Background Several patient-related factors have been identified with regard to the safety and efficacy of breast reconstructions. Using the largest database available in Europe, the presented study investigated the impact of cigarette smoking on deep inferior epigastric artery perforator (DIEP) free-flap breast reconstructions. Methods In total, 3,926 female patients underwent 4,577 free DIEP-flap breast reconstructions after malignancies in 22 different German breast cancer centers. The cases were divided into two groups: nonsmokers (NS) and smokers (S). Impact of smoking on surgical complications, controlled for covariates, and cluster effects within the cancer centers were analyzed by using generalized linear mixed models. Results Overall, there was no significant difference between the groups of patients regarding the rate of total flap loss. However, the rate of partial flap loss (0.9 vs. 3.2%, p < 0.001) and wound-healing disturbances requiring revision surgery (donor site: 1.5 vs. 4.0; recipient site: 1.3 vs. 3.6%, both p < 0.001) was significantly higher in smokers. Multivariable analysis identified smoking to be an independent risk factor for revision surgery (p = 0.001) and partial flap loss (p < 0.0001). Conclusion Our findings suggest that successful free tissue transfer can be achieved in smokers despite higher rates of partial flap losses and wound-healing disturbances. However, patients with a history of smoking requiring DIEP flap reconstruction should be critically evaluated preoperatively, informed in detail about the higher risk of complications and encouraged to quit smoking prior to surgery. ^* These authors have contributed equally to the preparation of the manuscript.

Background: The Laboratory Risk Indicator for Necrotizing Fasciitis score (LRINEC) is a simple tool used to support early diagnosis of Necrotizing Fasciitis (NF). The aim of this study was to investigate whether the LRINEC is suitable as a progression and prognosis parameter in patients with NF. Methods: In this retrospective study, laboratory data of 70 patients with NF were analyzed. The LRINEC was calculated for every patient at the time of hospital admission and postoperatively after surgical interventions. Furthermore, the LRINEC was examined as a prognostic factor for survival. Results: The overall lethality of our series was 20 out of 70 (28.6%). A highly significant LRINEC decrease was found for serial debridements. The largest decrease was observed after the first debridement. There was a significant difference between the initial LRINEC of deceased and surviving patients. A cut off value of >6.5 (7 LRINEC points) resulted in an optimal constellation of sensitivity (70%) and specificity (60%) to predict lethality in patients with NF. Conclusions: The LRINEC significantly decreases after surgical debridement. An initial LRINEC equal or greater than seven is an independent prognostic marker for lethality and can help to identify high-risk patients.

Background Preoperative ultrasound (US)-guided perforator mapping has immensely simplified perforator flap planning. It may be executed by the microsurgeon. Device settings and selection of ultrasound modes are of utmost significance for detection of low-flow microvessels. The following study evaluates different US modes. Methods A prospective complete data acquisition was performed from July 2018 to June 2019 in a subset of patients who underwent US-guided flap planning. Multifrequency linear transducers were used applying five US modes. Brightness (B)-mode, color flow (CF), power Doppler (PD), pulse wave (PW), and B-flow modes were evaluated regarding applicability by microsurgeons. Peak systolic velocity (PSV), end diastolic velocity (EDV), and resistance index (RI) were chosen to evaluate flow characteristics. US results were correlated to intraoperative findings. Results A total number of eight patients (six males and two females) undergoing anterolateral thigh (ALT) or superficial circumflex iliac artery perforator (SCIP) flap surgery received an extensive standardized US-guided perforator characterization. Qualitative evaluation was performed in B-mode, color-coded duplex sonography (CCDS), PD, and B-flow mode. Quantitative assessment was executed using PW-mode and CCDS measuring the microvessels' diameter (mm) and flow characteristics (PSV, EDV, and RI). CCDS provided a mean diameter of 1.93 mm (range: 1.2–2.8 ± 0.51), a mean systolic peak of 16.9 cm/s (range: 9.9–33.4 ± 7.79), and mean RI of 0.71 (range: 0.55–0.87 ± 0.09) for lower limb perforators. All perforators located with US were verified by intraoperative findings. An optimized, time-effective US mapping algorithm was derived. Qualitative parameters may be evaluated with B-mode, CF, or B-flow. Smallest microvessels may be assessed in PD-mode. Lowering pulse-repetition frequency (PRF)/scale is mandatory to image low-flow microvessels as perforators. Quantitative information may be obtained using PW-mode and the distance-measuring tool in CF-mode. Image and video materials are provided. Conclusion CCDS proved to be a powerful tool for preoperative perforator characterization when using a structured approach and mapping algorithm. Different techniques may be applied for specific visualizations and performed by the microsurgeon.

Background: Reliable, time- and cost-effective, and clinician-friendly diagnostic tools are cornerstones in facial palsy (FP) patient management. Different automated FP grading systems have been developed but revealed persisting downsides such as insufficient accuracy and cost-intensive hardware. We aimed to overcome these barriers and programmed an automated grading system for FP patients utilizing the House and Brackmann scale (HBS). Methods: Image datasets of 86 patients seen at the Department of Plastic, Hand, and Reconstructive Surgery at the University Hospital Regensburg, Germany, between June 2017 and May 2021, were used to train the neural network and evaluate its accuracy. Nine facial poses per patient were analyzed by the algorithm. Results: The algorithm showed an accuracy of 100%. Oversampling did not result in altered outcomes, while the direct form displayed superior accuracy levels when compared to the modular classification form (n = 86; 100% vs. 99%). The Early Fusion technique was linked to improved accuracy outcomes in comparison to the Late Fusion and sequential method (n = 86; 100% vs. 96% vs. 97%). Conclusions: Our automated FP grading system combines high-level accuracy with cost- and time-effectiveness. Our algorithm may accelerate the grading process in FP patients and facilitate the FP surgeon’s workflow.

Background: The grading process in facial palsy (FP) patients is crucial for time- and cost-effective therapy decision-making. The House-Brackmann scale (HBS) represents the most commonly used classification system in FP diagnostics. This study investigated the benefits of linking machine learning (ML) techniques with the HBS. Methods: Image datasets of 51 patients seen at the Department of Plastic, Hand, and Reconstructive Surgery at the University Hospital Regensburg, Germany, between June 2020 and May 2021, were used to build the neural network. A total of nine facial poses per patient were used to automatically determine the HBS. Results: The algorithm had an accuracy of 98%. The algorithm processed the real patient image series (i.e., nine images per patient) in 112 ms. For optimized accuracy, we found 30 training runs to be the most effective training length. Conclusion: We have developed an easy-to-use, time- and cost-efficient algorithm that provides highly accurate automated grading of FP patient images. In combination with our application, the algorithm may facilitate the FP surgeon’s clinical workflow.