Fluid balance and clinical outcomes in patients with aortic dissection: a retrospective case-control study based on ICU databases
Bibliographic record
Abstract
OBJECTIVES: Aortic dissection (AD) is a life-threatening condition that requires intensive care and management. This paper explores the role of fluid management in the clinical care of AD patients, which has been unclear despite the substantial existing research that has been conducted on the treatment of AD. DESIGN: A retrospective case-control study using data for AD patients from public databases. SETTING: Two public intensive care unit (ICU) databases with hospital courses from the USA, Medical Information Mart for Intensive Care (MIMIC)-IV critical care dataset and the eICU Collaborative Research Database, with data from 2008 to 2019. PARTICIPANTS: A total of 751 adult AD patients with detailed fluid management records from two databases were included. INTERVENTIONS: The mean 24-hour intake and output were calculated by dividing the total amount of intake and output by the number of days in the ICU, respectively. The mean 24-hour fluid balance was generated by subtracting the output from the intake. OUTCOME MEASURES: The relationship between the mean 24-hour fluid management and all-cause in-hospital death was assessed through univariate and multivariable regression analyses. RESULTS: A positive correlation was found between mean 24-hour fluid intake and in-hospital mortality among AD patients (OR 1.029, 95% CI (1.018, 1.041), p<0.001), whereas a negative correlation was revealed between mean 24-hour fluid output and in-hospital mortality (OR 0.941, 95% CI (0.914, 0.968), p<0.001). A similar result was found for mean 24-hour fluid balance (OR 1.030, 95% CI (1.019, 1.042), p<0.001), and the cut-off was selected to be 5.12 dL (AUC=0.778, OR 3.066, 95% CI (1.634, 5.753), p<0.001). CONCLUSIONS: This study stresses the importance of fluid balance in the clinical care of AD patients and provides new insights for optimising fluid management and monitoring strategies beyond the conventional focus on blood pressure and heart rate management.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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".