<b>OBJECTIVE </b>
<p>End-stage
kidney disease (ESKD) is a life-threatening complication of diabetes which can be prevented or delayed
by intervention. Hence, early detection of persons at increased risk is
essential.</p>
<p> </p>
<p><b>RESEARCH DESIGN AND METHODS </b></p>
<p>From
a population-based cohort of 5,460 clinically
diagnosed Danish adults with type 1 diabetes followed 2001-2016, we
developed a prediction model for ESKD accounting for the competing risk of
death. Poisson regression analysis was used to estimate the model based on information
routinely collected from clinical examinations. The effect of including
an extended set of predictors (lipids, alcohol intake etc.) was further
evaluated, and potential interactions identified in a survival tree analysis
were tested. The final model was externally validated in 9,175 adults from
Denmark and Scotland.</p>
<p> </p>
<p><b>RESULTS</b> </p>
<p>During a median follow-up of
10.4 years (interquartile limits: 5.1;14.7), 303 (5.5%) of the participants
(mean (SD) age 42.3 (16.5) years) developed ESKD and 764 (14.0%) died without
having developed ESKD. The final ESKD prediction model included age, male sex,
diabetes duration, estimated glomerular filtration rate, micro- and
macroalbuminuria, systolic blood pressure, HbA<sub>1c</sub>, smoking and
previous cardiovascular disease. Discrimination was excellent for 5-year risk
of ESKD event with a C-statistic of 0.888 (95%CI: 0.849;0.927) in the
derivation cohort and confirmed at 0.865 (0.811;0.919) and 0.961 (0.940;0.981)
in the external validation cohorts from Denmark and Scotland. </p>
<p> </p>
<p><b>CONCLUSIONS</b> </p>
<p>We have derived and validated
a novel, high-performing ESKD prediction model for risk stratification in the
adult type 1 diabetes population. This model may improve clinical decision
making and potentially guide early
intervention.</p>