Trajectories of fluid management after the initiation of renal replacement therapy in critically ill patients: a secondary analysis of the STARRT-AKI trial
Bibliographic record
Abstract
BACKGROUND: Fluid management is an essential component of renal replacement therapy (RRT) in critically ill patients. Both a positive cumulative fluid balance (CFB) and a high net ultrafiltration (NUF) rate have been reported to be associated with adverse outcomes in epidemiological studies, although the overall trajectory of fluid balance after RRT initiation is not well-described. We aimed to characterize trajectories of fluid management parameters during RRT and analyse the effect of CFB/NUF on outcomes as a trajectory rather than single or aggregated time points over the first week after initiation of RRT. METHODS: This is a secondary analysis using fluid balance data focusing on individuals enrolled in the standard-strategy arm of the STARRT-AKI trial who initiated RRT. Cumulative fluid balance (CFB) following RRT initiation and daily net ultrafiltration (NUF) adjusted for body weight during the first 7 days after initiation of RRT were the main independent exposures. We modeled the trajectory of fluid parameters using spline functions and used latent trajectory analysis methods to identify predominant trajectories to compare patients' characteristics and outcomes. We employed logistic regression and multivariable joint longitudinal models to compare the odds and determine the time-dependent association between fluid parameters (CFB and NUF) and 90-day mortality across and within the trajectory classes identified. RESULTS: We included 855 patients in the primary analysis. After excluding erroneous fluid balance data, we identified two distinct CFB/NUF trajectories. Class A (82.8%) was characterized by a slight increase in CFB and low/stable NUF during the week following RRT initiation while class B (17.2%) was characterized by an increasingly negative CFB with initially higher daily NUF during the first 4 days followed by a stabilization after day 4. In an adjusted analysis, individuals classified in class B were at lower risk for 90-day mortality (aOR: 0.48 CI 0.32; 0.70) p < 0.001) compared to class A. Time-dependent analysis revealed higher CFB was associated with mortality only in those with a class A trajectory (aHR 1.29, 95% CI 1.03-1.55, p = 0.03). CONCLUSIONS: Distinct CFB/NUF trajectories convey prognostic information beyond single-day fluid balance or NUF values and should be considered when formulating or interpreting fluid management strategies.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".