Impact of Negative Pressure Wound Therapy on Outcomes Following Pancreaticoduodenectomy: An NSQIP Analysis of 14,044 Patients
Bibliographic record
Abstract
Background: Despite ongoing efforts to improve the pancreaticoduodenectomy technique and perioperative care, surgical site infection (SSI) remains a contributor to morbidity. Efforts to reduce SSI include the use of negative pressure wound therapy (NPWT), but studies and meta-analyses have been met with conflicting results. We aimed to provide an up-to-date large-scale cohort study to assess the impact of NPWT on SSIs. Methods: Utilizing the National Surgical Quality Improvement Program database, we included patients undergoing a pancreaticoduodenectomy between 2017 and 2021 and divided patients into the NPWT and non-NPWT cohorts. A bivariate analysis was performed to compare baseline characteristics and complication rates between the cohorts. Multivariate logistic regression analysis was performed to assess the independent effect of NPWT on 30-day serious complication, 30-day mortality, and the development of deep or superficial SSI. A priori sensitivity analyses were performed in high-risk and malignancy cohorts. Results: Of the 14,044 included patients, 1689 (12.0%) patients had a prophylactic NPWT device, while 12,355 (88.0%) did not. Patients were more likely to have NPWT if they had higher ASA scores, had diabetes, were dialysis-dependent, or had a hard pancreas, but they were less likely if they were a smoker, had steroid use, or had a bleeding disorder. Most complications occurred similarly between the two cohorts, including superficial and deep SSI, but NPWT patients had a longer length of stay (10.4 d vs. 9.5 d, p < 0.001) and higher organ space SSI (22.6% vs. 17.4%, p < 0.001). Following multivariable modeling to control for demographic differences, NPWT was not independently associated with a difference in likelihood of SSI (aOR 0.94, p = 0.691) or serious complications (aOR 0.958, p = 0.669). Furthermore, the sensitivity analyses of both high-risk and malignant subgroup also did not see an independent association of NPWT on the rate of SSI (aOR 0.98, p = 0.898 and 0.96, p = 0.788, respectively). Conclusion: NPWT is used infrequently and is not significantly associated with improved outcomes including in the high-risk or malignant subgroups based on multivariable analysis for surgical site infections nor did it improve the outcomes of 30-day serious complications in these subgroups. Considering this and other studies showing the limited benefit of NPWT in all-comers and in high-risk cohorts, it remains unclear whether NPWT offers benefits following PD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".