Use of topical doxepin for the treatment of burn scar pruritus in adults: An inaccessible therapeutic alternative?

Burns ◽  
2020 ◽  
Vol 46 (1) ◽  
pp. 246-247
Author(s):  
P. Queruel ◽  
M.-N. Bartholomei ◽  
V. Chauvineau-Mortelette ◽  
E. Cabaret ◽  
J.-L. Bartoli ◽  
...  
1997 ◽  
Vol 59 (5) ◽  
pp. 713-715
Author(s):  
Izuho TAKEUCHI ◽  
Shoko NAGATA ◽  
Seita FUKUMARU ◽  
Keiko YAMAGUCHI ◽  
Ryo GUSHI ◽  
...  

1997 ◽  
Vol 59 (1) ◽  
pp. 45-47 ◽  
Author(s):  
Taiji KAWAKAMI ◽  
Osamu YAMAMOTO ◽  
Hiroshi YASUDA ◽  
Ysohinori SUENAGA ◽  
Masakazu ASAHI
Keyword(s):  

2010 ◽  
Vol 72 (3) ◽  
pp. 213-215
Author(s):  
Asuka YOSHIFUKU ◽  
Yuko HIGASHI ◽  
Hiroshi UCHIMIYA ◽  
Hiroshi SARUWATARI ◽  
Kentaro YONEKURA ◽  
...  

2021 ◽  
Vol 13 (10) ◽  
pp. 1966
Author(s):  
Christopher W Smith ◽  
Santosh K Panda ◽  
Uma S Bhatt ◽  
Franz J Meyer ◽  
Anushree Badola ◽  
...  

In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent.


2021 ◽  
Vol 42 (Supplement_1) ◽  
pp. S26-S27
Author(s):  
Rajiv Sood

Abstract Introduction Hypertrophic scarring after burn injury can be extremely painful, cause profound itching, and affect the way patients view themselves and how the outside world perceives them. We have utilized laser therapy as a modality for scar modulation for our patients since 2013. In 2014, we initiated and completed a prospective IRB approved study to evaluate the outcome of scars treated with fractional CO2 laser therapy (FLT) utilizing objective and subjective tools. Recently, we have completed a prospective study evaluating the use of pulse dye laser (PDL) therapy and the impact on post-burn pruritis. In reviewing the outcomes from these two studies, we have developed an evidence-based laser therapy algorithm for burn scar management. Methods The FLT study entailed a series of three CO2 laser treatments minimally 4–6 weeks apart with scar measurements and POSAS form completion performed prior to each laser treatment and four weeks after the last FLT. Scar measurements that included color, pliability, and scar thickness; and completion of the POSAS form were obtained prior to each laser therapy session and four weeks after the third laser treatment. The measurements of color, pliability, and scar thickness were measured with the Colorimeter, Cutometer, and ultrasound. The PDL study utilized the 5-D Itch scale to evaluate post-burn pruritis. A baseline measurement was obtained prior to any laser treatments. Each patient underwent two PDL sessions and a 5-D itch scale was completed four to six weeks after the second PDL session. The baseline measurement was then compared to the final 5-D itch scale measurement. Results Data from the FLT study is in Table 1 and shows that there were statistically significant improvements in the Patient and Observer POSAS scores, patient rated Itch score, scar thickness, and measured skin density. Changes to patient rated scar pain, scar color, and pliability were noted but were not of statistical significance. Data from the PDL study is in Table 2 and shows a statistically significant decrease in the treated patients’ post-burn pruritis. Conclusions In reviewing the outcomes of these two studies, we have developed an algorithm based on our studies. All of our patients undergoing laser therapy receive two PDL sessions that are four to six weeks apart followed by 3 FLT sessions. The use of both PDL and FLT decreases post-burn pruritis, decreases scar thickness, decreases pain, and increases patient satisfaction as shown in our research.


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