Effects of dapagliflozin on urinary output, fluid balance, and biochemistry in critically ill patients: a post-hoc secondary analysis of the DEFENDER trial
Bibliographic record
Abstract
BACKGROUND: Sodium-glucose cotransport-2 inhibitors (SGLT2i) have established benefits in diabetes mellitus, heart failure, and chronic kidney disease, but their physiological effects during critical illness remain unclear. We explored whether dapagliflozin affected urinary output, fluid balance, and other physiological parameters in critically ill patients with acute organ dysfunction. METHODS: This secondary analysis of the DEFENDER trial included 401 critically ill patients with acute organ dysfunction randomized to receive dapagliflozin 10 mg daily or standard care. We analyzed urinary output, fluid balance, electrolytes, acid-base status, glycemia, and vasopressor requirements over the first five days using Bayesian models. RESULTS: Dapagliflozin progressively increased urinary output (day 5: + 157 mL/day, 95% CrI -90 to 386, probability 90%) and decreased fluid balance (day 5: -290 mL/day, 95% CrI -564 to -27, probability 98%). Furosemide use was lower in the dapagliflozin group (overall -3%, 95% CrI -7% to 1%, probability 90%). Dapagliflozin had minimal effects on creatinine and electrolytes but was associated with progressive small decreases in pH (day 5: -0.02, probability 96%). Maximum glucose levels were consistently lower with dapagliflozin (-9 mg/dL overall, probability 83%). Norepinephrine requirements showed a time-dependent increase in the dapagliflozin group, with the expected dose difference reaching 0.034 mcg/kg/min by day 5 (probability 94%), and heterogeneity analysis revealed larger effects in patients with sepsis or on mechanical ventilation. CONCLUSION: This exploratory analysis suggests dapagliflozin may enhance diuresis and reduce loop diuretic requirements in critically ill patients, potentially at the cost of increased vasopressor needs. Glucose levels were likely slightly lower with dapagliflozin. Given the study's limitations and heterogeneous treatment effects, these findings should be considered hypothesis-generating pending confirmation in prospective trials.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".