Publications on digital amputation and replantation have been mostly derived from case series in high-volume hand surgery practices, and epidemiological studies are few. This study used a population-based dataset to illustrate the incidence of digital amputation, patient and hospital characteristics, and their relationships with replantation. A claim for reimbursement dataset (2008) was provided as a research database by the Bureau of National Health Insurance, Taiwan. Patients with ICD-9-CM coded as digital amputation (885 and 886) were included. These were cross-referenced with procedure codes for replantation procedures (84.21 and 84.22). We defined the patients who underwent thumb replantation (84.21) and thumb amputation (84.01) during a single hospitalization as replantation failure. Patient and hospital characteristics were studied with statistical analysis. In total, 2358 patients with digital amputation were admitted (1859 male, 499 female), mean age 39.2 ± 15.5 years. The incidence was 10.2/100,000 person-years. The highest incidence was 14.7/100,000 person-years in the age group 45-54 years. Machinery and powered hand tools caused 68.8% of digital amputations. Thumb amputation [odds ratio (OR): 1.35, p = 0.01], private hospital (OR: 1.40, p = 0.01), medical center (OR: 2.38, p < 0.001), regional hospital (OR: 2.41, p < 0.001) and hospitals with an annual volume >20 digital amputations (OR: 4.23, p < 0.001) were associated with higher attempt rates for replantation. Elderly patients (age >65 years) had higher risk of thumb replantation failure (OR: 32.30, p = 0.045), while hospitals with >20 annual replantations had lower risk (OR: 0.11, p = 0.02). Our study of the National Health Insurance database characterized the epidemiology of digital amputation patients undergoing replantation and the facilities in Taiwan where these procedures are performed. The hospitals treating more digital amputation patients had higher attempt rates and lower thumb failure rates.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Accurate assessment of the percentage of total body surface area (%TBSA) of burn wounds is crucial in the management of burn patients. The resuscitation fluid and nutritional needs of burn patients, their need for intensive unit care, and probability of mortality are all directly related to %TBSA. It is difficult to estimate a burn area of irregular shape by inspection. Many articles have reported discrepancy in estimating %TBSA by different doctors. Objective: We propose a method, based on deep learning, for burn wound detection, segmentation and calculation of % TBSA on a pixel-to-pixel basis. Methods: A two-step procedure was used to convert burn wound diagnosis into %TBSA. In the first step, images of burn wounds were collected and labeled by burn surgeons and the dataset was then input into two deep learning architectures, U-Net and Mask R-CNN, each configured with two different backbones, to segment the burn wounds. In the second step, we collected and labeled images of hands to create another dataset, which was also input into U-Net and Mask R-CNN to segment the hands. The percentage of TBSA of the burn wounds was then calculated by comparing the pixels of mask areas on the images of the burn wound and hand of the same patient according to the rule of hand, which says that one’s hand accounts for 0.8% of TBSA. Results: A total of 2591 images of burn wounds were collected and labeled to form the burn-wound dataset. The dataset was randomly split into a ratio of 8:1:1 to form the training, validation, and testing sets. Four hundred images of volar hands were collected and labeled to form the hand dataset, which was also split into three sets using the same method. For the images of burn wounds, Mask R-CNN with ResNet101 had the best segmentation result with a Dice coefficient (DC) of 0.9496, while U-Net with ResNet101 had a DC of 0.8545. For the hand images, U-Net and Mask R-CNN had similar performance with a DC of 0.9920 and 0.9910, respectively. Lastly, we conducted a test diagnosis in a burn patient. Mask R-CNN with ResNet-101 had on average less deviation (0.115% TBSA) from the ground truth than burn surgeons. Conclusions: This is one of the first studies to diagnose all depths of burn wounds and convert the segmentation results into %TBSA using different deep learning models. We aimed to assist medical staff in estimating burn size more accurately and thereby helping to provide precise care to burn victims.

Significance: With the increasing diabetic population worldwide, diabetic foot ulcers are a significant concern. This study aimed to compare the efficacy of skin substitutes, biomaterials, and topical agents with standard care. Recent Advances: A meta-analysis was conducted using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed, EMBASE, and Web of Science were searched using the following keywords: diabetes mellitus AND skin graft OR tissue replacement OR dressing OR drug. Two independent reviewers performed data collection and quality assessment of the eligible studies. The primary outcome was the 12–16-week healing rates and the secondary outcome was recurrence rates. Critical Issues: Thirty-eight randomized controlled trials, including 3862 patients, were analyzed. The studies exhibited low heterogeneity (τ2 = 0.10) without significant asymmetry (Egger’s test, p = 0.8852). After pooling direct and indirect estimates, placenta-based tissue products exhibited the best wound healing probability (P-score = 0.90), followed by skin substitutes with living cells (P-score = 0.70), acellular skin substitutes (P-score = 0.56), and advanced topical dressings (P-score = 0.34) compared with standard of care. The recurrence analysis showed significant improvement in the intervention group compared with the control group (11.21% vs. 15.15%). Future Directions: This network meta-analysis provides the relative effectiveness and rank of biomaterials and topical dressings in diabetic foot ulcer healing. The results could help clinical decision making. Keywords: Diabetic Foot Ulcer, Skin Substitute, Biological Dressing, Wound Healing

The aim of this study was to investigate the treatment of complicated keloids with helical tomotherapy (HT) and electron beam radiotherapy. From July 2018 to September 2018, 11 patients with 23 keloid lesions treated with HT were enrolled. Additionally, 11 patients with 20 lesions treated with electron beam radiotherapy in the same period were enrolled. Patients in both groups were treated within 24 h after surgical excision of the keloid lesion with 13.5 Gy in three consecutive daily fractions. The median follow-up period was 15 months. The local control rate was 91.3% and 80% in the HT group and the electron beam group, respectively. No acute adverse effects were observed in either group, but most patients exhibited pigmentation. No radiation-induced cancer occurred in these patients up to the time of this report. Pain and pruritus improved for all patients and more obviously for three patients with complicated keloids treated with HT. The measured surface dose was 103.7–112.5% and 92.8–97.6% of the prescribed dose in the HT group and the electron beam group, respectively. HT can be considered an alternative in cases where it is not feasible to use multiple electron fields, due to encouraging clinical outcomes.

Skin and soft tissue reconstruction has long been based on the reconstructive ladder. However, a skin substitute has become popular due to its predictable outcomes, without donor-site morbidity. The biodegradable temporizing matrix (BTM; NovoSorb, PolyNovo Ltd., Port Melbourne, Australia) is a synthetic skin substitute that has recently gained its clinical application. Compared with those of other dermal templates, the clinical efficacy and performance of the BTM are not well established, especially among the Asian population. This study aims to share our experience and strategy of using BTM in various wound conditions. The data of patients who underwent skin and soft tissue reconstruction with BTM at a single institution between January 2022 and December 2023 were reviewed. The patient demographics, wound characteristics, surgical details, secondary procedures, and complications were recorded and analyzed. Postoperative 6-month photographs were collected and independently evaluated by two plastic surgeons and two wound care center nurses using the Manchester Scar Scale (MSS). This study included 37 patients, consisting of 22 males and 15 females with a mean age of 51.8 years (range, 18–86 years old). The wound etiologies included trauma (67.6%), necrotizing soft tissue infection (16.2%), burns (10.8%), toe gangrene (2.7%), and scar excision (2.7%). The average wound area covered by BTM was 50.6 ± 47.6 cm². Among the patients, eight received concomitant flap surgery and BTM implantation, 20 (54.1%) underwent subsequent split-thickness skin grafts (STSG), and 17 had small wounds (mean: 21.6 cm²) healed by secondary intention. Infection was the most common complication, affecting six patients (n = 6 [16.2%]), five of whom were treated conservatively, and only one required debridement. Thirty-three patients (89.2%) had good BTM take, and only four had BTM failure, requiring further reconstruction. At the last follow-up, 35 out of the 37 patients (94.6%) achieved successful wound closure, and the total MSS score was 10.44 ± 2.94, indicating a satisfactory scar condition. The patients who underwent BTM grafting without STSG had better scar scores than those who received STSG (8.71 ± 2.60 vs. 11.18 ± 2.84, p = 0.039). In conclusion, the BTM is effective and feasible in treating various wounds, with relatively low complication rates, and it can thus be considered as an alternative for skin and soft tissue reconstruction. When combined with adipofasical flap reconstruction, it achieves a more comprehensive anatomical restoration.