Role of Filtration Fraction to Predict Early Circuit Loss During CRRT with Regional Citrate Anticoagulation: A Retrospective Cohort Study
Bibliographic record
Abstract
Background: Continuous renal replacement therapy (CRRT) is an effective treatment modality for kidney failure in acutely ill patients. Despite circuit anticoagulation with heparin, and more recently regional citrate anticoagulation (RCA), circuit loss due to premature filter coagulation remains a costly clinical challenge. The filtration fraction (FF) equation is the ratio of ultrafiltration to plasma flow rates delivered to the filter; a high FF corresponds to higher post-filter hematocrit. Based on current guidelines and expert consensus, a FF below 0.20 to 0.25 is recommended to minimize the occurrence of filter clotting. However, the role of FF in predicting early circuit loss with RCA is uncertain. Methods: We performed a retrospective analysis of CVVHDF episodes at a large academic center between January 2021 and December 2023. The aims of this study were to correlate the FF with circuit lifespan and assess the ability of FF to predict >48, >36 and <24 hours circuit loss by using logistic regression and calculating the area under the curve (AUC). Results: A total of 1087 circuits from 252 patients (mean age 61 years, 68% men) were included. RCA was used in 347 circuits, whereas 713 received local circuit heparin, systemic heparin or no anticoagulation. Out of all circuits, 443 (41%) were lost due to filter clotting (<72 hours) and 126 (12%) were changed at 72 hours per protocol. The mean FF at initiation was 0.23 (SD 0.07) which was similar between the two groups. High FF correlated with lower circuit lifespan for the heparin/no anticoagulation group (Pearson coefficient: -0.307; p<0.001), but not for RCA (Pearson coefficient: 0.023; p=0.73). FF predicted >48, >36 and <24 hours circuit loss in the heparin/no anticoagulation group (beta of -1.08, -1.20 and -1.23, respectively; p<0.001), but not for RCA (beta of 0.28, 0.09, -0.28, respectively; p>0.05). The AUC to predict early circuit loss <24 hours was 0.67 [0.62-0.72](p<0.001) for heparin/no anticoagulation and 0.52 [0.44-0.62](p=0.51) for RCA. Conclusion: Our study showed an association between FF and circuit lifespan for filters receiving heparin or no anticoagulation, however this was not observed when using RCA. FF is a modest predictor of circuit coagulation with heparin/no anticoagulation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".