Dataset Information

Derivation of a Predictive Model for Graft Loss Following Acute Kidney Injury in Kidney Transplant Recipients.

ABSTRACT:

Background

Acute kidney injury (AKI) is common in the kidney transplant population.

Objective

To derive a multivariable survival model that predicts time to graft loss following AKI.

Design

Retrospective cohort study using health care administrative and laboratory databases.

Setting

Southwestern Ontario (1999-2013) and Ottawa, Ontario, Canada (1996-2013).

Patients

We included first-time kidney only transplant recipients who had a hospitalization with AKI 6 months or greater following transplant.

Measurements

AKI was defined using the Acute Kidney Injury Network criteria (stage 1 or greater). The first episode of AKI was included in the analysis. Graft loss was defined by return to dialysis or repeat kidney transplant.

Methods

We performed a competing risk survival regression analysis using the Fine and Gray method and modified the model into a simple point system. Graft loss with death as a competing event was the primary outcome of interest.

Results

A total of 315 kidney transplant recipients who had a hospitalization with AKI 6 months or greater following transplant were included. The median (interquartile range) follow-up time was 6.7 (3.3-10.3) years. Graft loss occurred in 27.6% of the cohort. The final model included 6 variables associated with an increased risk of graft loss: younger age, increased severity of AKI, failure to recover from AKI, lower baseline estimated glomerular filtration rate, increased time from kidney transplant to AKI admission, and receipt of a kidney from a deceased donor. The risk score had a concordance probability of 0.75 (95% confidence interval [CI], 0.69-0.82). The predicted 5-year risk of graft loss fell within the 95% CI of the observed risk more than 95% of the time.

Limitations

The CIs of the estimates were wide, and model overfitting is possible due to the limited sample size; the risk score requires validation to determine its clinical utility.

Conclusions

Our prognostic risk score uses commonly available information to predict the risk of graft loss in kidney transplant patients hospitalized with AKI. If validated, this predictive model will allow clinicians to identify high-risk patients who may benefit from closer follow-up or targeted enrollment in future intervention trials designed to improve outcomes.

SUBMITTER: Molnar AO

PROVIDER: S-EPMC5308519 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Derivation of a Predictive Model for Graft Loss Following Acute Kidney Injury in Kidney Transplant Recipients.

Molnar Amber O AO van Walraven Carl C Fergusson Dean D Garg Amit X AX Knoll Greg G

Canadian journal of kidney health and disease 20170130

<h4>Background</h4>Acute kidney injury (AKI) is common in the kidney transplant population.<h4>Objective</h4>To derive a multivariable survival model that predicts time to graft loss following AKI.<h4>Design</h4>Retrospective cohort study using health care administrative and laboratory databases.<h4>Setting</h4>Southwestern Ontario (1999-2013) and Ottawa, Ontario, Canada (1996-2013).<h4>Patients</h4>We included first-time kidney only transplant recipients who had a hospitalization with AKI 6 mon ...[more]

PMID: 28270930

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Project description:Interstitial fibrosis, tubular atrophy, and inflammation are major contributors to kidney allograft failure. Here we sought an objective, quantitative pathological assessment of these lesions to improve predictive utility and constructed a deep-learning-based pipeline recognizing normal vs. abnormal kidney tissue compartments and mononuclear leukocyte infiltrates. Periodic acid- Schiff stained slides of transplant biopsies (60 training and 33 testing) were used to quantify pathological lesions specific for interstitium, tubules and mononuclear leukocyte infiltration. The pipeline was applied to the whole slide images from 789 transplant biopsies (478 baseline [pre-implantation] and 311 post-transplant 12-month protocol biopsies) in two independent cohorts (GoCAR: 404 patients, AUSCAD: 212 patients) of transplant recipients to correlate composite lesion features with graft loss. Our model accurately recognized kidney tissue compartments and mononuclear leukocytes. The digital features significantly correlated with revised Banff 2007 scores but were more sensitive to subtle pathological changes below the thresholds in the Banff scores. The Interstitial and Tubular Abnormality Score (ITAS) in baseline samples was highly predictive of one-year graft loss, while a Composite Damage Score in 12-month post-transplant protocol biopsies predicted later graft loss. ITASs and Composite Damage Scores outperformed Banff scores or clinical predictors with superior graft loss prediction accuracy. High/intermediate risk groups stratified by ITASs or Composite Damage Scores also demonstrated significantly higher incidence of estimated glomerular filtration rate decline and subsequent graft damage. Thus, our deep-learning approach accurately detected and quantified pathological lesions from baseline or post-transplant biopsies and demonstrated superior ability for prediction of post-transplant graft loss with potential application as a prevention, risk stratification or monitoring tool.

Project description:Rationale & objectiveDonor acute kidney injury (AKI) activates innate immunity, enhances HLA expression in the kidney allograft, and provokes recipient alloimmune responses. We hypothesized that injury and inflammation that manifested in deceased-donor urine biomarkers would be associated with higher rates of biopsy-proven acute rejection (BPAR) and allograft failure after transplantation.Study designProspective cohort.Setting & participants862 deceased donors for 1,137 kidney recipients at 13 centers.ExposuresWe measured concentrations of interleukin 18 (IL-18), kidney injury molecule 1 (KIM-1), and neutrophil gelatinase-associated lipocalin (NGAL) in deceased donor urine. We also used the Acute Kidney Injury Network (AKIN) criteria to assess donor clinical AKI.OutcomesThe primary outcome was a composite of BPAR and graft failure (not from death). A secondary outcome was the composite of BPAR, graft failure, and/or de novo donor-specific antibody (DSA). Outcomes were ascertained in the first posttransplant year.Analytical approachMultivariable Fine-Gray models with death as a competing risk.ResultsMean recipient age was 54 ± 13 (SD) years, and 82% received antithymocyte globulin. We found no significant associations between donor urinary IL-18, KIM-1, and NGAL and the primary outcome (subdistribution hazard ratio [HR] for highest vs lowest tertile of 0.76 [95% CI, 0.45-1.28], 1.20 [95% CI, 0.69-2.07], and 1.14 [95% CI, 0.71-1.84], respectively). In secondary analyses, we detected no significant associations between clinically defined AKI and the primary outcome or between donor biomarkers and the composite outcome of BPAR, graft failure, and/or de novo DSA.LimitationsBPAR was ascertained through for-cause biopsies, not surveillance biopsies.ConclusionsIn a large cohort of kidney recipients who almost all received induction with thymoglobulin, donor injury biomarkers were associated with neither graft failure and rejection nor a secondary outcome that included de novo DSA. These findings provide some reassurance that centers can successfully manage immunological complications using deceased-donor kidneys with AKI.

Dataset Information

Derivation of a Predictive Model for Graft Loss Following Acute Kidney Injury in Kidney Transplant Recipients.

Background

Objective

Design

Setting

Patients

Measurements

Methods

Results

Limitations

Conclusions

Publications

Derivation of a Predictive Model for Graft Loss Following Acute Kidney Injury in Kidney Transplant Recipients.

Similar Datasets

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