Risk Stratification of Surgical Site Outcomes by BMI & Flap Type in Autologous Breast Reconstruction

Impact of Technological Advancements on Short-term Outcomes in Flap Reconstruction after Soft Tissue Sarcoma Resection: A Retrospective Comparative Analysis

BACKGROUND

 Soft tissue sarcomas (STS) are rare malignancies requiring extensive surgical resection, often leading to significant soft tissue defects. Flap reconstruction is crucial for restoring function and appearance. Recent reconstructive microsurgery advancements, including high-resolution indocyanine green (ICG) imaging and ultra-high frequency ultrasonography (UHFU), have revolutionized preoperative planning and intraoperative guidance. We aimed to compare the surgical procedures and short-term outcomes of patients undergoing immediate flap reconstruction before and after our department's adoption of these technologies.

METHODS

 We retrospectively analyzed 276 patients who underwent immediate flap reconstruction post-sarcoma resection between May 2014 and December 2023. They were categorized into pre- and post-technology groups based on the introduction of ICG angiography and UHFU in July 2019. We collected demographic, surgical, and postoperative data and compared outcomes using Fisher's exact and t-tests.

RESULTS

 The muscle preservation rate at the donor site was significantly higher in the post-Tech than in the pre-Tech group (no muscle damage: 65% vs. 37%, incision muscle damage: 25% vs. 26%, and muscle resection: 10% vs. 37%; p < 0.01). The proportions of complications (21% vs. 36%, p = 0.01), flap complications (17% vs. 30%, p = 0.01), partial flap loss (5% vs. 17%, p < 0.01), and flap dehiscence (9% vs. 25%, p < 0.01) were low in the post-Tech group. In the stratified analysis of free-flap reconstruction, the post-Tech group had a shorter operative time (7:01 vs. 8:13, p = 0.03) and fewer takebacks due to compromised flap perfusion (4% vs. 15%, p = 0.03) compared with the pre-Tech group.

CONCLUSION

 The introduction of ICG angiography and UHFU has improved surgical outcomes in STS flap reconstructions. These technologies facilitate precise preoperative planning and intraoperative decision-making, resulting in reduced operative times, low complication rates, and enhanced muscle preservation at the donor site.

Effects of Marijuana Use in Patients Undergoing Abdominal Free Flap Breast Reconstruction

Background

Marijuana use has been associated with vascular inflammation and clotting, resulting in endothelial damage and arteritis. As marijuana use rises in the United States, few studies have evaluated its impact on surgical outcomes and wound healing in free flap breast reconstruction.

Methods

A retrospective cohort study of patients undergoing abdominal free flap breast reconstruction between 2016 and 2022 at a large metropolitan healthcare system was performed. Patient demographics, comorbidities, procedural details, and complications were analyzed. Minor complications were defined as skin or fat necrosis not requiring intervention, nipple loss, any wound requiring management in the clinic, hematoma, and seroma. Major complications were defined as reoperation, flap loss, cardiac or thromboembolic events, and hospital readmission. Active marijuana users were those with marijuana use within 12 weeks of surgery.

Results

In total, 168 patients underwent 276 deep inferior epigastric artery-based flaps for breast reconstruction. There were 21 active marijuana users. There were no significant differences in patient demographics, cancer treatment, or minor and major complications. However, there were higher rates of active nicotine use (P = 0.001) and anxiety/depression amongst active marijuana users (P = 0.002). Active users had higher rates of bilateral breast reconstruction (P = 0.029), but no significant differences in other operative details.

Conclusions

Active marijuana use of unknown frequency may be safe in patients undergoing breast free flap reconstruction. Advising marijuana abstinence preoperatively may not alter patient outcomes. Further studies of greater sample size are needed to evaluate marijuana's impact on outcomes associated with breast reconstruction using free flap.

Applying unsupervised machine learning approaches to characterize autologous breast reconstruction patient subgroups: an NSQIP analysis of 14,274 patients

BACKGROUND

Autologous breast reconstruction is composed of diverse techniques and results in a variety of outcome trajectories. We propose employing an unsupervised machine learning method to characterize such heterogeneous patterns in large-scale datasets.

METHODS

A retrospective cohort study of autologous breast reconstruction patients was conducted through the National Surgical Quality Improvement Program database. Patient characteristics, intraoperative variables, and occurrences of acute postoperative complications were collected. The cohort was classified into patient subgroups via the K-means clustering algorithm, a similarity-based unsupervised learning approach. The characteristics of each cluster were compared for differences from the complementary sample (p < 2 ×10-4) and validated with a test set.

RESULTS

A total of 14,274 female patients were included in the final study cohort. Clustering identified seven optimal subgroups, ordered by increasing rate of postoperative complication. Cluster 1 (2027 patients) featured breast reconstruction with free flaps (50%) and latissimus dorsi flaps (40%). In addition to its low rate of complications (14%, p < 2 ×10-4), its patient population was younger and with lower comorbidities when compared with the whole cohort. In the other extreme, cluster 7 (1112 patients) almost exclusively featured breast reconstruction with free flaps (94%) and possessed the highest rates of unplanned reoperations, readmissions, and dehiscence (p < 2 ×10-4). The reoperation profile of cluster 3 was also significantly different from the general cohort and featured lower proportions of vascular repair procedures (p < 8 ×10-4).

CONCLUSIONS

This study presents a novel, generalizable application of an unsupervised learning model to organize patient subgroups with associations between comorbidities, modality of breast reconstruction, and postoperative outcomes.