Clinical relevance of transcranial Doppler in a cardiac surgery setting: embolic load predicts difficult separation from cardiopulmonary bypass
Bibliographic record
Abstract
BACKGROUND: During cardiac surgery, transcranial Doppler (TCD) represents a non-invasive modality that allows measurement of red blood cell flow velocities in the cerebral arteries. TCD can also be used to detect and monitor embolic material in the cerebral circulation. Detection of microemboli is reported as a high intensity transient signal (HITS). The importance of cerebral microemboli during cardiac surgery has been linked to the increased incidence of postoperative renal failure, right ventricular dysfunction, and hemodynamic instability. The objective of this study is to determine whether the embolic load is associated with hemodynamic instability during cardiopulmonary bypass (CPB) separation and postoperative complications. METHODS: A retrospective single-centre cohort study of 354 patients undergoing cardiac surgery between December 2015 and March 2020 was conducted. Patients were divided in tertiles, where 117 patients had a low quantity of embolic material (LEM), 119 patients have a medium quantity of microemboli (MEM) and 118 patients who have a high quantity of embolic material (HEM). The primary endpoint was a difficult CPB separation. Multivariate logistic regression was used to determine the potential association between a difficult CPB separation and the number of embolic materials. RESULTS: Patients who had a difficult CPB separation had more HITS compared to patients who had a successful CPB separation (p < 0.001). In the multivariate analysis, patients with MEM decreased their odds of having a difficult CPB weaning compared to patients in the HEM group (OR = 0.253, CI 0.111-0.593; p = 0.001). In the postoperative period patients in the HEM group have a higher Time of Persistent Organ Dysfunction (TPOD), a longer stay in the ICU, a longer duration under vasopressor drugs and a higher mortality rate compared to those in the MEM and LEM groups. CONCLUSION: The result of this study suggests that a high quantity of cerebral embolic material increases the odds of having a difficult CPB separation. Also, it seems to be associated to more complex surgery, a longer CPB time, a higher TPOD and a longer stay in the ICU. Six out of eight patients who died in this cohort were in the HEM group.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".