Linear Correlation Between Mean Arterial Pressure and Urine Output in Critically Ill Patients
Bibliographic record
Abstract
OBJECTIVE: Mean arterial pressure (MAP) plays a significant role in regulating tissue perfusion and urine output (UO). The optimal MAP target in critically ill patients remains a subject of debate. We aimed to explore the relationship between MAP and UO. DESIGN: A retrospective observational study. SETTING: A general ICU in a tertiary medical center. PATIENTS: All critically ill patients admitted to the ICU for more than 10 hours. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: MAP values and hourly UO were collected in 5,207 patients. MAP levels were categorized into 10 groups of 5 mm Hg (from MAP < 60 mm Hg to MAP > 100 mg Hg), and 656,423 coupled hourly mean MAP and UO measurements were analyzed. Additionally, we compared the UO of individual patients in each MAP group with or without norepinephrine (NE) support or diuretics, as well as in patients with acute kidney injury (AKI).Hourly UO rose incrementally between MAP values of 65-100 mm Hg. Among 2,226 patients treated with NE infusion, mean UO was significantly lower in the MAP less than 60 mm Hg group (53.4 mL/hr; 95% CI, 49.3-57.5) compared with all other groups (p < 0.001), but no differences were found between groups of 75 less than or equal to MAP. Among 2500 patients with AKI, there was a linear increase in average UO from the MAP less than 60 mm Hg group (57.1 mL/hr; 95% CI, 54.2-60.0) to the group with MAP greater than or equal to 100 mm Hg (89.4 mL/hr; 95% CI, 85.7-93.1). When MAP was greater than or equal to 65 mm Hg, we observed a statistically significant trend of increased UO in periods without NE infusion. CONCLUSIONS: Our analysis revealed a linear correlation between MAP and UO within the range of 65-100 mm Hg, also observed in the subgroup of patients treated with NE or diuretics and in those with AKI. These findings highlight the importance of tissue perfusion to the maintenance of diuresis and achieving adequate fluid balance in critically ill patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".