ACE inhibitor or ARB treatment among patients with diabetes and chronic kidney disease
Bibliographic record
Abstract
OBJECTIVES: Many patients with type 2 diabetes (T2D) and chronic kidney disease (CKD) experience a delay in treatment or fail to initiate treatment with guideline-recommended angiotensin-converting enzyme inhibitors (ACEis) or angiotensin receptor blockers (ARBs) after CKD diagnosis. This study aimed to describe treatment patterns and treatment initiation after initial CKD diagnosis among patients with T2D. STUDY DESIGN: Retrospective analysis using data from the Optum Clinformatics Data Mart administrative claims database (January 2014-September 2018). METHODS: Adult patients with T2D entered the cohort if they met the criteria for CKD, defined as 2 laboratory test results 90 to 365 days apart (January 2014-September 2017) indicating CKD. Included were patients with no prior use of ACEis or ARBs or evidence of kidney disease in the 365 days prior to cohort entry (baseline). Patients were followed for a maximum of 365 days and were censored on death, disenrollment, or end of data. Patient demographics, comorbidities, and medication use were assessed at baseline, and treatments were assessed over a 1-year followup period. Multivariate logistic regression was used to identify factors associated with ACEi or ARB initiation. RESULTS: Among 15,400 eligible patients without prior ACEi or ARB treatment, only 17% initiated such therapy within a year after meeting CKD criteria. Patients who were White, resided in the northeastern United States, had more comorbidities, had less advanced albuminuria, or used sodium-glucose cotransporter 2 inhibitors were less likely to initiate treatment. CONCLUSIONS: A large proportion of patients with T2D meeting criteria for CKD do not initiate the recommended therapy within 1 year of CKD diagnosis, highlighting a need for new therapies that can slow the progression of CKD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".