Dataset Information

Archetypal Analysis of Injury in Kidney Transplant Biopsies Identifies Two Classes of Early AKI.

ABSTRACT: All transplanted kidneys are subjected to some degree of injury as a result of the donation-implantation process and various post-transplant stresses such as rejection. Because transplants are frequently biopsied, they present an opportunity to explore the full spectrum of kidney response-to-wounding from all causes. Defining parenchymal damage in transplanted organs is important for clinical management because it determines function and survival. In this study, we classified the scenarios associated with parenchymal injury in genome-wide microarray results from 1,526 kidney transplant indication biopsies collected during the INTERCOMEX study. We defined injury groups by using archetypal analysis (AA) of scores for gene sets and classifiers previously identified in various injury states. Six groups and their characteristics were defined in this population: No injury, minor injury, two classes of acute kidney injury ("AKI," AKI1, and AKI2), chronic kidney disease (CKD), and CKD combined with AKI. We compared the two classes of AKI, namely, AKI1 and AKI2. AKI1 had a poor function and increased parenchymal dedifferentiation but minimal response-to-injury and inflammation, instead having increased expression of PARD3, a gene previously characterized as being related to epithelial polarity and adherens junctions. In contrast, AKI2 had a poor function and increased response-to-injury, significant inflammation, and increased macrophage activity. In random forest analysis, the most important predictors of function (estimated glomerular filtration rate) and graft loss were injury-based molecular scores, not rejection scores. AKI1 and AKI2 differed in 3-year graft survival, with better survival in the AKI2 group. Thus, injury archetype analysis of injury-induced gene expression shows new heterogeneity in kidney response-to-wounding, revealing AKI1, a class of early transplants with a poor function but minimal inflammation or response to injury, a deviant response characterized as PC3, and an increased risk of failure. Given the relationship between parenchymal injury and kidney survival, further characterization of the injury phenotypes in kidney transplants will be important for an improved understanding that could have implications for understanding native kidney diseases (ClinicalTrials.gov #NCT01299168).

SUBMITTER: Halloran PF

PROVIDER: S-EPMC9021747 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundAcute kidney injury (AKI) in pediatric critical care patients is diagnosed using elevated serum creatinine, which occurs only after kidney impairment. There are no treatments other than supportive care for AKI once it has developed, so it is important to identify patients at risk to prevent injury. This study develops a machine learning model to learn pre-disease patterns of physiological measurements and predict pediatric AKI up to 48 h earlier than the currently established diagnostic guidelines.MethodsEHR data from 16,863 pediatric critical care patients between 1 month to 21 years of age from three independent institutions were used to develop a single machine learning model for early prediction of creatinine-based AKI using intelligently engineered predictors, such as creatinine rate of change, to automatically assess real-time AKI risk. The primary outcome is prediction of moderate to severe AKI (Stage 2/3), and secondary outcomes are prediction of any AKI (Stage 1/2/3) and requirement of renal replacement therapy (RRT). Predictions generate alerts allowing fast assessment and reduction of AKI risk, such as: "patient has 90% risk of developing AKI in the next 48 h" along with contextual information and suggested response such as "patient on aminoglycosides, suggest check level and review dose and indication".ResultsThe model was successful in predicting Stage 2/3 AKI prior to detection by conventional criteria with a median lead-time of 30 h at AUROC of 0.89. The model predicted 70% of subsequent RRT episodes, 58% of Stage 2/3 episodes, and 41% of any AKI episodes. The ratio of false to true alerts of any AKI episodes was approximately one-to-one (PPV 47%). Among patients predicted, 79% received potentially nephrotoxic medication after being identified by the model but before development of AKI.ConclusionsAs the first multi-center validated AKI prediction model for all pediatric critical care patients, the machine learning model described in this study accurately predicts moderate to severe AKI up to 48 h in advance of AKI onset. The model may improve outcome of pediatric AKI by providing early alerting and actionable feedback, potentially preventing or reducing AKI by implementing early measures such as medication adjustment.

Project description:Background and objectivesHow to best medically manage patients who survived hospitalized AKI is unclear. Use of renin-angiotensin system blockers in this setting may increase risk of recurrent AKI.Design, setting, participants, & measurementsThis is a cohort study of 10,242 members of an integrated health care delivery system in Northern California who experienced AKI and survived a hospitalization between January 1, 2006 and December 31, 2013. All study participants did not have prior heart failure or use of angiotensin-converting enzyme inhibitors (ACE-Is) or angiotensin receptor blockers (ARBs) up to 5 years prior. New receipt and time-updated exposure of ACE-Is/ARBs was identified on the basis of dispensed prescriptions found in outpatient health plan pharmacy databases. The main outcome of interest was subsequent episode of hospitalized AKI after discharge from an initial index hospitalization complicated by AKI. Recurrent AKI episode was defined using acute changes in serum creatinine concentrations. Marginal structural models were used to adjust for baseline and potential time-dependent confounders.ResultsForty-seven percent of the study population had a documented eGFR<60 ml/min per 1.73 m2 or documented proteinuria before hospitalization. With a median of 3 (interquartile range, 1-5) years of follow-up, 1853 (18%) patients initiated use of ACE-Is/ARBs and 2124 (21%) patients experienced recurrent AKI. Crude rate of recurrent AKI was 6.1 (95% confidence interval [95% CI], 5.9 to 6.4) per 100 person-years off ACE-Is/ARBs and 5.7 (95% CI, 4.9 to 6.5) per 100 person-years on ACE-Is/ARBs. In marginal structural causal inference models that adjusted for baseline and potential time-dependent confounders, exposure to ACE-I/ARB use was not associated with higher incidence of recurrent AKI (adjusted odds ratio, 0.71; 95% CI, 0.45 to 1.12).ConclusionsIn this study of AKI survivors without heart failure, new use of ACE-I/ARB therapy was not independently associated with increased risk of recurrent hospitalized AKI.

Dataset Information

Archetypal Analysis of Injury in Kidney Transplant Biopsies Identifies Two Classes of Early AKI.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets