Pulmonary hemodynamics and lung surgery: a narrative review
Bibliographic record
Abstract
Background and Objective: Lung cancer represents the leading cause of death from cancer.In suitable patients, lung resection surgery provides the highest chance of cure and thus it is one of the most commonly performed thoracic surgeries.Lung resection surgeries, including lobectomy and pneumonectomy are associated with high rates of cardiopulmonary complications.These complications may cause long-term morbidity which can be unrelated to the degree of lung function reduction.In particular, these sequelae could be due to cardiac impairment.These complications must be taken into consideration in the management of patients.The aim of the presented narrative review is to revisit the anatomy, the physiologic considerations of the right ventricle and pulmonary artery unit as well as the pre-operative hemodynamic and functional evaluation to understand the impact of one-lung ventilation and lung resection on pulmonary hemodynamics.Methods: An extended review of the literature through PudMed was conducted using the keyword related to pulmonary hemodynamics, right ventricular function, lung resection and preoperative cardiovascular evaluation.Publications in English and French, published before October 29th 2021, were considered. KeyContent and Findings: Relative reduction in right ventricular function happens following lung resection due to increased pulmonary vascular resistance and reduced lung compliance.Several components of the peri-operative care including pre-operative preparation, epidural analgesia and one-lung ventilation can influence cardiovascular performance after lung resection surgery.The surgical approach can also have an influence as patients that underwent video-assisted thoracoscopic surgery (VATS) generally had higher right heart systolic function compared to thoracotomy patients.Conclusions: Several components that are part of the peri-operative care of patient undergoing lung surgery can influence cardiovascular physiology.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".