Quality of care for people with chronic kidney disease: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: Guideline-based strategies to prevent chronic kidney disease (CKD) progression and complications are available, yet their implementation in clinical practice is uncertain. We aimed to synthesise the available evidence on the concordance of CKD care with clinical guidelines to identify gaps and inform future CKD care. DESIGN: Systematic review and meta-analysis. DATA SOURCES, PARTICIPANTS, AND OUTCOMES: We systematically searched MEDLINE (OVID), EMBASE (OVID) and CINAHL (EBSCOhost) (to 18 July 2025) for observational studies of adults with CKD reporting data on the quality of CKD care. We assessed data on quality indicators of CKD care across domains that related to patient monitoring (glomerular filtration rate and albuminuria), medications use (ACE inhibitors (ACEIs) and angiotensin receptor blockers (ARBs), statins) and treatment targets (blood pressure (BP) and HbA1c). Pooled estimates (95% CI) of the percentage of patients who met the quality indicators for CKD care were estimated using random effects model. RESULTS: 59 studies across 24 countries, including a total of 3 003 641 patients with CKD, were included. Across studies, 81.3% (95% CI: 75% to 87.6%) of patients received eGFR monitoring, 47.4% (95% CI: 40.0% to 54.7%) had albuminuria testing, and 90% (95% CI: 84.3% to 95.9%) had BP measured. ACEIs/ARBs were prescribed among 56.7% (95% CI: 51.5% to 62%), and statins among 56.6% (95% CI: 48.9% to 64.3%) of patients. BP (systolic BP ≤140/90 mm Hg) and HbA1c (<7%) targets were achieved in 56.5% (95% CI: 48.5% to 64.6%) and 43.5% (95% CI: 39.4% to 47.6%) of patients, respectively. Subgroup analysis indicated higher rates of proteinuria testing among patients with diabetes (52.2%) compared with those without (31.3%). CONCLUSIONS: Current evidence shows substantial variation in CKD care quality globally. Guideline-concordant care varied according to quality measures and across patient groups, with gaps in indicators like albuminuria testing. These findings underscore the need for effective quality improvement strategies to address gaps in CKD care, including increased albuminuria testing for risk stratification, together with systematic measures for monitoring care quality. PROSPERO REGISTRATION NUMBER: CRD42023391749.
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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 | 0.022 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.053 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".