Complications of Capsulectomies: An Analysis of the American College of Surgeons National Surgical Quality Improvement Program Database
Bibliographic record
Abstract
Background: Although plastic surgeons commonly perform capsulectomies for a variety of peri-prosthetic capsular conditions, the safety of capsulectomy remains unknown, and the literature lacks evidence describing its morbidity and complication rates for patients inquiring about its associated risks. Objectives: The present study aims to identify and define the complication rates associated with capsulectomies. Methods: An analysis of the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) database was performed between the years 2015 and 2018. All information pertaining to demographics, patient-related information, surgical indications, procedure-related information, outcomes, and complications were assessed. Results: The study identified 2231 cases of surgeon-reported capsulectomies; indications most commonly reported included capsular contracture (n = 638, 28.6%) and breast implant rupture (n = 403, 18.1%). In total, 141 patients (6.32%) were hospitalized for longer than 1 postoperative day (range, 2-28 days), while the overall complication rate was 3.0% (n = 67/2231 patients). Incidence of minor complications, representing superficial surgical site infections, was 0.8%, while the major complication rate was 2.24%. These included 7 cases of deep surgical site infections (0.3%), 19 organ space infections (0.9%), and 8 cases of wound dehiscence (0.4%). Eight patients developed sepsis (0.4%); 6 patients required transfusions (0.3%); 1 case of postoperative pneumonia and 1 myocardial infarction were also identified (n = 1 each, 0.0%). The overall reoperation and readmission rates were 2.0%, representing a readmission rate of 66% among patients with complications. Conclusions: The present study provides the first estimate of the incidence of complications associated with capsulectomies. Although the NSQIP database contains significant limitations, the data presented herein describe a complication profile that plastic surgeons can share with their patients during informed consent.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".