Risk Factors and Predictors of Prolonged Mechanical Ventilation Following Cardiac Surgery: A Narrative Review
Bibliographic record
Abstract
The subset of patients requiring prolonged mechanical ventilation is significantly high worldwide, making it an important topic of continuous and ongoing research. Over the years, various articles have shown that there may be predictors of prolonged ventilation that could be applied in healthcare to make it more patient-centered. The available literature suggests that authors have different definitions of "prolonged" ventilation. However, most critical care units embrace caution if a patient needs mechanical ventilation for more than 48 to 72 hours. The major benefits of mechanical ventilation are an overall decrease in the work of breathing and the facilitation of relatively easier pumping from an ailing heart. An elevated risk of prolonged ventilation after cardiac surgery exists in patients with higher classes of heart failure (as classified by the New York Heart Association (NYHA) or Canadian Cardiovascular Society (CCS)), a pre-existing congenital or acquired cardiac abnormality, and patients with renal failure, to name a few. The impact on quality of life has also been widely studied; as mortality rates increase with factors like age and days dependent on ventilation. Patients undergoing prolonged ventilation constitute an administrative challenge for critical care units, highlighting how multiple patients in this bracket can overwhelm the healthcare system. The use of prediction models in this context can aid healthcare delivery tremendously. Using different predictors, we can craft tailor-made treatment options and achieve the goal of more ventilator-free days per patient.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".