Effectiveness of Quality Improvement Strategies for the Management of CKD
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Quality improvement interventions have enhanced care for other chronic illnesses, but their effectiveness for patients with CKD is unknown. We sought to determine the effects of quality improvement strategies on clinical outcomes in adult patients with nondialysis-requiring CKD. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We conducted a systematic review of randomized trials, searching Medline and the Cochrane Effective Practice and Organization of Care database from January of 2003 to April of 2015. Eligible studies evaluated one or more of 11 prespecified quality improvement strategies, and prespecified study outcomes included at least one process of care measure, surrogate outcome, or hard clinical outcome. We used a random effects model to estimate the pooled risk ratio (RR; dichotomous data) or the mean difference (continuous data). RESULTS: =30,042 patients). Quality improvement strategies reduced dialysis incidence (seven trials; RR, 0.85; 95% confidence interval [95% CI], 0.74 to 0.97) and LDL cholesterol concentrations (four trials; mean difference, -17.6 mg/dl; 95% CI, -28.7 to -6.5), and increased the likelihood that patients received renin-angiotensin-aldosterone system inhibitors (nine trials; RR, 1.16; 95% CI, 1.06 to 1.27). We did not observe statistically significant effects on mortality, cardiovascular events, eGFR, glycated hemoglobin, and systolic or diastolic BP. CONCLUSIONS: Quality improvement interventions yielded significant beneficial effects on three elements of CKD care. Estimates of the effectiveness of quality improvement strategies were limited by study number and adherence to quality improvement principles. PODCAST: This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2017_09_06_CJASNPodcast_17_10.mp3.
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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 | 0.031 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.012 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".