Draft genome sequences for Ceratocystis fagacearum, C. harringtonii, Grosmannia penicillata, and Huntiella bhutanensis

Draft genomes for the fungi Ceratocystis fagacearum, C. harringtonii, Grosmannia penicillata, and Huntiella bhutanensis are presented. Ceratocystis fagacearum is a major causal agent of vascular wilt of oaks and other trees in the family Fagaceae. Ceratocystis harringtonii, previously known as C. populicola, causes disease in Populus species in the USA and Canada. Grosmannia penicillata is the causal agent of bluestain of sapwood on various conifers, including Picea spp. and Pinus spp. in Europe. Huntiella bhutanensis is a fungus in Ceratocystidaceae and known only in association with the bark beetle Ips schmutzenhorferi that infests Picea spinulosa in Bhutan. The availability of these genomes will facilitate further studies on these fungi.The Genomics Research Institute (University of Pretoria), the University of Pretoria Research Development Programme, the DST/NRF Center of Excellence in Tree Health Biotechnology (FABI, University of Pretoria), and the National Research Foundation (NRF) (Grant number 87332). Sequencing the genomes of Ceratocystis fagacearum, C. harringtonii, Huntiella bhutanensis was made possible through the Improving Academics Qualifications (IAQ) grant from the NRF.http://www.imafungus.orgam2017Forestry and Agricultural Biotechnology Institute (FABI)GeneticsMicrobiology and Plant Patholog

Draft genomes of Amanita jacksonii, Ceratocystis albifundus, Fusarium circinatum, Huntiella omanensis, Leptographium procerum, Rutstroemia sydowiana, and Sclerotinia echinophila

The genomes of fungi provide an important resource to resolve issues pertaining to their taxonomy, biology, and evolution. The genomes of Amanita jacksonii, Ceratocystis albifundus, a Fusarium circinatum variant, Huntiella omanensis, Leptographium procerum, Sclerotinia echinophila, and Rutstroemia sydowiana are presented in this genome announcement. These seven genomes are from a number of fungal pathogens and economically important species. The genome sizes range from 27 Mb in the case of Ceratocystis albifundus to 51.9 Mb for Rutstroemia sydowiana. The latter also encodes for a predicted 17 350 genes, more than double that of Ceratocystis albifundus. These genomes will add to the growing body of knowledge of these fungi and provide a value resource to researchers studying these fungi.The US Department of Agriculture (USDA) Agricultural Research Service, grants from the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Royal Ontario Museum to J.M.M.; Graduate Scholarships from the Consejo Nacional de Ciencia y Tecnologia (Mexico) and the University of Toronto to SSR; and a Undergraduate Student Research Award from NSERC to M.S. Financial support was provided by members of the Tree Protection Cooperative Program (TPCP), the Department of Science and Technology (DST)/ National Research Foundation (NRF) Centre of Excellence in Tree Health Biotechnology, and the Genomics Research Institute of the University of Pretoria. This project was supported by multiple grants from the NRF, South Africa, including the grant specific unique reference number (UID) 83924.http://www.imafungus.orgam201

Primary versus early secondary referral to a specialized neurotrauma center in patients with moderate/severe traumatic brain injury: a CENTER TBI study

Funder: ZNS - Hannelore Kohl Stiftung; doi: http://dx.doi.org/10.13039/501100007731Funder: Integra LifeSciences CorporationFunder: OneMindAbstract: Background: Prehospital care for patients with traumatic brain injury (TBI) varies with some emergency medical systems recommending direct transport of patients with moderate to severe TBI to hospitals with specialist neurotrauma care (SNCs). The aim of this study is to assess variation in levels of early secondary referral within European SNCs and to compare the outcomes of directly admitted and secondarily transferred patients. Methods: Patients with moderate and severe TBI (Glasgow Coma Scale < 13) from the prospective European CENTER-TBI study were included in this study. All participating hospitals were specialist neuroscience centers. First, adjusted between-country differences were analysed using random effects logistic regression where early secondary referral was the dependent variable, and a random intercept for country was included. Second, the adjusted effect of early secondary referral on survival to hospital discharge and functional outcome [6 months Glasgow Outcome Scale Extended (GOSE)] was estimated using logistic and ordinal mixed effects models, respectively. Results: A total of 1347 moderate/severe TBI patients from 53 SNCs in 18 European countries were included. Of these 1347 patients, 195 (14.5%) were admitted after early secondary referral. Secondarily referred moderate/severe TBI patients presented more often with a CT abnormality: mass lesion (52% vs. 34%), midline shift (54% vs. 36%) and acute subdural hematoma (77% vs. 65%). After adjusting for case-mix, there was a large European variation in early secondary referral, with a median OR of 1.69 between countries. Early secondary referral was not associated with functional outcome (adjusted OR 1.07, 95% CI 0.78–1.69), nor with survival at discharge (1.05, 0.58–1.90). Conclusions: Across Europe, substantial practice variation exists in the proportion of secondarily referred TBI patients at SNCs that is not explained by case mix. Within SNCs early secondary referral does not seem to impact functional outcome and survival after stabilisation in a non-specialised hospital. Future research should identify which patients with TBI truly benefit from direct transportation

The Potential Cost-Effectiveness of a Machine Learning Tool That Can Prevent Untimely Intensive Care Unit Discharge

Objectives: The machine learning prediction model Pacmed Critical (PC), currently under development, may guide intensivists in their decision-making process on the most appropriate time to discharge a patient from the intensive care unit (ICU). Given the financial pressure on healthcare budgets, this study assessed whether PC has the potential to be cost-effective compared with standard care, without the use of PC, for Dutch patients in the ICU from a societal perspective. Methods: A 1-year, 7-state Markov model reflecting the ICU care pathway and incorporating the PC decision tool was developed. A hypothetical cohort of 1000 adult Dutch patients admitted in the ICU was entered in the model. We used the literature, expert opinion, and data from Amsterdam University Medical Center for model parameters. The uncertainty surrounding the incremental cost-effectiveness ratio was assessed using deterministic and probabilistic sensitivity analyses and scenario analyses. Results: PC was a cost-effective strategy with an incremental cost-effectiveness ratio of €18 507 per quality-adjusted life-year. PC remained cost-effective over standard care in multiple scenarios and sensitivity analyses. The likelihood that PC will be cost-effective was 71% at a willingness-to-pay threshold of €30 000 per quality-adjusted life-year. The key driver of the results was the parameter “reduction in ICU length of stay.” Conclusions: We showed that PC has the potential to be cost-effective for Dutch ICUs in a time horizon of 1 year. This study is one of the first cost-effectiveness analyses of a machine learning device. Further research is needed to validate the effectiveness of PC, thereby focusing on the key parameter “reduction in ICU length of stay” and potential spill-over effects

The Potential Cost-Effectiveness of a Machine Learning Tool That Can Prevent Untimely Intensive Care Unit Discharge

Objectives: The machine learning prediction model Pacmed Critical (PC), currently under development, may guide intensivists in their decision-making process on the most appropriate time to discharge a patient from the intensive care unit (ICU). Given the financial pressure on healthcare budgets, this study assessed whether PC has the potential to be cost-effective compared with standard care, without the use of PC, for Dutch patients in the ICU from a societal perspective. Methods: A 1-year, 7-state Markov model reflecting the ICU care pathway and incorporating the PC decision tool was developed. A hypothetical cohort of 1000 adult Dutch patients admitted in the ICU was entered in the model. We used the literature, expert opinion, and data from Amsterdam University Medical Center for model parameters. The uncertainty surrounding the incremental cost-effectiveness ratio was assessed using deterministic and probabilistic sensitivity analyses and scenario analyses. Results: PC was a cost-effective strategy with an incremental cost-effectiveness ratio of €18 507 per quality-adjusted life-year. PC remained cost-effective over standard care in multiple scenarios and sensitivity analyses. The likelihood that PC will be cost-effective was 71% at a willingness-to-pay threshold of €30 000 per quality-adjusted life-year. The key driver of the results was the parameter “reduction in ICU length of stay.” Conclusions: We showed that PC has the potential to be cost-effective for Dutch ICUs in a time horizon of 1 year. This study is one of the first cost-effectiveness analyses of a machine learning device. Further research is needed to validate the effectiveness of PC, thereby focusing on the key parameter “reduction in ICU length of stay” and potential spill-over effects

The Potential Cost-Effectiveness of a Machine Learning Tool That Can Prevent Untimely Intensive Care Unit Discharge

OBJECTIVES: The machine learning prediction model Pacmed Critical (PC), currently under development, may guide intensivists in their decision-making process on the most appropriate time to discharge a patient from the intensive care unit (ICU). Given the financial pressure on healthcare budgets, this study assessed whether PC has the potential to be cost-effective compared with standard care, without the use of PC, for Dutch patients in the ICU from a societal perspective. METHODS: A 1-year, 7-state Markov model reflecting the ICU care pathway and incorporating the PC decision tool was developed. A hypothetical cohort of 1000 adult Dutch patients admitted in the ICU was entered in the model. We used the literature, expert opinion, and data from Amsterdam University Medical Center for model parameters. The uncertainty surrounding the incremental cost-effectiveness ratio was assessed using deterministic and probabilistic sensitivity analyses and scenario analyses. RESULTS: PC was a cost-effective strategy with an incremental cost-effectiveness ratio of €18 507 per quality-adjusted life-year. PC remained cost-effective over standard care in multiple scenarios and sensitivity analyses. The likelihood that PC will be cost-effective was 71% at a willingness-to-pay threshold of €30 000 per quality-adjusted life-year. The key driver of the results was the parameter "reduction in ICU length of stay." CONCLUSIONS: We showed that PC has the potential to be cost-effective for Dutch ICUs in a time horizon of 1 year. This study is one of the first cost-effectiveness analyses of a machine learning device. Further research is needed to validate the effectiveness of PC, thereby focusing on the key parameter "reduction in ICU length of stay" and potential spill-over effects

Draft genome of Cercospora zeina, Fusarium pininemorale, Hawksworthiomyces lignivorus, Huntiella decipiens and Ophiostoma ips

The genomes of Cercospora zeina, Fusarium pininemorale, Hawksworthiomyces lignivorus, Huntiella decipiens, and Ophiostoma ips are presented in this genome announcement. Three of these genomes are from plant pathogens and otherwise economically important fungal species. Fusarium pininemorale and H. decipiens are not known to cause significant disease but are closely related to species of economic importance. The genome sizes range from 25.99 Mb in the case of O. ips to 4.82 Mb for H. lignivorus. These genomes include the first reports of a genome from the genus Hawksworthiomyces. The availability of these genome data will allow the resolution of longstanding questions regarding the taxonomy of these species. In addition these genome sequences through comparative studies with closely related organisms will increase our understanding of how these species or close relatives cause disease.Genome sequencing of Cercospora zeina was done by the Phillip SanMiguel, Purdue Genomics Core Facility (USA), and was funded by National Research Foundation (NRF) CPRR, South Africa. The genome sequencing assembly and analyses of the Fusarium pininemorale, Hawksworthiomyces lignivorus, Huntiella decipiens, and Ophiostoma ips genomes were co-funded by the Tree Protection Co-operative Programme (TPCP) and the DST-NRF Centre of Excellence in Tree Health Biotechnology (CTHB) at the Forestry and Agricultural Biotechnology Institute (FABI), and the DST-NRF South African Research Chairs Initiative (SARChI) in Fungal Genomics.http://www.imafungus.orggv2018BiochemistryForestry and Agricultural Biotechnology Institute (FABI)GeneticsMicrobiology and Plant PathologyPlant Production and Soil Scienc

Risk factors for adverse outcomes during mechanical ventilation of 1152 COVID-19 patients: a multicenter machine learning study with highly granular data from the Dutch Data Warehouse

Background: The identification of risk factors for adverse outcomes and prolonged intensive care unit (ICU) stay in COVID-19 patients is essential for prognostication, determining treatment intensity, and resource allocation. Previous studies have determined risk factors on admission only, and included a limited number of predictors. Therefore, using data from the highly granular and multicenter Dutch Data Warehouse, we developed machine learning models to identify risk factors for ICU mortality, ventilator-free days and ICU-free days during the course of invasive mechanical ventilation (IMV) in COVID-19 patients. Methods: The DDW is a growing electronic health record database of critically ill COVID-19 patients in the Netherlands. All adult ICU patients on IMV were eligible for inclusion. Transfers, patients admitted for less than 24 h, and patients still admitted at time of data extraction were excluded. Predictors were selected based on the literature, and included medication dosage and fluid balance. Multiple algorithms were trained and validated on up to three sets of observations per patient on day 1, 7, and 14 using fivefold nested cross-validation, keeping observations from an individual patient in the same split. Results: A total of 1152 patients were included in the model. XGBoost models performed best for all outcomes and were used to calculate predictor importance. Using Shapley additive explanations (SHAP), age was the most important demographic risk factor for the outcomes upon start of IMV and throughout its course. The relative probability of death across age values is visualized in Partial Dependence Plots (PDPs), with an increase starting at 54 years. Besides age, acidaemia, low P/F-ratios and high driving pressures demonstrated a higher probability of death. The PDP for driving pressure showed a relative probability increase starting at 12 cmH(2)O. Conclusion: Age is the most important demographic risk factor of ICU mortality, ICU-free days and ventilator-free days throughout the course of invasive mechanical ventilation in critically ill COVID-19 patients. pH, P/F ratio, and driving pressure should be monitored closely over the course of mechanical ventilation as risk factors predictive of these outcomes

Predictors for extubation failure in COVID-19 patients using a machine learning approach

INTRODUCTION: Determining the optimal timing for extubation can be challenging in the intensive care. In this study, we aim to identify predictors for extubation failure in critically ill patients with COVID-19. METHODS: We used highly granular data from 3464 adult critically ill COVID patients in the multicenter Dutch Data Warehouse, including demographics, clinical observations, medications, fluid balance, laboratory values, vital signs, and data from life support devices. All intubated patients with at least one extubation attempt were eligible for analysis. Transferred patients, patients admitted for less than 24 h, and patients still admitted at the time of data extraction were excluded. Potential predictors were selected by a team of intensive care physicians. The primary and secondary outcomes were extubation without reintubation or death within the next 7 days and within 48 h, respectively. We trained and validated multiple machine learning algorithms using fivefold nested cross-validation. Predictor importance was estimated using Shapley additive explanations, while cutoff values for the relative probability of failed extubation were estimated through partial dependence plots. RESULTS: A total of 883 patients were included in the model derivation. The reintubation rate was 13.4% within 48 h and 18.9% at day 7, with a mortality rate of 0.6% and 1.0% respectively. The grandient-boost model performed best (area under the curve of 0.70) and was used to calculate predictor importance. Ventilatory characteristics and settings were the most important predictors. More specifically, a controlled mode duration longer than 4 days, a last fraction of inspired oxygen higher than 35%, a mean tidal volume per kg ideal body weight above 8 ml/kg in the day before extubation, and a shorter duration in assisted mode (< 2 days) compared to their median values. Additionally, a higher C-reactive protein and leukocyte count, a lower thrombocyte count, a lower Glasgow coma scale and a lower body mass index compared to their medians were associated with extubation failure. CONCLUSION: The most important predictors for extubation failure in critically ill COVID-19 patients include ventilatory settings, inflammatory parameters, neurological status, and body mass index. These predictors should therefore be routinely captured in electronic health records