Patterns of Care among Patients with Documented CKD in the United States
Bibliographic record
Abstract
Background: Guidelines for pharmacotherapy in chronic kidney disease (CKD) recommend a multifactorial strategy to slow kidney disease progression by inhibiting the renin-angiotensin-aldosterone system (RAASi; using angiotensin-converting enzyme inhibitors [ACEi] or angiotensin receptor blockers [ARB]) and lowering cardiovascular risk by using statins. In light of newer therapies available for CKD and the underdiagnosis of this condition, little is known regarding patterns of care in patients prior to documenting diagnosis. This study aimed to compare the patterns of care for RAASis and statins at the onset of CKD using laboratory data; vs documentation of CKD using ICD codes. Methods: A retrospective study using electronic health record-linked claims data from 2009-2020 Optum® Market Clarity was conducted. The cohort included adults with two estimated glomerular filtration rate (eGFR) measures <60mL/ min/1.73 m2, 3-12 months apart, followed by documented clinical diagnosis of CKD (using ICD 9/10 codes). The proportion of patients who were prescribed, administered, or ordered RAASis and/ or statins within ±90 days of: the second eGFR<60 mL/min/1.73 m2 (index date), and the date of first documented diagnosis in claims were computed. Results: A total of 1.39 million adults with laboratory evidence of CKD (mean [±SD] age 71 [±10]; 63% women; 87% Caucasian) were identified; of them, 740,088 (53.1%) received a documented diagnosis of CKD during follow-up, and formed the study cohort. Among those with documented CKD, 39% received RAASis and 36% received statins at index date; utilization increased to 46% and 45% respectively, at diagnosis. On average, use of RAASis increased from 38% between 2010-2012 to 48% between 2018-2020, while use of statins increased from 37% to 48% during the same time period. Conclusion: Despite the utilization of RAASis and statins increasing between onset of CKD and documented diagnosis, the overall low uptake of these guideline-recommended therapies (<50%) reinforces the need to improve pharmacologic management in CKD. As new treatments become available, optimizing care using evidence-based guideline-recommended therapies offers the opportunity of reducing risk of cardiorenal syndrome, and improving outcomes in CKD. Funding: Commercial Support - Boehringer Ingelheim
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".