| Item type: | Article | ||||
|---|---|---|---|---|---|
| Journal or Publication Title: | JAMA Network Open | ||||
| Publisher: | AMER MEDICAL ASSOC | ||||
| Place of Publication: | CHICAGO | ||||
| Volume: | 6 | ||||
| Number of Issue or Book Chapter: | 4 | ||||
| Page Range: | e231870 | ||||
| Date: | 2023 | ||||
| Institutions: | Medicine > Abteilung für Nephrologie | ||||
| Identification Number: |
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| Keywords: | RENAL-FUNCTION DECLINE; RISK; PROGRESSION | ||||
| Dewey Decimal Classification: | 600 Technology > 610 Medical sciences Medicine | ||||
| Status: | Published | ||||
| Refereed: | Yes, this version has been refereed | ||||
| Created at the University of Regensburg: | Yes | ||||
| Item ID: | 76038 |
Web of ScienceAbstract
IMPORTANCE Type 2 diabetes increases the risk of progressive diabetic kidney disease, but reliable prediction tools that can be used in clinical practice and aid in patients' understanding of disease progression are currently lacking. OBJECTIVE To develop and externally validate a model to predict future trajectories in estimated glomerular filtration rate (eGFR) in adults with type 2 diabetes ...
Abstract
IMPORTANCE Type 2 diabetes increases the risk of progressive diabetic kidney disease, but reliable prediction tools that can be used in clinical practice and aid in patients' understanding of disease progression are currently lacking. OBJECTIVE To develop and externally validate a model to predict future trajectories in estimated glomerular filtration rate (eGFR) in adults with type 2 diabetes and chronic kidney disease using data from 3 European multinational cohorts. DESIGN, SETTING, AND PARTICIPANTS This prognostic study used baseline and follow-up information collected between February 2010 and December 2019 from 3 prospective multinational cohort studies: PROVALID (Prospective Cohort Study in Patients with Type 2 Diabetes Mellitus for Validation of Biomarkers), GCKD (German Chronic Kidney Disease), and DIACORE (Diabetes Cohorte). A total of 4637 adult participants (aged 18-75 years) with type 2 diabetes and mildly to moderately impaired kidney function (baseline eGFR of >= 30 mL/min/1.73m(2)) were included. Data were analyzed between June 30, 2021, and January 31, 2023. MAIN OUTCOMES AND MEASURES Thirteen variables readily available from routine clinical care visits (age, sex, body mass index; smoking status; hemoglobin A(1c) [mmol/mol and percentage]; hemoglobin, and serum cholesterol levels; mean arterial pressure, urinary albumin-creatinine ratio, and intake of glucose-lowering, blood-pressure lowering, or lipid-lowering medication) were selected as predictors. Repeated eGFR measurements at baseline and follow-up visits were used as the outcome. A linear mixed-effects model for repeated eGFR measurements at study entry up to the last recorded follow-up visit (up to 5 years after baseline) was fit and externally validated. RESULTS Among 4637 adults with type 2 diabetes and chronic kidney disease (mean [SD] age at baseline, 63.5 [9.1] years; 2680 men [57.8%]; all of White race), 3323 participants from the PROVALID and GCKD studies (mean [SD] age at baseline, 63.2 [9.3] years; 1864 men [56.1%]) were included in the model development cohort, and 1314 participants from the DIACORE study (mean [SD] age at baseline, 64.5 [8.3] years; 816 men [62.1%]) were included in the external validation cohort, with a mean (SD) follow-up of 5.0 (0.6) years. Updating the random coefficient estimates with baseline eGFR values yielded improved predictive performance, which was particularly evident in the visual inspection of the calibration curve (calibration slope at 5 years: 1.09; 95% CI, 1.04-1.15). The prediction model had good discrimination in the validation cohort, with the lowest C statistic at 5 years after baseline (0.79; 95% CI, 0.77-0.80). The model also had predictive accuracy, with an R-2 ranging from 0.70 (95% CI, 0.63-0.76) at year 1 to 0.58 (95% CI, 0.53-0.63) at year 5. CONCLUSIONS AND RELEVANCE In this prognostic study, a reliable prediction model was developed and externally validated; the robust model was well calibrated and capable of predicting
Metadata last modified: 18 Mar 2025 10:08
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