Quality indicators for the detection and management of chronic kidney disease in primary care in Canada derived from a modified Delphi panel approach
Bibliographic record
Abstract
BACKGROUND: The detection and management of chronic kidney disease lies within primary care; however, performance measures applicable in the Canadian context are lacking. We sought to develop a set of primary care quality indicators for chronic kidney disease in the Canadian setting and to assess the current state of the disease's detection and management in primary care. METHODS: We used a modified Delphi panel approach, involving 20 panel members from across Canada (10 family physicians, 7 nephrologists, 1 patient, 1 primary care nurse and 1 pharmacist). Indicators identified from peer-reviewed and grey literature sources were subjected to 3 rounds of voting to develop a set of quality indicators for the detection and management of chronic kidney disease in the primary care setting. The final indicators were applied to primary care electronic medical records in the Electronic Medical Record Administrative data Linked Database (EMRALD) to assess the current state of primary care detection and management of chronic kidney disease in Ontario. RESULTS: Seventeen indicators made up the final list, with 1 under the category Prevalence, Incidence and Mortality; 4 under Screening, Diagnosis and Risk Factors; 11 under Management; and 1 under Referral to a Specialist. In a sample of 139 993 adult patients not on dialysis, 6848 (4.9%) had stage 3 or higher chronic kidney disease, with the average age of patients being 76.1 years (standard deviation [SD] 11.0); 62.9% of patients were female. Diagnosis and screening for chronic kidney disease were poorly performed. Only 27.1% of patients with stage 3 or higher disease had their diagnosis documented in their cumulative patient profile. Albumin-creatinine ratio testing was only performed for 16.3% of patients with a low estimated glomerular filtration rate (eGFR) and for 28.5% of patients with risk factors for chronic kidney disease. Family physicians performed relatively better with the management of chronic kidney disease, with 90.4% of patients with stage 3 or higher disease having an eGFR performed in the previous 18 months and 83.1% having a blood pressure recorded in the previous 9 months. INTERPRETATION: We propose a set of measurable indicators to evaluate the quality of the management of chronic kidney disease in primary care. These indicators may be used to identify opportunities to improve current practice in Canada.
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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 | 0.081 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| 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".