The Effect of Preoperative Pneumonia on Postsurgical Mortality and Morbidity: A NSQIP Analysis
Bibliographic record
Abstract
BACKGROUND: Currently, only indirect evidence suggests that preoperative pneumonia is a significant risk factor for poor postsurgical outcomes. Although this relationship is clinically intuitive, this is the first study that aims to quantify the extent to which pneumonia impacts morbidity and mortality. The objective of this study was to determine the impact of preoperative pneumonia on 30-day mortality and morbidity among both elective and emergency surgical patients. METHODS: We conducted a retrospective cohort study using 2008-2012 data from the American College of Surgeons National Surgical Quality Improvement Program database. Patients with preoperative pneumonia were matched to controls without preoperative pneumonia. Patient demographics and postoperative outcomes were extracted from the database, including 30-day mortality, specific morbidities (wound, cardiac, respiratory, urinary, central nervous system, thromboembolism and sepsis), composite morbidity, number of blood transfusions and number of patients that returned to the OR. Mortality and composite morbidity were further stratified. RESULTS: We obtained data for 137,174 patients, of whom 6933 (0.50%) had preoperative pneumonia. Overall, 6111 were successfully matched to 24,444 patients with no pneumonia. Postoperative mortality and composite morbidity were both higher in patients with pneumonia than in those without pneumonia, with an odds ratio of 1.37 (95% CI 1.26-1.48) and 1.68 (95% CI 1.58-1.79), respectively. CONCLUSION: Preoperative pneumonia significantly increased the rate of postoperative morbidity and mortality across several surgical settings and patient groups. It is our recommendation that elective surgery be delayed until after the pneumonia resolves.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".