A Population-Based Analysis of Quality Indicators in CKD
Bibliographic record
Abstract
Background and objectives Awareness of CKD remains low in comparison with other chronic diseases, such as diabetes, leading to low use of preventive medications and appropriate testing. The objective of this study was to evaluate the quality of care provided to people with and at risk of CKD. Design, setting, participants, & measurements We conducted a population-based analysis of all Albertans with eGFR=15–59 ml/min per 1.73 m 2 between April 1, 2011 and March 31, 2012 as well as patients with diabetes (as of March 31, 2012). We assessed multiple quality indicators in people with eGFR=15–59 ml/min per 1.73 m 2 , including appropriate risk stratification with albuminuria testing and preventive medication use and screened people with diabetes using urine albumin-to-creatinine ratio and serum creatinine measurements. Results Among 96,480 adults with eGFR=15–59 ml/min per 1.73 m 2 , we found that 17.0% of those without diabetes were appropriately risk stratified with a measure of albuminuria compared with 64.2% of those with diabetes ( P <0.001). Of those with eGFR=15–59 ml/min per 1.73 m 2 and moderate or severe albuminuria, 63.2% of those without diabetes received an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker compared with 82.1% in those with diabetes ( P <0.001). Statin use was also significantly lower in patients with eGFR=15–59 ml/min per 1.73 m 2 without diabetes (39.2%) compared with those with diabetes (64.6%; P <0.001). Among 235,649 adults with diabetes, only 41.8% received a urine albumin-to-creatinine ratio and 73.2% received a serum creatinine measurement over 1 year. Conclusions We identified large gaps in care, especially in those with CKD but no diabetes. The largest gap was in the prescription of guideline-concordant medication in those with CKD as well as appropriate screening for albuminuria in those with diabetes. Our work illustrates the importance of measuring health system performance as the first step in a quality improvement process to improve care and outcomes in CKD.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".