Assessing the quality of CKD care using process quality indicators: A scoping review
Bibliographic record
Abstract
INTRODUCTION: Assessing the quality of chronic kidney disease (CKD) management is crucial for optimal care and identifying care gaps. It is largely unknown which quality indicators have been widely used and the potential variations in the quality of CKD care. We sought to summarize process quality indicators for CKD and assess the quality of CKD care. METHODS: We searched databases including Medline (Ovid), PubMed, Cochrane Library, Web of Science, CINAHL, and Scopus from inception to June 20, 2024. Two reviewers screened the identified records, extracted relevant data, and classified categories and themes of quality indicators. RESULTS: We included 24 studies, extracted 30 quality indicators, and classified them into three categories with nine themes. The three categories included laboratory measures and monitoring of CKD progression and/or complications (monitoring of kidney markers, CKD mineral and bone disorder, anemia and malnutrition, electrolytes, and volume), use of guideline-recommended therapeutic agents (use of medications), and attainment of therapeutic targets (blood pressure, glycemia, and lipids). Among the frequently reported quality indicators (in five or more studies), the following have a median proportion of study participants achieving that quality indicator exceeding 50%: monitoring of kidney markers (Scr/eGFR), use of medications (ACEIs/ARBs, avoiding non-steroidal anti-inflammatory drugs (NSAIDs)), management of blood pressure (with a target of ≤140/90, or without specific targets), and monitoring for glycated hemoglobin A1c (HbA1c)). The presence of diabetes, hypertension, cardiovascular disease, or proteinuria was associated with higher achievement in indicators of monitoring of kidney markers, use of recommended medications, and management of blood pressure and glycemia. CONCLUSION: The quality of CKD management varies with quality indicators. A more consistent and complete reporting of key quality indicators is needed for future studies assessing CKD care quality.
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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 | 0.066 | 0.266 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.037 | 0.041 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".