Use of Bioimpedance Techniques in Patients With CKD: A Meta-Analysis
Bibliographic record
Abstract
Background: Bioimpedance technologies are increasingly used to determine extracellular volume status in patients with chronic kidney disease (CKD). We aimed to determine if this technology improves clinical outcomes as compared to usual care. Methods: We performed a systematic review and meta-analysis of trials comparing fluid management guided by Body-Composition Monitoring or Bioimpedance analysis to standard care in patients with CKD, including patients on dialysis. Our primary outcome was all-cause mortality. Secondary outcomes included blood pressure (BP) control, all-cause hospitalization, major adverse cardiovascular events (MACE), change in left ventricular mass index (LVMi), and residual renal function. The relative risk (RR) or Hedges' g standardized mean difference (SMD) were estimated using a random-effects model. Results: Our search identified 819 citations of which 12 randomized-controlled trials (RCTs) and one observational study were included (2670 patients with 1046 on peritoneal dialysis). No studies of non-dialysis dependent CKD patients met inclusion criteria. Mean age was 56 years and mean follow up was one year. There was no difference in all-cause mortality between the bioimpedance and the standard of care arms (RR 0.72, 95% confidence interval [CI] 0.47-1.11). Better diastolic BP control was observed in the bioimpedance arm of RCTs (SMD -0.18, 95% CI -0.34 to -0.02). No difference was observed between the two arms for MACE (RR 0.73, 95% CI 0.49-1.10) or LVMi (SMD -0.17, 95% CI -0.39 to 0.05). All-cause hospitalizations were not significantly different between the two groups (RR 1.08, 95% CI 0.92-1.26). Residual renal function could not be assessed. Conclusions: Amongst patients on dialysis, bioimpedance-guided volume management showed improved diastolic BP control but no significant difference in allcause mortality, MACE, and LVMi. Moreover, our study identified a knowledge gap in the use of this technology in non-dialysis dependent CKD patients and the possible effect it may have on clinical outcomes in this population.
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.049 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".