RESUMO
BACKGROUND: Childhood dyslipidemia is a common condition that can lead to atherosclerotic cardiovascular disease in adulthood. It is usually multifactorial. Screening for cholesterol disorders in children varies based on risk factors, with some guidelines recommending cascade screening for children with a clear family history of familial hypercholesterolemia, targeted screening for those with specific risk factors, and universal screening. Point-of-care testing (POCT) cholesterol tests offer potential advantages, including ease of use, portability, increased patient access, low cost, fewer medical or laboratory visits, and instant results. This study aimed to evaluate the effect of POCT cholesterol screening on the diagnosis of hypercholesterolemia in children in a family practice setting. METHODS: We used a POCT cholesterol analyzer to perform two different (universal and targeted) screening approaches for dyslipidemia in children. We used the NCEP guidelines for the classification of the results. RESULTS: We screened 183 children, 105 in the universal screening group and 78 in the targeted screening group. Eight patients in the targeted screening group had elevated cholesterol levels (p = 0.02). CONCLUSIONS: All participants received instant feedback and recommendations. Using a targeted screening approach, POCT could be a practical and effective tool for identifying at-risk children with hypercholesterolemia.
RESUMO
The optimal extubating moment is still a challenge in clinical practice. Respiratory pattern variability analysis in patients assisted through mechanical ventilation to identify this optimal moment could contribute to this process. This work proposes the analysis of this variability using several time series obtained from the respiratory flow and electrocardiogram signals, applying techniques based on artificial intelligence. 154 patients undergoing the extubating process were classified in three groups: successful group, patients who failed during weaning process, and patients who after extubating failed before 48 hours and need to reintubated. Power Spectral Density and time-frequency domain analysis were applied, computing Discrete Wavelet Transform. A new Q index was proposed to determine the most relevant parameters and the best decomposition level to discriminate between groups. Forward selection and bidirectional techniques were implemented to reduce dimensionality. Linear Discriminant Analysis and Neural Networks methods were implemented to classify these patients. The best results in terms of accuracy were, 84.61 ± 3.1% for successful versus failure groups, 86.90 ± 1.0% for successful versus reintubated groups, and 91.62 ± 4.9% comparing the failure and reintubated groups. Parameters related to Q index and Neural Networks classification presented the best performance in the classification of these patients.
Assuntos
Inteligência Artificial , Desmame do Respirador , Humanos , Desmame do Respirador/métodos , Respiração Artificial , Redes Neurais de Computação , Análise de OndaletasRESUMO
The determination of the optimal time of the patients in weaning trial process from mechanical ventilation, between patients capable of maintaining spontaneous breathing and patients that fail to maintain spontaneous breathing, is a very important task in intensive care unit. Wavelet Transform (WT) and Neural Networks (NN) techniques were applied in order to develop a classifier for the study of patients on weaning trial process. The respiratory pattern of each patient was characterized through different time series. Genetic Algorithms (GA) and Forward Selection were used as feature selection techniques. A classification performance of 77.00±0.06% of well classified patients, was obtained using a NN and GA combination, with only 6 variables of the 14 initials.