Modification and Assessment of the Bedside Pediatric Early Warning Score in the Pediatric Allogeneic Hematopoietic Cell Transplant Population

OBJECTIVES: To determine the validity of the Bedside Pediatric Early Warning Score system in the hematopoietic cell transplant population, and to determine if the addition of weight gain further strengthens the association with need for PICU admission. DESIGN: Retrospective cohort study of pediatric allogeneic hematopoietic cell transplant patients from 2009 to 2016. Daily Pediatric Early Warning Score and weights were collected during hospitalization. Logistic regression was used to identify associations between maximum Pediatric Early Warning Score or Pediatric Early Warning Score plus weight gain and the need for PICU intervention. The primary outcome was need for PICU intervention; secondary outcomes included mortality and intubation. SETTING: A large quaternary free-standing children's hospital. PATIENTS: One-hundred two pediatric allogeneic hematopoietic cell transplant recipients. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of the 102 hematopoietic cell transplant patients included in the study, 29 were admitted to the PICU. The median peak Pediatric Early Warning Score was 11 (interquartile range, 8-13) in the PICU admission cohort, compared with 4 (interquartile range, 3-5) in the cohort without a PICU admission (p < 0.0001). Pediatric Early Warning Score greater than or equal to 8 had a sensitivity of 76% and a specificity of 90%. The area under the receiver operating characteristics curve was 0.83. There was a high negative predictive value at this Pediatric Early Warning Score of 90%. When Pediatric Early Warning Score greater than or equal to 8 and weight gain greater than or equal to 7% were compared together, the area under the receiver operating characteristic curve increased to 0.88. CONCLUSIONS: In this study, a Pediatric Early Warning Score greater than or equal to 8 was associated with PICU admission, having a moderately high sensitivity and high specificity. This study adds to literature supporting Pediatric Early Warning Score monitoring for hematopoietic cell transplant patients. Combining weight gain with Pediatric Early Warning Score improved the discriminative ability of the model to predict the need for critical care, suggesting that incorporation of weight gain into Pediatric Early Warning Score may be beneficial for monitoring of hematopoietic cell transplant patients

Disparities and guideline adherence in drugs of abuse screening in intracerebral hemorrhage

OBJECTIVE: To characterize the pattern of urine drug screening in a cohort of intracerebral hemorrhage (ICH) patients at our academic centers. METHODS: We identified cases of primary ICH occurring from 2009 to 2011 in our academic centers. Demographic data, imaging characteristics, processes of care, and short-term outcomes were ascertained. We performed logistic regression to identify predictors for screening and evaluated preguideline and postguideline reiteration screening patterns. RESULTS: We identified 610 patients with primary ICH in 2009-2011; 379 (62.1%) were initially evaluated at an outside hospital. Overall, 142/610 (23.3%) patients were screened, with 21 positive for cocaine and 3 for amphetamine. Of patients <55 years of age, only 65/140 (46.4%) were screened. Black patients <55 years of age were screened more than nonblack patients <55 years of age (38/61 [62.3%] vs 27/79 [34.2%]; p = 0.0009). In the best multivariable model, age group (p = 0.0001), black race (p = 0.4529), first Glasgow Coma Scale score (p = 0.0492), current smoking (p < 0.0001), and age group × black race (p = 0.0097) were associated with screening. Guideline reiteration in 2010 did not improve the proportion <55 years of age who were screened: 42/74 (56.8%) were screened before and 23/66 (34.9%) after (p = 0.01). CONCLUSIONS: We found disparities in drugs of abuse (DOA) screening and suboptimal guideline adherence. Systematic efforts to improve screening for DOA are warranted. Improved identification of sympathomimetic exposure may improve etiologic classification and influence decision-making and prognosis counseling

High-Frequency Oscillatory Ventilation Use and Severe Pediatric ARDS in the Pediatric Hematopoietic Cell Transplant Recipient

INTRODUCTION: The effectiveness of high-frequency oscillatory ventilation (HFOV) in the pediatric hematopoietic cell transplant patient has not been established. We sought to identify current practice patterns of HFOV, investigate parameters during HFOV and their association with mortality, and compare the use of HFOV to conventional mechanical ventilation in severe pediatric ARDS. METHODS: This is a retrospective analysis of a multi-center database of pediatric and young adult allogeneic hematopoietic cell transplant subjects requiring invasive mechanical ventilation for critical illness from 2009 through 2014. Twelve United States pediatric centers contributed data. Continuous variables were compared using a Wilcoxon rank-sum test or a Kruskal-Wallis analysis. For categorical variables, univariate analysis with logistic regression was performed. RESULTS: The database contains 222 patients, of which 85 subjects were managed with HFOV. Of this HFOV cohort, the overall pediatric ICU survival was 23.5% (n = 20). HFOV survivors were transitioned to HFOV at a lower oxygenation index than nonsurvivors (25.6, interquartile range 21.1-36.8, vs 37.2, interquartile range 26.5-52.2, P = .046). Survivors were transitioned to HFOV earlier in the course of mechanical ventilation, (day 0 vs day 2, P = .002). No subject survived who was transitioned to HFOV after 1 week of invasive mechanical ventilation. We compared subjects with severe pediatric ARDS treated only with conventional mechanical ventilation versus early HFOV (within 2 d of invasive mechanical ventilation) versus late HFOV. There was a trend toward difference in survival (conventional mechanical ventilation 24%, early HFOV 30%, and late HFOV 9%, P = .08). CONCLUSIONS: In this large database of pediatric allogeneic hematopoietic cell transplant subjects who had acute respiratory failure requiring invasive mechanical ventilation for critical illness with severe pediatric ARDS, early use of HFOV was associated with improved survival compared to late implementation of HFOV, and the subjects had outcomes similar to those treated only with conventional mechanical ventilation

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

A global metagenomic map of urban microbiomes and antimicrobial resistance

We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities.Funding: the Tri-I Program in Computational Biology and Medicine (CBM) funded by NIH grant 1T32GM083937; GitHub; Philip Blood and the Extreme Science and Engineering Discovery Environment (XSEDE), supported by NSF grant number ACI-1548562 and NSF award number ACI-1445606; NASA (NNX14AH50G, NNX17AB26G), the NIH (R01AI151059, R25EB020393, R21AI129851, R35GM138152, U01DA053941); STARR Foundation (I13- 0052); LLS (MCL7001-18, LLS 9238-16, LLS-MCL7001-18); the NSF (1840275); the Bill and Melinda Gates Foundation (OPP1151054); the Alfred P. Sloan Foundation (G-2015-13964); Swiss National Science Foundation grant number 407540_167331; NIH award number UL1TR000457; the US Department of Energy Joint Genome Institute under contract number DE-AC02-05CH11231; the National Energy Research Scientific Computing Center, supported by the Office of Science of the US Department of Energy; Stockholm Health Authority grant SLL 20160933; the Institut Pasteur Korea; an NRF Korea grant (NRF-2014K1A4A7A01074645, 2017M3A9G6068246); the CONICYT Fondecyt Iniciación grants 11140666 and 11160905; Keio University Funds for Individual Research; funds from the Yamagata prefectural government and the city of Tsuruoka; JSPS KAKENHI grant number 20K10436; the bilateral AT-UA collaboration fund (WTZ:UA 02/2019; Ministry of Education and Science of Ukraine, UA:M/84-2019, M/126-2020); Kyiv Academic Univeristy; Ministry of Education and Science of Ukraine project numbers 0118U100290 and 0120U101734; Centro de Excelencia Severo Ochoa 2013–2017; the CERCA Programme / Generalitat de Catalunya; the CRG-Novartis-Africa mobility program 2016; research funds from National Cheng Kung University and the Ministry of Science and Technology; Taiwan (MOST grant number 106-2321-B-006-016); we thank all the volunteers who made sampling NYC possible, Minciencias (project no. 639677758300), CNPq (EDN - 309973/2015-5), the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science – MOE, ECNU, the Research Grants Council of Hong Kong through project 11215017, National Key RD Project of China (2018YFE0201603), and Shanghai Municipal Science and Technology Major Project (2017SHZDZX01) (L.S.