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Machine learning using the extreme gradient boosting (XGBoost) algorithm predicts 5-day delta of SOFA score at ICU admission in COVID-19 patients

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Journal of Intensive Medicine 2021年第2期1卷 110-116页

作者： Jonathan Montomoli Luca Romeo Sara Moccia Michele Bernardini Lucia Migliorelli Daniele Berardini Abele Donati Andrea Carsetti Maria Grazia Bocci Pedro David Wendel Garcia Thierry Fumeaux Philippe Guerci Reto Andreas Schüpbach Can Ince Emanuele Frontoni matthias peter hilty RISC-19-ICU Investigators Department of Anaesthesia and Intensive Care Infermi HospitalAUSL della RomagnaRimini 47923Italy Department of Information Engineering UniversitàPolitecnica delle MarcheAncona 60131Italy The BioRobotics Institute and Department of Excellence in Robotics and AI Scuola Superiore Sant’AnnaPisa 56127Italy Anesthesia and Intensive Care Unit Azienda Ospedaliero Universitaria Ospedali RiunitiAncona 60126Italy Department of Biomedical Sciences and Public Health UniversitàPolitecnica delle MarcheAncona 60126Italy Department of Anaesthesia and Intensive Care Fondazione Policlinico Universitario A.Gemelli IRCCSUniversitàCattolica del Sacro CuoreRome 00168Italy Institute of Intensive Care Medicine University Hospital of ZurichZurich 8091Switzerland Swiss Society of Intensive Care Medicine Basel 4001Switzerland Department of Anesthesiology and Critical Care Medicine University Hospital of NancyNancy 54511France Department of Intensive Care Erasmus MC University Medical Center RotterdamRotterdam3015 GDNetherlands 不详

Background:Accurate risk stratification of critically ill patients with coronavirus disease 2019(COVID-19)is essential for optimizing resource allocation,delivering targeted interventions,and maximizing patient survival *** learning(ML)techniques are attracting increased interest for the development of prediction models as they excel in the analysis of complex signals in data-rich environments such as critical ***:We retrieved data on patients with COVID-19 admitted to an intensive care unit(ICU)between March and October 2020 from the RIsk Stratification in COVID-19 patients in the Intensive Care Unit(RISC-19-ICU)*** applied the Extreme Gradient Boosting(XGBoost)algorithm to the data to predict as a binary out-come the increase or decrease in patients’Sequential Organ Failure Assessment(SOFA)score on day 5 after ICU *** model was iteratively cross-validated in different subsets of the study ***:The final study population consisted of 675 *** XGBoost model correctly predicted a decrease in SOFA score in 320/385(83%)critically ill COVID-19 patients,and an increase in the score in 210/290(72%)*** area under the mean receiver operating characteristic curve for XGBoost was significantly higher than that for the logistic regression model(0.86 vs.0.69,P<0.01[paired t-test with 95%confidence interval]).Conclusions:The XGBoost model predicted the change in SOFA score in critically ill COVID-19 patients admitted to the ICU and can guide clinical decision support systems(CDSSs)aimed at optimizing available resources.

关键词： Machine learning Extreme gradient boosting(XGBoost) COVID-19 Multiple organ failure Clinical decision support system(CDSS) Organ dysfunction score

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Association between serosal intestinal microcirculation and blood pressure during major abdominal surgery

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Journal of Intensive Medicine 2021年第1期1卷 59-64页

作者： Arthur LM Tavy Anton FJ de Bruin E Christiaan Boerma Can Ince matthias P hilty peter G Noordzij Djamila Boerma Mat van Iterson Department of Anesthesiology Intensive Care and Pain MedicineSt.Antonius HospitalNieuwegein 3435 CMthe Netherlands Department of Intensive Care Medical Center LeeuwardenLeeuwarden 8934 ADthe Netherlands Department of Intensive Care Medicine Erasmus MCUniversity Medical Center RotterdamRotterdam 3015 GDthe Netherlands Institute of Intensive Care Medicine University Hospital of ZurichZürich 8006Switzerland Department of Surgery St.Antonius HospitalNieuwegein 3435 CMthe Netherlands

Background:In clinical practice,blood pressure is used as a resuscitation goal on a daily basis,with the aim of maintaining adequate perfusion and oxygen delivery to target *** perfusion is often indicated as a key factor in the pathophysiology of anastomotic *** study was aimed at assessing the extent to which the microcirculation of the bowel coheres with blood pressure during abdominal ***:We performed a prospective and observational cohort *** patients undergoing abdominal surgery,the serosal microcirculation of either the small intestine or the colon was visualized using handheld vital mi-croscopy(HVM).From the acquired HVM image sequences,red blood cell velocity(RBCv)and total vessel density(TVD)were calculated using MicroTools and AVA software,*** association between microcircula-tory parameters and blood pressure was assessed using Pearson’s correlation *** considered a two-sided P-value of<0.050 to be ***:In 28 patients undergoing abdominal surgery,a total of 76 HVM images were *** RBCv was 335±96μm/s and the TVD was 13.7±3.4 mm/mm ^(2).Mean arterial pressure(MAP)was 71±12 mm Hg during microcirculatory *** was not correlated with RBCv(Pearson’s r=−0.049,P=0.800)or TVD(Pearson’s r=0.310,P=0.110).Conclusion:In 28 patients undergoing abdominal surgery,we found no association between serosal intestinal microcirculatory parameters and blood pressure.

关键词： Intestinal microcirculation Handheld vital microscopy Abdominal surgery Hemodynamic management

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马克斯·普朗克创新与竞争研究所就欧盟委员会“关于构建欧洲数据经济征求意见书”的立场声明

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电子知识产权 2017年第7期 92-100页

作者： Josef Drexl Reto M.hilty Jure Globocnik Franziska Greiner Daria Kim Heiko Richter peter R.Slowinski Gintar? Surblyt? Axel Walz Klaus Wiedemann 刘维张嘉莹马克斯.普朗克创新与竞争研究所上海交通大学凯原法学院上海大学法学院

马克斯·普朗克创新与竞争研究所是一家在马克斯·普朗克学会之下设立的研究机构,自五十一年前设立以来,一直致力于在既已确立的科学原理基础之上对知识产权和竞争法的分析与发展进行研究.马克斯·普朗克创新与竞争研究所承担着该领域... 详细信息

马克斯·普朗克创新与竞争研究所是一家在马克斯·普朗克学会之下设立的研究机构,自五十一年前设立以来,一直致力于在既已确立的科学原理基础之上对知识产权和竞争法的分析与发展进行研究.马克斯·普朗克创新与竞争研究所承担着该领域内基础问题的研究.基于其卓著的研究贡献,马克斯·普朗克创新与竞争研究所发起并引领着德国、欧洲以及国际层面重要的立法及理论研究。

关键词：普朗克马克斯竞争法研究所欧盟委员会创新欧洲意见书

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