Medical costs for managing chronic kidney disease and related complications in patients with chronic kidney disease and type 2 diabetes
Bibliographic record
Abstract
OBJECTIVE: To provide cost estimates for chronic kidney disease (CKD) management and major CKD complications among patients with CKD and type 2 diabetes (T2D). STUDY DESIGN: A retrospective cohort study of 52,599 adults with CKD and T2D using Optum Clinformatics claims data from 2014 to 2019. METHODS: Medical costs associated with CKD management, renal replacement therapies (RRTs), major CKD complications (eg, myocardial infarction, stroke, heart failure, atrial fibrillation, and hyperkalemia), and death were estimated using generalized estimating equations adjusting for baseline demographics, complications, and medical costs. Costs for CKD management, RRT, and major CKD complications were assessed in 4-month cycles. Mortality costs were assessed in the month before death. RESULTS: The estimated 4-month CKD management costs ranged from $7725 for stage I to II disease to $11,879 for stage V (without RRT), with high additional costs for dialysis and kidney transplantation ($87,538 and $124,271, respectively). The acute event costs were $31,063 for heart failure, $21,087 for stroke, and $21,016 for myocardial infarction in the first 4 months after the incident event, which all decreased substantially in subsequent 4-month cycles. The acute event costs of atrial fibrillation and hyperkalemia were $30,500 and $31,212 with hospitalization, and $5162 and $1782 without. The costs associated with cardiovascular-related death, renal-related death, and death from other causes were $17,031, $12,605, and $9900, respectively. CONCLUSIONS: Management of CKD and its complications incurs high medical costs for patients with CKD and T2D. Results from this study can be used to quantify the economic profile of emerging treatments and inform decision-making.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".