Impact of blood volume monitoring on fluid removal during intermittent hemodialysis of critically ill children with acute kidney injury
Bibliographic record
Abstract
BACKGROUND: In chronic pediatric patients treated with intermittent hemodialysis (IHD), blood volume monitoring (BVM) is commonly used to assess and manage volume status during the dialysis session. Minimal data exists on its use during IHD in critically ill children with acute kidney injury (AKI). In these cases, fluid removal may be limited by hemodynamic instability. METHODS: We present a retrospective study conducted in our pediatric intensive care unit. For eligible patients, demographic data and IHD treatment characteristics were recorded including BVM use, ultrafiltration (UF) volume per session, hypotensive episodes and intradialysis interventions. Hypotensive episodes and UF per IHD session were compared between IHD sessions with BVM (BVM group) and IHD sessions without BVM (control group). RESULTS: Twenty-three AKI patients with a median age of 11 years (1.8-18) and body weight of 36 kg (10-85) received 134 IHD sessions (70 with BVM and 64 without BVM). Hypotensive episodes occurred in 34% of all sessions with no significant difference between the BVM group and the control group: (95% CI: 22%, 44%) and 36% (95% CI: 24%, 48%), respectively, but UF per session was higher in the BVM group as compared to control (48 ± 27 mL/kg and 33 ± 26 mL/kg, respectively, P = 0.0001). The mean decrease in BVM did not exceed 13% over an entire dialysis session in patients without hypotension. CONCLUSION: In conclusion, in our experience of IHD sessions in critically ill children with AKI, the use of BVM allowed a higher UF in those with BVM without influencing the frequency of hypotensive episodes. Applying specific guidelines on BVM use may decrease hypotensive episodes during IHD treatment in critically ill patients.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".