1.Evaluation of PEscore performance in predicting platelet transfusion efficacy in hematological patients
Jun LI ; Lan WANG ; Yihua XIE ; Ziqi CHEN ; Gang ZHAO
Chinese Journal of Blood Transfusion 2025;38(6):797-802
Objective: To evaluate the performance of platelet efficacy score (PEscore) in predicting platelet transfusion efficacy in hematological patients. Methods: A total of 485 patients with hematological diseases, including 298 males (62.09±15.45 years) and 187 females (59.17±16.52 years) who received platelet transfusion from January 1, 2021 to December 31, 2024 were enrolled in this study. Clinical data of the patients such as diagnosis, gender, age, number of platelet transfusion, and platelet antibody data were analyzed to investigate the incidence and influencing factors of platelet transfusion refractoriness in hematological patients at our hospital. ROC curve was used to evaluate the performance of PEscore model in predicting platelet transfusion efficacy. The predictive performance of PEscore model was validated by calculating its sensitivity, specificity, and accuracy in 115 clinical cases. Results: The incidence of platelet transfusion refractoriness in 485 cases was 29.90% (145/485). Significant differences (P<0.05) were observed between the effective and ineffective platelet transfusion groups regarding the following factors: diagnosis: lymphoma [55.32% (26/47) vs 44.68% (21/47)], the number of previous platelet transfusions [≥25: 60.78% (31/51) vs 39.22% (20/51)], platelet antibody screening result [positive: 33.76% (53/157) vs 66.24% (104/157)], and platelet transfusion volume (×10
/L) [>6: 62.71% (74/118) vs 37.29% (44/118)]. The area under the ROC curve of PEscore was 0.876. The cut-off points and corresponding sensitivity and specificity were 19.90.59% and 69.44%, respectively. The results of clinical application showed that the sensitivity, specificity and accuracy of the PEscore model for predicting platelet transfusion were 87.50%, 93.41% and 92.17%, respectively. Conclusion: The incidence of platelet transfusion refractoriness in hematological patients is relatively high. PEscore prediction model has a good performance in predicting the effect of platelet transfusion, which can provide a reliable basis for predicting the effect of platelet transfusion in hematological patients before blood transfusion.
2.Risk factors for liver cancer in 504 patients with hepatitis B virus associated cirrhosis logistic regression analysis
Gang LI ; Hongliang SHANG ; Yuanyuan LIU ; Rui JIN ; Cheng WANG ; Yajuan XIE
Journal of Public Health and Preventive Medicine 2025;36(4):85-88
Objective Logistic regression model was used to analyze the risk factors of liver cancer in patients with hepatitis B virus-related cirrhosis. Methods A retrospective analysis was performed on 504 patients with hepatitis B cirrhosis who were treated in a hospital from April 2021 to April 2024. The occurrence of liver cancer was counted. The risk factors of liver cancer in patients with HBV-related cirrhosis were analyzed by logistic regression analysis. Results Among the 504 patients with hepatitis B cirrhosis, 101 patients developed liver cancer and 403 patients did not develop liver cancer, which were included in the liver cancer group (n=101) and the non-liver cancer group (n=403).. Among hepatitis B cirrhosis, the incidence rate of liver cancer was 20.04%. Compared with the non-liver cancer group, the proportion of patients with long-term drinking history, family history of liver cancer, history of diabetes mellitus, antiviral therapy, and HBV-DNA load>104 were higher in the liver cancer group (P<0.05). logistic regression analysis found that long-term drinking history (OR=3.077, 95%CI: 1.130-8.378, P=0.028), history of diabetes mellitus (OR=3.747, 95%CI: 1.765-7.954, P=0.001), no antiviral therapy (OR=3.466, 95%CI: 1.337-8.985, P=0.011) and HBV-DNA load>104 (OR=3.149, 95%CI: 1.353-7.328, P=0.008) could independently affect the occurrence of liver cancer in patients with hepatitis B cirrhosis. Conclusion According to logistic regression analysis, long-term drinking history, history of diabetes mellitus, no antiviral therapy, and HBV-DNA load>104 are risk factors for liver cancer in patients with HBV-related cirrhosis.
3.Effects of COL1A1 and SYTL2 on inflammatory cell infiltration and poor extracellular matrix remodeling of the vascular wall in thoracic aortic aneurysm
Xinsheng XIE ; Ye YUAN ; Yulong HUANG ; Xiang HONG ; Shichai HONG ; Gang CHEN ; Yihui CHEN ; Yue LIN ; Weifeng LU ; Weiguo FU ; Lixin WANG
Chinese Medical Journal 2024;137(9):1105-1114
Background::Thoracic aortic aneurysm (TAA) is a fatal cardiovascular disease, the pathogenesis of which has not yet been clarified. This study aimed to identify and validate the diagnostic markers of TAA to provide a strong theoretical basis for developing new methods to prevent and treat this disease.Methods::Gene expression profiles of the GSE9106, GSE26155, and GSE155468 datasets were acquired from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using the "limma" package in R. Least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), random forest, and binary logistic regression analyses were used to screen the diagnostic marker genes. Single-sample gene set enrichment analysis (ssGSEA) was used to estimate immune cell infiltration in TAA.Results::A total of 16 DEGs were identified. The enrichment and functional correlation analyses showed that DEGs were mainly associated with inflammatory response pathways and collagen-related diseases. Collagen type I alpha 1 chain ( COL1A1) and synaptotagmin like 2 ( SYTL2) were identified as diagnostic marker genes with a high diagnostic value for TAA. The expression of COL1A1 and SYTL2 was considerably higher in TAA vascular wall tissues than in the corresponding normal tissues, and there were significant differences in the infiltration of immune cells between TAA and normal vascular wall tissues. Additionally, COL1A1 and SYTL2 expression were associated with the infiltration of immune cells in the vascular wall tissue. Single-cell analysis showed that COL1A1 in TAA was mainly derived from fibroblasts and SYTL2 mainly from cluster of differentiation (CD)8 + T cells. In addition, single-cell analysis indicated that fibroblasts and CD8 + T cells in TAA were significantly higher than those in normal arterial wall tissue. Conclusions::COL1A1 and SYTL2 may serve as diagnostic marker genes for TAA. The upregulation of SYTL2 and COL1A1 may be involved in the inflammatory infiltration of the vessel wall and poor extracellular matrix remodeling, promoting the progression of TAA.
4.Analysis of the epidemic characteristics and disease burden of hospitalized children with viral myocarditis in China from 2016 to 2021
Luci HUANG ; Wei SHAO ; Lingyun GUO ; Yiliang FU ; Fei LI ; Hui XU ; Guoshuang FENG ; Lu GAO ; Zhengde XIE ; Yue YUAN ; Gang LIU ; Xiangpeng CHEN
Chinese Journal of Experimental and Clinical Virology 2024;38(4):432-438
Objective:This study aimed to provide basic data for the prevention, diagnosis and treatment of pediatric viral myocarditis (VMC) in China through analyzing the epidemic characteristics and disease burden of pediatric inpatients with VMC from 2016 to 2021.Methods:We performed a descriptive statistical analysis to the age, genders, seasons, regions and hospitalization cost and days of pediatric VMC inpatients and the death. All of the information was obtained from 27 Children′s hospitals or Maternal and Child Health hospitals of 23 provinces of China from 2016 to 2021.Results:A total of 7 647 599 cases including 1 646 VMC inpatients were admitted into our study. The annual numbers of hospitalizations were 173, 227, 313, 301, 295 and 337, with the hospitalized constituent ratios being 14.9/100 000, 17.9/100 000, 23.0/100 000, 20.5/100 000, 26.5/100 000 and 26.4/100 000 from 2016 to 2021. In recent 6 years, the proportion of VMC hospitalizations had increased yearly ( P<0.001), and had associated with the onset age ( P<0.001). Aged 12-≤18 years owned the highest hospitalized constituent ratio. The Northeast of China owned the largest number of VMC inpatients, and the East second to it. Among the 1 646 VMC children, there were 68 deaths, with the hospitalized case fatality rate of 4.13%. There were no significant differences between genders, age, seasons, years and fatality rate of VMC inpatients. For the diseases burden, the median of hospitalization days of all VMC inpatients was 10 days (IQR 6, 21), and the median of hospitalization cost was 1 1 842.3 RMB (IQR 6 969.22, 19 714.78). The median of hospitalization days of deceased VMC children was only 1 day (IQR 1, 3), the median cost could be 8 874.03 RMB (IQR 5 277.94, 5 6 151.59). Conclusions:In this study, we found that proportion of hospitalization of VMC children increased year by year, adolescence might be a risk factor of VMC. The fatality of VMC inpatients could be up to 4.13%, and the death led to a huge economic burden of society, family and individuals.
5.Residual neural network-101-feature pyramid network model based on CT for differentiating benign and malignant lung nodules
Gang LIU ; Xiaoting XIE ; Hui HE ; Fei LIU ; Xu MAO ; Jingyao SANG ; Haiyun YANG ; Yueyong XIAO
Chinese Journal of Interventional Imaging and Therapy 2024;21(7):414-417
Objective To observe the value of residual neural network(ResNet)-101-feature pyramid network(FPN)model based on CT for differentiating benign and malignant lung nodules.Methods Totally 2 040 lung nodules in 2 000 patients were retrospectively enrolled,including 1 150 benign and 890 malignant nodules.The nodules were divided into training set(n=1 632)and test set(n=408)at the ratio of 8∶2,the former including 881 benign and 751 malignant ones,while the latter including 269 benign and 139 malignant ones,respectively.Taken ResNet-101 as the backbone network,combined with FPN,a classification model was established based on chest CT,and the efficiency of this model alone and combined with evaluation of physicians for differentiating benign and malignant lung nodules were evaluated.Results Among 269 benign lung nodules in test set,ResNet-101-FPN model alone correctly diagnosed 214 nodules(214/269,79.55%),while combined with evaluation of physicians correctly diagnosed 230 ones(230/269,85.50%).For 139 malignant nodules in test set,ResNet-101-FPN model alone correctly diagnosed 124 nodules(124/139,89.21%),while combined with evaluation of physicians correctly diagnosed 131 ones(131/139,94.24%).The sensitivity,accuracy and precision of ResNet-101-FPN model combined with evaluation of physicians for distinguishing benign and malignant lung nodules were all higher,while the specificity of the combination was lower than those of ResNet-101-FPN model alone,but the differences were not significant(all P>0.05).Conclusion ResNet-101-FPN model could be used to distinguish benign and malignant lung nodules based on CT.Combining with evaluation of physicians could improve diagnostic efficiency of this model.
6.Surveillance of bacterial resistance in tertiary hospitals across China:results of CHINET Antimicrobial Resistance Surveillance Program in 2022
Yan GUO ; Fupin HU ; Demei ZHU ; Fu WANG ; Xiaofei JIANG ; Yingchun XU ; Xiaojiang ZHANG ; Fengbo ZHANG ; Ping JI ; Yi XIE ; Yuling XIAO ; Chuanqing WANG ; Pan FU ; Yuanhong XU ; Ying HUANG ; Ziyong SUN ; Zhongju CHEN ; Jingyong SUN ; Qing CHEN ; Yunzhuo CHU ; Sufei TIAN ; Zhidong HU ; Jin LI ; Yunsong YU ; Jie LIN ; Bin SHAN ; Yunmin XU ; Sufang GUO ; Yanyan WANG ; Lianhua WEI ; Keke LI ; Hong ZHANG ; Fen PAN ; Yunjian HU ; Xiaoman AI ; Chao ZHUO ; Danhong SU ; Dawen GUO ; Jinying ZHAO ; Hua YU ; Xiangning HUANG ; Wen'en LIU ; Yanming LI ; Yan JIN ; Chunhong SHAO ; Xuesong XU ; Wei LI ; Shanmei WANG ; Yafei CHU ; Lixia ZHANG ; Juan MA ; Shuping ZHOU ; Yan ZHOU ; Lei ZHU ; Jinhua MENG ; Fang DONG ; Zhiyong LÜ ; Fangfang HU ; Han SHEN ; Wanqing ZHOU ; Wei JIA ; Gang LI ; Jinsong WU ; Yuemei LU ; Jihong LI ; Qian SUN ; Jinju DUAN ; Jianbang KANG ; Xiaobo MA ; Yanqing ZHENG ; Ruyi GUO ; Yan ZHU ; Yunsheng CHEN ; Qing MENG ; Shifu WANG ; Xuefei HU ; Wenhui HUANG ; Juan LI ; Quangui SHI ; Juan YANG ; Abulimiti REZIWAGULI ; Lili HUANG ; Xuejun SHAO ; Xiaoyan REN ; Dong LI ; Qun ZHANG ; Xue CHEN ; Rihai LI ; Jieli XU ; Kaijie GAO ; Lu XU ; Lin LIN ; Zhuo ZHANG ; Jianlong LIU ; Min FU ; Yinghui GUO ; Wenchao ZHANG ; Zengguo WANG ; Kai JIA ; Yun XIA ; Shan SUN ; Huimin YANG ; Yan MIAO ; Mingming ZHOU ; Shihai ZHANG ; Hongjuan LIU ; Nan CHEN ; Chan LI ; Jilu SHEN ; Wanqi MEN ; Peng WANG ; Xiaowei ZHANG ; Yanyan LIU ; Yong AN
Chinese Journal of Infection and Chemotherapy 2024;24(3):277-286
Objective To monitor the susceptibility of clinical isolates to antimicrobial agents in tertiary hospitals in major regions of China in 2022.Methods Clinical isolates from 58 hospitals in China were tested for antimicrobial susceptibility using a unified protocol based on disc diffusion method or automated testing systems.Results were interpreted using the 2022 Clinical &Laboratory Standards Institute(CLSI)breakpoints.Results A total of 318 013 clinical isolates were collected from January 1,2022 to December 31,2022,of which 29.5%were gram-positive and 70.5%were gram-negative.The prevalence of methicillin-resistant strains in Staphylococcus aureus,Staphylococcus epidermidis and other coagulase-negative Staphylococcus species(excluding Staphylococcus pseudintermedius and Staphylococcus schleiferi)was 28.3%,76.7%and 77.9%,respectively.Overall,94.0%of MRSA strains were susceptible to trimethoprim-sulfamethoxazole and 90.8%of MRSE strains were susceptible to rifampicin.No vancomycin-resistant strains were found.Enterococcus faecalis showed significantly lower resistance rates to most antimicrobial agents tested than Enterococcus faecium.A few vancomycin-resistant strains were identified in both E.faecalis and E.faecium.The prevalence of penicillin-susceptible Streptococcus pneumoniae was 94.2%in the isolates from children and 95.7%in the isolates from adults.The resistance rate to carbapenems was lower than 13.1%in most Enterobacterales species except for Klebsiella,21.7%-23.1%of which were resistant to carbapenems.Most Enterobacterales isolates were highly susceptible to tigecycline,colistin and polymyxin B,with resistance rates ranging from 0.1%to 13.3%.The prevalence of meropenem-resistant strains decreased from 23.5%in 2019 to 18.0%in 2022 in Pseudomonas aeruginosa,and decreased from 79.0%in 2019 to 72.5%in 2022 in Acinetobacter baumannii.Conclusions The resistance of clinical isolates to the commonly used antimicrobial agents is still increasing in tertiary hospitals.However,the prevalence of important carbapenem-resistant organisms such as carbapenem-resistant K.pneumoniae,P.aeruginosa,and A.baumannii showed a downward trend in recent years.This finding suggests that the strategy of combining antimicrobial resistance surveillance with multidisciplinary concerted action works well in curbing the spread of resistant bacteria.
7.Changing distribution and resistance profiles of common pathogens isolated from urine in the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Yanming LI ; Mingxiang ZOU ; Wen'en LIU ; Yang YANG ; Fupin HU ; Demei ZHU ; Yingchun XU ; Xiaojiang ZHANG ; Fengbo ZHANG ; Ping JI ; Yi XIE ; Mei KANG ; Chuanqing WANG ; Pan FU ; Yuanhong XU ; Ying HUANG ; Ziyong SUN ; Zhongju CHEN ; Yuxing NI ; Jingyong SUN ; Yunzhuo CHU ; Sufei TIAN ; Zhidong HU ; Jin LI ; Yunsong YU ; Jie LIN ; Bin SHAN ; Yan DU ; Sufang GUO ; Lianhua WEI ; Fengmei ZOU ; Hong ZHANG ; Chun WANG ; Yunjian HU ; Xiaoman AI ; Chao ZHUO ; Danhong SU ; Dawen GUO ; Jinying ZHAO ; Hua YU ; Xiangning HUANG ; Yan JIN ; Chunhong SHAO ; Xuesong XU ; Chao YAN ; Shanmei WANG ; Yafei CHU ; Lixia ZHANG ; Juan MA ; Shuping ZHOU ; Yan ZHOU ; Lei ZHU ; Jinhua MENG ; Fang DONG ; Zhiyong LÜ ; Fangfang HU ; Han SHEN ; Wanqing ZHOU ; Wei JIA ; Gang LI ; Jinsong WU ; Yuemei LU ; Jihong LI ; Jinju DUAN ; Jianbang KANG ; Xiaobo MA ; Yanping ZHENG ; Ruyi GUO ; Yan ZHU ; Yunsheng CHEN ; Qing MENG ; Shifu WANG ; Xuefei HU ; Jilu SHEN ; Ruizhong WANG ; Hua FANG ; Bixia YU ; Yong ZHAO ; Ping GONG ; Kaizhen WENG ; Yirong ZHANG ; Jiangshan LIU ; Longfeng LIAO ; Hongqin GU ; Lin JIANG ; Wen HE ; Shunhong XUE ; Jiao FENG ; Chunlei YUE
Chinese Journal of Infection and Chemotherapy 2024;24(3):287-299
Objective To investigate the distribution and antimicrobial resistance profiles of the common pathogens isolated from urine from 2015 to 2021 in the CHINET Antimicrobial Resistance Surveillance Program.Methods The bacterial strains were isolated from urine and identified routinely in 51 hospitals across China in the CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021.Antimicrobial susceptibility was determined by Kirby-Bauer method,automatic microbiological analysis system and E-test according to the unified protocol.Results A total of 261 893 nonduplicate strains were isolated from urine specimen from 2015 to 2021,of which gram-positive bacteria accounted for 23.8%(62 219/261 893),and gram-negative bacteria 76.2%(199 674/261 893).The most common species were E.coli(46.7%),E.faecium(10.4%),K.pneumoniae(9.8%),E.faecalis(8.7%),P.mirabilis(3.5%),P.aeruginosa(3.4%),SS.agalactiae(2.6%),and E.cloacae(2.1%).The strains were more frequently isolated from inpatients versus outpatients and emergency patients,from females versus males,and from adults versus children.The prevalence of ESBLs-producing strains in E.coli,K.pneumoniae and P.mirabilis was 53.2%,52.8%and 37.0%,respectively.The prevalence of carbapenem-resistant strains in E.coli,K.pneumoniae,P.aeruginosa and A.baumannii was 1.7%,18.5%,16.4%,and 40.3%,respectively.Lower than 10%of the E.faecalis isolates were resistant to ampicillin,nitrofurantoin,linezolid,vancomycin,teicoplanin and fosfomycin.More than 90%of the E.faecium isolates were ressitant to ampicillin,levofloxacin and erythromycin.The percentage of strains resistant to vancomycin,linezolid or teicoplanin was<2%.The E.coli,K.pneumoniae,P.aeruginosa and A.baumannii strains isolated from ICU inpatients showed significantly higher resistance rates than the corresponding strains isolated from outpatients and non-ICU inpatients.Conclusions E.coli,Enterococcus and K.pneumoniae are the most common pathogens in urinary tract infection.The bacterial species and antimicrobial resistance of urinary isolates vary with different populations.More attention should be paid to antimicrobial resistance surveillance and reduce the irrational use of antimicrobial agents.
8.Changing resistance profiles of Enterococcus in hospitals across China:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Na CHEN ; Ping JI ; Yang YANG ; Fupin HU ; Demei ZHU ; Yingchun XU ; Xiaojiang ZHANG ; Yi XIE ; Mei KANG ; Chuanqing WANG ; Pan FU ; Yuanhong XU ; Ying HUANG ; Ziyong SUN ; Zhongju CHEN ; Yuxing NI ; Jingyong SUN ; Yunzhuo CHU ; Sufei TIAN ; Zhidong HU ; Jin LI ; Yunsong YU ; Jie LIN ; Bin SHAN ; Yan DU ; Sufang GUO ; Lianhua WEI ; Fengmei ZOU ; Hong ZHANG ; Chun WANG ; Yunjian HU ; Xiaoman AI ; Chao ZHUO ; Danhong SU ; Dawen GUO ; Jinying ZHAO ; Hua YU ; Xiangning HUANG ; Wen'en LIU ; Yanming LI ; Yan JIN ; Chunhong SHAO ; Xuesong XU ; Chao YAN ; Shanmei WANG ; Yafei CHU ; Lixia ZHANG ; Juan MA ; Shuping ZHOU ; Yan ZHOU ; Lei ZHU ; Jinhua MENG ; Fang DONG ; Zhiyong LÜ ; Fangfang HU ; Han SHEN ; Wanqing ZHOU ; Wei JIA ; Gang LI ; Jinsong WU ; Yuemei LU ; Jihong LI ; Jinju DUAN ; Jianbang KANG ; Xiaobo MA ; Yanping ZHENG ; Ruyi GUO ; Yan ZHU ; Yunsheng CHEN ; Qing MENG ; Shifu WANG ; Xuefei HU ; Jilu SHEN ; Ruizhong WANG ; Hua FANG ; Bixia YU ; Yong ZHAO ; Ping GONG ; Kaizhen WEN ; Yirong ZHANG ; Jiangshan LIU ; Longfeng LIAO ; Hongqin GU ; Lin JIANG ; Wen HE ; Shunhong XUE ; Jiao FENG ; Chunlei YUE
Chinese Journal of Infection and Chemotherapy 2024;24(3):300-308
Objective To understand the distribution and changing resistance profiles of clinical isolates of Enterococcus in hospitals across China from 2015 to 2021.Methods Antimicrobial susceptibility testing was conducted for the clinical isolates of Enterococcus according to the unified protocol of CHINET program by automated systems,Kirby-Bauer method,or E-test strip.The results were interpreted according to the Clinical & Laboratory Standards Institute(CLSI)breakpoints in 2021.WHONET 5.6 software was used for statistical analysis.Results A total of 124 565 strains of Enterococcus were isolated during the 7-year period,mainly including Enterococcus faecalis(50.7%)and Enterococcus faecalis(41.5%).The strains were mainly isolated from urinary tract specimens(46.9%±2.6%),and primarily from the patients in the department of internal medicine,surgery and ICU.E.faecium and E.faecalis strains showed low level resistance rate to vancomycin,teicoplanin and linezolid(≤3.6%).The prevalence of vancomycin-resistant E.faecalis and E.faecium was 0.1%and 1.3%,respectively.The prevalence of linezolid-resistant E.faecalis increased from 0.7%in 2015 to 3.4%in 2021,while the prevalence of linezolid-resistant E.faecium was 0.3%.Conclusions The clinical isolates of Enterococcus were still highly susceptible to vancomycin,teicoplanin,and linezolid,evidenced by a low resistance rate.However,the prevalence of linezolid-resistant E.faecalis was increasing during the 7-year period.It is necessary to strengthen antimicrobial resistance surveillance to effectively identify the emergence of antibiotic-resistant bacteria and curb the spread of resistant pathogens.
9.Changing resistance profiles of Enterobacter isolates in hospitals across China:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Shaozhen YAN ; Ziyong SUN ; Zhongju CHEN ; Yang YANG ; Fupin HU ; Demei ZHU ; Yi XIE ; Mei KANG ; Fengbo ZHANG ; Ping JI ; Zhidong HU ; Jin LI ; Sufang GUO ; Han SHEN ; Wanqing ZHOU ; Yingchun XU ; Xiaojiang ZHANG ; Xuesong XU ; Chao YAN ; Chuanqing WANG ; Pan FU ; Wei JIA ; Gang LI ; Yuanhong XU ; Ying HUANG ; Dawen GUO ; Jinying ZHAO ; Wen'en LIU ; Yanming LI ; Hua YU ; Xiangning HUANG ; Bin SHAN ; Yan DU ; Shanmei WANG ; Yafei CHU ; Yuxing NI ; Jingyong SUN ; Yunsong YU ; Jie LIN ; Chao ZHUO ; Danhong SU ; Lianhua WEI ; Fengmei ZOU ; Yan JIN ; Chunhong SHAO ; Jihong LI ; Lixia ZHANG ; Juan MA ; Yunzhuo CHU ; Sufei TIAN ; Jinju DUAN ; Jianbang KANG ; Ruizhong WANG ; Hua FANG ; Fangfang HU ; Yunjian HU ; Xiaoman AI ; Fang DONG ; Zhiyong LÜ ; Hong ZHANG ; Chun WANG ; Yong ZHAO ; Ping GONG ; Lei ZHU ; Jinhua MENG ; Xiaobo MA ; Yanping ZHENG ; Jinsong WU ; Yuemei LU ; Ruyi GUO ; Yan ZHU ; Kaizhen WEN ; Yirong ZHANG ; Chunlei YUE ; Jiangshan LIU ; Wenhui HUANG ; Shunhong XUE ; Xuefei HU ; Hongqin GU ; Jiao FENG ; Shuping ZHOU ; Yan ZHOU ; Yunsheng CHEN ; Qing MENG ; Bixia YU ; Jilu SHEN ; Rui DOU ; Shifu WANG ; Wen HE ; Longfeng LIAO ; Lin JIANG
Chinese Journal of Infection and Chemotherapy 2024;24(3):309-317
Objective To examine the changing antimicrobial resistance profile of Enterobacter spp.isolates in 53 hospitals across China from 2015 t0 2021.Methods The clinical isolates of Enterobacter spp.were collected from 53 hospitals across China during 2015-2021 and tested for antimicrobial susceptibility using Kirby-Bauer method or automated testing systems according to the CHINET unified protocol.The results were interpreted according to the breakpoints issued by the Clinical & Laboratory Standards Institute(CLSI)in 2021(M100 31st edition)and analyzed with WHONET 5.6 software.Results A total of 37 966 Enterobacter strains were isolated from 2015 to 2021.The proportion of Enterobacter isolates among all clinical isolates showed a fluctuating trend over the 7-year period,overall 2.5%in all clinical isolates amd 5.7%in Enterobacterale strains.The most frequently isolated Enterobacter species was Enterobacter cloacae,accounting for 93.7%(35 571/37 966).The strains were mainly isolated from respiratory specimens(44.4±4.6)%,followed by secretions/pus(16.4±2.3)%and urine(16.0±0.9)%.The strains from respiratory samples decreased slightly,while those from sterile body fluids increased over the 7-year period.The Enterobacter strains were mainly isolated from inpatients(92.9%),and only(7.1±0.8)%of the strains were isolated from outpatients and emergency patients.The patients in surgical wards contributed the highest number of isolates(24.4±2.9)%compared to the inpatients in any other departement.Overall,≤ 7.9%of the E.cloacae strains were resistant to amikacin,tigecycline,polymyxin B,imipenem or meropenem,while ≤5.6%of the Enterobacter asburiae strains were resistant to these antimicrobial agents.E.asburiae showed higher resistance rate to polymyxin B than E.cloacae(19.7%vs 3.9%).Overall,≤8.1%of the Enterobacter gergoviae strains were resistant to tigecycline,amikacin,meropenem,or imipenem,while 10.5%of these strains were resistant to polycolistin B.The overall prevalence of carbapenem-resistant Enterobacter was 10.0%over the 7-year period,but showing an upward trend.The resistance profiles of Enterobacter isolates varied with the department from which they were isolated and whether the patient is an adult or a child.The prevalence of carbapenem-resistant E.cloacae was the highest in the E.cloacae isolates from ICU patients.Conclusions The results of the CHINET Antimicrobial Resistance Surveillance Program indicate that the proportion of Enterobacter strains in all clinical isolates fluctuates slightly over the 7-year period from 2015 to 2021.The Enterobacter strains showed increasing resistance to multiple antimicrobial drugs,especially carbapenems over the 7-year period.
10.Changing resistance profiles of Proteus,Morganella and Providencia in hospitals across China:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Yunmin XU ; Xiaoxue DONG ; Bin SHAN ; Yang YANG ; Fupin HU ; Demei ZHU ; Yingchun XU ; Xiaojiang ZHANG ; Ping JI ; Fengbo ZHANG ; Yi XIE ; Mei KANG ; Chuanqing WANG ; Pan FU ; Yuanhong XU ; Ying HUANG ; Ziyong SUN ; Zhongju CHEN ; Yuxing NI ; Jingyong SUN ; Yunzhuo CHU ; Sufei TIAN ; Zhidong HU ; Jin LI ; Yunsong YU ; Jie LIN ; Sufang GUO ; Lianhua WEI ; Fengmei ZOU ; Hong ZHANG ; Chun WANG ; Yunjian HU ; Xiaoman AI ; Chao ZHUO ; Danhong SU ; Dawen GUO ; Jinying ZHAO ; Hua YU ; Xiangning HUANG ; Wen'en LIU ; Yanming LI ; Yan JIN ; Chunhong SHAO ; Xuesong XU ; Chao YAN ; Shanmei WANG ; Yafei CHU ; Lixia ZHANG ; Juan MA ; Shuping ZHOU ; Yan ZHOU ; Lei ZHU ; Jinhua MENG ; Fang DONG ; Hongyan ZHENG ; Fangfang HU ; Han SHEN ; Wanqing ZHOU ; Wei JIA ; Gang LI ; Jinsong WU ; Yuemei LU ; Jihong LI ; Jinju DUAN ; Jianbang KANG ; Xiaobo MA ; Yanping ZHENG ; Ruyi GUO ; Yan ZHU ; Yunsheng CHEN ; Qing MENG ; Shifu WANG ; Xuefei HU ; Jilu SHEN ; Wenhui HUANG ; Ruizhong WANG ; Hua FANG ; Bixia YU ; Yong ZHAO ; Ping GONG ; Kaizhen WEN ; Yirong ZHANG ; Jiangshan LIU ; Longfeng LIAO ; Hongqin GU ; Lin JIANG ; Wen HE ; Shunhong XUE ; Jiao FENG ; Chunlei YUE
Chinese Journal of Infection and Chemotherapy 2024;24(4):410-417
Objective To understand the changing distribution and antimicrobial resistance profiles of Proteus,Morganella and Providencia in hospitals across China from January 1,2015 to December 31,2021 in the CHINET Antimicrobial Resistance Surveillance Program.Methods Antimicrobial susceptibility testing was carried out following the unified CHINET protocol.The results were interpreted in accordance with the breakpoints in the 2021 Clinical & Laboratory Standards Institute(CLSI)M100(31 st Edition).Results A total of 32 433 Enterobacterales strains were isolated during the 7-year period,including 24 160 strains of Proteus,6 704 strains of Morganella,and 1 569 strains of Providencia.The overall number of these Enterobacterales isolates increased significantly over the 7-year period.The top 3 specimen source of these strains were urine,lower respiratory tract specimens,and wound secretions.Proteus,Morganella,and Providencia isolates showed lower resistance rates to amikacin,meropenem,cefoxitin,cefepime,cefoperazone-sulbactam,and piperacillin-tazobactam.For most of the antibiotics tested,less than 10%of the Proteus and Morganella strains were resistant,while less than 20%of the Providencia strains were resistant.The prevalence of carbapenem-resistant Enterobacterales(CRE)was 1.4%in Proteus isolates,1.9%in Morganella isolates,and 15.6%in Providencia isolates.Conclusions The overall number of clinical isolates of Proteus,Morganella and Providencia increased significantly in the 7-year period from 2015 to 2021.The prevalence of CRE strains also increased.More attention should be paid to antimicrobial resistance surveillance and rational antibiotic use so as to prevent the emergence and increase of antimicrobial resistance.


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