Risk Assessment of Kidney Disease Progression and Efficacy of SGLT2 Inhibition in Patients With Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVE: To develop a risk assessment tool to identify patients with type 2 diabetes (T2D) at higher risk for kidney disease progression and who might benefit more from sodium-glucose cotransporter 2 (SGLT2) inhibition. RESEARCH DESIGN AND METHODS: A total of 41,204 patients with T2D from four Thrombolysis In Myocardial Infarction (TIMI) clinical trials were divided into derivation (70%) and validation cohorts (30%). Candidate predictors of kidney disease progression (composite of sustained ≥40% decline in estimated glomerular filtration rate [eGFR], end-stage kidney disease, or kidney death) were selected with multivariable Cox regression. Efficacy of dapagliflozin was assessed by risk categories (low: <0.5%; intermediate: 0.5 to <2%; high: ≥2%) in Dapagliflozin Effect on Cardiovascular Events (DECLARE)-TIMI 58. RESULTS: There were 695 events over a median follow-up of 2.4 years. The final model comprised eight independent predictors of kidney disease progression: atherosclerotic cardiovascular disease, heart failure, systolic blood pressure, T2D duration, glycated hemoglobin, eGFR, urine albumin-to-creatinine ratio, and hemoglobin. The c-indices were 0.798 (95% CI, 0.774-0.821) and 0.798 (95% CI, 0.765-0.831) in the derivation and validation cohort, respectively. The calibration plot slope (deciles of predicted vs. observed risk) was 0.98 (95% CI, 0.93-1.04) in the validation cohort. Whereas relative risk reductions with dapagliflozin did not differ across risk categories, there was greater absolute risk reduction in patients with higher baseline risk, with a 3.5% absolute risk reduction in kidney disease progression at 4 years in the highest risk group (≥1%/year). Results were similar with the 2022 Chronic Kidney Disease Prognosis Consortium risk prediction model. CONCLUSIONS: Risk models for kidney disease progression can be applied in patients with T2D to stratify risk and identify those who experience a greater magnitude of benefit from SGLT2 inhibition.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".