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 an...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.展开更多
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 Colleg...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.展开更多
After the impoundment of the Three Gorges Reservoir（TGR）, the hydrological situation of the reservoir has changed greatly. The concentration and distribution of typical persistent organic pollutants in water and sed...After the impoundment of the Three Gorges Reservoir（TGR）, the hydrological situation of the reservoir has changed greatly. The concentration and distribution of typical persistent organic pollutants in water and sediment have also changed accordingly. In this study, the concentration, distribution and potential sources of 16 polycyclic aromatic hydrocarbons（PAHs） and 6 phthalic acid esters（PAEs） during the water drawdown and impoundment periods were investigated in water and sediment from the TGR. According to our results, PAHs and PAEs showed temporal and spatial variations. The mean ΣPAH and ΣPAE concentrations in water and sediment were both higher during the water impoundment period than during the water drawdown period. The water samples from the main stream showed larger ΣPAH concentration fluctuations than those from tributaries. Both the PAH and PAE concentrations meet the Chinese national water environmental quality standard（GB 3838-2002）. PAH monomers with 2–3 rings and 4 rings were dominant in water, and 4-ring and 5–6-ring PAHs were dominant in sediment. Di-n-butyl phthalate（DBP） and di-2-ethylhexyl phthalate（DEHP）were the dominant PAE pollutants in the TGR. DBP and DEHP had the highest concentrations in water and sediment, respectively. The main source of PAHs in water from the TGR was petroleum and emissions from coal and biomass combustion, whereas the main sources of PAHs in sediments included coal and biomass combustion, petroleum, and petroleum combustion. The main source of PAEs in water was domestic waste, and the plastics and heavy chemical industries were the main sources of PAEs in sediment.展开更多
基金supported by the National Key R&D Program of China(No.2020YFC0861000)the CAMS Innovation Fund for Medical Sciences(CIFMS)(No.2020-I2 M-CoV19-001)+4 种基金the China International Medical Exchange Foundation Special Fund for Young and Middle-aged Medical Research(No.Z-2018-35-1902)2020 CMB Open Competition Program(No.20-381)CAMS Endowment Fund(No.2021-CAMS-JZ004)the Chinese Medical Information and Big Data Association(CHMIA)Special Fund for Emergency Project,and Beijing Municipal Natural Science Foundation(M21019)the CAMS Endowment Fund(No.2021-CAMS-JZ004).
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.
基金National High Level Hospital Clinical Research Funding (2023-PUMCH-G-001)Chinese Academy of Medical Sciences and Peking Union Medical Hospital (K3872)+1 种基金Beijing Municipal Natural Science Foundation General Program (M21019)Beijing Municipal Natural Science Foundation-Haidian Original Innovation Unite Foundation Key Program (L222019).
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.
基金supported by the Public Interest Scientific Research Fund of the Ministry of Water Resource of China(No.201501042)the National Natural Science Foundation of China(Nos.51309019,51379016)+3 种基金the Young Elite Scientist Sponsorship Program by CAST(No.2015QNRC001)the State-level Public Welfare Scientific Research Institutes Basic Scientific Research Business Project of China(No.CKSF2017062/SH)the Technology Demonstration Project of the Ministry of Water Resources of China(No.SF-201602)supported by the Brook Byers Institute for Sustainable Systems,Georgia Institute of Technology(Georgia Tech Hightower No.1365802)
文摘After the impoundment of the Three Gorges Reservoir（TGR）, the hydrological situation of the reservoir has changed greatly. The concentration and distribution of typical persistent organic pollutants in water and sediment have also changed accordingly. In this study, the concentration, distribution and potential sources of 16 polycyclic aromatic hydrocarbons（PAHs） and 6 phthalic acid esters（PAEs） during the water drawdown and impoundment periods were investigated in water and sediment from the TGR. According to our results, PAHs and PAEs showed temporal and spatial variations. The mean ΣPAH and ΣPAE concentrations in water and sediment were both higher during the water impoundment period than during the water drawdown period. The water samples from the main stream showed larger ΣPAH concentration fluctuations than those from tributaries. Both the PAH and PAE concentrations meet the Chinese national water environmental quality standard（GB 3838-2002）. PAH monomers with 2–3 rings and 4 rings were dominant in water, and 4-ring and 5–6-ring PAHs were dominant in sediment. Di-n-butyl phthalate（DBP） and di-2-ethylhexyl phthalate（DEHP）were the dominant PAE pollutants in the TGR. DBP and DEHP had the highest concentrations in water and sediment, respectively. The main source of PAHs in water from the TGR was petroleum and emissions from coal and biomass combustion, whereas the main sources of PAHs in sediments included coal and biomass combustion, petroleum, and petroleum combustion. The main source of PAEs in water was domestic waste, and the plastics and heavy chemical industries were the main sources of PAEs in sediment.
