Post-Operative Haemodynamic Monitoring of Patients undergoing Corrective Open Heart Surgery for Structural Heart Defects
Bibliographic record
Abstract
Background: Accurate hemodynamic monitoring is essential when identifying and treating critically ill pediatric patients. Effective perioperative care directed by sophisticated hemodynamic monitoring can lower problems and enhance results, even though the best monitoring method is still up for debate. Objective: to compare monitoring methods of cardiac output and systemic hemodynamics post-cardiac surgery and its correlation to the clinical status of patients (reflected by clinical signs and laboratory assessment) and outcome, focusing on non-invasive cardiometry and echocardiography. Methodology: A cross-sectional study was conducted on 40 pediatric patients under five years old who underwent corrective cardiac surgery for congenital heart lesions. Hemodynamic parameters were assessed using echocardiography and ICON, alongside clinical evaluation and biomarkers (BNP and lactate), at 6 and 24 hours postoperatively. Statistical analysis was employed to identify significant correlations and trends. Results: Significant hemodynamic changes were observed in the immediate post-operative period. Clinical assessment detected low cardiac output in 47% of patients at 6 hours, decreasing to 45% at 24 hours. TAPSE and ejection fraction, measured via echocardiography, showed significant correlations with clinical signs of low cardiac output at 6 and 24 hours, respectively. ICON parameters, such as thoracic fluid content (TFC), demonstrated significant trends, correlating with fluid balance and time on bypass. However, ICON lacked the precision of echocardiography for certain measures; its continuous, non-invasive monitoring provided valuable insights into hemodynamic trends. Conclusion: Hemodynamic monitoring post-operative cardiac surgery with early management of instability decreases the risk of complications and prolonged hospital stay. Clinical assessment of low cardiac output symptoms is crucial in the hemodynamic monitoring of patients, paying attention to vital data. Echocardiography and electrical cardiometry parameters have significant correlations with each other, including contractility (TAPSE, CI) and fluid assessment values (IVC collapsibility and TFC). BNP levels are best used not as a "stand-alone" test but in conjunction with existing multivariable risk indexes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".