Myocardial blood flow in patients with sepsis
Bibliographic record
Abstract
Abstract The high mortality of systemic infection results from its associated cardiovascular depression. Cardiac depression typically normalizes if the patient recovers from the infection, but recent studies suggest that these patients have an increased long‐term risk of developing cardiovascular disease after the septic episode. These findings have reignited interest in sepsis‐associated cardiac function and myocardial blood flow, which remain poorly understood in humans. We studied cardiac function and myocardial microvascular perfusion using gadolinium‐contrast magnetic resonance imaging in a cohort of patients during the initial recovery period of sepsis ( n = 16) or septic shock ( n = 5) and after full recovery 1–2 months later ( n = 13). In addition, hepatic, splenic, and renal cortical perfusion were also assessed. With infection, cardiac output (+27%), the rate‐pressure‐product (+21%), and the left ventricle (LV) peak‐ejection (+36%) and peak‐filling (+35%) rates increased compared to full recovery (all p < 0.05). Onset of LV myocardial perfusion and the time to peak LV myocardial perfusion of gadolinium‐contrast occurred earlier during initial than after recovery, with a numerically higher LV myocardium wash‐in rate (23 ± 22 vs. 14 ± 14 s −1 ; p = 0.07). LV myocardial fibrosis was not seen in any patients. Renal cortical, splenic, and hepatic perfusion parameters including onset, time‐to‐peak, and wash‐in rates of gadolinium contrast were comparable between initial and full recovery, except for a lower hepatic wash‐in rate during initial recovery (13 ± 10 vs. 22 ± 16 s −1 ; p = 0.03). Our study supports that myocardial microvascular dysfunction is unlikely to contribute to cardiovascular disease after severe infection. Conversely, hepatic hypoperfusion during sepsis may explain the commonly observed hepatic dysfunction in sepsis.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".