Dataset Information

Predicting kidney failure from longitudinal kidney function trajectory: A comparison of models.

ABSTRACT:

Rationale & objective

Early prediction of chronic kidney disease (CKD) progression to end-stage kidney disease (ESKD) currently use Cox models including baseline estimated glomerular filtration rate (eGFR) only. Alternative approaches include a Cox model that includes eGFR slope determined over a baseline period of time, a Cox model with time varying GFR, or a joint modeling approach. We studied if these more complex approaches may further improve ESKD prediction.

Study design

Prospective cohort.

Setting & participants

We re-used data from two CKD cohorts including patients with baseline eGFR >30ml/min per 1.73m2. MASTERPLAN (N = 505; 55 ESKD events) was used as development dataset, and NephroTest (N = 1385; 72 events) for validation.

Predictors

All models included age, sex, eGFR, and albuminuria, known prognostic markers for ESKD.

Analytical approach

We trained the models on the MASTERPLAN data and determined discrimination and calibration for each model at 2 years follow-up for a prediction horizon of 2 years in the NephroTest cohort. We benchmarked the predictive performance against the Kidney Failure Risk Equation (KFRE).

Results

The C-statistics for the KFRE was 0.94 (95%CI 0.86 to 1.01). Performance was similar for the Cox model with time-varying eGFR (0.92 [0.84 to 0.97]), eGFR (0.95 [0.90 to 1.00]), and the joint model 0.91 [0.87 to 0.96]). The Cox model with eGFR slope showed the best calibration.

Conclusion

In the present studies, where the outcome was rare and follow-up data was highly complete, the joint models did not offer improvement in predictive performance over more traditional approaches such as a survival model with time-varying eGFR, or a model with eGFR slope.

SUBMITTER: van den Brand JAJG

PROVIDER: S-EPMC6508737 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

Predicting kidney failure from longitudinal kidney function trajectory: A comparison of models.

van den Brand Jan A J G JAJG Dijkstra Tjeerd M H TMH Wetzels Jack J Stengel Bénédicte B Metzger Marie M Blankestijn Peter J PJ Lambers Heerspink Hiddo J HJ Gansevoort Ron T RT

PloS one 20190509 5

<h4>Rationale & objective</h4>Early prediction of chronic kidney disease (CKD) progression to end-stage kidney disease (ESKD) currently use Cox models including baseline estimated glomerular filtration rate (eGFR) only. Alternative approaches include a Cox model that includes eGFR slope determined over a baseline period of time, a Cox model with time varying GFR, or a joint modeling approach. We studied if these more complex approaches may further improve ESKD prediction.<h4>Study design</h4>Pro ...[more]

PMID: 31071186

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Project description:BACKGROUND:Renal transplant patients are mandated to have continuous assessment of their kidney function over time to monitor disease progression determined by changes in blood urea nitrogen (BUN), serum creatinine (Cr), and estimated glomerular filtration rate (eGFR). Multivariate analysis of these outcomes that aims at identifying the differential factors that affect disease progression is of great clinical significance. Thus our study aims at demonstrating the application of different joint modeling approaches with random coefficients on a cohort of renal transplant patients and presenting a comparison of their performance through a pseudo-simulation study. The objective of this comparison is to identify the model with best performance and to determine whether accuracy compensates for complexity in the different multivariate joint models. METHODS AND RESULTS:We propose a novel application of multivariate Generalized Linear Mixed Models (mGLMM) to analyze multiple longitudinal kidney function outcomes collected over 3 years on a cohort of 110 renal transplantation patients. The correlated outcomes BUN, Cr, and eGFR and the effect of various covariates such patient's gender, age and race on these markers was determined holistically using different mGLMMs. The performance of the various mGLMMs that encompass shared random intercept (SHRI), shared random intercept and slope (SHRIS), separate random intercept (SPRI) and separate random intercept and slope (SPRIS) was assessed to identify the one that has the best fit and most accurate estimates. A bootstrap pseudo-simulation study was conducted to gauge the tradeoff between the complexity and accuracy of the models. Accuracy was determined using two measures; the mean of the differences between the estimates of the bootstrapped datasets and the true beta obtained from the application of each model on the renal dataset, and the mean of the square of these differences. The results showed that SPRI provided most accurate estimates and did not exhibit any computational or convergence problem. CONCLUSION:Higher accuracy was demonstrated when the level of complexity increased from shared random coefficient models to the separate random coefficient alternatives with SPRI showing to have the best fit and most accurate estimates.

Project description:BackgroundOutcomes after acute kidney injury (AKI) are well described, but not for those already under nephrology clinic care. This is where discussions about kidney failure risk are commonplace. We evaluated whether the established kidney failure risk equation (KFRE) should account for previous AKI episodes when used in this setting.MethodsThis observational cohort study included 7491 people referred for nephrology clinic care in British Columbia in 2003-09 followed to 2016. Predictors were previous Kidney Disease: Improving Global Outcomes-based AKI, age, sex, proteinuria, estimated glomerular filtration rate (eGFR) and renal diagnosis. Outcomes were 5-year kidney failure and death. We developed cause-specific Cox models (AKI versus no AKI) for kidney failure and death, stratified by eGFR (</?30?mL/min/1.73?m2). We also compared prediction models comparing the 5-year KFRE with two refitted models, one with and one without AKI as a predictor.ResultsAKI was associated with increased kidney failure (33.1% versus 26.3%) and death (23.8% versus 16.8%) (P ?<?0.001). In Cox models, AKI was independently associated with increased kidney failure in those with an eGFR ?30?mL/min/1.73?m2 {hazard ratio [HR] 1.35 [95% confidence interval (CI) 1.07-1.70]}, no increase in those with eGFR <30?mL/min/1.73?m2 ([HR 1.05 95% CI 0.91-1.21)] and increased mortality in both subgroups [respective HRs 1.89 (95% CI 1.56-2.30) and 1.43 (1.16-1.75)]. Incorporating AKI into a refitted kidney failure prediction model did not improve predictions on comparison of receiver operating characteristics (P?=?0.16) or decision curve analysis. The original KFRE calibrated poorly in this setting, underpredicting risk.ConclusionsAKI carries a poorer long-term prognosis among those already under nephrology care. AKI may not alter kidney failure risk predictions, but the use of prediction models without appreciating the full impact of AKI, including increased mortality, would be simplistic. People with kidney diseases have risks beyond simply kidney failure. This complexity and variability of outcomes of individuals is important.

Dataset Information

Predicting kidney failure from longitudinal kidney function trajectory: A comparison of models.

Rationale & objective

Study design

Setting & participants

Predictors

Analytical approach

Results

Conclusion

Publications

Predicting kidney failure from longitudinal kidney function trajectory: A comparison of models.

Similar Datasets

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