Organ Perfusion Pressure Predicts Outcomes in Cardiogenic Shock Patients
Bibliographic record
Abstract
AIMS: The diagnosis of cardiogenic shock (CS) relies upon signs and/or symptoms of end-organ hypoperfusion. The combination of hypoperfusion and systemic congestion identifies patients at particularly high risk. This study evaluated organ perfusion pressure (OPP), calculated as mean arterial pressure minus invasive central venous pressure, as a predictor of outcomes in CS. METHODS AND RESULTS: All consecutive patients with acute myocardial infarction-related CS (AMI-CS) or acutely decompensated heart failure-related CS (ADHF-CS) enrolled in the multicentre Altshock-2 registry between January 2020 and November 2023 were included. The primary outcome was in-hospital all-cause mortality. Overall, 316 patients were included (mean age: 64 ± 13 years, 62 [20%] female, median left ventricular ejection fraction: 22% [interquartile range, IQR 15-30%], 261 [85.9%] SCAI stage C or worse, median OPP at presentation: 57.0 mmHg [IQR 47.0-69.8 mmHg]). A total of 117 (37%) patients died during the hospitalization. Low OPP (i.e. <57.0 mmHg) was associated with significantly higher in-hospital all-cause mortality (hazard ratio [HR] 1.757, 95% confidence interval [CI] 1.208-2.556, p = 0.003), whereas low mean arterial pressure alone was not (HR 1.323, 95% CI 0.901-1.941, p = 0.153). After multivariable adjustment for significant clinical data available at first bedside assessment (age and Sequential Organ Failure Assessment score), low OPP still predicted significantly higher in-hospital all-cause mortality (HR per mmHg decrease: 1.016, 95% CI 1.004-1.029, p = 0.010). Low OPP appeared particularly powerful in predicting higher in-hospital all-cause mortality among ADHF-CS patients (HR 3.172, p = 0.002). CONCLUSION: In this multicentre, observational, prospective study on patients hospitalized for CS, lower OPP on admission was associated with significantly higher in-hospital all-cause mortality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".