Achieving high convective volume in hemodiafiltration: Lessons learned after successful implementation in the <scp>HDFit</scp> trial
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: High-volume online hemodiafiltration (OL-HDF) associates with improved outcomes compared to hemodialysis (HD), provided adequate dosing is achieved as estimated from convective volume (CV). Achievement of high CV and its impact on biochemical indicators following a standardized protocol converting HD patients to OL-HDF has not been systematically reported. We assessed the success of implementation of OL-HDF in clinics naïve to the modality. DESIGN, SETTING, PARTICIPANTS, AND MEASUREMENTS: We analyzed the results of the implementation of postdilution OL-HDF in patients randomized to the HDF arm of a clinical trial (impact of hemoDiaFIlTration on physical activity and self-reported outcomes: a randomized controlled trial (HDFit) trial [ClinicalTrials.gov:NCT02787161]). The day before randomization of the first patient to OL-HDF at each clinic staff started a 3-day in-person training module on operation of Fresenius 5008 CorDiax machine in HDF mode. Patients were converted from high-flux HD to OL-HDF under oversight of trainers. OL-HDF was performed over a 6-months follow-up with a CV target of 22 L/treatment. We characterized median achieved CV >22 L/treatment record and analyzed the impact of HDF on biochemical variables. RESULTS: Ninety-seven patients (mean age 53 ± 16 years, 29% with diabetes, and 11% had a catheter) from 13 clinics randomized to the OL-HDF arm of the trial were converted from HD to HDF. Median CV > 22 L/treatment was achieved in 99% (94/95) of OL-HDF patients throughout follow-up. Monthly mean CV ranged from 27.1 L to 27.5 L. OL-HDF provided an increased single pool Kt/V at 3-months (0.2 [95% CI: 0.1-0.3]) and 6-months (0.2 [95% CI: 0.1-0.4]) compared to baseline, and reduced phosphate at 3-months (-0.4 mg/dL [95% CI: -0.8 to -0.12]) of follow-up. CONCLUSIONS: High-volume online hemodiafiltration was successfully implemented with 99% of patients achieving protocol defined CV target. Monthly mean CV was consistently >22 L/treatment during follow-up. Kt/V increased, and phosphate decreased with OL-HDF. Findings resulting from a short training period in several dialysis facilities appear to suggest HDF is an easily implementable technique.
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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 | 0.048 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".