Ressuscitação direcionada ao tempo de enchimento capilar baseada em fenótipo hemodinâmico no choque séptico precoce: protocolo de estudo do ensaio clínico randomizado ANDROMEDA-SHOCK-2
Bibliographic record
Abstract
BACKGROUND: Early reversion of sepsis-induced tissue hypoperfusion is essential for survival in septic shock. However, consensus regarding the best initial resuscitation strategy is lacking given that interventions designed for the entire population with septic shock might produce unnecessary fluid administration. This article reports the rationale, study design and analysis plan of the ANDROMEDA-2 study, which aims to determine whether a peripheral perfusion-guided strategy consisting of capillary refill time-targeted resuscitation based on clinical and hemodynamic phenotypes is associated with a decrease in a composite outcome of mortality, time to organ support cessation, and hospital length of stay compared to standard care in patients with early (< 4 hours of diagnosis) septic shock. METHODS: The ANDROMEDA-2 study is a multicenter, multinational randomized controlled trial. In the intervention group, capillary refill time will be measured hourly for 6 hours. If abnormal, patients will enter an algorithm starting with pulse pressure assessment. Patients with pulse pressure less than 40mmHg will be tested for fluid responsiveness and receive fluids accordingly. In patients with pulse pressure > 40mmHg, norepinephrine will be titrated to maintain diastolic arterial pressure > 50mmHg. Patients who fail to normalize capillary refill time after the previous steps will be subjected to critical care echocardiography for cardiac dysfunction evaluation and subsequent management. Finally, vasopressor and inodilator tests will be performed to further optimize perfusion. A sample size of 1,500 patients will provide 88% power to demonstrate superiority of the capillary refill time-targeted strategy. CONCLUSIONS: If hemodynamic phenotype-based, capillary refill time-targeted resuscitation demonstrates to be a superior strategy, care processes in septic shock resuscitation can be optimized with bedside tools.
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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 | 0.022 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".