Multicenter validation of PIM3 and PIM2 in Brazilian pediatric intensive care units

ObjectiveTo validate the PIM3 score in Brazilian PICUs and compare its performance with the PIM2.MethodsObservational, retrospective, multicenter study, including patients younger than 16 years old admitted consecutively from October 2013 to September 2019. We assessed the Standardized Mortality Ratio (SMR), the discrimination capability (using the area under the receiver operating characteristic curve – AUROC), and the calibration. To assess the calibration, we used the calibration belt, which is a curve that represents the correlation of predicted and observed values and their 95% Confidence Interval (CI) through all the risk ranges. We also analyzed the performance of both scores in three periods: 2013–2015, 2015–2017, and 2017–2019.Results41,541 patients from 22 PICUs were included. Most patients aged less than 24 months (58.4%) and were admitted for medical conditions (88.6%) (respiratory conditions = 53.8%). Invasive mechanical ventilation was used in 5.8%. The median PICU length of stay was three days (IQR, 2–5), and the observed mortality was 1.8% (763 deaths). The predicted mortality by PIM3 was 1.8% (SMR 1.00; 95% CI 0.94–1.08) and by PIM2 was 2.1% (SMR 0.90; 95% CI 0.83–0.96). Both scores had good discrimination (PIM3 AUROC = 0.88 and PIM2 AUROC = 0.89). In calibration analysis, both scores overestimated mortality in the 0%–3% risk range, PIM3 tended to underestimate mortality in medium-risk patients (9%–46% risk range), and PIM2 also overestimated mortality in high-risk patients (70%–100% mortality risk).ConclusionsBoth scores had a good discrimination ability but poor calibration in different ranges, which deteriorated over time in the population studied

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

ATLANTIC EPIPHYTES: a data set of vascular and non-vascular epiphyte plants and lichens from the Atlantic Forest

Epiphytes are hyper-diverse and one of the frequently undervalued life forms in plant surveys and biodiversity inventories. Epiphytes of the Atlantic Forest, one of the most endangered ecosystems in the world, have high endemism and radiated recently in the Pliocene. We aimed to (1) compile an extensive Atlantic Forest data set on vascular, non-vascular plants (including hemiepiphytes), and lichen epiphyte species occurrence and abundance; (2) describe the epiphyte distribution in the Atlantic Forest, in order to indicate future sampling efforts. Our work presents the first epiphyte data set with information on abundance and occurrence of epiphyte phorophyte species. All data compiled here come from three main sources provided by the authors: published sources (comprising peer-reviewed articles, books, and theses), unpublished data, and herbarium data. We compiled a data set composed of 2,095 species, from 89,270 holo/hemiepiphyte records, in the Atlantic Forest of Brazil, Argentina, Paraguay, and Uruguay, recorded from 1824 to early 2018. Most of the records were from qualitative data (occurrence only, 88%), well distributed throughout the Atlantic Forest. For quantitative records, the most common sampling method was individual trees (71%), followed by plot sampling (19%), and transect sampling (10%). Angiosperms (81%) were the most frequently registered group, and Bromeliaceae and Orchidaceae were the families with the greatest number of records (27,272 and 21,945, respectively). Ferns and Lycophytes presented fewer records than Angiosperms, and Polypodiaceae were the most recorded family, and more concentrated in the Southern and Southeastern regions. Data on non-vascular plants and lichens were scarce, with a few disjunct records concentrated in the Northeastern region of the Atlantic Forest. For all non-vascular plant records, Lejeuneaceae, a family of liverworts, was the most recorded family. We hope that our effort to organize scattered epiphyte data help advance the knowledge of epiphyte ecology, as well as our understanding of macroecological and biogeographical patterns in the Atlantic Forest. No copyright restrictions are associated with the data set. Please cite this Ecology Data Paper if the data are used in publication and teaching events. © 2019 The Authors. Ecology © 2019 The Ecological Society of Americ

Avaliação do tempo até a recidiva e do tempo até o óbito do carcinoma hepatocelular em pacientes transplantados: uma abordagem de riscos competitivos e modelo multi-estado

A análise de sobrevivência é uma técnica estatística utilizada quando é de interesse conhecer o tempo até a ocorrência de um determinado evento. No Brasil, em 2010, as neoplasias representavam a segunda principal causa de morte e o tumor de fígado destacou-se entre os mais frequentes. O carcinoma hepatocelular (CHC) é o principal tumor primário do fígado. No mundo, o CHC é o sétimo câncer mais comum e ocupa a terceira posição em mortalidade por câncer. A expectativa de vida para quem contrai o CHC é de aproximadamente 6 meses a partir do diagnóstico e, por isso, o transplante é de extrema importância e urgência. Diante disto, o objetivo do presente estudo é avaliar o tempo até a recidiva e o tempo até o óbito em pacientes que foram submetidos a um transplante por CHC. Na análise dos dados comparou-se o método clássico de análise de sobrevivência com o método de riscos competitivos. Além disso, foi estimado um modelo multi-estado para avaliar o risco de transição entre transplante, recidiva e óbito. Foram utilizados os dados do Hospital Federal de Bonsucesso, coletados no período entre janeiro de 2001 e abril de 2012. O referencial teórico mostrou que a análise de riscos competitivos é a mais apropriada para o conjunto dos dados. As variáveis associadas à recidiva foram tipo de doador, nível de alfa-feto proteína, invasão vascular e número de nódulos. Em relação ao óbito, as variáveis associadas foram idade, diferenciação tumoral, tamanho do nódulo e hepatite C

Modelo exponencial por partes para dados de sobrevivência com longa duração

Exportado OPUSMade available in DSpace on 2019-08-11T15:49:03Z (GMT). No. of bitstreams: 1 dissertacao_final.pdf: 665951 bytes, checksum: b1471cb65258c677625248827bee9888 (MD5) Previous issue date: 22O Modelo Exponencial por Partes (MEP) é um modelo bastante utilizado principalmente em análise de sobrevivência. Ao utilizar esse modelo, uma partição do eixo do tempo em um número finito de intervalos é estabelecida e, em seguida, uma taxa de falha constante é considerada para cada um dos intervalos. Portanto, o MEP aproxima uma função continua, a saber a taxa de falha, através de seguimentos de reta. Por essa razão, o MEP é um modelo bastante flexível, embora este seja um modelo paramétrico, é frequentemente considerado como não paramétrico. O presente trabalho propõe uma abordagem bayesiana dinâmica que permite a obtenção de uma distribuição suavizada exata para os parâmetros representando a taxa de falha. Além disso a partição do eixo do tempo (e, consequentemente, o número de intervalos) será considerada como uma quantidade desconhecida a ser estimada. Toda a abordagem proposta será utilizada para modelar a fração de cura em uma população, o que ocorre quando uma parte dos indivíduos em um estudo _e considerada curada e, portanto, nunca experimentará o evento de interesse. Para que seja possível uma comparação, o caso com grade fixa também será considerado. Por fim, será mostrada uma aplicação a fim de ilustrar os conceitos apresentados.The Piecewise Exponential Model (PEM) is a very utilized model, mainly in survival analysis. When using this model, one considers a partition of the time axis into a finite number of intervals and, after that, a constant failure rate is considered to each interval. Therefore the PEM approximates a continuous function, the failure rate, through line segments. For this reason, the PEM is a very exible model and, although it is a parametric model, it is often considered as non parametric one. The present work proposes a Bayesian dynamic approach that allows one to obtain the exact smoothed distribution for the parameters representing the failure rate. Moreover, the partition of the time grid(and, consequently, the number of intervals), will be considered as an unknown quantity to be estimated. This entire approach will be used to model the cure fraction in a population, which occurs when a part of the individuals in a study is considered cured and, therefore, will never experience the event of interest. For comparison purposes, the fixed time grid will also be considered. Lastly, in order to illustrate this approach, an application will be shown

Quality of child anthropometric data from SISVAN, Brazil, 2008-2017

OBJECTIVE: To evaluate the quality of anthropometric data of children recorded in the Food and Nutrition Surveillance System (SISVAN) from 2008 to 2017. METHOD: Descriptive study on the quality of anthropometric data of children under five years of age admitted in primary care services of the Unified Health System, from the individual databases of SISVAN. Data quality was annually assessed using the indicators: coverage, completeness, sex ratio, age distribution, weight and height digit preference, implausible z-score values, standard deviation, and normality of z-scores. RESULTS: In total, 73,745,023 records and 29,852,480 children were identified. Coverage increased from 17.7% in 2008 to 45.4% in 2017. Completeness of birth date, weight, and height corresponded to almost 100% in all years. The sex ratio was balanced and approximately similar to the expected ratio, ranging from 0.8 to 1. The age distribution revealed higher percentages of registrations from the ages of two to four years until mid-2015. A preference for terminal digits “zero” and “five” was identified among weight and height records. The percentages of implausible z-scores exceeded 1% for all anthropometric indices, with values decreasing from 2014 onwards. A high dispersion of z-scores, including standard deviations between 1.2 and 1.6, was identified mainly in the indices including height and in the records of children under two years of age and residents in the North, Northeast, and Midwest regions. The distribution of z-scores was symmetric for all indices and platykurtic for height/age and weight/age. CONCLUSIONS: The quality of SISVAN anthropometric data for children under five years of age has improved substantially between 2008 and 2017. Some indicators require attention, particularly for height measurements, whose quality was lower especially among groups more vulnerable to nutritional problems.OBJETIVOS: Avaliar a qualidade dos dados antropométricos de crianças registradas no Sistema de Vigilância Alimentar e Nutricional (Sisvan) no período 2008-2017. MÉTODOS: Estudo descritivo sobre a qualidade dos dados antropométricos de crianças menores de 5 anos atendidas nos serviços de atenção primária do Sistema Único de Saúde, a partir das bases de dados individuais do Sisvan. A qualidade dos dados foi avaliada anualmente por meio dos indicadores: cobertura, completude, razão entre sexos, distribuição da idade, preferência por dígitos de peso e estatura, valores de escore-z implausíveis, desvio-padrão e normalidade dos escores-z. RESULTADOS: N o t otal, 7 3.745.023 r egistros e 2 9.852.480 c rianças f oram i dentificados. A cobertura aumentou de 17,7% em 2008 para 45,4% em 2017. A completude da data de nascimento, peso e estatura correspondeu a quase 100% para todos os anos. A razão entre sexos foi equilibrada e aproximadamente similar a razão esperada, variando entre 0,8 e 1. A distribuição da idade revelou maiores percentuais de registros entre as idades de 2 a 4 anos até meados de 2015. Uma preferência pelos dígitos terminais “zero” e “cinco” foi identificada entre os registros de peso e estatura. As porcentagens de escores-z implausíveis excederam 1% para todos os índices antropométricos, com redução dos valores a partir de 2014. Uma alta dispersão dos escores-z, incluindo desvios-padrão entre 1,2 e 1,6, foi identificada principalmente nos índices incluindo estatura e nos registros de crianças menores de 2 anos e residentes das regiões Norte, Nordeste e Centro-Oeste. A distribuição dos escores-z foi simétrica para todos os índices e platicúrtica para estatura/idade e peso/idade. CONCLUSÕES: A qualidade dos dados antropométricos do Sisvan para crianças menores de 5 anos melhorou substancialmente entre 2008 e 2017. Alguns indicadores requerem atenção, sobretudo para medidas de estatura, cuja qualidade foi principalmente inferior entre os grupos mais vulneráveis a agravos nutricionais

Identifying biologically implausible values in big longitudinal data: an example applied to child growth data from the Brazilian food and nutrition surveillance system

Abstract Background Several strategies for identifying biologically implausible values in longitudinal anthropometric data have recently been proposed, but the suitability of these strategies for large population datasets needs to be better understood. This study evaluated the impact of removing population outliers and the additional value of identifying and removing longitudinal outliers on the trajectories of length/height and weight and on the prevalence of child growth indicators in a large longitudinal dataset of child growth data. Methods Length/height and weight measurements of children aged 0 to 59 months from the Brazilian Food and Nutrition Surveillance System were analyzed. Population outliers were identified using z-scores from the World Health Organization (WHO) growth charts. After identifying and removing population outliers, residuals from linear mixed-effects models were used to flag longitudinal outliers. The following cutoffs for residuals were tested to flag those: -3/+3, -4/+4, -5/+5, -6/+6. The selected child growth indicators included length/height-for-age z-scores and weight-for-age z-scores, classified according to the WHO charts. Results The dataset included 50,154,738 records from 10,775,496 children. Boys and girls had 5.74% and 5.31% of length/height and 5.19% and 4.74% of weight values flagged as population outliers, respectively. After removing those, the percentage of longitudinal outliers varied from 0.02% (+6) to 1.47% (+3) for length/height and from 0.07 to 1.44% for weight in boys. In girls, the percentage of longitudinal outliers varied from 0.01 to 1.50% for length/height and from 0.08 to 1.45% for weight. The initial removal of population outliers played the most substantial role in the growth trajectories as it was the first step in the cleaning process, while the additional removal of longitudinal outliers had lower influence on those, regardless of the cutoff adopted. The prevalence of the selected indicators were also affected by both population and longitudinal (to a lesser extent) outliers. Conclusions Although both population and longitudinal outliers can detect biologically implausible values in child growth data, removing population outliers seemed more relevant in this large administrative dataset, especially in calculating summary statistics. However, both types of outliers need to be identified and removed for the proper evaluation of trajectories