Chronic disease management interventions for people with chronic kidney disease in primary care: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Primary care providers manage the majority of patients with chronic kidney disease (CKD), although the most effective chronic disease management (CDM) strategies for these patients are unknown. We assessed the efficacy of CDM interventions used by primary care providers managing patients with CKD. Methods: The Medline, Embase and Cochrane Central databases were systematically searched (inception to November 2014) for randomized controlled trials (RCTs) assessing education-based and computer-assisted CDM interventions targeting primary care providers managing patients with CKD in the community. The efficacy of CDM interventions was assessed using quality indicators [use of angiotensin-converting enzyme inhibitor (ACEI) or angiotensin receptor blocker (ARB), proteinuria measurement and achievement of blood pressure (BP) targets] and clinical outcomes (change in BP and glomerular filtration rate). Two independent reviewers evaluated studies for inclusion, quality and extracted data. Random effects models were used to estimate pooled odds ratios (ORs) and weighted mean differences for outcomes of interest. Results: Five studies (188 clinics; 494 physicians; 42 852 patients with CKD) were included. Two studies compared computer-assisted intervention strategies with usual care, two studies compared education-based intervention strategies with computer-assisted intervention strategies and one study compared both these intervention strategies with usual care. Compared with usual care, computer-assisted CDM interventions did not increase the likelihood of ACEI/ARB use among patients with CKD {pooled OR 1.00 [95% confidence interval (CI) 0.83-1.21]; I2 = 0.0%}. Similarly, education-related CDM interventions did not increase the likelihood of ACEI/ARB use compared with computer-assisted CDM interventions [pooled OR 1.12 (95% CI 0.77-1.64); I2 = 0.0%]. Inconsistencies in reporting methods limited further pooling of data. Conclusions: To date, there have been very few randomized trials testing CDM interventions targeting primary care providers with the goal of improving care of people with CKD. Those conducted to date have shown minimal impact, suggesting that other strategies, or multifaceted interventions, may be required to enhance care for patients with CKD in the community.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.035 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".