Existing Psychiatric Diagnoses Among Breast Cancer Patients Interact with Outcomes After Autologous and Implant-Based Bilateral Breast Reconstruction: A Propensity Score Matched Analysis.

BACKGROUND: Breast reconstruction is an integral postoncologic procedure that has been associated with improved mental health and psychological outcomes. The possible interaction between existing psychiatric diagnoses hospital courses and postoperative complications warrants further exploration. METHODS: Bilateral breast reconstruction patients were identified from the 2016 to 2018 Healthcare Cost and Utilization Project-National Inpatient Sample (HCUP - NIS). Number and type of psychiatric diagnoses within the cohort were then evaluated using a host of ICD-10 codes. A propensity score analysis was applied to control for confounding variables such as demographics, existing comorbidities, and hospital characteristics. A binary logistic regression model was then used to identify the prediction value of psychiatric diagnosis and its interaction with modality of reconstruction for objective outcomes like length of hospital stay, treatment charge, and postoperative complications. RESULTS: A total of 10,114 patients were identified as the final cohort of breast reconstruction patients. 2621 (25.9%) patients possessed an average of 1.4 ± 0.6 existing psychiatric diagnoses. Presence of at least 1 psychiatric diagnosis was a strong predictor alone for extended length of stay (OR: 1.34, 95% CI: 1.28-1.41, P < .001) and occurrence of postoperative complications (OR: 1.31, 95% CI: 1.21-1.41, P < .001). Psychiatric diagnosis displayed a significant interaction with modality of breast reconstruction and conferred a lower increase in risk of extended length of stay in autologous reconstruction when compared to implant-based reconstruction (OR: 0.80, 95% CI: 0.72-0.89, P < .001). CONCLUSION: Existing psychiatric diagnoses were shown to strongly predict and modulate risk of adverse postoperative outcomes depending on modality of reconstruction.

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.

Correlating state-specific and national trends in breast reconstruction after Medicaid expansion: A decade-long update on the Affordable Care Act's impact.

While disparities in access to reconstruction persist, a comprehensive analysis comparing state-based outcomes and national patterns in breast reconstruction as a result of Medicaid expansion has never been examined. In this study, we investigated how breast reconstruction rates changed as a result of Medicaid expansion and compared these state-based findings to national counterparts. Patient data from the Healthcare Cost and Utilization Project among states that chose to expand Medicaid were compared with those from states that did not expand. The difference-in-differences estimate of expansion to nonexpansion states was 7.05 (p = 0.10) for implant-based reconstruction, -11.56 (p = 0.01) for autologous reconstruction, and -7.08 (p = 0.18) for overall reconstruction. Comparing rates of nonexpansion states to national trends yielded estimates of -0.06 (p = 0.04), 0.06 (p = 0.01), and 0.004 (p = 0.90) for implant-based, autologous, and overall breast reconstruction, respectively. Similarly, comparing rates of expansion states to national trends yielded estimates of 0.02 (p = 0.38), -0.05 (p = 0.03), and -0.02 (p = 0.44) for implant-based, autologous, and overall breast reconstruction, respectively. In this study on national health policy, Medicaid expansion was associated with a significant increase in autologous rates while state-specific trends alone did not appear to predict the national outcomes of sweeping legislative changes that were differentially applied among states.

Augmenting Breast Implant Research: Accessible Methods for Fabricating Miniature Smooth and Textured Breast Implants in a Laboratory Setting.

BACKGROUND: Because of the association of textured breast implants with breast implant-associated anaplastic large cell lymphoma, anatomically shaped breast implants, which rely on a textured surface to maintain rotational stability, have been recalled from the market. The dearth of anatomically shaped implants on the market reflects a need for novel breast implant technology, which has been traditionally developed by commercial breast implant manufacturers due to the complexities of implant manufacturing. To increase the accessibility of preclinical breast implant research, miniature breast implants made from polydimethylsiloxane were designed and fabricated for high throughput and low-cost prototyping and in vivo testing of both smooth and textured implants in a laboratory setting. METHODS: Two-piece negative molds measuring 2 × 1 cm were constructed in Fusion360 and 3D printed in Polysmooth filament. Textured molds were painted with a mixture of an epoxy and fine sugar or granular salt to create textured surfaces, while molds for smooth implants were smoothed using ethanol spray. Molds were injected with polydimethylsiloxane and cured for 12 hours at 37°C. The surface topography of laboratory-made implants and commercial textured and smooth implant shells was analyzed using scanning electron microscopy and implants were evaluated in vivo in an immunocompetent rodent model. RESULTS: Implants retained the original dome shape of the 3D-printed molds. Qualitative assessment of scanning electron microscopy images demonstrated similar surface topography between laboratory-made and commercial smooth and textured implants. There was no statistical difference in the diameter or density of the surface indentations of the Allergan's textured implant compared with laboratory-made textured implants ( P > 0.05). Finally, the surface topography and thickness of laboratory-made implant capsules were similar to previously published data using industry made miniature silicone devices implanted in rats. CONCLUSIONS: This study demonstrates a low-cost, highly customizable approach to fabricate miniature smooth and textured breast implant prototypes for in vivo studies. The accessibility of this implant fabrication strategy allows nonindustry investigators to develop novel implant designs more rapidly for preclinical investigation.