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| 1 | Classification of the Gut Microbiota of Patients in Intensive Care Units During Development of Sepsis and Septic Shock显示文摘The gut microbiota of intensive care unit(ICU)patients displays extreme dysbiosis associated with increased susceptibility to organ failure,sepsis,and septic shock.However,such dysbiosis is difficult to characterize owing to the high dimensional complexity of the gut microbiota.We tested whether the concept of enterotype can be applied to the gut microbiota of ICU patients to describe the dysbiosis.We collected 131 fecal samples from 64 ICU patients diagnosed with sepsis or septic shock and performed 16S rRNA gene sequencing to dissect their gut microbiota compositions.During the development of sepsis or septic shock and during various medical treatments,the ICU patients always exhibited two dysbiotic microbiota patterns,or ICU-enterotypes,which could not be explained by host properties such as age,sex,and body mass index,or external stressors such as infection site and antibiotic use.ICU-enterotype I(ICU E1)comprised predominantly Bacteroides and an unclassified genus of Enterobacteriaceae,while ICU-enterotype II(ICU E2)comprised predominantly Enterococcus.Among more critically ill patients with Acute Physiology and Chronic Health Evaluation II(APACHE II)scores>18,septic shock was more likely to occur with ICU E1(P=0.041).Additionally,ICU E1 was correlated with high serum lactate levels(P=0.007).Therefore,different patterns of dysbiosis were correlated with different clinical outcomes,suggesting that ICU-enterotypes should be diagnosed as independent clinical indices.Thus,the microbial-based human index classifier we propose is precise and effective for timely monitoring of ICU-enterotypes of individual patients.This work is a first step toward precision medicine for septic patients based on their gut microbiota profiles. | Wanglin Liu Mingyue Cheng Jinman Li Peng Zhang Hang Fan Qinghe Hu Maozhen Han Longxiang Su Huaiwu He Yigang Tong Kang Ning Yun Long | 2020 | Genomics, Proteomics & Bioinformatics2020，18，6： | 7 |
| 2 | Prognosis and weaning of elderly multiple organ dysfunction syndrome patients with invasive mechanical ventilation显示文摘 | Xiao Kun Su Longxiang Han Bingchao Guo Chao Feng Lin Jiang Zhaoxu Wang Huijuan Lin Yong Jia Yanhong She Danyang Xie Lixin | 2014 | Chinese Medical Journal2014，，1： | 7 |
| 3 | Quality metrics and outcomes among critically ill patients in China:results of the national clinical quality control indicators for critical care medicine survey 2015-2019显示文摘Background:It is crucial to improve the quality of care provided to ICU patient,therefore a national survey of the medical quality of intensive care units(ICUs)was conducted to analyze adherence to quality metrics and outcomes among critically ill patients in China from 2015 to 2019.Methods:This was an ICU-level study based on a 15-indicator online survey conducted in China.Considering that ICU care quality may vary between secondary and tertiary hospitals,direct standardization was adopted to compare the rates of ICU quality indicators among provinces/regions.Multivariate analysis was performed to identify potential factors for in-hospital mortality and factors related to ventilator-associated pneumonia(VAP),catheter-related bloodstream infections(CRBSIs),and catheter-associated urinary tract infections(CAUTIs).Results:From the survey,the proportions of structural indicators were 1.83%for the number of ICU inpatients relative to the total number of inpatients,1.44%for ICU bed occupancy relative to the total inpatient bed occupancy,and 51.08%for inpatients with Acute Physiology and Chronic Health Evaluation II scores≥15.The proportions of procedural indicators were 74.37%and 76.60%for 3-hour and 6-hour surviving sepsis campaign bundle compliance,respectively,62.93%for microbiology detection,58.24%for deep vein thrombosis prophylaxis,1.49%for unplanned endotracheal extubations,1.99%for extubated inpatients reintubated within 48 hours,6.38%for unplanned transfer to the ICU,and 1.20%for 48-hour ICU readmission.The proportions of outcome indicators were 1.28‰for VAP,3.06‰for CRBSI,3.65‰for CAUTI,and 10.19%for in-hospital mortality.Although the indicators varied greatly across provinces and regions,the treatment level of ICUs in China has been stable and improved based on various quality control indicators in the past 5 years.The overall mortality rate has dropped from 10.19%to approximately 8%.Conclusions:The quality indicators of medical care in China’s ICUs are heterogeneous,which is reflected in geographic disparities and grades of hospitals.This study is of great significance for improving the homogeneity of ICUs in China. | Xi Rui Fen Dong Xudong Ma Longxiang Su Guangliang Shan Yanhong Guo Yun Long Dawei Liu Xiang Zhou | 2022 | Chinese Medical Journal2022，，9： | 4 |
| 4 | Heliox as a driving gas to atomize inhaled drugs on acute exacerbation of chronic obstructive pulmonary disease: a prospective clinical study显示文摘 | Xiao Yongjiu Su Longxiang Han Bingchao Zhang Xin Xie Lixin | 2014 | Chinese Medical Journal2014，，1： | 4 |
| 5 | Diagnostic Value of Dynamics Serum sCD163, sTREM-1, PCT, and CRP in Differentiating Sepsis, Severity Assessment, and Prognostic Prediction显示文摘 | Longxiang Su Lin Feng Qing Song Hongjun Kang Xingang Zhang Zhixin Liang Yanhong Jia Dan Feng Changting Liu Lixin Xie Celeste C. Finnerty | 2013 | Mediators of Inflammation2013，，： | 2 |
| 6 | Value of solu- ble TREM-1, procalcitonin, and C-reactive protein serum levels as biomarkers for detecting bacteremia among sepsis patients with new fever in intensive care units: a prospective cohort study 显示文摘 | Longxiang Su Bingchao Han Changting Liu | 2012 | BMC Infectious Diseases2012，12，157： | 1 |
| 7 | Identification of novelbiomarkers for sepsis prognosis via urinary proteomic analysis usingiTRAQ labeling and 2D-LC-MS/MS 显示文摘 | Su Longxiang Cao Lichao Zhou Ruo | 2013 | PLoS One2013，8，54： | 1 |
| 8 | Diagnostic value of dy namics serum sCD163, sTREM-1, PCT, and CRP in differentiating sepsis, severity assessment, and prognostic prediction 显示文摘 | Longxiang Su Lin Feng Qing Song | 2013 | Media- tors lnflamm2013，14，1： | 1 |
| 9 | Value of solu- ble TREM-1, proealeitonin, and C-roaetive protein serum levels as biomarkers for detecting baeteremia among sepsis patients with new fever in intensive care units: a prospeetive eohort study 显示文摘 | Longxiang Su Bingchao Han Changting Liu | 2012 | BMC Infectious Diseases2012，12，157： | 1 |
| 10 | cAMP-PGC1α途径诱导的抗Warburg效应驱动胶质母细胞瘤细胞分化为星形胶质细胞显示文摘文章简介多形性胶质母细胞瘤(GBM)是人类最具侵袭性的肿瘤之一。虽然分化治疗已被提出作为治疗GBM的潜在方法,但诱导分化的机制仍然很不清楚。在此,课题组利用c AMP激活剂建立了GBM诱导分化模型,该模型特异性地指导GBM分化为星形胶质细胞。 | Fan Xing Yizhao Luan Jing Cai Sihan Wu Jialuo Mai Jiayu Gu Haipeng Zhang Kai Li Yuan Lin Xiao Xiao Jiankai Liang Yuan Li Wenli Chen Yaqian Tan Longxiang Sheng Bingzheng Lu Wanjun Lu Mingshi Gao Pengxin Qiu Xingwen Su Wei Yin Jun Hu Zhongping Chen Ke Sai Jing Wang Furong Chen Yinsheng Chen Shida Zhu Dongbing Liu Shiyuan Cheng 谢志 朱文博 颜光美 | 2018 | 科学新闻2018，0，4： | 1 |
| 11 | Mean airway pressure has the potential to become the core pressure indicator of mechanical ventilation: Raising to the front from behind the clinical scenes显示文摘Mean airway pressure(Pmean)is a common pressure monitoring parameter of mechanical ventilators that is closely correlated with mean alveolar pressure and represents stresses applied to the lung parenchyma during ventilation.Pmean is determined by the peak inspiratory pressure,positive end-expiratory pressure(PEEP),and inspiratory-to-expiratory time ratio with dynamic and real-time characteristics,which represents mechanical power affected by the ventilator mode.Additionally,Pmean is an important parameter that affects hemodynamics.Tidal forces and PEEP increase pulmonary vascular resistance(PVR)in direct proportion to their effects on Pmean.Therefore,Pmean is increasingly considered to be related to the prognosis of patients on mechanical ventilation.We propose a 3P strategy(Pmean,central venous pressure[CVP],and perfusion index[PI])which is indicated to achieve circulation protection mechanical ventilation with flow priority.Titrating the appropriate CVP and meeting PI to ensure tissue perfusion with a lower Pmean are the core purposes.Pmean links the circulatory and respiratory systems and is expected to become a potential parameter for intelligent ventilation. | Longxiang Su Pan Pan Dawei Liu Yun Long | 2021 | Journal of Intensive Medicine2021，1，2： | 1 |
| 12 | 2 - 30 Progress in Analyzing RIBLL1 17F+p Experimental Data显示文摘 | He Jianjun Hu Jun Xu Shiwei Chen Zhiqiang Zhang Xueying Wang Jiansong Yu Xiangqing Li Long Zhang Liyong Yang Yanyun Ma Peng Wang Hongwei Tian Wendong Zhang Xueheng Su Jun Li Ertao Hu Zhengguo Guo Zhongyan Xu Xing Yuan Xiaohua Lu Wan Lei Xiangguo Yu Yuhong Tang Shuwen Ye Ruiping Chen Jinda Jin Shilun Du Chengming Wang Shitao Ma Junbing Liu Longxiang Bai Zhen Sun Zhiyu Li Xiangqing Zhang Yuhu Zhou Xiaohong Xu Hushan | 2010 | IMP & HIRFL Annual Report2010，，1： | 0 |
| 13 | Initial 24-h perfusion index of ICU admission is associated with acutekidney injury in perioperative critically ill patients: A retrospective cohortanalysis显示文摘Background:The relationship between perfusion index(PI)and organ dysfunction in patients in the intensivecare unit(ICU)is not clear.This study aimed to explore the relationship between PI and renal function in theperioperative critical care setting and evaluate the predictive efficiency of PI on patients with acute kidney injury(AKI)in the ICU.Methods:This retrospective analysis involved 12,979 patients who had undergone an operation and were admitted to the ICU in Peking Union Medical College Hospital from January 2014 to December 2019.The distributionof average PI in the first 24 h after ICU admission and its correlation with AKI was calculated by Cox regression.Receiver operating characteristic(ROC)curves were generated to compare the ability of PI,mean arterial pressure(MAP),creatinine,blood urea nitrogen(BUN),and central venous pressure(CVP)to discriminate AKI in thefirst 48 h in all perioperative critically ill patients.Results:Average PI in the first 24 h served as an independent protective factor of AKI(Odds ratio[OR]=0.786,95%confidence interval[CI]:0.704–0.873,P<0.0001).With a decrease in PI by one unit,the incidence of AKIincreased 1.74 times.Among the variables explored for the prediction of AKI(PI,MAP,creatine,BUN,and CVP),PI yielded the highest area under the ROC curve,with a sensitivity of 64.34%and specificity of 70.14%.A cut-offvalue of PI≤2.12 could be used to predict AKI according to the Youden index.Moreover,patients in the low PIgroup(PI≤2.12)exhibited a marked creatine elevation at 24–48 h with a slower decrease compared with thosein the high PI group(PI>2.12).Conclusions:As a local blood flow indicator,the initial 24-h average PI for perioperative critically ill patients canpredict AKI during their first 120 h in the ICU. | Shengjun Liu Longxiang Su Changjing Zhuge Huaiwu He Yun Long | 2023 | Journal of Intensive Medicine2023，3，3： | 0 |
| 14 | Prediction of mechanical ventilation outcome by early abdominal-visceral-blood-flow-and-function score in critically ill patients after cardiopulmonary bypass in the ICU: A prospective observational study显示文摘Background:Abdominal organs are important organs that sense and respond to ischemia and hypoxia,but there are few evaluation methods.We use ultrasonography to evaluate abdominal organ function and blood flow in patients with mechanical ventilation(MV)after cardiopulmonary bypass and to obtain a semiquantitative score for abdominal organ function and blood flow.Methods:Patients with cardiopulmonary bypass in the Critical Care Department of Peking Union Medical College Hospital in China from March to July 2021 were enrolled in this prospective observational study.The correlation of the abdominal-visceral-blood-flow-and-function score(AVBFS)with the duration of MV,number of days spent in the intensive care unit(ICU),acute physiology and chronic health evaluation II(APACHE-II),sequential organ failure assessment(SOFA),lactate,epinephrine,and norepinephrine use was analyzed,and the results were used to assess the predictive value of the receiver operating characteristic curve(ROC)regression analysis score for the duration of MV.Results:Of the 92 patients who underwent cardiopulmonary bypass,41 were finally included.The AVBFS were significantly correlated with the duration of MV,number of days spent in the ICU,APACHE-II score,SOFA score,and norepinephrine use time.The AVBFS in a group of patients using ventilators≥36 h were significantly higher than those obtained for a group of patients using ventilators<36 h(P<0.05).The evaluation results for the AVBFS at 0-12 h after ICU admission were as follows:area under the ROC curve(AUC)=0.876(95%confidence interval[CI]:0.767 to 0.984),cut-off value=2.5,specificity=0.842,and sensitivity=0.773.Conclusions:Abdominal visceral organ function and blood perfusion can be used to evaluate gastrointestinal function.It is related to early and late extubation after cardiac surgery. | Chaofu Yue Longxiang Su Jun Wang Na Cui Yuankai Zhou Wei Cheng Bo Tang Xi Rui Huaiwu He Yun Long | 2024 | Journal of Intensive Medicine2024，4，1： | 0 |
| 15 | Evaluation of ICUs and weight of quality control indicators:an exploratory study based on Chinese ICU quality data from 2015 to 2020显示文摘This study aimed to explore key quality control factors that affected the prognosis of intensive care unit(ICU)patients in Chinese mainland over six years(2015–2020).The data for this study were from 31 provincial and municipal hospitals(3425 hospital ICUs)and included 2110685 ICU patients,for a total of 27607376 ICU hospitalization days.We found that 15 initially established quality control indicators were good predictors of patient prognosis,including percentage of ICU patients out of all inpatients(%),percentage of ICU bed occupancy of total inpatient bed occupancy(%),percentage of all ICU inpatients with an APACHE II score≥15(%),three-hour(surviving sepsis campaign)SSC bundle compliance(%),six-hour SSC bundle compliance(%),rate of microbe detection before antibiotics(%),percentage of drug deep venous thrombosis(DVT)prophylaxis(%),percentage of unplanned endotracheal extubations(%),percentage of patients reintubated within 48 hours(%),unplanned transfers to the ICU(%),48-h ICU readmission rate(%),ventilator associated pneumonia(VAP)(per 1000 ventilator days),catheter related blood stream infection(CRBSI)(per 1000 catheter days),catheter-associated urinary tract infections(CAUTI)(per 1000 catheter days),in-hospital mortality(%).When exploratory factor analysis was applied,the 15 indicators were divided into 6 core elements that varied in weight regarding quality evaluation:nosocomial infection management(21.35%),compliance with the Surviving Sepsis Campaign guidelines(17.97%),ICU resources(17.46%),airway management(15.53%),prevention of deep-vein thrombosis(14.07%),and severity of patient condition(13.61%).Based on the different weights of the core elements associated with the 15 indicators,we developed an integrated quality scoring system defined as F score=21.35%xnosocomial infection management+17.97%xcompliance with SSC guidelines+17.46%×ICU resources+15.53%×airway management+14.07%×DVT prevention+13.61%×severity of patient condition.This evidence-based quality scoring system will help in assessing the key elements of quality management and establish a foundation for further optimization of the quality control indicator system. | Longxiang Su Xudong Ma Sifa Gao Zhi Yin Yujie Chen Wenhu Wang Huaiwu He Wei Du Yaoda Hu Dandan Ma Feng Zhang Wen Zhu Xiaoyang Meng Guoqiang Sun Lian Ma Huizhen Jiang Guangliang Shan Dawei Liu Xiang Zhou China-NCCQC | 2023 | Frontiers of Medicine2023，17，4： | 0 |
| 16 | Multidimensional dynamic prediction model for hospitalized patients with the omicron variant in China显示文摘Purpose:To establish dynamic prediction models by machine learning using daily multidimensional data for coronavirus disease 2019(COVID-19)patients.Methods:Hospitalized COVID-19 patients at Peking Union Medical College Hospital from Nov 2nd,2022,to Jan 13th,2023,were enrolled in this study.The outcome was defined as deterioration or recovery of the patient's condition.Demographics,comorbidities,laboratory test results,vital signs,and treatments were used to train the model.To predict the following days,a separate XGBoost model was trained and validated.The Shapley additive explanations method was used to analyze feature importance.Results:A total of 995 patients were enrolled,generating 7228 and 3170 observations for each prediction model.In the deterioration prediction model,the minimum area under the receiver operating characteristic curve(AUROC)for the following 7 days was 0.786(95%CI 0.721-0.851),while the AUROC on the next day was 0.872(0.831-0.913).In the recovery prediction model,the minimum AUROC for the following 3 days was 0.675(0.583-0.767),while the AUROC on the next day was 0.823(0.770-0.876).The top 5 features for deterioration prediction on the 7th day were disease course,length of hospital stay,hypertension,and diastolic blood pressure.Those for recovery prediction on the 3rd day were age,D-dimer levels,disease course,creatinine levels and corticosteroid therapy.Conclusion:The models could accurately predict the dynamics of Omicron patients’conditions using daily multidimensional variables,revealing important features including comorbidities(e.g.,hyperlipidemia),age,disease course,vital signs,D-dimer levels,corticosteroid therapy and oxygen therapy. | Yujie Chen Yao Wang Jieqing Chen Xudong Ma Longxiang Su Yuna Wei Linfeng Li Dandan Ma Feng Zhang Wen Zhu Xiaoyang Meng Guoqiang Sun Lian Ma Huizhen Jiang Chang Yin Taisheng Li Xiang Zhou China National Critical Care Quality Control Center Group | 2023 | Infectious Disease Modelling2023，8，4： | 0 |
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