Use of Absorbable Dermal Staples in Bilateral Breast Reduction Surgery: Effects on Operative Times, Surgical Outcomes, and Procedure Charges.

BACKGROUND: Use of the absorbable deep dermal stapler in wound closure has become more common in plastic surgery because of its possible reduction in operative times and subsequent decrease in operative room costs. In this study, we examine the effects of this stapler on operative times and postoperative complications in bilateral reduction mammaplasties. METHODS: A retrospective, observational cohort study was conducted via electronic chart review on patients who underwent bilateral reduction mammaplasties. Patients were stratified by wound closure method. One group was closed with sutures only, and in the other group, deep dermal staples were used during closure of the inframammary fold incision. Incidences of patient comorbidities and postoperative complications were compared. In addition, a financial cost analysis was performed. RESULTS: The final patient cohort included 62 patients. Operative time was reduced by an average of 21.8 minutes when using deep dermal staples during closure, compared with when closing solely with sutures (P = 0.032). When controlling for mass of breast tissue removed and type of pedicle, deep dermal staple closure still predicted a reduction of 26.5 (SE, 9.9) minutes in operative time (P = 0.010). Postoperative complications were not affected by wound closure method (odds ratio, 4.36; 95% confidence interval, 0.91-31.7, P = 0.087). Though not statistically significant, financial charge was decreased with usage of deep dermal staples (P = 0.34). CONCLUSIONS: Use of absorbable deep dermal staples produces a significant decrease in operative time for reduction mammaplasties with no increase in postoperative complication rates.

When a Good Flap Turns Bad: A Temporal Predictive Model for Free Flap Complications.

BACKGROUND: Microsurgical cases are complex plastic surgery procedures with a significant risk of acute postoperative complications. In this study, we use a large-scale database to investigate the temporal progression of complications after microsurgical procedures and the risk imparted by acute postoperative complications on subsequent reconstructive outcomes. METHODS: Microsurgery cases were extracted from the National Surgical Quality Improvement Program database by Current Procedural Terminology codes. Postoperative complications were collected for 30 days after surgery and stratified into four temporal periods (postoperative days [PODs] 0-6, 7-13, 14-20, 21-30). Postoperative complication occurrences were incorporated into a weighted multivariate logistic regression model to identify significant predictors of adverse outcomes (p < 0.05). Separately, a regression model was calculated for the time between index operation and reoperation and additional complications. RESULTS: The final cohort comprised 19,517 patients, 6,140 (31.5%) of which experienced at least one complication in the first 30 days after surgery. The occurrence of prior complications in the postoperative period was a significant predictor of future adverse outcomes following the initial week after surgery (p < 0.001). Upon predictive analysis, overall model performance was highest in PODs 7 to 13 (71.1% accuracy and the area under a receiver operating characteristic curve 0.684); 2,578 (13.2%) patients underwent at least one reoperation within the first 2 weeks after surgery. The indication for reoperation (p < 0.001) and number of days since surgery (p = 0.0038) were significant predictors of future complications after reoperation. CONCLUSION: Prior occurrence of complications in an earlier postoperative week, as well as timing and nature of reoperation, were shown to be significant predictors of future complications.

Characterizing differential risk effects of body mass index for Asian patients in the United States undergoing breast reconstruction.

BACKGROUND: Asian patient populations continue to be underrepresented in both plastic surgery research and rates of breast reconstruction. Better elucidation of reconstruction in Asian women may help guide patient-directed counseling. This study investigates the differential effect of body mass index (BMI), a well-known risk factor, for Asian patients in outcomes after breast reconstruction. METHODS: Asian and White breast reconstruction patients were identified by CPT code in the National Surgical Quality Improvement Program. Within each cohort, BMI was converted into percentile ranks for standardized comparisons between cohorts. The effects of BMI on occurrence of complications for Asian and White patients were then quantified with multivariate logistic regression models. RESULTS: The final cohort included 86,514 White patients and 4813 Asian patients, of which 9876 (11%) and 424 (8.8%) experienced at least one postoperative complication. The average BMI of White patients who experienced complications was 29.2 ± 6.3 kg/m2, a higher average than that of Asian patients, 25.6 ± 4.8 kg/m2. Higher BMI percentile was a significant predictor of increased risk of complications in White patients (OR: 1.005, 95% CI: 1.004-1.006, p < 0.001). In Asian patients, however, BMI percentile was not a significant predictor of postoperative complications (OR: 1.001, 95% CI: 0.997-1.005, p = 0.62). BMI percentile significantly predicted risk of unplanned reoperation in both cohorts (p < 0.001 and p = 0.029, respectively). CONCLUSIONS: Whereas BMI is a direct predictor of complications in White populations, this effect is held more inconsistently for Asian patients. Such trends can guide more informed interpretations of BMI in current risk algorithms.

Impact of Sociodemographic and Hospital Factors on Inpatient Bilateral Reduction Mammaplasty: A National Inpatient Sample Analysis.

Background: Although reduction mammaplasty remains a common procedure in plastic surgery, its interaction with sociodemographic and economic disparities has remained relatively uncharacterized on a nationwide scale. Methods: Patients who underwent reduction mammaplasty were identified within the 2016-2018 National Inpatient Sample databases. In addition to clinical comorbidities, sociodemographic characteristics, hospital-level variables, and postoperative outcomes of each patient were collected for analysis. Statistical analyses, including univariate comparison and multivariate logistic regression, were applied to the cohort to determine significant predictors of adverse outcomes, described as extended length of stay, higher financial cost, and postoperative complications. Results: The final patient cohort included 414 patients who underwent inpatient reduction mammaplasty. The average age was 45.2 ±â€…14.5 years. The average length of stay was 1.6 ±â€…1.5 days, and the average hospital charge was $53,873.81 ± $36,014.50. Sixty (14.5%) patients experienced at least one postoperative complication. Black race and treatment within a nonmetropolitan or rural county predicted postoperative complications (P < 0.01). Black race, lower relative income, and concurrent abdominal contouring procedures also predicted occurrence of extended length of stay (P < 0.01). Hospital factors, including larger bed capacity and for-profit ownership, predicted high hospital charges (P < 0.05). Severity of comorbidities, measured by a clinical index, also predicted all three outcomes (P < 0.001). Conclusion: In addition to well-described clinical variables, multiple sociodemographic and economic disparities affect outcomes in inpatient reduction mammaplasty.

Predictors of extended length of stay following outpatient reduction mammaplasty.

PURPOSE: Reduction mammaplasty has transitioned into a largely outpatient procedure in the United States. Following planned outpatient procedures, patients may still be admitted for additional inpatient care, incurring clinical and economic burden. Prior literature has not explored the preoperative and perioperative determinants of extended lengths of stay (LOS) after breast reduction surgery. METHODS: Patients who underwent scheduled outpatient reduction mammaplasty were identified via current procedural terminology code from the 2013 to 2021 National Surgical Quality Improvement Program databases. The primary outcome was extended LOS, defined as an LOS greater than 1 day. The most significant predictor variables were identified through bivariate association, and a binary logistic regression model was used to characterize predictive associations (p < 0.05). RESULTS: In this study, 33,924 patients were included in the final cohort of planned outpatient reduction mammaplasty cases. Among them 325 (1.0%) patients had extended LOS. Concurrent liposuction, body contouring, and increased operative time were the most significant predictors of extended LOS (p < 0.001), followed by older age, higher body mass index, bleeding disorder, history of diabetes, higher American Society of Anesthesiologists class, and White race (p < 0.05). When adjusted for other confounding variables, extended LOS was also a significant predictor of increased risk of postoperative complications after discharge (OR: 1.85, 95% confidence intervals: 1.27-2.69, p = 0.0012). CONCLUSION: Extended LOS after planned outpatient reduction mammaplasty is associated with specific comorbidities, and is a significant predictor of postoperative complications following hospital discharge. DATA AVAILABILITY STATEMENT: The data that support the findings of this study are publicly available.

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.

Systematic under-ascertainment of anaplastic large cell lymphoma cases in cancer registries: report from a comprehensive cancer center.

INTRODUCTION: Breast implant-associated anaplastic large cell lymphoma (ALCL) has been rapidly rising in the US and around the world, leading to a mandated "black-box" label on all silicone- and saline-filled implants by the Food and Drug Administration (FDA). Because regulatory decisions in the US and around the world have been influenced primarily by risk estimates derived from cancer registries, it is important to determine their validity in identifying cases of ALCL. METHOD: We reviewed all cases of ALCL submitted to the New York State Cancer Registry from a large comprehensive cancer center in New York City from 2007 to 2019. To determine the possibility of misdiagnosis or under-diagnosis of ALCL cases reported to cancer registries, we accessed the sensitivity and specificity of the ICD-O-3 codes 9714 (ALCL) and 9702 (Mature T-cell lymphoma, not otherwise specified [T-NOS]) to identify pathologically-proven ALCL. RESULTS: We reviewed 2286,164 pathology reports from 47,466 unique patients with primary cancers. Twenty-eight cases of histologically-proven ALCL were identified. The sensitivity and specificity of the ICD-O-3 code 9714 (ALCL) were 82% and 100%, respectively. The sensitivity of the combined codes 9714/9702 (ALCL/T-NOS) was 96% and the specificity was 44%. CONCLUSION: Previous epidemiological studies that influenced regulatory decisions by the FDA may have systematically underestimated the risk of ALCL by at least 20%. We encourage updated global risk estimates of breast ALCL using methods that ensure adequate case ascertainment.

Inferior altitudinal visual loss and mask-wearing practices: A case series.

Face mask-wearing practices and their impact on the visual field bear particular importance in the coronavirus disease 2019 (COVID-19) pandemic era. This case series examines 10 participants with no history of ocular impairment or visual field defects who underwent age-corrected visual field testing in both eyes with different types of face masks. Wearing duckbill N95 masks was consistently associated with increased accuracy errors in the inferior altitudinal visual field when compared to wearing surgical masks or no masks. These findings support public health guidance that has previously attributed the risks of falls and accidents to face mask wearing.