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 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.
Background and objectivesCritically ill patients with worsening AKI are at high risk for poor outcomes. Predicting which patients will experience progression of AKI remains elusive. We sought to develop and validate a risk model for predicting severe AKI within 72 hours after intensive care unit admission.Design, setting, participants, & measurementsWe applied least absolute shrinkage and selection operator regression methodology to two prospectively enrolled, critically ill cohorts of patients who met criteria for the systemic inflammatory response syndrome, enrolled within 24–48 hours after hospital admission. The risk models were derived and internally validated in 1075 patients and externally validated in 262 patients. Demographics and laboratory and plasma biomarkers of inflammation or endothelial dysfunction were used in the prediction models. Severe AKI was defined as Kidney Disease Improving Global Outcomes (KDIGO) stage 2 or 3.ResultsSevere AKI developed in 62 (8%) patients in the derivation, 26 (8%) patients in the internal validation, and 15 (6%) patients in the external validation cohorts. In the derivation cohort, a three-variable model (age, cirrhosis, and soluble TNF receptor-1 concentrations [ACT]) had a c-statistic of 0.95 (95% confidence interval [95% CI], 0.91 to 0.97). The ACT model performed well in the internal (c-statistic, 0.90; 95% CI, 0.82 to 0.96) and external (c-statistic, 0.93; 95% CI, 0.89 to 0.97) validation cohorts. The ACT model had moderate positive predictive values (0.50–0.95) and high negative predictive values (0.94–0.95) for severe AKI in all three cohorts.ConclusionsACT is a simple, robust model that could be applied to improve risk prognostication and better target clinical trial enrollment in critically ill patients with AKI.
Background Few studies of biomarkers as predictors of outcome in infection have been performed in tropical, low- and middle-income countries where the burden of sepsis is highest. We evaluated whether selected biomarkers could predict 28-day mortality in infected patients in rural Thailand. Methods Four thousand nine hundred eighty-nine adult patients admitted with suspected infection to a referral hospital in northeast Thailand were prospectively enrolled within 24 h of admission. In a secondary analysis of 760 patients, interleukin-8 (IL-8), soluble tumor necrosis factor receptor 1 (sTNFR-1), angiopoietin-1 (Ang-1), angiopoietin-2 (Ang-2), and soluble triggering receptor expressed by myeloid cells 1 (sTREM-1) were measured in the plasma. Association with 28-day mortality was evaluated using regression; a parsimonious biomarker model was selected using the least absolute shrinkage and selection operator (LASSO) method. Discrimination of mortality was assessed by receiver operating characteristic curve analysis and verified by multiple methods. Results IL-8, sTNFR-1, Ang-2, and sTREM-1 concentrations were strongly associated with death. LASSO identified a three-biomarker model of sTREM-1, Ang-2, and IL-8, but sTREM-1 alone provided comparable mortality discrimination (p = 0.07). sTREM-1 alone was comparable to a model of clinical variables (area under receiver operating characteristic curve [AUC] 0.81, 95% confidence interval [CI] 0.77–0.85 vs AUC 0.79, 95% CI 0.74–0.84; p = 0.43). The combination of sTREM-1 and clinical variables yielded greater mortality discrimination than clinical variables alone (AUC 0.83, 95% CI 0.79–0.87; p = 0.004). Conclusions sTREM-1 predicts mortality from infection in a tropical, middle-income country comparably to a model derived from clinical variables and, when combined with clinical variables, can further augment mortality prediction. Trial registration The Ubon-sepsis study was registered on ClinicalTrials.gov (NCT02217592), 2014.
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