1.Association between ambient particulate matter exposure and risk of benign prostatic hyperplasia in middle-aged and older men: A longitudinal cohort study based on CHARLS
Hanxiao HU ; Chuchu LIU ; Yuyuan HU ; Jiali CHEN ; Lingyi WANG ; Xiaobo LIU ; Yue WU
Journal of Environmental and Occupational Medicine 2026;43(5):630-636
Background Benign prostatic hyperplasia (BPH) is a common chronic urinary disease in middle-aged and older men, yet the impact of long-term exposure to atmospheric particulate matter (PM) on its pathogenesis remains unclear. Objective To investigate the association between PM exposure and the risk of incident BPH in middle-aged and older men. Methods Based on four waves of follow-up data (2011–2018) from the China Health and Retirement Longitudinal Study (CHARLS), 4766 participants were enrolled. Robust Poisson regression models were employed to assess the association between exposure to PM (PM1, PM2.5, and PM10) and the risk of incident BPH. Relative risks (RR) and their corresponding 95% confidence intervals (95%CI) were calculated. Dose-response relationships were fitted using restricted cubic splines (RCS). Subgroup analyses were performed to explore potential effect modifications, and multiple imputation was used to handle missing data. Results Over a mean follow-up of 6 years, 914 incident BPH cases were identified among the4766 participants (cumulative incidence: 19.18%). After adjusting for confounders, each 10 μg·m−3 increase in PM1, PM2.5, and PM10 concentrations was associated with a 13.1% (RR=1.131, 95%CI: 1.063, 1.203), 8.5% (RR=1.085, 95%CI: 1.050, 1.122), and 5.1% (RR=1.051, 95%CI: 1.034, 1.069) increased risk of BPH, respectively. RCS analysis showed that no nonlinear relationship was found between PM1 and PM2.5 and the risk of BPH (P>0.05); however, a nonlinear association was observed for PM10 (P=0.03), with the risk increment slowing beyond 100 μg·m−3. Subgroup and sensitivity analyses confirmed the robustness of these findings. Conclusion Long-term exposure to ambient particulate matter may be associated with an increased risk of incident BPH in middle-aged and older men.
2.Association between ambient particulate matter exposure and risk of benign prostatic hyperplasia in middle-aged and older men: A longitudinal cohort study based on CHARLS
Hanxiao HU ; Chuchu LIU ; Yuyuan HU ; Jiali CHEN ; Lingyi WANG ; Xiaobo LIU ; Yue WU
Journal of Environmental and Occupational Medicine 2026;43(5):630-636
Background Benign prostatic hyperplasia (BPH) is a common chronic urinary disease in middle-aged and older men, yet the impact of long-term exposure to atmospheric particulate matter (PM) on its pathogenesis remains unclear. Objective To investigate the association between PM exposure and the risk of incident BPH in middle-aged and older men. Methods Based on four waves of follow-up data (2011–2018) from the China Health and Retirement Longitudinal Study (CHARLS), 4766 participants were enrolled. Robust Poisson regression models were employed to assess the association between exposure to PM (PM1, PM2.5, and PM10) and the risk of incident BPH. Relative risks (RR) and their corresponding 95% confidence intervals (95%CI) were calculated. Dose-response relationships were fitted using restricted cubic splines (RCS). Subgroup analyses were performed to explore potential effect modifications, and multiple imputation was used to handle missing data. Results Over a mean follow-up of 6 years, 914 incident BPH cases were identified among the4766 participants (cumulative incidence: 19.18%). After adjusting for confounders, each 10 μg·m−3 increase in PM1, PM2.5, and PM10 concentrations was associated with a 13.1% (RR=1.131, 95%CI: 1.063, 1.203), 8.5% (RR=1.085, 95%CI: 1.050, 1.122), and 5.1% (RR=1.051, 95%CI: 1.034, 1.069) increased risk of BPH, respectively. RCS analysis showed that no nonlinear relationship was found between PM1 and PM2.5 and the risk of BPH (P>0.05); however, a nonlinear association was observed for PM10 (P=0.03), with the risk increment slowing beyond 100 μg·m−3. Subgroup and sensitivity analyses confirmed the robustness of these findings. Conclusion Long-term exposure to ambient particulate matter may be associated with an increased risk of incident BPH in middle-aged and older men.
3.Expert consensus on the standardized application of whole exome sequencing technology in the diagnosis of genetic disorders
Yun BAO ; Yanjie FAN ; Meng SU ; Bingbing WU ; Xiaobo HU ; Jian WANG ; Yongguo YU ; Taosheng HUANG
Chinese Journal of Medical Genetics 2025;42(1):1-11
Next generation sequencing (NGS) technology is playing an increasingly important role in the diagnosis of genetic diseases. Whole exome sequencing (WES), which targets the coding regions of the genome, has been widely used in the diagnosis of genetic diseases for its low cost and high efficiency. However, compared to conventional methods, the Next Generation Sequencing (NGS) process is intricate, and there is variability in the expertise of data analysts and variant interpreters, which may lead to inconsistencies in the outcomes. To ensure the quality of testing and enhance the diagnostic rate of diseases, this consensus has provided recommendations regarding the laboratory setup, operational procedures, data analysis, result interpretation, and quality control for WES, with an aim to standardize its application in the detection of genetic disorders.
4.Disease burden of chronic obstructive pulmonary disease under the hierarchical medical system based on medical internet of things
Huanying WANG ; Fengli SI ; Yiqun JIANG ; Peng WU ; Xiaobo SONG ; Bangfeng ZHAO ; Chunfeng SHENG ; Xun XU ; Fan LI ; Tingting WU
Chinese Journal of General Practitioners 2025;24(8):978-984
Objective:To evaluate the impact of implementing a regional hierarchical medical management model based on the medical internet of things (medical IoT) on the frequency of emergency department visits and hospitalizations, as well as related medical expenses, in patients with chronic obstructive pulmonary disease (COPD).Methods:This retrospective study included COPD patients enrolled in the regional hierarchical medical management system based on Medical IoT across 21 community health service centers in Songjiang District, Shanghai, between July 2017 and May 2018. Utilizing patient data from the year prior to enrollment as the baseline, changes in the number of emergency visits, hospitalizations, and associated medical costs during the first and second years of management were compared. Changes for patients receiving drug treatment were also analyzed.Results:A total of 973 COPD patients were enrolled. The mean age was 75.2±17.0 years, and 64.34% (626/973) were male. Compared to baseline, all COPD patients in the first year of management showed significant reductions: emergency visits decreased by 33.67%, total emergency costs by 45.60%, hospitalizations by 27.15%, and total hospitalization costs by 25.42%. In the second year, reductions were: emergency visits by 28.08%, total emergency costs by 36.10%, hospitalizations by 35.26%, and total hospitalization costs by 18.13% (all P<0.05). Among patients receiving drug therapy, reductions in the first year were: emergency visits by 39.66%, total emergency costs by 47.54%, hospitalizations by 25.19%, and total hospitalization costs by 28.40%. In the second year, reductions were: emergency visits by 46.98%, total emergency costs by 45.99%, hospitalizations by 41.98%, and total hospitalization costs by 24.94% (all P<0.05). No significant differences were observed before and after management for patients without drug treatment. Conclusion:The implementation of the regional hierarchical medical management model based on Medical IoT significantly reduced the frequency of emergency visits and hospitalizations, as well as related costs, for COPD patients.
5.Impacts of the frequency of orthogonal image guided-verification on set-up error correction and PTV margins in postoperative radiotherapy for breast cancer
Fangfen DONG ; Bing WU ; Zhixin WANG ; Jiaming LI ; Miaoyun HUANG ; Yong YANG ; Benhua XU ; Xiaobo LI
Chinese Journal of Radiological Medicine and Protection 2025;45(1):37-42
Objective:To explore the impacts of different frequencies of orthogonal image-guided verification on set-up error correction and the reference values of planning target volume (PTV) margins in postoperative radiotherapy for breast cancer, in order to provide recommendations of the verification frequency in clinical practice.Methods:A total of 80 breast cancer patients who received postoperative intensity-modulated radiotherapy for breast cancer at the Affiliated Union Hospital of Fujian Medical University from January 2021 to January 2022 were enrolled. Orthogonal image-guided verification was conducted before each radiation treatment to determine initial and residual set-up errors. The error data were statistically analyzed by categorizing patients into groups A, B, C, D, E, and F, based on assumed verification frequencies (daily, every 2, 3, 4, and 5 d, and no correction). The magnitude, distribution, and differences of set-up errors of various groups were analyzed, and the PTV margins were calculated using a reference formula.Results:The collected initial set-up errors of the 80 patients were (3.49±3.43), (2.87±2.88), and (2.75±2.35) mm, in x, y, and z directions, respectively. The residual set-up errors decreased gradually with increasing verification frequency. In the case of daily image-guided verification, the residual set-up errors in x, y, and z directions decreased to (1.63±0.90), (1.63±0.93), and (1.55±0.90)mm, respectively. The reference values of PTV margins calculated under different verification frequencies decreased with an increase in the verification frequency. Under daily verification, the reference values of PTV margins calculated based on set-up errors decreased from initial 6.60, 6.29, and 4.22 mm (without correction) to 1.27, 1.37, and 1.28 mm, respectively. Differences in the reference values under verification frequencies every 3, 4, and 5 d were less than 0.81 mm. Conclusions:Daily image-guided verification (including correction) can effectively reduce set-up errors in image-guided radiotherapy for breast cancer. The set-up errors differ under varying verification frequencies, with a higher frequency corresponding to better correction effects and smaller PTV margins required.
6.A review of artificial intelligence in acute stroke imaging diagnosis
Xiaowei SONG ; Xiaobo JIA ; Jian WU
Chinese Journal of Cerebrovascular Diseases 2025;22(2):75-80
Artificial intelligence(AI)technology is advancing rapidly and has demonstrated significant potential in medical image processing.In recent years,various research initiatives and products based on AI technology have been implemented in the diagnosis and treatment of strokes,enhancing both efficiency and accuracy to some extent.However,AI technology still encounters several challenges in the diagnosis and treatment of strokes.This paper reviewed the existing related AI technologies and applications and explores future research directions.
7.Distribution and resistance profiles of bacterial strains isolated from cerebrospinal fluid in hospitals across China:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Juan MA ; Lixia ZHANG ; Yang YANG ; Fupin HU ; Demei ZHU ; Han SHEN ; Wanqing ZHOU ; Wenen LIU ; Yanming LI ; Yi XIE ; Mei KANG ; Dawen GUO ; Jinying ZHAO ; Zhidong HU ; Jin LI ; Shanmei WANG ; Yafei CHU ; Yunsong YU ; Jie LIN ; Yingchun XU ; Xiaojiang ZHANG ; Jihong LI ; Bin SHAN ; Yan DU ; Ping JI ; Fengbo ZHANG ; Chao ZHUO ; Danhong SU ; Lianhua WEI ; Fengmei ZOU ; Xiaobo MA ; Yanping ZHENG ; Yuanhong XU ; Ying HUANG ; Yunzhuo CHU ; Sufei TIAN ; Hua YU ; Xiangning HUANG ; Sufang GUO ; Xuesong XU ; Chao YAN ; Fangfang HU ; Yan JIN ; Chunhong SHAO ; Wei JIA ; Gang LI ; Jinsong WU ; Yuemei LU ; Fang DONG ; Zhiyong LÜ ; Lei ZHU ; Jinhua MENG ; Shuping ZHOU ; Yan ZHOU ; Chuanqing WANG ; Pan FU ; Yunjian HU ; Xiaoman AI ; Ziyong SUN ; Zhongju CHEN ; Hong ZHANG ; Chun WANG ; Yuxing NI ; Jingyong SUN ; Kaizhen WEN ; Yirong ZHANG ; Ruyi GUO ; Yan ZHU ; Jinju DUAN ; Jianbang KANG ; Xuefei HU ; Shifu WANG ; Yunsheng CHEN ; Qing MENG ; Yong ZHAO ; Ping GONG ; Ruizhong WANG ; Hua FANG ; Jilu SHEN ; Jiangshan LIU ; Hongqin GU ; Jiao FENG ; Shunhong XUE ; Bixia YU ; Wen HE ; Lin JIANG ; Longfeng LIAO ; Chunlei YUE ; Wenhui HUANG
Chinese Journal of Infection and Chemotherapy 2025;25(3):279-289
Objective To investigate the distribution and antimicrobial resistance profiles of common pathogens isolated from cerebrospinal fluid(CSF)in CHINET program from 2015 to 2021.Methods The bacterial strains isolated from CSF were identified in accordance with clinical microbiology practice standards.Antimicrobial susceptibility test was conducted using Kirby-Bauer method and automated systems per the unified CHINET protocol.Results A total of 14 014 bacterial strains were isolated from CSF samples from 2015 to 2021,including the strains isolated from inpatients(95.3%)and from outpatient and emergency care patients(4.7%).Overall,19.6%of the isolates were from children and 80.4%were from adults.Gram-positive and Gram-negative bacteria accounted for 68.0%and 32.0%,respectively.Coagulase negative Staphylococcus accounted for 73.0%of the total Gram-positive bacterial isolates.The prevalence of MRSA was 38.2%in children and 45.6%in adults.The prevalence of MRCNS was 67.6%in adults and 69.5%in children.A small number of vancomycin-resistant Enterococcus faecium(2.2%)and linezolid-resistant Enterococcus faecalis(3.1%)were isolated from adult patients.The resistance rates of Escherichia coli and Klebsiella pneumoniae to ceftriaxone were 52.2%and 76.4%in children,70.5%and 63.5%in adults.The prevalence of carbapenem-resistant E.coli and K.pneumoniae(CRKP)was 1.3%and 47.7%in children,6.4%and 47.9%in adults.The prevalence of carbapenem-resistant Acinetobacter baumannii(CRAB)and Pseudomonas aeruginosa(CRPA)was 74.0%and 37.1%in children,81.7%and 39.9%in adults.Conclusions The data derived from antimicrobial resistance surveillance are crucial for clinicians to make evidence-based decisions regarding antibiotic therapy.Attention should be paid to the Gram-negative bacteria,especially CRKP and CRAB in central nervous system(CNS)infections.Ongoing antimicrobial resistance surveillance is helpful for optimizing antibiotic use in CNS infections.
8.Changing antibiotic resistance profiles of the bacterial strains isolated from geriatric patients in hospitals across China:data from CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Xiaoman AI ; Yunjian HU ; Chunyue GE ; Yang YANG ; Fupin HU ; Demei ZHU ; Yingchun XU ; Xiaojiang ZHANG ; Hui LI ; 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 ; 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 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 2025;25(3):290-302
Objective To investigate the antimicrobial resistance of clinical isolates from elderly patients(≥65 years)in major medical institutions across China.Methods Bacterial strains were isolated from elderly patients in 52 hospitals participating in the CHINET Antimicrobial Resistance Surveillance Program during the period from 2015 to 2021.Antimicrobial susceptibility test was carried out by disk diffusion method and automated systems according to the same CHINET protocol.The data were interpreted in accordance with the breakpoints recommended by the Clinical and Laboratory Standards Institute(CLSI)in 2021.Results A total of 514 715 nonduplicate clinical isolates were collected from elderly patients in 52 hospitals from January 1,2015 to December 31,2021.The number of isolates accounted for 34.3%of the total number of clinical isolates from all patients.Overall,21.8%of the 514 715 strains were gram-positive bacteria,and 78.2%were gram-negative bacteria.Majority(90.9%)of the strains were isolated from inpatients.About 42.9%of the strains were isolated from respiratory specimens,and 22.9%were isolated from urine.More than half(60.7%)of the strains were isolated from male patients,and 39.3%isolated from females.About 51.1%of the strains were isolated from patients aged 65-<75 years.The prevalence of methicillin-resistant strains(MRSA)was 38.8%in 32 190 strains of Staphylococcus aureus.No vancomycin-or linezolid-resistant strains were found.The resistance rate of E.faecalis to most antibiotics was significantly lower than that of Enterococcus faecium,but a few vancomycin-resistant strains(0.2%,1.5%)and linezolid-resistant strains(3.4%,0.3%)were found in E.faecalis and E.faecium.The prevalence of penicillin-susceptible S.pneumoniae(PSSP),penicillin-intermediate S.pneumoniae(PISP),and penicillin-resistant S.pneumoniae(PRSP)was 94.3%,4.0%,and 1.7%in nonmeningitis S.pneumoniae isolates.The resistance rates of Klebsiella spp.(Klebsiella pneumoniae 93.2%)to imipenem and meropenem were 20.9%and 22.3%,respectively.Other Enterobacterales species were highly sensitive to carbapenem antibiotics.Only 1.7%-7.8%of other Enterobacterales strains were resistant to carbapenems.The resistance rates of Acinetobacter spp.(Acinetobacter baumannii 90.6%)to imipenem and meropenem were 68.4%and 70.6%respectively,while 28.5%and 24.3%of P.aeruginosa strains were resistant to imipenem and meropenem,respectively.Conclusions The number of clinical isolates from elderly patients is increasing year by year,especially in the 65-<75 age group.Respiratory tract isolates were more prevalent in male elderly patients,and urinary tract isolates were more prevalent in female elderly patients.Klebsiella isolates were increasingly resistant to multiple antimicrobial agents,especially carbapenems.Antimicrobial resistance surveillance is helpful for accurate empirical antimicrobial therapy in elderly patients.
9.Changing resistance profiles of Haemophilus influenzae and Moraxella catarrhalis isolates in hospitals across China:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Hui FAN ; Chunhong SHAO ; Jia WANG ; Yang YANG ; Fupin HU ; Demei ZHU ; Yunsheng CHEN ; Qing MENG ; Hong ZHANG ; Chun WANG ; Fang DONG ; Wenqi SONG ; Kaizhen WEN ; Yirong ZHANG ; Chuanqing WANG ; Pan FU ; Chao ZHUO ; Danhong SU ; Jiangwei KE ; Shuping ZHOU ; Hua ZHANG ; Fangfang HU ; Mei KANG ; Chao HE ; Hua YU ; Xiangning HUANG ; Yingchun XU ; Xiaojiang ZHANG ; Wenen LIU ; Yanming LI ; Lei ZHU ; Jinhua MENG ; Shifu WANG ; Bin SHAN ; Yan DU ; Wei JIA ; Gang LI ; Jiao FENG ; Ping GONG ; Miao SONG ; Lianhua WEI ; Xin WANG ; Ruizhong WANG ; Hua FANG ; Sufang GUO ; Yanyan WANG ; Dawen GUO ; Jinying ZHAO ; Lixia ZHANG ; Juan MA ; Han SHEN ; Wanqing ZHOU ; Ruyi GUO ; Yan ZHU ; Jinsong WU ; Yuemei LU ; Yuxing NI ; Jingrong SUN ; Xiaobo MA ; Yanqing ZHENG ; Yunsong YU ; Jie LIN ; Ziyong SUN ; Zhongju CHEN ; Zhidong HU ; Jin LI ; Fengbo ZHANG ; Ping JI ; Yunjian HU ; Xiaoman AI ; Jinju DUAN ; Jianbang KANG ; Xuefei HU ; Xuesong XU ; Chao YAN ; Yi LI ; Shanmei WANG ; Hongqin GU ; Yuanhong XU ; Ying HUANG ; Yunzhuo CHU ; Sufei TIAN ; Jihong LI ; Bixia YU ; Cunshan KOU ; Jilu SHEN ; Wenhui HUANG ; Xiuli YANG ; Likang ZHU ; Lin JIANG ; Wen HE ; Chunlei YUE
Chinese Journal of Infection and Chemotherapy 2025;25(1):30-38
Objective To investigate the distribution and antimicrobial resistance profiles of clinically isolated Haemophilus influenzae and Moraxella catarrhalis in hospitals across China from 2015 to 2021,and provide evidence for rational use of antimicrobial agents.Methods Data of H.influenzae and M.catarrhalis strains isolated from 2015 to 2021 in CHINET program were collected for analysis,and antimicrobial susceptibility testing was performed by disc diffusion method or automated systems according to the uniform protocol of CHINET.The results were interpreted according to the CLSI breakpoints in 2022.Beta-lactamases was detected by using nitrocefin disk.Results From 2015 to 2021,a total of 43 642 strains of Haemophilus species were isolated,accounting for 2.91%of the total clinical isolates and 4.07%of Gram-negative bacteria in CHINET program.Among the 40 437 strains of H.influenzae,66.89%were isolated from children and 33.11%were isolated from adults.More than 90%of the H.influenzae strains were isolated from respiratory tract specimens.The prevalence of β-lactamase was 53.79%in H.influenzae strains.The H.influenzae strains isolated from children showed higher resistance rate than the strains isolated from adults.Overall,779 strains of H.influenzae did not produce β-lactamase but were resistant to ampicillin(BLNAR).Beta-lactamase-producing strains showed significantly higher resistance rates to these antimicrobial agents than the β-lactamase-nonproducing strains.Of the 16 191 M.catarrhalis strains,80.06%were isolated from children and 19.94%isolated from adults.M.catarrhalis strains were mostly susceptible to both amoxicillin-clavulanic acid and cefuroxime,evidenced by resistance rate lower than 2.0%.Conclusions The emergence of antibiotic-resistant H.influenzae due to β-lactamase production poses a challenge for clinical anti-infective treatment.Therefore,it is very important to implement antibiotic resistance surveillance for H.influenzae and guide rational antibiotic use.All local clinical microbiology laboratories should actively improve antibiotic susceptibility testing and strengthen antibiotic resistance surveillance for H.influenzae.
10.Changing distribution and antimicrobial resistance profiles of clinical isolates in children:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Qing MENG ; Lintao ZHOU ; Yunsheng CHEN ; Yang YANG ; Fupin HU ; Demei ZHU ; Chuanqing WANG ; Aimin WANG ; Lei ZHU ; Jinhua MENG ; Hong ZHANG ; Chun WANG ; Fang DONG ; Zhiyong LÜ ; Shuping ZHOU ; Yan ZHOU ; Shifu WANG ; Fangfang HU ; Yingchun XU ; Xiaojiang ZHANG ; Zhaoxia ZHANG ; Ping JI ; Wei JIA ; Gang LI ; Kaizhen WEN ; Yirong ZHANG ; Yan JIN ; Chunhong SHAO ; Yong ZHAO ; Ping GONG ; Chao ZHUO ; Danhong SU ; Bin SHAN ; Yan DU ; Sufang GUO ; Jiao FENG ; Ziyong SUN ; Zhongju CHEN ; Wen'en LIU ; Yanming LI ; Xiaobo MA ; Yanping ZHENG ; Dawen GUO ; Jinying ZHAO ; Ruizhong WANG ; Hua FANG ; Lixia ZHANG ; Juan MA ; Jihong LI ; Zhidong HU ; Jin LI ; Yuxing NI ; Jingyong SUN ; Ruyi GUO ; Yan ZHU ; Yi XIE ; Mei KANG ; Yuanhong XU ; Ying HUANG ; Shanmei WANG ; Yafei CHU ; Hua YU ; Xiangning HUANG ; Lianhua WEI ; Fengmei ZOU ; Han SHEN ; Wanqing ZHOU ; Yunzhuo CHU ; Sufei TIAN ; Shunhong XUE ; Hongqin GU ; Xuesong XU ; Chao YAN ; Bixia YU ; Jinju DUAN ; Jianbang KANG ; Jiangshan LIU ; Xuefei HU ; Yunsong YU ; Jie LIN ; Yunjian HU ; Xiaoman AI ; Chunlei YUE ; Jinsong WU ; Yuemei LU
Chinese Journal of Infection and Chemotherapy 2025;25(1):48-58
Objective To understand the changing composition and antibiotic resistance of bacterial species in the clinical isolates from outpatient and emergency department(hereinafter referred to as outpatients)and inpatient children over time in various hospitals,and to provide laboratory evidence for rational antibiotic use.Methods The data on clinically isolated pathogenic bacteria and antimicrobial susceptibility of isolates from outpatients and inpatient children in the CHINET program from 2015 to 2021 were collected and analyzed.Results A total of 278 471 isolates were isolated from pediatric patients in the CHINET program from 2015 to 2021.About 17.1%of the strains were isolated from outpatients,primarily group A β-hemolytic Streptococcus,Escherichia coli,and Staphylococcus aureus.Most of the strains(82.9%)were isolated from inpatients,mainly SS.aureus,E.coli,and H.influenzae.The prevalence of methicillin-resistant S.aureus(MRSA)in outpatients(24.5%)was lower than that in inpatient children(31.5%).The MRSA isolates from outpatients showed lower resistance rates to the antibiotics tested than the strains isolated from inpatient children.The prevalence of vancomycin-resistant Enterococcus faecalis or E.faecium and penicillin-resistant S.pneumoniae was low in either outpatients or inpatient children.S.pneumoniae,β-hemolytic Streptococcus and S.viridans showed high resistance rates to erythromycin.The prevalence of erythromycin-resistant group A β-hemolytic Streptococcus was higher in outpatients than that in inpatient children.The prevalence of β-lactamase-producing H.influenzae showed an overall upward trend in children,but lower in outpatients(45.1%)than in inpatient children(59.4%).The prevalence of carbapenem-resistant Klebsiella pneumoniae(CRKpn),carbapenem-resistant Pseudomonas aeruginosa(CRPae)and carbapenem-resistant Acinetobacter baumannii(CRAba)was 14%,11.7%,47.8%in outpatients,but 24.2%,20.6%,and 52.8%in inpatient children,respectively.The prevalence of multidrug-resistant E.coli,K.pneumoniae,Proteus mirabilis,P.aeruginosa and A.baumannii strains was lower in outpatients than in inpatient children.The prevalence of fluoroquinolone-resistant E.coli,ESBLs-producing K.pneumoniae,ESBLs-producing P.mirabilis,carbapenem-resistant E.coli(CREco),CRKpn,and CRPae was lower in children in outpatients than in inpatient children,but the prevalence of CRAba in 2021 was higher than in inpatient children.Conclusions The distribution of clinical isolates from children is different between outpatients and inpatients.The prevalence of MRSA,ESBL,and CRO was higher in inpatient children than in outpatients.Antibiotics should be used rationally in clinical practice based on etiological diagnosis and antimicrobial susceptibility test results.Ongoing antimicrobial resistance surveillance and prevention and control of hospital infections are crucial to curbing bacterial resistance.

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