Epidemiology and Outcomes of AKI Treated With Continuous Kidney Replacement Therapy: The Multicenter CRRTnet Study
Bibliographic record
Abstract
Rationale & Objective Continuous kidney replacement therapy (CKRT) is the predominant form of acute kidney replacement therapy used for critically ill adult patients with acute kidney injury (AKI). Given the variability in CKRT practice, a contemporary understanding of its epidemiology is necessary to improve care delivery. Study Design Multicenter, prospective living registry. Setting & Population 1,106 critically ill adults with AKI requiring CKRT from December 2013 to January 2021 across 5 academic centers and 6 intensive care units. Patients with pre-existing kidney failure and those with coronavirus 2 infection were excluded. Exposure CKRT for more than 24 hours. Outcomes Hospital mortality, kidney recovery, and health care resource utilization. Analytical Approach Data were collected according to preselected timepoints at intensive care unit admission and CKRT initiation and analyzed descriptively. Results Patients' characteristics, contributors to AKI, and CKRT indications differed among centers. Mean (standard deviation) age was 59.3 (13.9) years, 39.7% of patients were women, and median [IQR] APACHE-II (acute physiologic assessment and chronic health evaluation) score was 30 [25-34]. Overall, 41.1% of patients survived to hospital discharge. Patients that died were older (mean age 61 vs. 56.8, P <0.001), had greater comorbidity (median Charlson score 3 [1-4] vs. 2 [1-3], P < 0.001), and higher acuity of illness (median APACHE-II score 30 [25-35] vs. 29 [24-33], P =0.003). The most common condition predisposing to AKI was sepsis (42.6%), and the most common CKRT indications were oliguria/anuria (56.2%) and fluid overload (53.9%). Standardized mortality ratios were similar among centers. Limitations The generalizability of these results to CKRT practices in nonacademic centers or low-and middle-income countries is limited. Conclusions In this registry, sepsis was the major contributor to AKI and fluid management was collectively the most common CKRT indication. Significant heterogeneity in patient- and CKRT-specific characteristics was found in current practice. These data highlight the need for establishing benchmarks of CKRT delivery, performance, and patient outcomes. Data from this registry could assist with the design of such studies.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".