
  • Parikh CR, Jani A, Melnikov VY, et al (2004) Urinary interleukin-18 is a marker of human acute tubular necrosis. Am J Kidney Dis 43:405–14 [CrossRef] [PubMed] [Google Scholar]
  • Haase M, Bellomo R, Story D, et al (2008) Urinary interleukin18 does not predict acute kidney injury after adult cardiac surgery: a prospective observational cohort study. Crit Care 12:R96 [CrossRef] [PubMed] [Google Scholar]
  • Pozzoli S, Simonini M, Manunta P (2018) Predicting acute kidney injury: current status and future challenges. J Nephrol 31:209–23 [CrossRef] [PubMed] [Google Scholar]
  • Marx D, Metzger J, Pejchinovski M, et al (2018) Proteomics and metabolomics for AKI diagnosis. Semin Nephrol 38:63–87 [CrossRef] [PubMed] [Google Scholar]
  • Klein J, Bascands J-L, Mischak H, Schanstra JP (2016) The role of urinary peptidomics in kidney disease research. Kidney Int 89:539–45 [CrossRef] [PubMed] [Google Scholar]
  • Weiss RH, Kim K (2011) Metabolomics in the study of kidney diseases. Nat Rev Nephrol 8:22–33 [Google Scholar]
  • Izquierdo-Garcia JL, Nin N, Cardinal-Fernandez P, et al (2019) Identification of novel metabolomic biomarkers in an experimental model of septic acute kidney injury. Am J Physiol Renal Physiol 316:F54–F62 [CrossRef] [PubMed] [Google Scholar]
  • Ichai C, Vinsonneau C, Souweine B, et al (2016) Acute kidney injury in the perioperative period and in intensive care units (excluding renal replacement therapies). Ann Intensive Care 6:48 [CrossRef] [PubMed] [Google Scholar]
  • Janke AT, Overbeek DL, Kocher KE, Levy PD (2016) Exploring the potential of predictive analytics and big data in emergency care. Ann Emerg Med 67:227–36 [CrossRef] [PubMed] [Google Scholar]
  • Jensen PB, Jensen LJ, Brunak S (2012) Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet 13:395–405 [CrossRef] [PubMed] [Google Scholar]
  • Sutherland SM, Chawla LS, Kane-Gill SL, et al (2016) Utilizing electronic health records to predict acute kidney injury risk and outcomes: workgroup statements from the 15(th) ADQI Consensus Conference. Can J Kidney Health Dis 3:11 [Google Scholar]
  • Wijeysundera DN, Karkouti K, Dupuis J-Y, et al (2007) Derivation and validation of a simplified predictive index for renal replacement therapy after cardiac surgery. JAMA 297:1801–9 [CrossRef] [PubMed] [Google Scholar]
  • Chiofolo C, Chbat N, Ghosh E, et al (2019) Automated continuous acute kidney injury prediction and surveillance: a random forest model. Mayo Clin Proc 94:783–92 [CrossRef] [PubMed] [Google Scholar]
  • Martinez DA, Levin SR, Klein EY, et al (2020) Early prediction of acute kidney injury in the emergency department with machine-learning methods applied to electronic health record data. Ann Emerg Med 76:501–14 [CrossRef] [PubMed] [Google Scholar]
  • Cheungpasitporn W, Kashani K (2016) Electronic data systems and acute kidney injury. Contrib Nephrol 187:73–83 [CrossRef] [PubMed] [Google Scholar]
  • Barton AL, Williams SBM, Dickinson SJ, et al (2020) Acute kidney injury in primary care: a review of patient follow-up, mortality, and hospital admissions following the introduction of an AKI alert system. Nephron 144:498–505 [CrossRef] [PubMed] [Google Scholar]
  • Barker J, Smith-Byrne K, Sayers O, et al (2021) Electronic alerts for acute kidney injury across primary and secondary care. BMJ Open Qual 10:e000956 [CrossRef] [PubMed] [Google Scholar]
  • Kolhe NV, Staples D, Reilly T, et al (2015) Impact of compliance with a care bundle on acute kidney injury outcomes: a prospective observational study. PloS One 10:e0132279 [CrossRef] [PubMed] [Google Scholar]
  • Hodgson LE, Roderick PJ, Venn RM, et al (2018) The ICE-AKI study: impact analysis of a clinical prediction rule and electronic AKI alert in general medical patients. PloS One 13:e0200584 [CrossRef] [PubMed] [Google Scholar]
  • Le Dorze M, Bouglé A, Deruddre S, Duranteau J (2012) Renal Doppler ultrasound: a new tool to assess renal perfusion in critical illness. Shock 37:360–5 [CrossRef] [PubMed] [Google Scholar]
  • Schnell D, Reynaud M, Venot M, et al (2014) Resistive index or color-Doppler semi-quantitative evaluation of renal perfusion by inexperienced physicians: results of a pilot study. Minerva Anestesiol 80:1273–81 [PubMed] [Google Scholar]
  • Zhi HJ, Li Y, Wang B, et al (2020) Renal echography for predicting acute kidney injury in critically ill patients: a prospective observational study. Ren Fail 42:263–9 [CrossRef] [PubMed] [Google Scholar]
  • Saade A, Bourmaud A, Schnell D, et al (2022) Performance of Doppler-based resistive index and semiquantitative renal perfusion in predicting persistent acute kidney injury according to operator experience: post hoc analysis of a prospective multicenter study. Crit Care Med 50:e361–e369 [PubMed] [Google Scholar]

Les statistiques affichées correspondent au cumul d'une part des vues des résumés de l'article et d'autre part des vues et téléchargements de l'article plein-texte (PDF, Full-HTML, ePub... selon les formats disponibles) sur la platefome Vision4Press.

Les statistiques sont disponibles avec un délai de 48 à 96 heures et sont mises à jour quotidiennement en semaine.

Le chargement des statistiques peut être long.