Baseline characteristics of participants with diabetes in the development and validation cohorts. Median (interquartile-range, IQR) or frequencies (%) are given.

Derivation of development (left panel) and validation (right panel) cohorts.

Performance of prediction models in the development and validation cohorts.
Optimism-corrected performance measures were computed by internal validation with 500 bootstrap resamples. Results for the outcome ‘alive without incidence and progression of CKD’ (CKD) and ROC curves can be found in Supplemental Table 3 and Supplemental Figure 3, respectively.

Explanation of the predictor d-UACRtp. Micro- and macroalbuminuria were defined as urinary-albumin creatinine ratio (UACR) above 30 mg/g (3.4 mg/mmol) and 300 mg/g (33.9 mg/mmol), respectively. To predict incidence or progression of chronic kidney disease (CKD) after 5.5 years the current (at baseline) UACR measurement is clearly an important predictor. Therefore, for each participant, we calculated d-UACRtp as the difference between each participant’s UACR at baseline and the cutoff that was relevant for this participant. For individuals with normoalbuminuria at baseline [UACR<30 mg/g] the relevant cutoff for incidence of albuminuria is 30 mg/g and for individuals with microalbuminuria at baseline [30 Note, that partial explained variation estimates of individual predictors in a model do not always sum up to the explained variation of the full model because of correlation among predictors. d-UACRtp is the main determinant of the outcome state alive with incidence or progression of chronic kidney disease (CKD), whereas age and eGFR CKD-EPI are the main determinants of death in this population of early CKD stages.

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Figure 3:

Predicted probabilities for incidence or progression of chronic kidney disease after 5.5 years and death within 5.5 years computed by the laboratory model. For a hypothetical male, 65-year old individual with normoalbuminuria (top panels) or with microalbuminuria (bottom panels) predicted probabilities were computed by inserting eGFR CKD-EPI values and selected UACR values into the laboratory model. The plots reveal that the predicted probability for death within 5.5 years is higher in an individual with microalbuminuria at baseline than in an individual with normoalbuminuria. As a consequence, an individual with microalbuminuria at baseline has a lower predicted probability for CKD after 5.5 years than an individual with normoalbuminuria.

Performance of Prediction Models in the Development and Validation Cohorts for the Outcome ‘Alive without incidence or progression of chronic kidney disease.

Supplemental Figure 1. Kaplan‐Meier Plot Comparing Follow‐Up of ONTARGET and ORIGIN

Prediction equations:
(a) Laboratory Prediction Equation, (b) Clinical Prediction Equation

Decision curve analysis for the laboratory (left panel) and the clinical model (right panel) for the outcome state ‘alive with incidence and progression of CKD’ based on the development cohort (first row) and the validation cohort (second row).

Scatterplot comparing eGFR CKD‐EPI and eGFR MDRD

Receiver‐Operating Curves (ROC) for the Laboratory and the Clinical Model based on the Development and Validation Cohort.

Calibration plots

Predicted Probabilities for incidence or progression of chronic kidney disease after 5.5 years and death within 5.5 years computed by the laboratory model.
(a) Hypothetical male individual aged 60, 65 or 70.
(b) Hypothetical female individual aged 60, 65 or 70.