Children with severe burns display no sex differences in exercise capacity at hospital discharge or adaptation after exercise rehabilitation training

Rivas, Eric; Herndon, David N.; Chapa, Martha; Cambiaso‐Daniel, Janos; Rontoyanni, Victoria G.; Gutierrez, Ileana L.; Sanchez, Kevin; Glover, Shauna; Suman, Oscar E.

doi:10.1016/j.burns.2018.01.014

Cited by 6 publications

(7 citation statements)

References 32 publications

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“…Many research reports [3][4][5] have lamented a worrisome situation in poor countries where high cases (up to 90%) of burns injuries are recorded with high percentage rate of mortality and was attributed to lack of social awareness programs related to prevention and safety measures, while most of the cost-effective and safety measures are practically implemented in high income countries [1]. In general, studies [6,7] have shown that about 265,000 deaths are reported every year, as such burns have become eminent health concern globally.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Abubakar

Ajuji

Yahya

2020

ASI

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While visual assessment is the standard technique for burn evaluation, computer-aided diagnosis is increasingly sought due to high number of incidences globally. Patients are increasingly facing challenges which are not limited to shortage of experienced clinicians, lack of accessibility to healthcare facilities and high diagnostic cost. Certain number of studies were proposed in discriminating burn and healthy skin using machine learning leaving a huge and important gap unaddressed; whether burns and related skin injuries can be effectively discriminated using machine learning techniques. Therefore, we specifically use transfer learning by leveraging pre-trained deep learning models due to deficient dataset in this paper, to discriminate two classes of skin injuries—burnt skin and injured skin. Experiments were extensively conducted using three state-of-the-art pre-trained deep learning models that includes ResNet50, ResNet101 and ResNet152 for image patterns extraction via two transfer learning strategies—fine-tuning approach where dense and classification layers were modified and trained with features extracted by base layers and in the second approach support vector machine (SVM) was used to replace top-layers of the pre-trained models, trained using off-the-shelf features from the base layers. Our proposed approach records near perfect classification accuracy in categorizing burnt skin ad injured skin of approximately 99.9%.

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Section: Introductionmentioning

confidence: 99%

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Abubakar

Ajuji

Yahya

2020

ASI

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“…The negative situation in those countries was attributed to lack of social awareness programs related to prevention and safety measures, while most of the cost-effective and safety measures are practically implemented in high income countries [5]. In general, studies [6,7] have shown that about 265,000 deaths are reported every year, as such burns have become eminent health concern globally. Assessment of burn injury clinically by experience personnel via visual observation is very subjective and totally dependent on the experience of the personnel with recorded high accuracy of approximately 75% [8], in addition of been painful due to invasive nature (blanching).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Abubakar¹,

Ajuji²,

Yahya³

2020

Preprint

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While visual assessment is the standard technique for burn evaluation, computer-aided diagnosis is increasingly sought due to high number of incidences globally. Patients are increasingly facing challenges which are not limited to shortage of experienced clinicians, lack of accessibility to healthcare facilities, and high diagnostic cost. Certain number of studies were proposed in discriminating burn and healthy skin using machine learning leaving a huge and important gap unaddressed; whether burns and related skin injuries can be effectively discriminated using machine learning techniques. Therefore, we specifically use pre-trained deep learning models due to deficient dataset to train a new model from scratch. Experiments were extensively conducted using three stateof-the-art pre-trained deep learning models that includes ResNet50, ResNet101 and ResNet152 for image patterns extraction via two transfer learning strategies: fine-tuning approach where dense and classification layers were modified and trained with features extracted by base layers, and in the second approach support vector machine (SVM) was used to replace top-layers of the pre-trained models, trained using off-the-shelf features from the base layers. Our proposed approach records near perfect classification accuracy of approximately 99.9%.

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“…Prior assessment of exercise capacity with the Steep Ramp Test (SRT) predicted exercise capacity 6 months after discharge, and can therefore be used to identify "at risk" patients at an early stage. Until now, all longitudinal studies on exercise capacity during the first year after pediatric burns were performed by the research group of the Shriners Hospital for Children in Galveston, U.S.A. [6,[14][15][16][17][18][22][23][24][25][26][27]. It is hard to make a fair comparison between these studies and our findings, as the Galveston patients suffered from more extensive burns compared to our participants (>30% or >40% versus <35% TBSA burned, respectively).…”

Section: Discussionmentioning

confidence: 68%

“…These studies included children and adolescents with extensive burns (>30% or >40% of total body surface area (TBSA)). In this exceptional group of patients, exercise capacity was shown to be affected up to 5 years post-burn, despite significant improvements caused by structured exercise and/or drug interventions [14][15][16]20,21,[24][25][26][27]. Fortunately, such extensive pediatric burns are rare in both the Netherlands and the U.S.A. [28], and this will also be the case in other developed countries.…”

Section: Introductionmentioning

confidence: 90%

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Predictability of exercise capacity following pediatric burns: a preliminary investigation

Akkerman

Mouton

Groot

et al. 2019

Disability and Rehabilitation

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Purpose: Describe the course of exercise capacity in pediatric burn patients during the initial 6 months after hospital discharge, and examine whether its recovery can be predicted from burn characteristics, sociodemographic characteristics, and/or prior assessment. Materials and methods: Exercise capacity was assessed at discharge, and 6 weeks, 3 months, and 6 months after discharge using the Steep Ramp Test (SRT). Results: Twenty-four pediatric patients with burns affecting 0.1-34% of total body surface area were included. At group level, exercise capacity was low at discharge and did not reach healthy reference values within 6 months, despite significant improvement over time. At individual level, the course of exercise capacity varied widely. Six months after discharge, 48% of participants scored more than one standard deviation below healthy age-and sex-specific reference values. SRT outcomes at 6 weeks and 3 months were the best predictors of exercise capacity 6 months after discharge, explaining, respectively, 76% and 93% of variance. Conclusions: Forty-eight percent of participants did not achieve healthy reference values of exercise capacity and were therefore considered "at risk" for diminished functioning. Our preliminary conclusion that early assessment of exercise capacity with the SRT can timely identify those patients, needs to be strengthened by further research. ä IMPLICATIONS FOR REHABILITATIONPediatric burns can be considered as a chronic medical condition because of the lifelong consequences. Exercise capacity is reduced following-even minor -pediatric burns. Recovery patterns vary widely: some pediatric burn patients achieve healthy levels of exercise capacity without specific intervention, while others do not. The Steep Ramp Test can be used to assess exercise capacity, identifying those "at risk" for adverse outcomes at an early stage.Patients "at risk" should be encouraged to play sports and adopt an active lifestyle.

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Children with severe burns display no sex differences in exercise capacity at hospital discharge or adaptation after exercise rehabilitation training

Abstract: The burn injury does not have sex-dependent effects on LBM or exercise capacity in severely burn injured children. Differences in relative peak VO and peak HR suggest the need for burn specific exercise programs for improving the efficacy of a rehabilitation program.

Cited by 6 publications

References 32 publications

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Predictability of exercise capacity following pediatric burns: a preliminary investigation

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