1.Analyzing the influencing factors of occupational burnout among disease control and prevention staffs in Sichuan Province
Chaoxue WU ; Shuang DONG ; Liang WANG ; Xunbo DU ; Lin ZHAO ; Dan SHAO ; Quanquan XIAO ; Lijun ZHOU ; Chongkun XIAO ; Heng YUAN
China Occupational Medicine 2025;52(3):288-292
Objective To assess the situation and influencing factors of occupational burnout among the staff at the Center for Disease Control and Prevention (CDC) in Sichuan Province. Methods A total of 1 038 CDC staff members in Sichuan Province were selected as the study subjects using the stratified random sampling method. Occupational burnout of the staff was assessed using the Maslach Burnout Inventory General Survey via an online questionnaire. Results The detection rate of occupational burnout was 42.3% (439/1 038). Binary logistic regression analysis result showed that, after controlling for confounding factors such as education level and alcohol consumption, CDC staffs aged at 20-<31, 31-<41, and 41-<51 years were at higher risk of occupational burnout compared with those ≥51 years (all P<0.05). CDC staffs with 5-<10 or ≥10 years of service had higher occupational burnout risk compared with those with <5 years (both P<0.05). CDC staffs with poor or fair health status, irregular diet, and poor sleep quality had higher risk of occupational burnout compared with those healthy, have regular diet, and good sleep quality (all P<0.05). The risk of occupational burnout increased with higher overtime frequency (all P<0.05). Conclusion Occupational burnout among CDC staffs in Sichuan Province is relatively high. Age, years of service, health status, diet, sleep quality, and overtime frequency are key influencing factors.
2.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.
3.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.
4.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.
5.Changing distribution and resistance profiles of Klebsiella strains in hospitals across China:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Chuyue ZHUO ; Yingyi GUO ; Chao ZHUO ; 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 ; 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 ; 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):418-426
Objective To understand the changing distribution and antimicrobial resistance profiles of Klebsiella strains in 52 hospitals across China in the CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021.Methods Antimicrobial susceptibility testing was carried out according to the unified CHINET protocol.The susceptibility results were interpreted according to the breakpoints in the Clinical & Laboratory Standards Institute(CLSI)M100 document.Results A total of 241,549 nonduplicate Klebsiella strains were isolated from 2015 to 2021,including Klebsiella pneumoniae(88.0%),Klebsiella aerogenes(5.8%),Klebsiella oxytoca(5.7%),and other Klebsiella species(0.6%).Klebsiella strains were mainly isolated from respiratory tract(48.49±5.32)%.Internal medicine(22.79±3.28)%,surgery(17.98±3.10)%,and ICU(14.03±1.39)%were the top 3 departments where Klebsiella strains were most frequently isolated.K.pneumoniae isolates showed higher resistance rate to most antimicrobial agents compared to other Klebsiella species.Klebsiella isolates maintained low resistance rates to tigecycline and polymyxin B.ESBLs-producing K.pneumoniae and K.oxytoca strains showed higher resistance rates to all the antimicrobial agents tested compared to the corresponding ESBLs-nonproducing strains.The K.pneumoniae and carbapenem-resistant K.pneumoniae(CRKP)strains isolated from ICU patients demonstrated higher resistance rates to majority of the antimicrobial agents tested than the strains isolated from non-ICU patients.The CRKP strains isolated from adult patients had higher resistance rates to most of the antimicrobial agents tested than the corresponding CRKP strains isolated from paediatric patients.Conclusions The prevalence of carbapenem-resistant strains in Klebsiella isolates increased greatly from 2015 to 2021.However,the Klebsiella isolates remained highly susceptible to tigecycline and polymyxin B.Antimicrobial resistance surveillance should still be strengthened for Klebsiella strains.
6.Changing resistance profiles of Staphylococcus isolates in hospitals across China:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Yuling XIAO ; Mei KANG ; Yi XIE ; Yang YANG ; Fupin HU ; Demei ZHU ; Yingchun XU ; Xiaojiang ZHANG ; Ping JI ; Fengbo ZHANG ; 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 ; 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(5):570-580
Objective To investigate the changing distribution and antibiotic resistance profiles of clinical isolates of Staphylococcus in hospitals across China from 2015 to 2021.Methods Antimicrobial susceptibility testing was conducted for the clinical isolates of Staphylococcus according to the unified protocol of CHINET(China Antimicrobial Surveillance Network)using disk diffusion method and commercial automated systems.The CHINET antimicrobial resistance surveillance data from 2015 to 2021 were interpreted according to the 2021 CLSI breakpoints and analyzed using WHONET 5.6.Results During the period from 2015 to 2021,a total of 204,771 nonduplicate strains of Staphylococcus were isolated,including 136,731(66.8%)strains of Staphylococcus aureus and 68,040(33.2%)strains of coagulase-negative Staphylococcus(CNS).The proportions of S.aureus isolates and CNS isolates did not show significant change.S.aureus strains were mainly isolated from respiratory specimens(38.9±5.1)%,wound,pus and secretions(33.6±4.2)%,and blood(11.9±1.5)%.The CNS strains were predominantly isolated from blood(73.6±4.2)%,cerebrospinal fluid(12.1±2.5)%,and pleural effusion and ascites(8.4±2.1)%.S.aureus strains were mainly isolated from the patients in ICU(17.0±7.3)%,outpatient and emergency(11.6±1.7)%,and department of surgery(11.2±0.9)%,whereas CNS strains were primarily isolated from the patients in ICU(32.2±9.7)%,outpatient and emergency(12.8±4.7)%,and department of internal medicine(11.2±1.9)%.The prevalence of methicillin-resistant strains was 32.9%in S.aureus(MRSA)and 74.1%in CNS(MRCNS).Over the 7-year period,the prevalence of MRSA decreased from 42.1%to 29.2%,and the prevalence of MRCNS decreased from 82.1%to 68.2%.MRSA showed higher resistance rates to all the antimicrobial agents tested except trimethoprim-sulfamethoxazole than methicillin-susceptible S.aureus(MSSA).Over the 7-year period,MRSA strains showed decreasing resistance rates to gentamicin,rifampicin,and levofloxacin,MRCNS showed decreasing resistance rates to gentamicin,erythromycin,rifampicin,and trimethoprim-sulfamethoxazole,but increasing resistance rate to levofloxacin.No vancomycin-resistant strains were detected.The prevalence of linezolid-resistant MRCNS increased from 0.2%to 2.3%over the 7-year period.Conclusions Staphylococcus remains the major pathogen among gram-positive bacteria.MRSA and MRCNS were still the principal antibiotic-resistant gram-positive bacteria.No S.aureus isolates were found resistant to vancomycin or linezolid,but linezolid-resistant strains have been detected in MRCNS isolates,which is an issue of concern.
7.Establishment and validation of a laboratory-based multiparameter model for predicting bone marrow metastasis in malignant tumors
Haocheng LI ; Wei XU ; Zhonghua DU ; Lin SONG ; Dan LIU ; Huihui SHAO ; Chunhe ZHAO ; Weiqi CUI ; Linlin QU
Chinese Journal of Laboratory Medicine 2024;47(11):1248-1255
Objective:To establish and validate the prediction model for bone marrow metastasis (BMM) in malignant tumors by screening out laboratory multiparameters.Methods:This case-control study collected 444 cases of malignant tumor patients who were hospitalized in the First Hospital of Jilin University from March 2018 to March 2024, including 243 cases for model establishment set and 201 cases for model validation set. The model establishment set was divided into BMM positive group (81 cases) and BMM negative group (162 cases), and the model validation set was divided into positive group (67 cases) and a negative group (134 cases). We collected patients′ clinical information such as gender, age, clinical diagnosis, and results of 47 laboratory tests including routine blood analysis, coagulation, liver function, tumor markers, potassium, sodium, chloride, and calcium ion tests, bone marrow morphology, and bone marrow biopsy. BMM was taken as the outcome event, differencial variables were analyzed using inter group comparisons, the correlation among parameters was analyzed using Pearson correlation analysis, the risk factors for BMM were analyzed using multivariate conditional logistic regression analysis, to establish logistic model, followed by efficiency evaluation on BMM predictive model using receiver operating characteristic (ROC) curves.Results:In the model establishment set, Pearson correlation analysis of 28 parameters that differed between the BMM positive and negative groups revealed that the correlation coefficients of 17 parameters, including mean platelet volume (MPV), hematocrit (HCT), hemoglobin (HGB), and prothrombin time (PT), were no more than 0.6 ( P<0.05). Further multivariate conditional logistic regression analysis demonstrated that MPV, HGB, HCT, PT, red cell distribution width (RDW), platelet count (PLT), alkaline phosphatase (ALP), chloride (Cl -), and mean erythrocyte hemoglobin concentration (MCHC) were the risk factors of BMM occurence in malignancy [MPV ( OR=9.929, 95% CI 2.688-71.335), HCT ( OR=8.232, 95% CI 6.223-9.841), HGB ( OR=4.300, 95% CI 1.947-16.577), PT ( OR=3.738, 95% CI 1.359-11.666), RDW ( OR=1.995, 95% CI 1.275-3.807), ALP ( OR=1.025, 95% CI 1.012-1.045), PLT ( OR=1.014, 95% CI 1.002-1.031), MCHC ( OR=0.724, 95% CI 0.523-0.880) and Cl -( OR=0.703, 95% CI 0.472-0.967)]. In the model establishment set, combiation of risk factors provided an AUC of 0.943 (95% CI 0.898-0.987, P<0.001), a sensitivity of 86.3%, and a specificity of 89.2% for BMM prediction. In the model validation set, the AUC was 0.924 (95% CI 0.854-0.960, P<0.001), with a sensitivity and specificity of 86.7% and 83.8%, respectively. Conclusion:This study built and validated a multiple-parameter model for BMM, which may facilitate the timely detection of BMM and provide reference for decision making of bone marrow aspiration.
8.Evidence-based guideline for clinical diagnosis and treatment of acute combination fractures of the atlas and axis in adults (version 2023)
Yukun DU ; Dageng HUANG ; Wei TIAN ; Dingjun HAO ; Yongming XI ; Baorong HE ; Bohua CHEN ; Tongwei CHU ; Jian DONG ; Jun DONG ; Haoyu FENG ; Shunwu FAN ; Shiqing FENG ; Yanzheng GAO ; Zhong GUAN ; Yong HAI ; Lijun HE ; Yuan HE ; Dianming JIANG ; Jianyuan JIANG ; Weiqing KONG ; Bin LIN ; Bin LIU ; Baoge LIU ; Chunde LI ; Fang LI ; Feng LI ; Guohua LYU ; Li LI ; Qi LIAO ; Weishi LI ; Xiaoguang LIU ; Yong LIU ; Zhongjun LIU ; Shibao LU ; Fei LUO ; Jianyi LI ; Yong QIU ; Limin RONG ; Yong SHEN ; Huiyong SHEN ; Jun SHU ; Yueming SONG ; Tiansheng SUN ; Jiang SHAO ; Jiwei TIAN ; Yan WANG ; Zhe WANG ; Zheng WANG ; Xiangyang WANG ; Hong XIA ; Jinglong YAN ; Liang YAN ; Wen YUAN ; Jie ZHAO ; Jianguo ZHANG ; Yue ZHU ; Xuhui ZHOU ; Mingwei ZHAO
Chinese Journal of Trauma 2023;39(4):299-308
The acute combination fractures of the atlas and axis in adults have a higher rate of neurological injury and early death compared with atlas or axial fractures alone. Currently, the diagnosis and treatment choices of acute combination fractures of the atlas and axis in adults are controversial because of the lack of standards for implementation. Non-operative treatments have a high incidence of bone nonunion and complications, while surgeries may easily lead to the injury of the vertebral artery, spinal cord and nerve root. At present, there are no evidence-based Chinese guidelines for the diagnosis and treatment of acute combination fractures of the atlas and axis in adults. To provide orthopedic surgeons with the most up-to-date and effective information in treating acute combination fractures of the atlas and axis in adults, the Spinal Trauma Group of Orthopedic Branch of Chinese Medical Doctor Association organized experts in the field of spinal trauma to develop the Evidence-based guideline for clinical diagnosis and treatment of acute combination fractures of the atlas and axis in adults ( version 2023) by referring to the "Management of acute combination fractures of the atlas and axis in adults" published by American Association of Neurological Surgeons (AANS)/Congress of Neurological Surgeons (CNS) in 2013 and the relevant Chinese and English literatures. Ten recommendations were made concerning the radiological diagnosis, stability judgment, treatment rules, treatment options and complications based on medical evidence, aiming to provide a reference for the diagnosis and treatment of acute combination fractures of the atlas and axis in adults.
9.Predictive value of BMI combined with preoperative oxygenation index for postoperative hypoxemia in Stanford type A aortic dissection
Jin-Zhen ZHAO ; Ping LV ; Peng ZHU ; Song-Lin DU ; Jun WAN ; Dong-Qi AN ; Shao-Yi ZHENG
Medical Journal of Chinese People's Liberation Army 2023;48(12):1445-1450
Objectives To analyze the risk factors and their predictive value for postoperative hypoxemia in Type-A aortic dissection(TAAD).Methods A single-center retrospective study was conducted among 146 consecutive patients diagnosed as TAAD and undergone aortic arch surgery from January 2018 to June 2021 in Nanfang Hospital of Southern Medical University.According to the lowest postoperative PaO2/FiO2 ratio within 24 hours,the patients were classified into two groups:hypoxemia group(PaO2/FiO2≤200 mmHg)and non-hypoxemia group(PaO2/FiO2>200 mmHg).The difference of preoperative oxygen index,duration of mechanical ventilation and mortality in hospital were analyzed between the two groups.The independent risk factors for postoperative hypoxemia were evaluated by multivariate logistic regression and the predictive value was analyzed by receiver operator character(ROC)curves.Results For TAAD patients,the incidence of postoperative hypoxemia was 45.9%.Compared to non-hypoxemia group,hypoxemia group exhibited longer duration of mechanical ventilation(P<0.001)and longer intensive care unit(ICU)length of stay(P<0.05).Moreover,patients with hypoxemia presented higher mortality during hospital(P=0.011).Multivariate regression analysis identified BMI as independent risk factor(OR=1.701,P<0.001)and preoperation PaO2/FiO2 ratio as protective factors for postoperative hypoxemia in patients with TAAD(OR=0.987,P=0.004).Area under the ROC curve of BMI was 0.848,the optimal cut-off point of BMI was 25.8 kg/m2.Area under the ROC curve of pre-operation PaO2/FiO2 ratio was 0.808,the optimal cut-off point of preoperation PaO2/FiO2 ratio was 265 mmHg.Conclusions BMI higher than 25.8 kg/m2 is an independent risk factor and preoperation PaO2/FiO2 ratio higher than 265 mmHg is a protective factor for postoperative hypoxemia in patients with TAAD.Subjects with hypoxemia had longer duration of mechanical ventilation,ICU stay and higher mortality.
10.Relationship between body mass index and sexual development in Chinese children.
Xiao Qin XU ; Jian Wei ZHANG ; Rui Min CHEN ; Jing Si LUO ; Shao Ke CHEN ; Rong Xiu ZHENG ; Di WU ; Min ZHU ; Chun Lin WANG ; Yan LIANG ; Hui YAO ; Hai Yan WEI ; Zhe SU ; Mireguli MAIMAITI ; Hong Wei DU ; Fei Hong LUO ; Pin LI ; Shu Ting SI ; Wei WU ; Ke HUANG ; Guan Ping DONG ; Yun Xian YU ; Jun Fen FU
Chinese Journal of Pediatrics 2022;60(4):311-316
Objective: To investigate the relationship between body mass index (BMI) and sexual development in Chinese children. Methods: A nationwide multicenter and population-based large cross-sectional study was conducted in 13 provinces, autonomous regions and municipalities of China from January 2017 to December 2018. Data on sex, age, height, weight were collected, BMI was calculated and sexual characteristics were analyzed. The subjects were divided into four groups based on age, including ages 3-<6 years, 6-<10 years, 10-<15 years and 15-<18 years. Multiple Logistic regression models were used for evaluating the associations of BMI with sexual development in children. Dichotomous Logistic regression was used to compare the differences in the distribution of early and non-early puberty among normal weight, overweight and obese groups. Curves were drawn to analyze the relationship between the percentage of early puberty and BMI distribution in girls and boys at different Tanner stages. Results: A total of 208 179 healthy children (96 471 girls and 111 708 boys) were enrolled in this study. The OR values of B2, B3 and B4+ in overweight girls were 1.72 (95%CI: 1.56-1.89), 3.19 (95%CI: 2.86-3.57), 7.14 (95%CI: 6.33-8.05) and in obese girls were 2.05 (95%CI: 1.88-2.24), 4.98 (95%CI: 4.49-5.53), 11.21 (95%CI: 9.98-12.59), respectively; while the OR values of G2, G3, G4+ in overweight boys were 1.27 (95%CI: 1.17-1.38), 1.52 (95%CI: 1.36-1.70), 1.88 (95%CI: 1.66-2.14) and in obese boys were 1.27 (95%CI: 1.17-1.37), 1.59 (95%CI: 1.43-1.78), and 1.93 (95%CI: 1.70-2.18) (compared with normal weight Tanner 1 group,all P<0.01). Analysis in different age groups found that OR values of obese girls at B2 stage and boys at G2 stage were 2.02 (95%CI: 1.06-3.86) and 2.32 (95%CI:1.05-5.12) in preschool children aged 3-<6 years, respectively (both P<0.05). And in the age group of 6-10 years, overweight girls had a 5.45-fold risk and obese girls had a 12.54-fold risk of B3 stage compared to girls with normal BMI. Compared with normal weight children, the risk of early puberty was 2.67 times higher in overweight girls, 3.63 times higher in obese girls, and 1.22 times higher in overweight boys, 1.35 times higher in obese boys (all P<0.01). Among the children at each Tanner stages, the percentage of early puberty increased with the increase of BMI, from 5.7% (80/1 397), 16.1% (48/299), 13.8% (27/195) to 25.7% (198/769), 65.1% (209/321), 65.4% (157/240) in girls aged 8-<9, 10-<11 and 11-<12 years, and 6.6% (34/513), 18.7% (51/273), 21.6% (57/264) to 13.3% (96/722), 46.4% (140/302), 47.5% (105/221) in boys aged 9-<10, 12-<13 and 13-<14 years, respectively. Conclusions: BMI is positively correlated with sexual development in both Chinese boys and girls, and the correlation is stronger in girls. Obesity is a risk factor for precocious puberty in preschool children aged 3-<6 years, and 6-<10 years of age is a high risk period for early development in obese girls.
Adolescent
;
Body Mass Index
;
Child
;
Child, Preschool
;
China/epidemiology*
;
Cross-Sectional Studies
;
Female
;
Humans
;
Male
;
Obesity/epidemiology*
;
Overweight/epidemiology*
;
Puberty
;
Puberty, Precocious
;
Sexual Development

Result Analysis
Print
Save
E-mail