Online Hemodiafiltration Compared to Conventional Hemodialysis in Critically Ill Patients
Bibliographic record
Abstract
Introduction Online hemodiafiltration (HDF) has been increasingly used for improved clearance of middle molecular weight toxins. The impact of this mode of clearance is unknown in critically ill patients. We aimed to determine whether the use of HDF in acute kidney injury (AKI) is associated with lower mortality and improved kidney recovery up to 90 days after initiation of therapy. Methods Single-center retrospective cohort study using data from 2017 to 2020 of adults with AKI who initiated intermittent renal replacement therapy (IRRT) in the intensive care unit (ICU), using either hemodialysis (HD) or HDF depending on the maintenance status of the water system without regards for patient characteristics. We assessed association with patient-events and session-events using time-dependent Cox models and general estimating equations models, respectively. Results We included 182 adults with AKI for whom 848 IRRT sessions were performed in the ICU. The 90-day mortality rate was 43 of 182 (24.6%). There was no significant association with the use of HDF and mortality (adjusted hazard ratio [aHR]: 0.85 (0.43; 1.67) P = 0.64), kidney recovery (aHR: 1.18 (0.76; 1.84) P = 0.47), or intradialytic hypotension (adjusted odds ratio [aOR]: 0.91 confidence interval [CI]: 0.64–1.28 P = 0.58). HDF treatment was associated with a lower rate of subsequent vasopressor use (aOR: 0.60 CI: 0.36–0.99 P = 0.047) and a greater reduction of the neutrophil-to-lymphocyte ratio (NLR) following the first session (−15.0% vs. +5.1%, P = 0.047) but was also associated with increased risk of filter thrombosis during treatment (aOR: 2.42 CI: 1.67–3.50 P < 0.001). Conclusion The use of HDF in the setting of AKI was not associated with a differential risk of mortality or kidney recovery.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".