Monitorização do sistema nervoso autônomo em ambiente de cuidados intensivos como ferramenta de prognóstico. Revisão sistemática / Autonomic nervous system monitoring in intensive care as a prognostic tool. Systematic review

RESUMO Objetivo: Apresentar uma revisão sistemática do uso da monitorização do sistema nervoso autônomo como ferramenta de prognóstico, verificando a variabilidade da frequência cardíaca nas unidades de cuidados intensivos. Métodos: Revisão de literatura publicada até julho de 2016 na PubMed/MEDLINE de estudos realizados em unidades de cuidados intensivos, sobre a monitorização do sistema nervoso autônomo, por meio da análise da variabilidade da frequência cardíaca, como ferramenta de prognóstico - estudo da mortalidade. Foram utilizados os seguintes termos em inglês no campo de pesquisa: ("autonomic nervous system" OR "heart rate variability") AND ("intensive care" OR "critical care" OR "emergency care" OR "ICU") AND ("prognosis" OR "prognoses" OR "mortality"). Resultados: A probabilidade de morte nos doentes aumentou com a diminuição da variabilidade da frequência cardíaca, estudada por meio da variância da frequência cardíaca, desacoplamento cardíaco, volatilidade da frequência cardíaca, integer heart rate variability, desvio padrão de todos os intervalos RR normais, raiz quadrada da média do quadrado das diferenças entre intervalos RR adjacentes, poder total, componente de baixa frequência, componente de muito baixa frequência, razão entre o componente de baixa frequência e o componente de alta frequência), razão entre expoentes fractais de curto e longo prazo, entropia de Shannon, entropia multiescalar e entropia aproximada. Conclusão: Nos doentes internados em unidades de cuidados intensivos, independentemente da patologia que motivou o internamento, a variabilidade da frequência cardíaca varia de forma inversa com a gravidade clínica e com o prognóstico.

ABSTRACT Objective: To present a systematic review of the use of autonomic nervous system monitoring as a prognostic tool in intensive care units by assessing heart rate variability. Methods: Literature review of studies published until July 2016 listed in PubMed/Medline and conducted in intensive care units, on autonomic nervous system monitoring, via analysis of heart rate variability as a prognostic tool (mortality study). The following English terms were entered in the search field: ("autonomic nervous system" OR "heart rate variability") AND ("intensive care" OR "critical care" OR "emergency care" OR "ICU") AND ("prognosis" OR "prognoses" OR "mortality"). Results: There was an increased likelihood of death in patients who had a decrease in heart rate variability as analyzed via heart rate variance, cardiac uncoupling, heart rate volatility, integer heart rate variability, standard deviation of NN intervals, root mean square of successive differences, total power, low frequency, very low frequency, low frequency/high frequency ratio, ratio of short-term to long-term fractal exponents, Shannon entropy, multiscale entropy and approximate entropy. Conclusion: In patients admitted to intensive care units, regardless of the pathology, heart rate variability varies inversely with clinical severity and prognosis.

Reticular pattern detection in dermoscopy: an approach using Curvelet Transform

Abstract Introduction Dermoscopy is a non-invasive in vivo imaging technique, used in dermatology in feature identification, among pigmented melanocytic neoplasms, from suspicious skin lesions. Often, in the skin exam is possible to ascertain markers, whose identification and proper characterization is difficult, even when it is used a magnifying lens and a source of light. Dermoscopic images are thus a challenging source of a wide range of digital features, frequently with clinical correlation. Among these markers, one of particular interest to diagnosis in skin evaluation is the reticular pattern. Methods This paper presents a novel approach (avoiding pre-processing, e.g. segmentation and filtering) for reticular pattern detection in dermoscopic images, using texture spectral analysis. The proposed methodology involves a Curvelet Transform procedure to identify features. Results Feature extraction is applied to identify a set of discriminant characteristics in the reticular pattern, and it is also employed in the automatic classification task. The results obtained are encouraging, presenting Sensitivity and Specificity of 82.35% and 76.79%, respectively. Conclusions These results highlight the use of automatic classification, in the context of artificial intelligence, within a computer-aided diagnosis strategy, as a strong tool to help the human decision making task in clinical practice. Moreover, the results were obtained using images from three different sources, without previous lesion segmentation, achieving to a rapid, robust and low complexity methodology. These properties boost the presented approach to be easily used in clinical practice as an aid to the diagnostic process.

On the geometric modulation of skin lesion growth: a mathematical model for melanoma

Abstract Introduction Early detection of suspicious skin lesions is critical to prevent skin malignancies, particularly the melanoma, which is the most dangerous form of human skin cancer. In the last decade, image processing techniques have been an increasingly important tool for early detection and mathematical models play a relevant role in mapping the progression of lesions. Methods This work presents an algorithm to describe the evolution of the border of the skin lesion based on two main measurable markers: the symmetry and the geometric growth path of the lesion. The proposed methodology involves two dermoscopic images of the same melanocytic lesion obtained at different moments in time. By applying a mathematical model based on planar linear transformations, measurable parameters related to symmetry and growth are extracted. Results With this information one may compare the actual evolution in the lesion with the outcomes from the geometric model. First, this method was tested on predefined images whose growth was controlled and the symmetry known which were used for validation. Then the methodology was tested in real dermoscopic melanoma images in which the parameters of the mathematical model revealed symmetry and growth rates consistent with a typical melanoma behavior. Conclusions The method developed proved to show very accurate information about the target growth markers (variation on the growth along the border, the deformation and the symmetry of the lesion trough the time). All the results, validated by the expected phantom outputs, were similar to the ones on the real images.