Dataset Information

Comparison of methods for renal risk prediction in patients with type 2 diabetes (ZODIAC-36).

ABSTRACT:

Background

Patients with diabetes are at high risk of death prior to reaching end-stage renal disease, but most models predicting the risk of kidney disease do not take this competing risk into account. We aimed to compare the performance of Cox regression and competing risk models for prediction of early- and late-stage renal complications in type 2 diabetes.

Methods

Patients with type 2 diabetes participating in the observational ZODIAC study were included. Prediction models for (micro)albuminuria and 50% increase in serum creatinine (SCr) were developed using Cox regression and competing risk analyses. Model performance was assessed by discrimination and calibration.

Results

During a total follow-up period of 10 years, 183 out of 640 patients (28.6%) with normoalbuminuria developed (micro)albuminuria, and 22 patients (3.4%) died without developing (micro)albuminuria (i.e. experienced the competing event). Seventy-nine out of 1,143 patients (6.9%) reached the renal end point of 50% increase in SCr, while 219 (19.2%) died without developing the renal end point. Performance of the Cox and competing risk models predicting (micro)albuminuria was similar and differences in predicted risks were small. However, the Cox model increasingly overestimated the risk of increase in SCr in presence of a substantial number of competing events, while the performance of the competing risk model was quite good.

Conclusions

In this study, we demonstrated that, in case of substantial numbers of competing events, it is important to account for the competing risk of death in renal risk prediction in patients with type 2 diabetes.

SUBMITTER: Riphagen IJ

PROVIDER: S-EPMC4361549 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

Comparison of methods for renal risk prediction in patients with type 2 diabetes (ZODIAC-36).

Riphagen Ineke J IJ Kleefstra Nanne N Drion Iefke I Alkhalaf Alaa A van Diepen Merel M Cao Qi Q Groenier Klaas H KH Landman Gijs W D GW Navis Gerjan G Bilo Henk J G HJ Bakker Stephan J L SJ

PloS one 20150316 3

<h4>Background</h4>Patients with diabetes are at high risk of death prior to reaching end-stage renal disease, but most models predicting the risk of kidney disease do not take this competing risk into account. We aimed to compare the performance of Cox regression and competing risk models for prediction of early- and late-stage renal complications in type 2 diabetes.<h4>Methods</h4>Patients with type 2 diabetes participating in the observational ZODIAC study were included. Prediction models for ...[more]

PMID: 25775414

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Project description:Complexity analysis of glucose time series with Detrended Fluctuation Analysis (DFA) has been proved to be useful for the prediction of type 2 diabetes mellitus (T2DM) development. We propose a modified DFA algorithm, review some of its characteristics and compare it with other metrics derived from continuous glucose monitorization in this setting. Several issues of the DFA algorithm were evaluated: (1) Time windowing: the best predictive value was obtained including all time-windows from 15 minutes to 24 hours. (2) Influence of circadian rhythms: for 48-hour glucometries, DFA alpha scaling exponent was calculated on 24-hour sliding segments (1-hour gap, 23-hour overlap), with a median coefficient of variation of 3.2%, which suggests that analysing time series of at least 24-hour length avoids the influence of circadian rhythms. (3) Influence of pretreatment of the time series through integration: DFA without integration was more sensitive to the introduction of white noise and it showed significant predictive power to forecast the development of T2DM, while the pretreated time series did not. (4) Robustness of an interpolation algorithm for missing values: The modified DFA algorithm evaluates the percentage of missing values in a time series. Establishing a 2% error threshold, we estimated the number and length of missing segments that could be admitted to consider a time series as suitable for DFA analysis. For comparison with other metrics, a Principal Component Analysis was performed and the results neatly tease out four different components. The first vector carries information concerned with variability, the second represents mainly DFA alpha exponent, while the third and fourth vectors carry essentially information related to the two "pre-diabetic behaviours" (impaired fasting glucose and impaired glucose tolerance). The scaling exponent obtained with the modified DFA algorithm proposed has significant predictive power for the development of T2DM in a high-risk population compared with other variability metrics or with the standard DFA algorithm.

Project description:Background and objectivesQuantitative data for prediction of incidence and progression of early CKD are scarce in individuals with type 2 diabetes. Therefore, two risk prediction models were developed for incidence and progression of CKD after 5.5 years and the relative effect of predictors were ascertained.Design, setting, participants, & measurementsBaseline and prospective follow-up data of two randomized clinical trials, ONgoing Telmisartan Alone and in combination with Ramipril Global Endpoint Trial (ONTARGET) and Outcome Reduction with Initial Glargine Intervention (ORIGIN), were used as development and independent validation cohorts, respectively. Individuals aged ≥55 years with type 2 diabetes and normo- or microalbuminuria at baseline were included. Incidence or progression of CKD after 5.5 years was defined as new micro- or macroalbuminuria, doubling of creatinine, or ESRD. The competing risk of death was considered as an additional outcome state in the multinomial logistic models.ResultsOf the 6766 ONTARGET participants with diabetes, 1079 (15.9%) experienced incidence or progression of CKD, and 1032 (15.3%) died. The well calibrated, parsimonious laboratory prediction model incorporating only baseline albuminuria, eGFR, sex, and age exhibited an externally validated c-statistic of 0.68 and an R(2) value of 10.6%. Albuminuria, modeled to depict the difference between baseline urinary albumin/creatinine ratio and the threshold for micro- or macroalbuminuria, was mostly responsible for the predictive performance. Inclusion of clinical predictors, such as glucose control, diabetes duration, number of prescribed antihypertensive drugs, previous vascular events, or vascular comorbidities, increased the externally validated c-statistic and R(2) value only to 0.69 and 12.1%, respectively. Explained variation was largely driven by renal and not clinical predictors.ConclusionsAlbuminuria and eGFR were the most important factors to predict onset and progression of early CKD in individuals with type 2 diabetes. However, their predictive ability is modest. Inclusion of demographic, clinical, and other laboratory predictors barely improved predictive performance.

Dataset Information

Comparison of methods for renal risk prediction in patients with type 2 diabetes (ZODIAC-36).

Background

Methods

Results

Conclusions

Publications

Comparison of methods for renal risk prediction in patients with type 2 diabetes (ZODIAC-36).

Similar Datasets

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