Pavan K Bhatraju1,2, Leila R Zelnick2, Jerald Herting3, Ronit Katz2, Carmen Mikacenic1, Susanna Kosamo1, Eric D Morrell1, Cassianne Robinson-Cohen2, Carolyn S Calfee4,5,6, Jason D Christie7,8, Kathleen D Liu9,10, Michael A Matthay4,5,6, William O Hahn11, Victoria Dmyterko1, Natalie S J Slivinski12, Jim A Russell13,14, Keith R Walley13,14, David C Christiani15,16,17, W Conrad Liles18, Jonathan Himmelfarb2, Mark M Wurfel1,2. 1. 1 Division of Pulmonary, Critical Care, and Sleep Medicine. 2. 2 Kidney Research Institute, Division of Nephrology, and. 3. 3 Department of Sociology, and. 4. 4 Department of Medicine. 5. 5 Department of Anesthesia and Perioperative Care. 6. 6 Cardiovascular Research Institute. 7. 7 Division of Pulmonary, Allergy, and Critical Care and. 8. 8 Center for Clinical Epidemiology and Biostatistics, Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania. 9. 9 Division of Nephrology, and. 10. 10 Division of Critical Care Medicine, University of California, San Francisco, San Francisco, California. 11. 11 Division of Allergy and Infectious Diseases, Department of Medicine. 12. 12 University of Leeds, Leeds, United Kingdom. 13. 13 Centre for Heart Lung Innovation and. 14. 14 Division of Critical Care Medicine, Department of Medicine, St. Paul's Hospital, University of British Columbia, Vancouver, British Columbia, Canada. 15. 15 Department of Environmental Health and. 16. 16 Department of Epidemiology, Harvard School of Public Health, Harvard University, Boston, Massachusetts; and. 17. 17 Pulmonary and Critical Care Division, Department of Medicine, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts. 18. 18 Department of Medicine, University of Washington, Seattle, Washington.
Abstract
RATIONALE: Currently, no safe and effective pharmacologic interventions exist for acute kidney injury (AKI). One reason may be that heterogeneity exists within the AKI population, thereby hampering the identification of specific pathophysiologic pathways and therapeutic targets. OBJECTIVE: The aim of this study was to identify and test whether AKI subphenotypes have prognostic and therapeutic implications. METHODS: First, latent class analysis methodology was applied independently in two critically ill populations (discovery [n = 794] and replication [n = 425]) with AKI. Second, a parsimonious classification model was developed to identify AKI subphenotypes. Third, the classification model was applied to patients with AKI in VASST (Vasopressin and Septic Shock Trial; n = 271), and differences in treatment response were determined. In all three populations, AKI was defined using serum creatinine and urine output. MEASUREMENTS AND MAIN RESULTS: A two-subphenotype latent class analysis model had the best fit in both the discovery (P = 0.004) and replication (P = 0.004) AKI groups. The risk of 7-day renal nonrecovery and 28-day mortality was greater with AKI subphenotype 2 (AKI-SP2) relative to AKI subphenotype 1 (AKI-SP1). The AKI subphenotypes discriminated risk for poor clinical outcomes better than the Kidney Disease: Improving Global Outcomes stages of AKI. A three-variable model that included markers of endothelial dysfunction and inflammation accurately determined subphenotype membership (C-statistic 0.92). In VASST, vasopressin compared with norepinephrine was associated with improved 90-day mortality in AKI-SP1 (27% vs. 46%, respectively; P = 0.02), but no significant difference was observed in AKI-SP2 (45% vs. 49%, respectively; P = 0.99) and the P value for interaction was 0.05. CONCLUSIONS: This analysis identified two molecularly distinct AKI subphenotypes with different clinical outcomes and responses to vasopressin therapy. Identification of AKI subphenotypes could improve risk prognostication and may be useful for predictive enrichment in clinical trials.
RATIONALE: Currently, no safe and effective pharmacologic interventions exist for acute kidney injury (AKI). One reason may be that heterogeneity exists within the AKI population, thereby hampering the identification of specific pathophysiologic pathways and therapeutic targets. OBJECTIVE: The aim of this study was to identify and test whether AKI subphenotypes have prognostic and therapeutic implications. METHODS: First, latent class analysis methodology was applied independently in two critically ill populations (discovery [n = 794] and replication [n = 425]) with AKI. Second, a parsimonious classification model was developed to identify AKI subphenotypes. Third, the classification model was applied to patients with AKI in VASST (Vasopressin and Septic Shock Trial; n = 271), and differences in treatment response were determined. In all three populations, AKI was defined using serum creatinine and urine output. MEASUREMENTS AND MAIN RESULTS: A two-subphenotype latent class analysis model had the best fit in both the discovery (P = 0.004) and replication (P = 0.004) AKI groups. The risk of 7-day renal nonrecovery and 28-day mortality was greater with AKI subphenotype 2 (AKI-SP2) relative to AKI subphenotype 1 (AKI-SP1). The AKI subphenotypes discriminated risk for poor clinical outcomes better than the Kidney Disease: Improving Global Outcomes stages of AKI. A three-variable model that included markers of endothelial dysfunction and inflammation accurately determined subphenotype membership (C-statistic 0.92). In VASST, vasopressin compared with norepinephrine was associated with improved 90-day mortality in AKI-SP1 (27% vs. 46%, respectively; P = 0.02), but no significant difference was observed in AKI-SP2 (45% vs. 49%, respectively; P = 0.99) and the P value for interaction was 0.05. CONCLUSIONS: This analysis identified two molecularly distinct AKI subphenotypes with different clinical outcomes and responses to vasopressin therapy. Identification of AKI subphenotypes could improve risk prognostication and may be useful for predictive enrichment in clinical trials.
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