Pre-operative patient optimization to prevent postoperative pulmonary complications—Insights and roles for the respiratory therapist: A narrative review
Bibliographic record
Abstract
BACKGROUND: The preoperative period has gained recognition as a crucial time to identify and manage preoperative medical conditions for preventing perioperative complications. Consequently, preoperative clinics have now become an essential component of perioperative care at many large hospitals. As the prevalence of preoperative clinics continues to grow, and the field of perioperative medicine progresses, respiratory therapists (RTs) will inevitably find a growing role to participate in preoperative patient optimization to mitigate pulmonary complications. METHODS: Keyword searches on perioperative pulmonary complications were conducted on the Medline database via PubMed and identified over 2000 candidate articles for review. Articles were included if they were English only and resulted with one or more of the following search terms; pulmonary complications, postoperative complications, postoperative pulmonary complications (PPCs), prehabilitation, incentive spirometry, smoking cessation, noninvasive ventilation. Preference was given for meta-analyses, randomized controlled trials, and systematic reviews. Publications within the past two decades were given additional preference toward final inclusion. The authors discussed eligible articles in group meetings over the span of multiple years to assess relevance and quality of data for narrowing eligible articles to the final selection of publications for the review. FINDINGS: The following narrative review examines preoperative optimization strategies to prevent PPCs and highlight areas where RTs may play a key role. After examining challenges in defining PPCs, the review examines key risk models available to predict PPCs and their implications for subsequent discussion on preventive measures that RTs may assist with in a multidisciplinary team. CONCLUSION: RTs can reduce the health care burden of PPCs by assisting fellow perioperative clinicians in providing respiratory care for patients with premorbid conditions. While much of our review focused on pre-existing pulmonary pathologies and both the pharmacological and nonpharmacological optimization of these pathologies, there are other factors contributing to PPCs deserving future exploration.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".