Background: Whether continuous renal replacement therapy (CRRT) should be applied to critically ill patients with both acute kidney injury (AKI) and cancer remains controversial because of poor expected outcomes. The present study determined prognostic factors for all-cause in-hospital mortality in patients with AKI and cancer undergoing CRRT. Methods: We included 471 patients with AKI and cancer who underwent CRRT at the intensive care unit of a Korean tertiary hospital from 2013 to 2020, and classified them by malignancy type. The primary outcomes were 28-day all-cause mortality rate and prognostic factors for in-hospital mortality. The secondary outcome was renal replacement therapy (RRT) dependency at hospital discharge.Results: The 28-day mortality rates were 58.8% and 82% in the solid and hematologic malignancy groups, respectively. Body mass index (BMI), presence of oliguria, Sequential Organ Failure Assessment (SOFA) score, and albumin level were common predictors of 28-day mortality in the solid and hematologic malignancy groups. A high heart rate and the presence of severe acidosis were prognostic factors only in the solid malignancy group. Among the survivors, the proportion with RRT dependency was 25.0% and 33.3% in the solid and hematologic malignancy groups, respectively. Conclusion:The 28-day mortality rate of cancer patients with AKI undergoing CRRT was high in both the solid and hematologic malignancy groups. BMI, presence of oliguria, SOFA score, and albumin level were common predictors of 28-day mortality in the solid and hematologic malignancy groups, but a high heart rate and severe acidosis were prognostic factors only in the solid malignancy group.

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Background: Immunoglobulin A nephropathy (IgAN) is the most prevalent form of glomerulonephritis worldwide. Prediction of disease progression in IgAN can help to provide individualized treatment based on accurate risk stratification. Methods: We performed proton nuclear magnetic resonance-based metabolomics analyses of serum and urine samples from healthy controls, non-progressor (NP), and progressor (P) groups to identify metabolic profiles of IgAN disease progression. Metabolites that were significantly different between the NP and P groups were selected for pathway analysis. Subsequently, we analyzed multivariate area under the receiver operating characteristic (ROC) curves to evaluate the predictive power of metabolites associated with IgAN progression. Results: We observed several distinct metabolic fingerprints of the P group involving the following metabolic pathways: glycolipid metabolism; valine, leucine, and isoleucine biosynthesis; aminoacyl-transfer RNA biosynthesis; glycine, serine, and threonine metabolism; and glyoxylate and dicarboxylate metabolism. In multivariate ROC analyses, the combinations of serum glycerol, threonine, and proteinuria (area under the curve [AUC], 0.923; 95% confidence interval [CI], 0.667–1.000) and of urinary leucine, valine, and proteinuria (AUC, 0.912; 95% CI, 0.667–1.000) showed the highest discriminatory ability to predict IgAN disease progression. Conclusion: This study identified serum and urine metabolites profiles that can aid in the identification of progressive IgAN and proposed perturbed metabolic pathways associated with the identified metabolites.