Association of continuous renal replacement therapy downtime with fluid balance gap and clinical outcomes: a retrospective cohort analysis utilizing EHR and machine data
Bibliographic record
Abstract
BACKGROUND: Fluid balance gap (FBgap-prescribed vs. achieved) is associated with hospital mortality. Downtime is an important quality indicator for the delivery of continuous renal replacement therapy (CRRT). We examined the association of CRRT downtime with FBgap and clinical outcomes including mortality. METHODS: This is a retrospective cohort study of critically ill adults receiving CRRT utilizing both electronic health records (EHR) and CRRT machine data. FBgap was calculated as achieved minus prescribed fluid balance. Downtime, or percent treatment time loss (%TTL), was defined as CRRT downtime in relation to the total CRRT time. Data collection stopped upon transition to intermittent hemodialysis when applicable. Linear and logistic regression models were used to analyze the association of %TTL with FBgap and hospital mortality, respectively. Covariates included demographics, Sequential Organ Failure Assessment (SOFA) score at CRRT initiation, use of organ support devices, and the interaction between %TTL and machine alarms. RESULTS: We included 3630 CRRT patient-days from 500 patients with a median age of 59.5 years (IQR 50-67). Patients had a median SOFA score at CRRT initiation of 13 (IQR 10-16). Median %TTL was 8.1% (IQR 4.3-12.5) and median FBgap was 17.4 mL/kg/day (IQR 8.2-30.4). In adjusted models, there was a significant positive relationship between FBgap and %TTL only in the subgroup with higher alarm frequency (6 + alarms per CRRT-day) (β = 0.87 per 1% increase, 95%CI 0.48-1.26). No association was found in the subgroups with lower alarm frequency (0-2 and 3-5 alarms). There was no statistical evidence for an association between %TTL and hospital mortality in the adjusted model with the interaction term of alarm frequency. CONCLUSIONS: In critically ill adult patients undergoing CRRT, %TTL was associated with FBgap only in the subgroup with higher alarm frequency, but not in the other subgroups with lower alarms. No association between %TTL and mortality was observed. More frequent alarms, possibly indicating unexpected downtime, may suggest compromised CRRT delivery and could negatively impact FBgap.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".