Noninvasive Hemodynamic Characterization of Shock and Preshock Using Echocardiography in Cardiac Intensive Care Unit Patients
Bibliographic record
Abstract
BACKGROUND: Shock and preshock are defined on the basis of the presence of hypotension, hypoperfusion, or both. We sought to determine the hemodynamic underpinnings of shock and preshock noninvasively using transthoracic echocardiography (TTE). METHODS AND RESULTS: We included Mayo Clinic cardiac intensive care unit patients from 2007 to 2015 with TTE within 1 day of admission. Hypotension and hypoperfusion at the time of cardiac intensive care unit admission were used to define 4 groups. TTE findings were evaluated across these groups, and in-hospital mortality was evaluated according to TTE findings in each group. We included 5375 patients with a median age of 69.2 years (36.8% women). The median left ventricular ejection fraction was 50%. Groups based on hypotension and hypoperfusion were assigned as follows: no hypotension or hypoperfusion, 59.7%; isolated hypotension, 15.3%; isolated hypoperfusion, 16.4%; and both hypotension and hypoperfusion, 8.7%. Most TTE variables of interest varied across these groups, with worse biventricular function, lower forward flow, and higher filling pressures as the degree of hemodynamic compromise increased. In-hospital mortality occurred in 8.2%, and inpatient deaths had more TTE parameter abnormalities. In-hospital mortality increased with the degree of hemodynamic compromise, and a marked gradient in in-hospital mortality was observed when the clinical classification of shock and preshock was combined with TTE findings reflecting worse biventricular function, lower forward flow, or higher filling pressures. CONCLUSIONS: Substantial differences in cardiac function are observed between cardiac intensive care unit patients with preshock and shock using TTE, and the combination of the clinical and TTE hemodynamic assessment provides robust mortality risk stratification.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 | 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".