1.Establishment and validation of a predictive model for increased drainage volume after open transforaminal lumbar interbody fusion
Yin HU ; Hai-long YU ; Hong-wen GU ; Kang-en HAN ; Shi-lei TANG ; Yuan-hang ZHAO ; Zhi-hao ZHANG ; Jun-chao LI ; Le XING ; Hong-wei WANG
Journal of Regional Anatomy and Operative Surgery 2025;34(11):981-986
Objective To analyze the risk factors for increased drainage volume after open transforaminal lumbar interbody fusion(TLIF),and to establish a predictive model and then validate it.Methods The clinical data of 680 patients who underwent open TLIF at the General Hospital of Northern Theater Command from January 2016 to December 2019 were collected and the patients were randomly divided into the training group(n=476)and the validation group(n=204).Taking the predictive factors screened out by LASSO regression analysis as independent variables,a multivariate Logistic regression predictive model was constructed.The model was internally validated through the receiver operating characteristic(ROC)curve,Hosmer-Lemeshow goodness-of-fit test,and calibration curve,and its clinical utility was assessed via decision curve analysis(DCA).Results LASSO regression analysis screened out four predictive variables:age,number of surgical segments,operative duration,and intraoperative blood loss.The multivariate Logistic regression predictive model demonstrated that age≥60 years,number of surgical segments≥4,operative duration≥2 hours,and intraoperative blood loss≥200 mL were independent influencing factors for the increased postoperative drainage volume in patients undergoing TLIF(P<0.05).ROC curve analysis revealed an area under the curve(AUC)of 0.816(95%CI:0.798 to 0.867)in the training group and 0.783(95%CI:0.685 to 0.823)in the validation group,indicating that the predictive model had good discriminatory ability.Additionally,the Hosmer-Lemeshow goodness-of-fit test and calibration curve indicated that the predictive model had a good degree of fit,and the predicted probability was basically consistent with the actual probability,demonstrating a good calibration.The DCA results confirmed that this predictive model could be applied in clinical practice.Conclusion The risk factors for increased drainage volume after open TLIF include age,number of surgical segments,operative duration,and intraoperative blood loss.The predictive model established based on these factors demonstrates good performance,and it can be applied in clinical guidance for the selection of drainage tube removal time after TLIF.
2.Establishment and validation of a predictive model for increased drainage volume after open transforaminal lumbar interbody fusion
Yin HU ; Hai-long YU ; Hong-wen GU ; Kang-en HAN ; Shi-lei TANG ; Yuan-hang ZHAO ; Zhi-hao ZHANG ; Jun-chao LI ; Le XING ; Hong-wei WANG
Journal of Regional Anatomy and Operative Surgery 2025;34(11):981-986
Objective To analyze the risk factors for increased drainage volume after open transforaminal lumbar interbody fusion(TLIF),and to establish a predictive model and then validate it.Methods The clinical data of 680 patients who underwent open TLIF at the General Hospital of Northern Theater Command from January 2016 to December 2019 were collected and the patients were randomly divided into the training group(n=476)and the validation group(n=204).Taking the predictive factors screened out by LASSO regression analysis as independent variables,a multivariate Logistic regression predictive model was constructed.The model was internally validated through the receiver operating characteristic(ROC)curve,Hosmer-Lemeshow goodness-of-fit test,and calibration curve,and its clinical utility was assessed via decision curve analysis(DCA).Results LASSO regression analysis screened out four predictive variables:age,number of surgical segments,operative duration,and intraoperative blood loss.The multivariate Logistic regression predictive model demonstrated that age≥60 years,number of surgical segments≥4,operative duration≥2 hours,and intraoperative blood loss≥200 mL were independent influencing factors for the increased postoperative drainage volume in patients undergoing TLIF(P<0.05).ROC curve analysis revealed an area under the curve(AUC)of 0.816(95%CI:0.798 to 0.867)in the training group and 0.783(95%CI:0.685 to 0.823)in the validation group,indicating that the predictive model had good discriminatory ability.Additionally,the Hosmer-Lemeshow goodness-of-fit test and calibration curve indicated that the predictive model had a good degree of fit,and the predicted probability was basically consistent with the actual probability,demonstrating a good calibration.The DCA results confirmed that this predictive model could be applied in clinical practice.Conclusion The risk factors for increased drainage volume after open TLIF include age,number of surgical segments,operative duration,and intraoperative blood loss.The predictive model established based on these factors demonstrates good performance,and it can be applied in clinical guidance for the selection of drainage tube removal time after TLIF.
3.Expert consensus on ethical requirements for artificial intelligence (AI) processing medical data.
Cong LI ; Xiao-Yan ZHANG ; Yun-Hong WU ; Xiao-Lei YANG ; Hua-Rong YU ; Hong-Bo JIN ; Ying-Bo LI ; Zhao-Hui ZHU ; Rui LIU ; Na LIU ; Yi XIE ; Lin-Li LYU ; Xin-Hong ZHU ; Hong TANG ; Hong-Fang LI ; Hong-Li LI ; Xiang-Jun ZENG ; Zai-Xing CHEN ; Xiao-Fang FAN ; Yan WANG ; Zhi-Juan WU ; Zun-Qiu WU ; Ya-Qun GUAN ; Ming-Ming XUE ; Bin LUO ; Ai-Mei WANG ; Xin-Wang YANG ; Ying YING ; Xiu-Hong YANG ; Xin-Zhong HUANG ; Ming-Fei LANG ; Shi-Min CHEN ; Huan-Huan ZHANG ; Zhong ZHANG ; Wu HUANG ; Guo-Biao XU ; Jia-Qi LIU ; Tao SONG ; Jing XIAO ; Yun-Long XIA ; You-Fei GUAN ; Liang ZHU
Acta Physiologica Sinica 2024;76(6):937-942
As artificial intelligence technology rapidly advances, its deployment within the medical sector presents substantial ethical challenges. Consequently, it becomes crucial to create a standardized, transparent, and secure framework for processing medical data. This includes setting the ethical boundaries for medical artificial intelligence and safeguarding both patient rights and data integrity. This consensus governs every facet of medical data handling through artificial intelligence, encompassing data gathering, processing, storage, transmission, utilization, and sharing. Its purpose is to ensure the management of medical data adheres to ethical standards and legal requirements, while safeguarding patient privacy and data security. Concurrently, the principles of compliance with the law, patient privacy respect, patient interest protection, and safety and reliability are underscored. Key issues such as informed consent, data usage, intellectual property protection, conflict of interest, and benefit sharing are examined in depth. The enactment of this expert consensus is intended to foster the profound integration and sustainable advancement of artificial intelligence within the medical domain, while simultaneously ensuring that artificial intelligence adheres strictly to the relevant ethical norms and legal frameworks during the processing of medical data.
Artificial Intelligence/legislation & jurisprudence*
;
Humans
;
Consensus
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Computer Security/standards*
;
Confidentiality/ethics*
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Informed Consent/ethics*
4. Effects of metabolites of eicosapentaenoic acid on promoting transdifferentiation of pancreatic OL cells into pancreatic β cells
Chao-Feng XING ; Min-Yi TANG ; Qi-Hua XU ; Shuai WANG ; Zong-Meng ZHANG ; Zi-Jian ZHAO ; Yun-Pin MU ; Fang-Hong LI
Chinese Pharmacological Bulletin 2024;40(1):31-38
Aim To investigate the role of metabolites of eicosapentaenoic acid (EPA) in promoting the transdifferentiation of pancreatic α cells to β cells. Methods Male C57BL/6J mice were injected intraperitoneally with 60 mg/kg streptozocin (STZ) for five consecutive days to establish a type 1 diabetes (T1DM) mouse model. After two weeks, they were randomly divided into model groups and 97% EPA diet intervention group, 75% fish oil (50% EPA +25% DHA) diet intervention group, and random blood glucose was detected every week; after the model expired, the regeneration of pancreatic β cells in mouse pancreas was observed by immunofluorescence staining. The islets of mice (obtained by crossing GCG
5.Abnormal changes of white matter structure in temporal lobe epilepsy patients with sleep disorders based on diffusion kurtosis imaging
Min GUO ; Yanjing LI ; Boxing SHEN ; Hong LUO ; Ruohan YUAN ; Jie HU ; Xing TANG
Journal of Practical Radiology 2024;40(1):1-5
Objective To investigate the microstructural changes of temporal lobe epilepsy(TLE)in patients with sleep disorders based on diffusion kurtosis imaging(DKI).Methods This research prospectively included 38 TLE patients(case group)and 20 healthy controls(HC)(HC group).Participants used sleep questionnaires to evaluate their sleep status.All TLE patients were divided into groups with and without sleep disorders according to the diagnostic criteria and scale scores of sleep disorders.The mean kurtosis(MK),mean diffusivity(MD),and fractional anisotropy(FA)of the relevant region of interest(ROI)were measured by DKI sequence.The differences of sleep quality scores and DKI parameters between groups were further compared via independent samples t-test and one-way analysis of variance.Results The Epworth sleepiness scale(ESS),Athens insomnia scale(AIS),and Pittsburgh sleep qual-ity index(PSQI)scores of TLE patients with sleep disorders were significantly higher than those of HC group(P<0.05).The FA and MK values in TLE patients were significantly lower than those in HC group,while the MD value of TLE patients were substan-tially higher than that of HC group(P<0.05).The values of MK and FA in left TLE patients with sleep disorders were significantly lower than those of without sleep disorders(P<0.05),while there was no significant difference in MD value between the two groups(P>0.05).MK value of right TLE patients with sleep disor-ders was significantly lower than that of without sleep disorders(P<0.05),however,there were no significant differences in MD and FA values between the two groups(P>0.05).Conclusion Quantitative DKI analysis revealed differences in DKI parameters in TLE patients combined with sleep disorders,inferring a specific white matter fiber damage in this group and providing imaging data to support the personalized treatment and prognostic assessment of these patients.
6.Willingness to preventive treatments and related factors among college freshmen with latent tuberculosis infection in Changzhou
Chinese Journal of School Health 2024;45(12):1802-1806
Objective:
To investigate the willingness to accept preventive treatments and its related factors among college freshmen with latent tuberculosis infection (LTBI), so as to provide the evidence for preventive treatment intervention measures for students with LTBI.
Methods:
Cluster sampling method was used to select 368 LTBI freshmen from 8 colleges and universities in Changzhou in September 2023, who conducted a questionnaire survey on the willingness to receive preventive treatment. General demographic data were collected and relevant data were collected using tuberculosis knowledge scale, General Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9), Adaptation, Partnership, Growth, Affection and Resolve (APGAR), and a self developed Stigma Scale. A binary Logistic regression model was constructed with the willingness to accept preventive treatment as the dependent variable to analyze the willingness to accept preventive treatment and the influencing factors.
Results:
A total of 253 LTBI college freshmen were willing to take preventive treatment, the acceptance rate was 68.75%. The rate of willingness to accept preventive treatment for LTBI was higher among students whose fathers had an education level of high school, compared to those whose fathers had an education level of junior high school or below ( OR =2.16, P <0.05). LTBI students whose per capita family income was >5 000-10 000 yuan and >10 000 yuan were more willing to accept LTBI preventive treatment than those whose per capita family income was <3 000 yuan ( OR =2.72, 4.46, P <0.05). LTBI students who engaged in physical exercise for more than 2 hours per week were more willing to accept than those who exercised less than 0.5 hours per week ( OR =1.91, P <0.05). LTBI students with high levels of tuberculosis knowledge and stigma were more likely to receive preventive treatment ( OR =1.18, 1.11, P < 0.05). LTBI students with high PHQ-9 ( OR =0.85) and GAD-7 ( OR =0.92) scores were more likely to refuse preventive treatment ( P <0.05).
Conclusion
The present study revealed a moderate level of willingness of LTBI students to preventive treatment in Changzhou City, and the acceptance is affected by family factors, healthy lifestyles, tuberculosis knowledge and psychological status.
7.Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients (version 2024)
Yao LU ; Yang LI ; Leiying ZHANG ; Hao TANG ; Huidan JING ; Yaoli WANG ; Xiangzhi JIA ; Li BA ; Maohong BIAN ; Dan CAI ; Hui CAI ; Xiaohong CAI ; Zhanshan ZHA ; Bingyu CHEN ; Daqing CHEN ; Feng CHEN ; Guoan CHEN ; Haiming CHEN ; Jing CHEN ; Min CHEN ; Qing CHEN ; Shu CHEN ; Xi CHEN ; Jinfeng CHENG ; Xiaoling CHU ; Hongwang CUI ; Xin CUI ; Zhen DA ; Ying DAI ; Surong DENG ; Weiqun DONG ; Weimin FAN ; Ke FENG ; Danhui FU ; Yongshui FU ; Qi FU ; Xuemei FU ; Jia GAN ; Xinyu GAN ; Wei GAO ; Huaizheng GONG ; Rong GUI ; Geng GUO ; Ning HAN ; Yiwen HAO ; Wubing HE ; Qiang HONG ; Ruiqin HOU ; Wei HOU ; Jie HU ; Peiyang HU ; Xi HU ; Xiaoyu HU ; Guangbin HUANG ; Jie HUANG ; Xiangyan HUANG ; Yuanshuai HUANG ; Shouyong HUN ; Xuebing JIANG ; Ping JIN ; Dong LAI ; Aiping LE ; Hongmei LI ; Bijuan LI ; Cuiying LI ; Daihong LI ; Haihong LI ; He LI ; Hui LI ; Jianping LI ; Ning LI ; Xiying LI ; Xiangmin LI ; Xiaofei LI ; Xiaojuan LI ; Zhiqiang LI ; Zhongjun LI ; Zunyan LI ; Huaqin LIANG ; Xiaohua LIANG ; Dongfa LIAO ; Qun LIAO ; Yan LIAO ; Jiajin LIN ; Chunxia LIU ; Fenghua LIU ; Peixian LIU ; Tiemei LIU ; Xiaoxin LIU ; Zhiwei LIU ; Zhongdi LIU ; Hua LU ; Jianfeng LUAN ; Jianjun LUO ; Qun LUO ; Dingfeng LYU ; Qi LYU ; Xianping LYU ; Aijun MA ; Liqiang MA ; Shuxuan MA ; Xainjun MA ; Xiaogang MA ; Xiaoli MA ; Guoqing MAO ; Shijie MU ; Shaolin NIE ; Shujuan OUYANG ; Xilin OUYANG ; Chunqiu PAN ; Jian PAN ; Xiaohua PAN ; Lei PENG ; Tao PENG ; Baohua QIAN ; Shu QIAO ; Li QIN ; Ying REN ; Zhaoqi REN ; Ruiming RONG ; Changshan SU ; Mingwei SUN ; Wenwu SUN ; Zhenwei SUN ; Haiping TANG ; Xiaofeng TANG ; Changjiu TANG ; Cuihua TAO ; Zhibin TIAN ; Juan WANG ; Baoyan WANG ; Chunyan WANG ; Gefei WANG ; Haiyan WANG ; Hongjie WANG ; Peng WANG ; Pengli WANG ; Qiushi WANG ; Xiaoning WANG ; Xinhua WANG ; Xuefeng WANG ; Yong WANG ; Yongjun WANG ; Yuanjie WANG ; Zhihua WANG ; Shaojun WEI ; Yaming WEI ; Jianbo WEN ; Jun WEN ; Jiang WU ; Jufeng WU ; Aijun XIA ; Fei XIA ; Rong XIA ; Jue XIE ; Yanchao XING ; Yan XIONG ; Feng XU ; Yongzhu XU ; Yongan XU ; Yonghe YAN ; Beizhan YAN ; Jiang YANG ; Jiangcun YANG ; Jun YANG ; Xinwen YANG ; Yongyi YANG ; Chunyan YAO ; Mingliang YE ; Changlin YIN ; Ming YIN ; Wen YIN ; Lianling YU ; Shuhong YU ; Zebo YU ; Yigang YU ; Anyong YU ; Hong YUAN ; Yi YUAN ; Chan ZHANG ; Jinjun ZHANG ; Jun ZHANG ; Kai ZHANG ; Leibing ZHANG ; Quan ZHANG ; Rongjiang ZHANG ; Sanming ZHANG ; Shengji ZHANG ; Shuo ZHANG ; Wei ZHANG ; Weidong ZHANG ; Xi ZHANG ; Xingwen ZHANG ; Guixi ZHANG ; Xiaojun ZHANG ; Guoqing ZHAO ; Jianpeng ZHAO ; Shuming ZHAO ; Beibei ZHENG ; Shangen ZHENG ; Huayou ZHOU ; Jicheng ZHOU ; Lihong ZHOU ; Mou ZHOU ; Xiaoyu ZHOU ; Xuelian ZHOU ; Yuan ZHOU ; Zheng ZHOU ; Zuhuang ZHOU ; Haiyan ZHU ; Peiyuan ZHU ; Changju ZHU ; Lili ZHU ; Zhengguo WANG ; Jianxin JIANG ; Deqing WANG ; Jiongcai LAN ; Quanli WANG ; Yang YU ; Lianyang ZHANG ; Aiqing WEN
Chinese Journal of Trauma 2024;40(10):865-881
Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.
8.A multicenter study of neonatal stroke in Shenzhen,China
Li-Xiu SHI ; Jin-Xing FENG ; Yan-Fang WEI ; Xin-Ru LU ; Yu-Xi ZHANG ; Lin-Ying YANG ; Sheng-Nan HE ; Pei-Juan CHEN ; Jing HAN ; Cheng CHEN ; Hui-Ying TU ; Zhang-Bin YU ; Jin-Jie HUANG ; Shu-Juan ZENG ; Wan-Ling CHEN ; Ying LIU ; Yan-Ping GUO ; Jiao-Yu MAO ; Xiao-Dong LI ; Qian-Shen ZHANG ; Zhi-Li XIE ; Mei-Ying HUANG ; Kun-Shan YAN ; Er-Ya YING ; Jun CHEN ; Yan-Rong WANG ; Ya-Ping LIU ; Bo SONG ; Hua-Yan LIU ; Xiao-Dong XIAO ; Hong TANG ; Yu-Na WANG ; Yin-Sha CAI ; Qi LONG ; Han-Qiang XU ; Hui-Zhan WANG ; Qian SUN ; Fang HAN ; Rui-Biao ZHANG ; Chuan-Zhong YANG ; Lei DOU ; Hui-Ju SHI ; Rui WANG ; Ping JIANG ; Shenzhen Neonatal Data Network
Chinese Journal of Contemporary Pediatrics 2024;26(5):450-455
Objective To investigate the incidence rate,clinical characteristics,and prognosis of neonatal stroke in Shenzhen,China.Methods Led by Shenzhen Children's Hospital,the Shenzhen Neonatal Data Collaboration Network organized 21 institutions to collect 36 cases of neonatal stroke from January 2020 to December 2022.The incidence,clinical characteristics,treatment,and prognosis of neonatal stroke in Shenzhen were analyzed.Results The incidence rate of neonatal stroke in 21 hospitals from 2020 to 2022 was 1/15 137,1/6 060,and 1/7 704,respectively.Ischemic stroke accounted for 75%(27/36);boys accounted for 64%(23/36).Among the 36 neonates,31(86%)had disease onset within 3 days after birth,and 19(53%)had convulsion as the initial presentation.Cerebral MRI showed that 22 neonates(61%)had left cerebral infarction and 13(36%)had basal ganglia infarction.Magnetic resonance angiography was performed for 12 neonates,among whom 9(75%)had involvement of the middle cerebral artery.Electroencephalography was performed for 29 neonates,with sharp waves in 21 neonates(72%)and seizures in 10 neonates(34%).Symptomatic/supportive treatment varied across different hospitals.Neonatal Behavioral Neurological Assessment was performed for 12 neonates(33%,12/36),with a mean score of(32±4)points.The prognosis of 27 neonates was followed up to around 12 months of age,with 44%(12/27)of the neonates having a good prognosis.Conclusions Ischemic stroke is the main type of neonatal stroke,often with convulsions as the initial presentation,involvement of the middle cerebral artery,sharp waves on electroencephalography,and a relatively low neurodevelopment score.Symptomatic/supportive treatment is the main treatment method,and some neonates tend to have a poor prognosis.
9.Antimicrobial resistance of bacteria from blood specimens:surveillance re-port from Hunan Province Antimicrobial Resistance Surveillance System,2012-2021
Hong-Xia YUAN ; Jing JIANG ; Li-Hua CHEN ; Chen-Chao FU ; Chen LI ; Yan-Ming LI ; Xing-Wang NING ; Jun LIU ; Guo-Min SHI ; Man-Juan TANG ; Jing-Min WU ; Huai-De YANG ; Ming ZHENG ; Jie-Ying ZHOU ; Nan REN ; An-Hua WU ; Xun HUANG
Chinese Journal of Infection Control 2024;23(8):921-931
Objective To understand the change in distribution and antimicrobial resistance of bacteria isolated from blood specimens of Hunan Province,and provide for the initial diagnosis and treatment of clinical bloodstream infection(BSI).Methods Data reported from member units of Hunan Province Antimicrobial Resistance Survei-llance System from 2012 to 2021 were collected.Bacterial antimicrobial resistance surveillance method was imple-mented according to the technical scheme of China Antimicrobial Resistance Surveillance System(CARSS).Bacteria from blood specimens and bacterial antimicrobial susceptibility testing results were analyzed by WHONET 5.6 soft-ware and SPSS 27.0 software.Results A total of 207 054 bacterial strains were isolated from blood specimens from member units in Hunan Province Antimicrobial Resistance Surveillance System from 2012 to 2021,including 107 135(51.7%)Gram-positive bacteria and 99 919(48.3%)Gram-negative bacteria.There was no change in the top 6 pathogenic bacteria from 2012 to 2021,with Escherichia coli(n=51 537,24.9%)ranking first,followed by Staphylococcus epidermidis(n=29 115,14.1%),Staphylococcus aureus(n=17 402,8.4%),Klebsiella pneu-moniae(17 325,8.4%),Pseudomonas aeruginosa(n=4 010,1.9%)and Acinetobacter baumannii(n=3 598,1.7%).The detection rate of methicillin-resistant Staphylococcus aureus(MRSA)decreased from 30.3%in 2015 to 20.7%in 2021,while the detection rate of methicillin-resistant coagulase-negative Staphylococcus(MRCNS)showed an upward trend year by year(57.9%-66.8%).No Staphylococcus was found to be resistant to vancomy-cin,linezolid,and teicoplanin.Among Gram-negative bacteria,constituent ratios of Escherichia coli and Klebsiella pneumoniae were 43.9%-53.9%and 14.2%-19.5%,respectively,both showing an upward trend(both P<0.001).Constituent ratios of Pseudomonas aeruginosa and Acinetobacter baumannii were 3.6%-5.1%and 3.0%-4.5%,respectively,both showing a downward trend year by year(both P<0.001).From 2012 to 2021,resistance rates of Escherichia coli to imipenem and ertapenem were 1.0%-2.0%and 0.6%-1.1%,respectively;presenting a downward trend(P<0.001).The resistant rates of Klebsiella pneumoniae to meropenem and ertapenem were 7.4%-13.7%and 4.8%-6.4%,respectively,presenting a downward trend(both P<0.001).The resistance rates of Pseudomonas aeruginosa and Acinetobacter baumannii to carbapenem antibiotics were 7.1%-15.6%and 34.7%-45.7%,respectively.The trend of resistance to carbapenem antibiotics was relatively stable,but has de-creased compared with 2012-2016.The resistance rates of Escherichia coli to the third-generation cephalosporins from 2012 to 2021 were 41.0%-65.4%,showing a downward trend year by year.Conclusion The constituent ra-tio of Gram-negative bacillus from blood specimens in Hunan Province has been increasing year by year,while the detection rate of carbapenem-resistant Gram-negative bacillus remained relatively stable in the past 5 years,and the detection rate of coagulase-negative Staphylococcus has shown a downward trend.
10.Antimicrobial resistance of bacteria from cerebrospinal fluid specimens:surveillance report from Hunan Province Antimicrobial Resistance Survei-llance System,2012-2021
Jun LIU ; Li-Hua CHEN ; Chen-Chao FU ; Chen LI ; Yan-Ming LI ; Xing-Wang NING ; Guo-Min SHI ; Jing-Min WU ; Huai-De YANG ; Hong-Xia YUAN ; Ming ZHENG ; Nan REN ; An-Hua WU ; Xun HUANG ; Man-Juan TANG
Chinese Journal of Infection Control 2024;23(8):932-941
Objective To investigate changes in the distribution and antimicrobial resistance of bacteria isolated from cerebrospinal fluid(CSF)specimens in Hunan Province,and provide reference for correct clinical diagnosis and rational antimicrobial use.Methods Data reported by member units of Hunan Province Antimicrobial Resistance Surveillance System from 2012 to 2021 were collected according to China Antimicrobial Resistance Surveillance Sys-tem(CARSS)technical scheme.Data of bacteria isolated from CSF specimens and antimicrobial susceptibility tes-ting results were analyzed with WHONET 5.6 and SPSS 20.0 software.Results A total of 11 837 bacterial strains were isolated from CSF specimens from member units of Hunan Province Antimicrobial Resistance Surveillance Sys-tem from 2012 to 2021.The top 5 strains were coagulase-negative Staphylococcus(n=6 397,54.0%),Acineto-bacter baumannii(n=764,6.5%),Staphylococcus aureus(n=606,5.1%),Enterococcus faecium(n=465,3.9%),and Escherichia coli(n=447,3.8%).The detection rates of methicillin-resistant coagulase-negative Staphyloco-ccus(MRCNS)and methicillin-resistant Staphylococcus aureus(MRSA)were 58.9%-66.3%and 34.4%-62.1%,respectively.No Staphylococcus spp.were found to be resistant to vancomycin,linezolid,and teicoplanin.The de-tection rate of Enterococcus faecium was higher than that of Enterococcus faecalis,and the resistance rates of En-terococcus f aecium to penicillin,ampicillin,high concentration streptomycin and levofloxacin were all higher than those of Enterococcus faecalis(all P=0.001).Resistance rate of Streptococcus pneumoniae to penicillin was 85.0%,at a high level.Resistance rate of Escherichia coli to ceftriaxone was>60%,while resistance rates to enzyme inhibitors and carbapenem antibiotics were low.Resistance rate of Klebsiella pneumoniae to ceftriaxone was>60%,to en-zyme inhibitors piperacillin/tazobactam and cefoperazone/sulbactam was>30%,to carbapenem imipenem and me-ropenem was about 30%.Resistance rates of Acinetobacter baumannii to most tested antimicrobial agents were>60%,to imipenem and meropenem were 59.0%-79.4%,to polymyxin B was low.Conclusion Among the bac-teria isolated from CSF specimens,coagulase-negative Staphylococcus accounts for the largest proportion,and the overall resistance of pathogenic bacteria is relatively serious.Bacterial antimicrobial resistance surveillance is very important for the effective treatment of central nerve system infection.


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