Inotrope and vasopressor use in cardiogenic shock: what, when and why?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Despite increasing interest in the management of cardiogenic shock (CS), mortality rates remain unacceptably high. The mainstay of supportive treatment includes vasopressors and inotropes. These medications are recommended in international guidelines and are widely used despite limited evidence supporting safety and efficacy in CS. RECENT FINDINGS: The OptimaCC trial further supports that norepinephrine should continue to be the first-line vasopressor of choice in CS. The CAPITAL DOREMI trial found that milrinone is not superior to dobutamine in reducing morbidity and mortality in CS. Two studies currently underway will offer the first evidence of the necessity of inotrope therapy in placebo-controlled trials: CAPITAL DOREMI2 will randomize CS patients to inotrope or placebo in the initial resuscitation of shock to evaluate the efficacy of inotrope therapy and LevoHeartShock will examine the efficacy of levosimendan against placebo in early CS requiring vasopressor therapy. SUMMARY: Review of the current literature fails to show significant mortality benefit with any specific vasopressor or inotropic in CS patients. The upcoming DOREMI 2 and levosimendan versus placebo trials will further tackle the question of inotrope necessity in CS. At this time, inotrope selection should be guided by physician experience, availability, cost, and most importantly, individual patients' response to therapy.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".