Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Designing acute kidney injury clinical trials

Abstract

Acute kidney injury (AKI) is a common clinical condition with various causes and is associated with increased mortality. Despite advances in supportive care, AKI increases not only the risk of premature death compared with the general population but also the risk of developing chronic kidney disease and progressing towards kidney failure. Currently, no specific therapy exists for preventing or treating AKI other than mitigating further injury and supportive care. To address this unmet need, novel therapeutic interventions targeting the underlying pathophysiology must be developed. New and well-designed clinical trials with appropriate end points must be subsequently designed and implemented to test the efficacy of such new interventions. Herein, we discuss predictive and prognostic enrichment strategies for patient selection, as well as primary and secondary end points that can be used in different clinical trial designs (specifically, prevention and treatment trials) to evaluate novel interventions and improve the outcomes of patients at a high risk of AKI or with established AKI.

Key points

  • Acute kidney injury (AKI) is a very heterogeneous syndrome; therefore, enrichment strategies are required to reduce heterogeneity within the study patient cohort and to reduce sample size. Several factors, including risk scores, biomarkers and clinical features, can be used for prognostic and predictive enrichment.

  • Careful selection of end points is crucial for any trial. Different end points are required for phase II and phase III trials, and for prevention, attenuation and treatment trials.

  • The occurrence of AKI is a key end point in prevention or attenuation trials and is mainly assessed based on current AKI definitions (based on serum creatinine and urine output) but biomarkers can be used as surrogate end points in phase II trials.

  • Major adverse kidney events cannot be used in prevention trials, but different components in this composite end point should always be reported when used in treatment trials.

  • The trajectory of AKI is an important outcome in intervention trials, because clinical measures or drugs might influence the course of AKI and subsequently influence the mortality rate.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: General consideration of designing a trial.
Fig. 2: Overview of the timing of the intervention, patient population and relevant outcomes.

Similar content being viewed by others

References

  1. Hoste, E. A. J. et al. Global epidemiology and outcomes of acute kidney injury. Nat. Rev. Nephrol. 14, 607–625 (2018).

    Article  CAS  PubMed  Google Scholar 

  2. Zarbock, A., Koyner, J. L., Hoste, E. A. J. & Kellum, J. A. Update on perioperative acute kidney injury. Anesth. Analg. 127, 1236–1245 (2018).

    Article  PubMed  Google Scholar 

  3. Pickkers, P., Murray, P. T. & Ostermann, M. New drugs for acute kidney injury. Intensive Care Med. 48, 1796–1798 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Gomberg-Maitland, M. et al. New trial designs and potential therapies for pulmonary artery hypertension. J. Am. Coll. Cardiol. 62, D82–91 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  5. US Food and Drug Administration. Enrichment Strategies for Clinical Trials to Support Determination of Effectiveness of Human Drugs and Biological Products Guidance For Industry. USA: U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER) (2019).

  6. Chertow, G. M. et al. Preoperative renal risk stratification. Circulation 95, 878–884 (1997).

    Article  CAS  PubMed  Google Scholar 

  7. Fortescue, E. B., Bates, D. W. & Chertow, G. M. Predicting acute renal failure after coronary bypass surgery: cross-validation of two risk-stratification algorithms. Kidney Int. 57, 2594–2602 (2000).

    Article  CAS  PubMed  Google Scholar 

  8. Eriksen, B. O., Hoff, K. R. & Solberg, S. Prediction of acute renal failure after cardiac surgery: retrospective cross-validation of a clinical algorithm. Nephrol. Dial. Transpl. 18, 77–81 (2003).

    Article  Google Scholar 

  9. Thakar, C. V., Arrigain, S., Worley, S., Yared, J. P. & Paganini, E. P. A clinical score to predict acute renal failure after cardiac surgery. J. Am. Soc. Nephrol. 16, 162–168 (2005).

    Article  PubMed  Google Scholar 

  10. Candela-Toha, A. et al. Predicting acute renal failure after cardiac surgery: external validation of two new clinical scores. Clin. J. Am. Soc. Nephrol. 3, 1260–1265 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Meersch, M. 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. 43, 1551–1561 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Zarbock, A. et al. Prevention of cardiac surgery-associated acute kidney injury by implementing the KDIGO guidelines in high-risk patients identified by biomarkers: the PrevAKI-multicenter randomized controlled trial. Anesth. Analg. 133, 292–302 (2021).

    Article  CAS  PubMed  Google Scholar 

  13. Weiss, R. et al. Effect of glutamine administration after cardiac surgery on kidney damage in patients at high risk for acute kidney injury: a randomized controlled trial. Anesth. Analg. https://doi.org/10.1213/ANE.0000000000006288 (2022).

  14. Hoste, E. et al. Identification and validation of biomarkers of persistent acute kidney injury: the RUBY study. Intensive Care Med. 46, 943–953 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Massoth, C. et al. Comparison of C-C motif chemokine ligand 14 with other biomarkers for adverse kidney events after cardiac surgery. J. Thorac. Cardiovasc. Surg. 165, 199–207.e2 (2023).

    Article  PubMed  Google Scholar 

  16. Woodcock, J. & LaVange, L. M. Master protocols to study multiple therapies, multiple diseases, or both. N. Engl. J. Med. 377, 62–70 (2017).

    Article  CAS  PubMed  Google Scholar 

  17. Zarbock, A. et al. Effect of remote ischemic preconditioning on kidney injury among high-risk patients undergoing cardiac surgery: a randomized clinical trial. JAMA 313, 2133–2141 (2015).

    Article  CAS  PubMed  Google Scholar 

  18. US National Library of Medicine. ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/NCT05199493 (2023).

  19. Bhatraju, P. K. et al. Identification of acute kidney injury subphenotypes with differing molecular signatures and responses to vasopressin therapy. Am. J. Respir. Crit. Care Med. 199, 863–872 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Moledina, D. G. et al. Urine interleukin-9 and tumor necrosis factor-α for prognosis of human acute interstitial nephritis. Nephrol. Dial. Transpl. 36, 1851–1858 (2021).

    Article  Google Scholar 

  21. Seymour, C. W. et al. Precision medicine for all? Challenges and opportunities for a precision medicine approach to critical illness. Crit. Care 21, 257 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Legrand, M. et al. Optimizing the design and analysis of future AKI trials. J. Am. Soc. Nephrol. 33, 1459–1470 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Katsanos, A. H. et al. Blood pressure reduction and secondary stroke prevention: a systematic review and metaregression analysis of randomized clinical trials. Hypertension 69, 171–179 (2017).

    Article  CAS  PubMed  Google Scholar 

  24. Cook, D. et al. Dalteparin versus unfractionated heparin in critically ill patients. N. Engl. J. Med. 364, 1305–1314 (2011).

    Article  CAS  PubMed  Google Scholar 

  25. KDIGO. KDIGO clinical practice guideline for acute kidney injury. Kidney Int. Suppl. 2, 1–138 (2012).

    Google Scholar 

  26. Billings, F. T. T. & Shaw, A. D. Clinical trial endpoints in acute kidney injury. Nephron Clin. Pract. 127, 89–93 (2014).

    Article  CAS  PubMed  Google Scholar 

  27. Kashani, K. et al. Discovery and validation of cell cycle arrest biomarkers in human acute kidney injury. Crit. Care 17, R25 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Koyner, J. L. et al. Tissue inhibitor metalloproteinase-2 (TIMP-2)IGF-binding protein-7 (IGFBP7) levels are associated with adverse long-term outcomes in patients with AKI. J. Am. Soc. Nephrol. 26, 1747–1754 (2015).

    Article  CAS  PubMed  Google Scholar 

  29. Priyanka, P. et al. The impact of acute kidney injury by serum creatinine or urine output criteria on major adverse kidney events in cardiac surgery patients. J. Thorac. Cardiovasc. Surg. 162, 143–151 e147 (2021).

    Article  PubMed  Google Scholar 

  30. Saadat-Gilani, K., Zarbock, A. & Meersch, M. Perioperative renoprotection: clinical implications. Anesth. Analg. 131, 1667–1678 (2020).

    Article  PubMed  Google Scholar 

  31. Thielmann, M. et al. Teprasiran, a small interfering RNA, for the prevention of acute kidney injury in high-risk patients undergoing cardiac surgery: a randomized clinical study. Circulation 144, 1133–1144 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Park, J. H., Shim, J. K., Song, J. W., Soh, S. & Kwak, Y. L. Effect of atorvastatin on the incidence of acute kidney injury following valvular heart surgery: a randomized, placebo-controlled trial. Intensive Care Med. 42, 1398–1407 (2016).

    Article  CAS  PubMed  Google Scholar 

  33. Prowle, J. R. et al. Serum creatinine changes associated with critical illness and detection of persistent renal dysfunction after AKI. Clin. J. Am. Soc. Nephrol. 9, 1015–1023 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. McCallum, W., Tighiouart, H., Ku, E., Salem, D. & Sarnak, M. J. Acute declines in estimated glomerular filtration rate on enalapril and mortality and cardiovascular outcomes in patients with heart failure with reduced ejection fraction. Kidney Int. 96, 1185–1194 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Al-Jaghbeer, M., Dealmeida, D., Bilderback, A., Ambrosino, R. & Kellum, J. A. Clinical decision support for in-hospital AKI. J. Am. Soc. Nephrol. 29, 654–660 (2018).

    Article  PubMed  Google Scholar 

  36. Inker, L. A. et al. New creatinine- and cystatin C-based equations to estimate GFR without race. N. Engl. J. Med. 385, 1737–1749 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Hoste, E. A. et al. RIFLE criteria for acute kidney injury are associated with hospital mortality in critically ill patients: a cohort analysis. Crit. Care 10, R73 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Uchino, S. et al. Acute renal failure in critically ill patients: a multinational, multicenter study. JAMA 294, 813–818 (2005).

    Article  CAS  PubMed  Google Scholar 

  39. Bell, M. et al. Cystatin C is correlated with mortality in patients with and without acute kidney injury. Nephrol. Dial. Transpl. 24, 3096–3102 (2009).

    Article  CAS  Google Scholar 

  40. Kellum, J. A. et al. Classifying AKI by urine output versus serum creatinine level. J. Am. Soc. Nephrol. 26, 2231–2238 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Nateghi Haredasht, F. et al. Comparison between cystatin C- and creatinine-based estimated glomerular filtration rate in the follow-up of patients recovering from a stage-3 AKI in ICU. J. Clin. Med. 11, 7264 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Fuhrman, D. Y. & Kellum, J. A. Biomarkers for diagnosis, prognosis and intervention in acute kidney injury. Contrib. Nephrol. 187, 47–54 (2016).

    Article  PubMed  Google Scholar 

  43. Coca, S. G., Yalavarthy, R., Concato, J. & Parikh, C. R. Biomarkers for the diagnosis and risk stratification of acute kidney injury: a systematic review. Kidney Int. 73, 1008–1016 (2008).

    Article  CAS  PubMed  Google Scholar 

  44. Siew, E. D., Ware, L. B. & Ikizler, T. A. Biological markers of acute kidney injury. J. Am. Soc. Nephrol. 22, 810–820 (2011).

    Article  PubMed  Google Scholar 

  45. Liu, K. D. et al. Clinical adjudication in acute kidney injury studies: findings from the pivotal TIMP-2*IGFBP7 biomarker study. Nephrol. Dial. Transpl. 31, 1641–1646 (2016).

    Article  CAS  Google Scholar 

  46. Bosch, J. P. Renal reserve: a functional view of glomerular filtration rate. Semin. Nephrol. 15, 381–385 (1995).

    CAS  PubMed  Google Scholar 

  47. Ronco, C. et al. Renal functional reserve in pregnancy. Nephrol. Dial. Transpl. 3, 157–161 (1988).

    CAS  Google Scholar 

  48. Bosch, J. P. et al. Renal functional reserve in humans. Effect of protein intake on glomerular filtration rate. Am. J. Med. 75, 943–950 (1983).

    Article  CAS  PubMed  Google Scholar 

  49. Barai, S., Gambhir, S., Prasad, N., Sharma, R. K. & Ora, M. Functional renal reserve capacity in different stages of chronic kidney disease. Nephrology 15, 350–353 (2010).

    Article  PubMed  Google Scholar 

  50. Christiadi, D. et al. Cystatin C kidney functional reserve: a simple method to predict outcome in chronic kidney disease. Nephrol. Dial. Transpl. 37, 1118–1124 (2022).

    Article  CAS  Google Scholar 

  51. Husain-Syed, F. et al. Persistent decrease of renal functional reserve in patients after cardiac surgery-associated acute kidney injury despite clinical recovery. Nephrol. Dial. Transpl. 34, 308–317 (2019).

    Article  CAS  Google Scholar 

  52. Husain-Syed, F. et al. Preoperative renal functional reserve predicts risk of acute kidney injury after cardiac operation. Ann. Thorac. Surg. 105, 1094–1101 (2018).

    Article  PubMed  Google Scholar 

  53. Levey, A. S. & James, M. T. Acute kidney injury. Ann. Intern. Med. 167, ITC66–ITC80 (2017).

    Article  PubMed  Google Scholar 

  54. Lameire, N. H. et al. Acute kidney injury: an increasing global concern. Lancet 382, 170–179 (2013).

    Article  PubMed  Google Scholar 

  55. Chertow, G. M., Burdick, E., Honour, M., Bonventre, J. V. & Bates, D. W. Acute kidney injury, mortality, length of stay, and costs in hospitalized patients. J. Am. Soc. Nephrol. 16, 3365–3370 (2005).

    Article  PubMed  Google Scholar 

  56. Chawla, L. S., Eggers, P. W., Star, R. A. & Kimmel, P. L. Acute kidney injury and chronic kidney disease as interconnected syndromes. N. Engl. J. Med. 371, 58–66 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Kudose, S., Hoshi, M., Jain, S. & Gaut, J. P. Renal histopathologic findings associated with severity of clinical acute kidney injury. Am. J. Surg. Pathol. 42, 625–635 (2018).

    Article  PubMed  Google Scholar 

  58. Ostermann, M. et al. Recommendations on acute kidney injury biomarkers from the acute disease quality initiative consensus conference: a consensus statement. JAMA Netw. Open 3, e2019209 (2020).

    Article  PubMed  Google Scholar 

  59. Haase, M. et al. The outcome of neutrophil gelatinase-associated lipocalin-positive subclinical acute kidney injury: a multicenter pooled analysis of prospective studies. J. Am. Coll. Cardiol. 57, 1752–1761 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Molinari, L. et al. Utility of biomarkers for sepsis-associated acute kidney injury staging. JAMA Netw. Open 5, e2212709 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Molinari, L., Landsittel, D. P., Kellum, J. A., ProCess & Pro, G.-A. K. I. I. Use of kidney injury molecule-1 for sepsis-associated acute kidney injury staging. Nephrol. Dial. Transpl. 38, 1560–1563 (2023).

    Article  Google Scholar 

  62. Weisbord, S. D. et al. Outcomes after angiography with sodium bicarbonate and acetylcysteine. N. Engl. J. Med. 378, 603–614 (2018).

    Article  CAS  PubMed  Google Scholar 

  63. Self, W. H. et al. Balanced crystalloids versus saline in noncritically ill adults. N. Engl. J. Med. 378, 819–828 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Semler, M. W. et al. Balanced crystalloids versus saline in critically ill adults. N. Engl. J. Med. 378, 829–839 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Santacruz, C. A., Pereira, A. J., Celis, E. & Vincent, J. L. Which multicenter randomized controlled trials in critical care medicine have shown reduced mortality? a systematic review. Crit. Care Med. 47, 1680–1691 (2019).

    Article  PubMed  Google Scholar 

  66. Kent, D. M. & Hayward, R. A. Limitations of applying summary results of clinical trials to individual patients: the need for risk stratification. JAMA 298, 1209–1212 (2007).

    Article  CAS  PubMed  Google Scholar 

  67. Rothwell, P. M. Clinical trials are too often founded on poor quality pre-clinical research. J. Neurol. 252, 1115 (2005).

    Article  CAS  PubMed  Google Scholar 

  68. Rothwell, P. M. External validity of randomised controlled trials: “to whom do the results of this trial apply?”. Lancet 365, 82–93 (2005).

    Article  PubMed  Google Scholar 

  69. Hoste, E. A. et al. Epidemiology of acute kidney injury in critically ill patients: the multinational AKI-EPI study. Intensive Care Med. 41, 1411–1423 (2015).

    Article  PubMed  Google Scholar 

  70. The VA/NIH Acute Renal Failure Trial Network. Intensity of renal support in critically ill patients with acute kidney injury. N. Engl. J. Med. 359, 7–20 (2008).

    Article  PubMed Central  Google Scholar 

  71. Pocock, S. J., Ariti, C. A., Collier, T. J. & Wang, D. The win ratio: a new approach to the analysis of composite endpoints in clinical trials based on clinical priorities. Eur. Heart J. 33, 176–182 (2012).

    Article  PubMed  Google Scholar 

  72. Chawla, L. S. et al. Acute kidney disease and renal recovery: consensus report of the Acute Disease Quality Initiative (ADQI) 16 Workgroup. Nat. Rev. Nephrol. 13, 241–257 (2017).

    Article  PubMed  Google Scholar 

  73. Kellum, J. A., Sileanu, F. E., Bihorac, A., Hoste, E. A. & Chawla, L. S. Recovery after acute kidney injury. Am. J. Respir. Crit. Care Med. 195, 784–791 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  74. Siew, E. D. et al. Predictors of recurrent AKI. J. Am. Soc. Nephrol. 27, 1190–1200 (2016).

    Article  CAS  PubMed  Google Scholar 

  75. Liu, K. D. et al. Risk factors for recurrent acute kidney injury in a large population-based cohort. Am. J. Kidney Dis. 73, 163–173 (2019).

    Article  PubMed  Google Scholar 

  76. Lameire, N. H. et al. Harmonizing acute and chronic kidney disease definition and classification: report of a kidney disease: improving global outcomes (KDIGO) consensus conference. Kidney Int. 100, 516–526 (2021).

    Article  PubMed  Google Scholar 

  77. See, E. J. et al. Long-term risk of adverse outcomes after acute kidney injury: a systematic review and meta-analysis of cohort studies using consensus definitions of exposure. Kidney Int. 95, 160–172 (2019).

    Article  PubMed  Google Scholar 

  78. Coca, S. G., Singanamala, S. & Parikh, C. R. Chronic kidney disease after acute kidney injury: a systematic review and meta-analysis. Kidney Int. 81, 442–448 (2012).

    Article  PubMed  Google Scholar 

  79. Pannu, N., James, M., Hemmelgarn, B., Klarenbach, S. & Alberta Kidney Disease, N. Association between AKI, recovery of renal function, and long-term outcomes after hospital discharge. Clin. J. Am. Soc. Nephrol. 8, 194–202 (2013).

    Article  PubMed  Google Scholar 

  80. Heung, M. et al. Acute kidney injury recovery pattern and subsequent risk of CKD: an analysis of veterans health administration data. Am. J. Kidney Dis. 67, 742–752 (2016).

    Article  PubMed  Google Scholar 

  81. Hickson, L. J. et al. Predictors of outpatient kidney function recovery among patients who initiate hemodialysis in the hospital. Am. J. Kidney Dis. 65, 592–602 (2015).

    Article  PubMed  Google Scholar 

  82. Lee, B. J. et al. Pre-admission proteinuria impacts risk of non-recovery after dialysis-requiring acute kidney injury. Kidney Int. 93, 968–976 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Venkatachalam, M. A. et al. Acute kidney injury: a springboard for progression in chronic kidney disease. Am. J. Physiol. Renal Physiol. 298, F1078–1094 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Lewington, A. & Kanagasundaram, S. Renal association clinical practice guidelines on acute kidney injury. Nephron Clin. Pract. 118, c349–390 (2011).

    Article  PubMed  Google Scholar 

  85. Ftouh, S. & Thomas, M., Acute Kidney Injury Guideline Development, G. Acute kidney injury: summary of NICE guidance. BMJ 347, f4930 (2013).

    Article  PubMed  Google Scholar 

  86. The FHN Trial Group. et al. In-center hemodialysis six times per week versus three times per week. N. Engl. J. Med. 363, 2287–2300 (2010).

    Article  PubMed Central  Google Scholar 

  87. Redfors, B. et al. The win ratio approach for composite endpoints: practical guidance based on previous experience. Eur. Heart J. 41, 4391–4399 (2020).

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

A.Z. received funding from the German Research Foundation (KFO 342/2, ZA428/18-2, and ZA428/21-1 to A.Z.).

Author information

Authors and Affiliations

Authors

Contributions

A.Z., L.G.F., R.B. and J.A.K. designed and conceptualized the review. All authors contributed to writing of the manuscript and reviewed and/or edited the manuscript before submission.

Corresponding author

Correspondence to Alexander Zarbock.

Ethics declarations

Competing interests

A.Z. has received consulting fees from Astute-bioMérieux, Baxter, Bayer, Novartis, Guard Therapeutics, AM Pharma, Paion, Fresenius, research funding from Astute-bioMérieux, Fresenius, Baxter, and speakers fees from Astute-bioMérieux, Fresenius, Baxter; L.G.F. has received research support and lecture fees from Ortho Clinical Diagnostics, Baxter, Exthera and bioMérieux and consulting fees from La Jolla Pharmaceuticals and Paion; M.O. received research funding from Baxter, Fresenius Medical, bioMérieux and La Jolla Pharma. She also received speaker honoraria from Baxter, Fresenius Medical, bioMérieux and Gilead, which was used for research purposes in the institution; C.R. has been on the advisory boards or speaker’s bureau for Asahi, Aferetica, Baxter, bioMérieux, Cytosorbents, B. Braun, GE, Medica, Medtronic, Jafron and AstraZeneca; S.M.B. is a scientific adviser for Baxter, BioPorto, Novartis, exerts clinical adjudication for BioPorto, is on the DSMB of the I-SPY-COVID trial, and is supported by a Canada Research Chair in Critical Care Outcomes and Systems Evaluation; R.L.M. has consulting/advisory relationships with Baxter, AM Pharma, bioMérieux, Intercept, Mallinckrodt, GE Healthcare; Medtronic, CHF Solutions, Sphingotec, Abiomed, Nova Biomed, Sanofi, Renasym, Alexion, Fresenius, Abbott and Renibus; R.B. has received grant money, speaker’s fees, and advisory board fees from Baxter Acute Care, Jafron Biomedical, CSL Behring, AM Pharma and Paion; J.A.K. has received grant support and consulting fees from Astute Medical and Alere, unrelated to the current study.

Peer review

Peer review information

Nature Reviews Nephrology thanks S. Gaudry, C. Parikh, S. Vaara, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Glossary

Endotype

An endotype is a subtype of a condition, which is defined by a distinct pathobiological or functional mechanism. Patients with a specific endotype present within phenotypic clusters of diseases.

Phenotype

A phenotype is an observable characteristic or trait of a disease, such as physiological or biochemical properties, without any implication of a mechanism. A clinical phenotype is the presentation of a disease in a given individual.

Win ratio

The win ratio is a new method for examining composite end points that accounts for the relative priorities of its components and allows the components to be different types of outcomes.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zarbock, A., Forni, L.G., Ostermann, M. et al. Designing acute kidney injury clinical trials. Nat Rev Nephrol 20, 137–146 (2024). https://doi.org/10.1038/s41581-023-00758-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41581-023-00758-1

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing