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:IntroductionKidney transplantation is the best therapeutical option for CKD patients. Graft loss risk factors are usually estimated with the cox method. Competing risk analysis could be useful to determine the impact of different events affecting graft survival, the occurrence of an outcome of interest can be precluded by another. We aimed to determine the risk factors for graft loss in the presence of mortality as a competing event.MethodsA retrospective cohort of 1454 kidney transplant recipients who were transplanted between July 1, 2008, to May 31, 2019, in Colombiana de Trasplantes, were analyzed to determine risk factors of graft loss and mortality at 5 years post-transplantation. Kidney and patient survival probabilities were estimated by the competing risk analysis. The Fine and Gray method was used to fit a multivariable model for each outcome. Three variable selection methods were compared, and the bootstrapping technique was used for internal validation as split method for resample. The performance of the final model was assessed calculating the prediction error, brier score, c-index and calibration plot.ResultsGraft loss occurred in 169 patients (11.6%) and death in 137 (9.4%). Cumulative incidence for graft loss and death was 15.8% and 13.8% respectively. In a multivariable analysis, we found that BKV nephropathy, serum creatinine and increased number of renal biopsies were significant risk factors for graft loss. On the other hand, recipient age, acute cellular rejection, CMV disease were risk factors for death, and recipients with living donor had better survival compared to deceased-donor transplant and coronary stent. The c-index were 0.6 and 0.72 for graft loss and death model respectively.ConclusionWe developed two prediction models for graft loss and death 5 years post-transplantation by a unique transplant program in Colombia. Using a competing risk multivariable analysis, we were able to identify 3 significant risk factors for graft loss and 5 significant risk factors for death. This contributes to have a better understanding of risk factors for graft loss in a Latin-American population. The predictive performance of the models was mild.

Project description:Background:Calcineurin inhibitors (CNI; cyclosporine, tacrolimus) are critical for kidney transplant immunosuppression, but have multiple potential drug interactions, such as with macrolide antibiotics. Macrolide antibiotics (clarithromycin, erythromycin, and azithromycin) are often used to treat atypical infections. Clarithromycin and erythromycin inhibit CNI metabolism and increase the risk of CNI nephrotoxicity, while azithromycin does not. Objective:To determine the frequency of CNI-macrolide co-prescriptions, the proportion who receive post-prescription monitoring, and the risk of adverse drug events in kidney transplant recipients. Design:Retrospective cohort study. Setting:We used linked health care databases in Alberta, Canada. Patients:We included 293 adult kidney transplant recipients from 2008-2015 who were co-prescribed a CNI and macrolide. Measurements:The primary outcome was a composite of all-cause hospitalization, acute kidney injury (creatinine increase ?0.3 mg/dL or 1.5 times baseline), or death within 30 days of the macrolide prescription. Methods:We identified CNI-macrolide co-prescriptions and compared outcomes in those who received clarithromycin/erythromycin versus azithromycin. We used a linear mixed-effects model to examine the mean change in serum creatinine and estimated glomerular filtration rate (eGFR). Results:Of the 293 recipients who were co-prescribed a CNI and a macrolide, 38% (n = 112) were prescribed clarithromycin/erythromycin while 62% (n = 181) were prescribed azithromycin. Compared with azithromycin users, clarithromycin/erythromycin users were less likely to have outpatient serum creatinine monitoring post-prescription (56% vs 69%, P = .03). There was no significant difference in the primary outcome between the 2 groups (17% vs 11%, P = .11); however, the risk of all-cause hospitalization was higher in the clarithromycin/erythromycin group (10% vs 3%, P = .02). The mean decrement in eGFR was significantly greater in the clarithromycin/erythromycin versus azithromycin group (-5.4 vs -1.9 mL/min/1.73 m2, P < .05). Limitations:We did not have CNI levels to correlate with the timing of CNI-macrolide co-prescriptions. We also did not have information regarding the indications for macrolide prescriptions. Conclusion:Clarithromycin and erythromycin were frequently co-prescribed in kidney transplant recipients on CNIs despite known drug interactions. Clarithromycin/erythromycin use was associated with a higher risk of hospitalization compared with azithromycin users. Safer prescribing practices in kidney transplant recipients are warranted.

Project description:Some patients will develop chronic kidney disease after a hospitalization with acute kidney injury; however, no risk-prediction tools have been developed to identify high-risk patients requiring follow-up.To derive and validate predictive models for progression of acute kidney injury to advanced chronic kidney disease.Data from 2 population-based cohorts of patients with a prehospitalization estimated glomerular filtration rate (eGFR) of more than 45 mL/min/1.73 m2 and who had survived hospitalization with acute kidney injury (defined by a serum creatinine increase during hospitalization > 0.3 mg/dL or > 50% of their prehospitalization baseline), were used to derive and validate multivariable prediction models. The risk models were derived from 9973 patients hospitalized in Alberta, Canada (April 2004-March 2014, with follow-up to March 2015). The risk models were externally validated with data from a cohort of 2761 patients hospitalized in Ontario, Canada (June 2004-March 2012, with follow-up to March 2013).Demographic, laboratory, and comorbidity variables measured prior to discharge.Advanced chronic kidney disease was defined by a sustained reduction in eGFR less than 30 mL/min/1.73 m2 for at least 3 months during the year after discharge. All participants were followed up for up to 1 year.The participants (mean [SD] age, 66 [15] years in the derivation and internal validation cohorts and 69 [11] years in the external validation cohort; 40%-43% women per cohort) had a mean (SD) baseline serum creatinine level of 1.0 (0.2) mg/dL and more than 20% had stage 2 or 3 acute kidney injury. Advanced chronic kidney disease developed in 408 (2.7%) of 9973 patients in the derivation cohort and 62 (2.2%) of 2761 patients in the external validation cohort. In the derivation cohort, 6 variables were independently associated with the outcome: older age, female sex, higher baseline serum creatinine value, albuminuria, greater severity of acute kidney injury, and higher serum creatinine value at discharge. In the external validation cohort, a multivariable model including these 6 variables had a C statistic of 0.81 (95% CI, 0.75-0.86) and improved discrimination and reclassification compared with reduced models that included age, sex, and discharge serum creatinine value alone (integrated discrimination improvement, 2.6%; 95% CI, 1.1%-4.0%; categorical net reclassification index, 13.5%; 95% CI, 1.9%-25.1%) or included age, sex, and acute kidney injury stage alone (integrated discrimination improvement, 8.0%; 95% CI, 5.1%-11.0%; categorical net reclassification index, 79.9%; 95% CI, 60.9%-98.9%).A multivariable model using routine laboratory data was able to predict advanced chronic kidney disease following hospitalization with acute kidney injury. The utility of this model in clinical care requires further research.

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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