Assessing the Quality of Care for People with CKD: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Effective strategies for managing CKD are available, but the extent to which implementation of these strategies is consistent with guideline recommendations is uncertain. We aimed to synthesize available data on the quality of CKD care globally. Methods: EMBASE, PubMed, and CINAHL were systematically searched (inception–2023) for observational studies reporting on the quality of CKD care across domains related to patient monitoring (eGFR, albuminuria), appropriate medication use (ACEIs, ARBs, statins, NSAIDs), and treatment targets (BP, HbA1c) according to management recommendations in international CKD guidelines. Pooled estimates (95% CI) of the percentage of patients who met the quality indicators for CKD care were obtained using random effects meta-analysis. Results: 58 studies across 22 countries, including a total of 2,969,039 patients with CKD, were included. The reporting of and adherence to quality indicators for CKD care varied substantially across the included studies (Figure 1). Summary estimates of the percentage of CKD patients who met key indicators showed that (1) eGFR was monitored in 81% (75–87%) of patients, albuminuria in 47% (40–54%) and BP in 90% (84–95%); (2) ACEIs/ARBs were prescribed in 56% (51–62%), statins in 56% (48–64%), and NSAIDs withheld in 81% (77–86%) and (3) a BP target of ≤140/90 mmHg was achieved in 56% (48–64%) patients. Conclusion: Current evidence suggests substantial variation in the reporting and quality of CKD care. Concordance with guideline recommendations varied across quality indicators and patient groups, with opportunities for considerable improvement, particularly albuminuria testing. Effective quality improvement strategies to address gaps in CKD care, along with systematic approaches for monitoring care quality, are needed.Figure 1: Forest plot summarising the percentage of patients with CKD who met the quality indicators for CKD care
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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 | 0.028 | 0.067 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.052 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".