PubMed ID:
35339377
Public Release Type:
Journal
Publication Year: 2022
Affiliation: artment of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, United States of America b Bayer HealthCare Pharmaceuticals Inc. (US), Bayer Boulevard Whippany, NJ, United States of America c Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, United States of America
DOI:
https://doi.org/10.1016/j.jdiacomp.2022.108180
Authors:
Farej R,
Putnam N,
Ye W,
Ding X,
Kong SX,
Wang D,
Kuo S,
Herman WH,
Lott L,
Elliott JC,
Singh R
Request IDs:
21415
Studies:
Chronic Renal Insufficiency Cohort Study
,
Diabetes Control and Complications Trial / Epidemiology of Diabetes Interventions and Complications
,
Diabetes Prevention Program
,
Diabetes Prevention Program Outcomes Study
Aims: To develop a set of prediction models for end-stage kidney disease (ESKD), cardiovascular outcomes, and mortality in patients with type 2 diabetes (T2D) and chronic kidney disease (CKD) using commonly measured clinical variables. Methods: We studied 1432 participants with T2D and CKD enrolled in the Chronic Renal Insufficiency Cohort, followed for a median period of 7 years. We used Cox proportional-hazards models to model the six outcomes (ESKD, stroke, myocardial infarction (MI), congestive heart failure (CHF), death before ESKD, and all-cause mortality). We internally evaluated these models using concordance and calibration measures. Results: The newly developed six prediction models included 15 predictors: age at diabetes diagnosis, sex, blood pressure, body mass index, hemoglobin A1c, high density lipoprotein cholesterol, urine protein-to-creatinine ratio, estimated glomerular filtration rate, smoking status, and history of stroke, MI, CHF, ESKD, and amputation. The resulting models demonstrated good/strong discrimination (cross-validation C-index range: 0.70 to 0.90) and calibration. Conclusions: This study provided an internally validated and useful tool for predicting individual adverse outcomes and mortality in patients with T2D and CKD. These models may inform optimal use of targeted health interventions.