BACKGROUND: Breast projection is a critical element of breast reconstruction aesthetics, but little has been published regarding breast projection as the firm expander is changed to a softer implant. Quantitative data representing this loss in projection may enhance patient education and improve our management of patient expectations. METHODS: Female patients who were undergoing immediate tissue-expander breast reconstruction with the senior author were enrolled in this prospective study. Three-dimensional camera software was used for all patient photographs and data analysis. Projection was calculated as the distance between the chest wall and the point of maximal projection of the breast form. Values were calculated for final tissue expander expansion and at varying intervals 3, 6, and 12 months after implant placement. RESULTS: Fourteen breasts from 12 patients were included in the final analysis. Twelve of the 14 breasts had a loss of projection at three months following the implant placement or beyond. The percentage of projection lost in these 12 breasts ranged from 6.30% to 43.4%, with an average loss of projection of 21.05%. CONCLUSIONS: This study is the first prospective quantitative analysis of temporal changes in breast projection after expander-implant reconstruction. By prospectively capturing projection data with three-dimensional photographic software, we reveal a loss of projection in this population by three months post-implant exchange. These findings will not only aid in managing patient expectations, but our methodology provides a foundation for future objective studies of the breast form.
Breast Implants ; Breast* ; Esthetics ; Female ; Humans ; Mammaplasty ; Patient Education as Topic ; Prospective Studies* ; Statistics as Topic ; Thoracic Wall ; Tissue Expansion Devices

BACKGROUND: Despite advances in surgical techniques, sterile protocols, and perioperative antibiotic regimens, surgical site infections (SSIs) remain a significant problem. We investigated the relationship between wound classification (i.e., clean, clean/contaminated, contaminated, dirty) and SSI rates in plastic surgery. METHODS: We performed a retrospective review of a multi-institutional, surgical outcomes database for all patients undergoing plastic surgery procedures from 2006-2010. Patient demographics, wound classification, and 30-day outcomes were recorded and analyzed by multivariate logistic regression. RESULTS: A total of 15,289 plastic surgery cases were analyzed. The overall SSI rate was 3.00%, with superficial SSIs occurring at comparable rates across wound classes. There were similar rates of deep SSIs in the clean and clean/contaminated groups (0.64%), while rates reached over 2% in contaminated and dirty cases. Organ/space SSIs occurred in less than 1% of each wound classification. Contaminated and dirty cases were at an increased risk for deep SSIs (odds ratios, 2.81 and 2.74, respectively); however, wound classification did not appear to be a significant predictor of superficial or organ/space SSIs. Clean/contaminated, contaminated, and dirty cases were at increased risk for a postoperative complication, and contaminated and dirty cases also had higher odds of reoperation and 30-day mortality. CONCLUSIONS: Analyzing a multi-center database, we found that wound classification was a significant predictor of overall complications, reoperation, and mortality, but not an adequate predictor of surgical site infections. When comparing infections for a given wound classification, plastic surgery had lower overall rates than the surgical population at large.
Demography ; Humans ; Postoperative Complications ; Reoperation ; Retrospective Studies ; Surgery, Plastic ; Wound Infection

BACKGROUND: Understanding risk factors that increase readmission rates may help enhance patient education and set system-wide expectations. We aimed to provide benchmark data on causes and predictors of readmission following inpatient plastic surgery. METHODS: The 2011 National Surgical Quality Improvement Program dataset was reviewed for patients with both "Plastics" as their recorded surgical specialty and inpatient status. Readmission was tracked through the "Unplanned Readmission" variable. Patient characteristics and outcomes were compared using chi-squared analysis and Student's t-tests for categorical and continuous variables, respectively. Multivariate regression analysis was used for identifying predictors of readmission. RESULTS: A total of 3,671 inpatient plastic surgery patients were included. The unplanned readmission rate was 7.11%. Multivariate regression analysis revealed a history of chronic obstructive pulmonary disease (COPD) (odds ratio [OR], 2.01; confidence interval [CI], 1.12-3.60; P=0.020), previous percutaneous coronary intervention (PCI) (OR, 2.69; CI, 1.21-5.97; P=0.015), hypertension requiring medication (OR, 1.65; CI, 1.22-2.24; P<0.001), bleeding disorders (OR, 1.70; CI, 1.01-2.87; P=0.046), American Society of Anesthesiologists (ASA) class 3 or 4 (OR, 1.57; CI, 1.15-2.15; P=0.004), and obesity (body mass index > or =30) (OR, 1.43; CI, 1.09-1.88, P=0.011) to be significant predictors of readmission. CONCLUSIONS: Inpatient plastic surgery has an associated 7.11% unplanned readmission rate. History of COPD, previous PCI, hypertension, ASA class 3 or 4, bleeding disorders, and obesity all proved to be significant risk factors for readmission. These findings will help to benchmark inpatient readmission rates and manage patient and hospital system expectations.
Dataset ; Hemorrhage ; Humans ; Hypertension ; Inpatients* ; Obesity ; Patient Education as Topic ; Patient Readmission ; Percutaneous Coronary Intervention ; Pulmonary Disease, Chronic Obstructive ; Quality Improvement ; Risk Factors ; Surgery, Plastic* ; Track and Field