1.Carvedilol to prevent hepatic decompensation of cirrhosis in patients with clinically significant portal hypertension stratified by new non-invasive model (CHESS2306)
Chuan LIU ; Hong YOU ; Qing-Lei ZENG ; Yu Jun WONG ; Bingqiong WANG ; Ivica GRGUREVIC ; Chenghai LIU ; Hyung Joon YIM ; Wei GOU ; Bingtian DONG ; Shenghong JU ; Yanan GUO ; Qian YU ; Masashi HIROOKA ; Hirayuki ENOMOTO ; Amr Shaaban HANAFY ; Zhujun CAO ; Xiemin DONG ; Jing LV ; Tae Hyung KIM ; Yohei KOIZUMI ; Yoichi HIASA ; Takashi NISHIMURA ; Hiroko IIJIMA ; Chuanjun XU ; Erhei DAI ; Xiaoling LAN ; Changxiang LAI ; Shirong LIU ; Fang WANG ; Ying GUO ; Jiaojian LV ; Liting ZHANG ; Yuqing WANG ; Qing XIE ; Chuxiao SHAO ; Zhensheng LIU ; Federico RAVAIOLI ; Antonio COLECCHIA ; Jie LI ; Gao-Jun TENG ; Xiaolong QI
Clinical and Molecular Hepatology 2025;31(1):105-118
Background:
s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model.
Methods:
Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort.
Results:
In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM).
Conclusions
Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.
2.Carvedilol to prevent hepatic decompensation of cirrhosis in patients with clinically significant portal hypertension stratified by new non-invasive model (CHESS2306)
Chuan LIU ; Hong YOU ; Qing-Lei ZENG ; Yu Jun WONG ; Bingqiong WANG ; Ivica GRGUREVIC ; Chenghai LIU ; Hyung Joon YIM ; Wei GOU ; Bingtian DONG ; Shenghong JU ; Yanan GUO ; Qian YU ; Masashi HIROOKA ; Hirayuki ENOMOTO ; Amr Shaaban HANAFY ; Zhujun CAO ; Xiemin DONG ; Jing LV ; Tae Hyung KIM ; Yohei KOIZUMI ; Yoichi HIASA ; Takashi NISHIMURA ; Hiroko IIJIMA ; Chuanjun XU ; Erhei DAI ; Xiaoling LAN ; Changxiang LAI ; Shirong LIU ; Fang WANG ; Ying GUO ; Jiaojian LV ; Liting ZHANG ; Yuqing WANG ; Qing XIE ; Chuxiao SHAO ; Zhensheng LIU ; Federico RAVAIOLI ; Antonio COLECCHIA ; Jie LI ; Gao-Jun TENG ; Xiaolong QI
Clinical and Molecular Hepatology 2025;31(1):105-118
Background:
s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model.
Methods:
Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort.
Results:
In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM).
Conclusions
Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.
3.Carvedilol to prevent hepatic decompensation of cirrhosis in patients with clinically significant portal hypertension stratified by new non-invasive model (CHESS2306)
Chuan LIU ; Hong YOU ; Qing-Lei ZENG ; Yu Jun WONG ; Bingqiong WANG ; Ivica GRGUREVIC ; Chenghai LIU ; Hyung Joon YIM ; Wei GOU ; Bingtian DONG ; Shenghong JU ; Yanan GUO ; Qian YU ; Masashi HIROOKA ; Hirayuki ENOMOTO ; Amr Shaaban HANAFY ; Zhujun CAO ; Xiemin DONG ; Jing LV ; Tae Hyung KIM ; Yohei KOIZUMI ; Yoichi HIASA ; Takashi NISHIMURA ; Hiroko IIJIMA ; Chuanjun XU ; Erhei DAI ; Xiaoling LAN ; Changxiang LAI ; Shirong LIU ; Fang WANG ; Ying GUO ; Jiaojian LV ; Liting ZHANG ; Yuqing WANG ; Qing XIE ; Chuxiao SHAO ; Zhensheng LIU ; Federico RAVAIOLI ; Antonio COLECCHIA ; Jie LI ; Gao-Jun TENG ; Xiaolong QI
Clinical and Molecular Hepatology 2025;31(1):105-118
Background:
s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model.
Methods:
Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort.
Results:
In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM).
Conclusions
Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.
4.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.
5.Antimicrobial resistance profile of clinical isolates in hospitals across China:report from the CHINET Antimicrobial Resistance Surveillance Program,2023
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 ; Hua FANG ; Penghui ZHANG ; Bixia YU ; Ping GONG ; Haixia SHI ; Kaizhen WEN ; Yirong ZHANG ; Xiuli YANG ; Yiqin ZHAO ; Longfeng LIAO ; Jinhua WU ; Hongqin GU ; Lin JIANG ; Meifang HU ; Wen HE ; Jiao FENG ; Lingling YOU ; Dongmei WANG ; Dong'e WANG ; Yanyan LIU ; Yong AN ; 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 ; Jianping WANG ; Mingming ZHOU ; Shihai ZHANG ; Hongjuan LIU ; Nan CHEN ; Chan LI ; Cunshan KOU ; Shunhong XUE ; Jilu SHEN ; Wanqi MEN ; Peng WANG ; Xiaowei ZHANG ; Xiaoyan ZENG ; Wen LI ; Yan GENG ; Zeshi LIU
Chinese Journal of Infection and Chemotherapy 2024;24(6):627-637
Objective To monitor the susceptibility of clinical isolates to antimicrobial agents in healthcare facilities in major regions of China in 2023.Methods Clinical isolates collected from 73 hospitals across China were tested for antimicrobial susceptibility using a unified protocol based on disc diffusion method or automated testing systems.Results were interpreted using the 2023 Clinical & Laboratory Standards Institute (CLSI) breakpoints.Results A total of 445199 clinical isolates were collected in 2023,of which 29.0% were gram-positive and 71.0% 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) (MRSA,MRSE and MRCNS) was 29.6%,81.9% and 78.5%,respectively.Methicillin-resistant strains showed significantly higher resistance rates to most antimicrobial agents than methicillin-susceptible strains (MSSA,MSSE and MSCNS).Overall,92.9% of MRSA strains were susceptible to trimethoprim-sulfamethoxazole and 91.4% of MRSE strains were susceptible to rifampicin.No vancomycin-resistant strains were found.Enterococcus faecalis had 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 93.1% in the isolates from children and and 95.9% in the isolates from adults.The resistance rate to carbapenems was lower than 15.0% for most Enterobacterales species except for Klebsiella,22.5% and 23.6% of which were resistant to imipenem and meropenem,respectively .Most Enterobacterales isolates were highly susceptible to tigecycline,colistin and polymyxin B,with resistance rates ranging from 0.6% to 10.0%.The resistance rate to imipenem and meropenem was 21.9% and 17.4% for Pseudomonas aeruginosa,respectively,and 67.5% and 68.1% for Acinetobacter baumannii,respectively.Conclusions Increasing resistance to the commonly used antimicrobial agents is still observed in clinical bacterial isolates.However,the prevalence of important crabapenem-resistant organisms such as crabapenem-resistant K.pneumoniae,P.aeruginosa,and A.baumannii showed a slightly decreasing trend.This finding suggests that strengthening bacterial resistance surveillance and multidisciplinary linkage are important for preventing the occurrence and development of bacterial resistance.
6.Antimicrobial resistance profile of clinical isolates in hospitals across China:report from the CHINET Antimicrobial Resistance Surveillance Program,2023
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 ; Hua FANG ; Penghui ZHANG ; Bixia YU ; Ping GONG ; Haixia SHI ; Kaizhen WEN ; Yirong ZHANG ; Xiuli YANG ; Yiqin ZHAO ; Longfeng LIAO ; Jinhua WU ; Hongqin GU ; Lin JIANG ; Meifang HU ; Wen HE ; Jiao FENG ; Lingling YOU ; Dongmei WANG ; Dong'e WANG ; Yanyan LIU ; Yong AN ; 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 ; Jianping WANG ; Mingming ZHOU ; Shihai ZHANG ; Hongjuan LIU ; Nan CHEN ; Chan LI ; Cunshan KOU ; Shunhong XUE ; Jilu SHEN ; Wanqi MEN ; Peng WANG ; Xiaowei ZHANG ; Xiaoyan ZENG ; Wen LI ; Yan GENG ; Zeshi LIU
Chinese Journal of Infection and Chemotherapy 2024;24(6):627-637
Objective To monitor the susceptibility of clinical isolates to antimicrobial agents in healthcare facilities in major regions of China in 2023.Methods Clinical isolates collected from 73 hospitals across China were tested for antimicrobial susceptibility using a unified protocol based on disc diffusion method or automated testing systems.Results were interpreted using the 2023 Clinical & Laboratory Standards Institute (CLSI) breakpoints.Results A total of 445199 clinical isolates were collected in 2023,of which 29.0% were gram-positive and 71.0% 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) (MRSA,MRSE and MRCNS) was 29.6%,81.9% and 78.5%,respectively.Methicillin-resistant strains showed significantly higher resistance rates to most antimicrobial agents than methicillin-susceptible strains (MSSA,MSSE and MSCNS).Overall,92.9% of MRSA strains were susceptible to trimethoprim-sulfamethoxazole and 91.4% of MRSE strains were susceptible to rifampicin.No vancomycin-resistant strains were found.Enterococcus faecalis had 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 93.1% in the isolates from children and and 95.9% in the isolates from adults.The resistance rate to carbapenems was lower than 15.0% for most Enterobacterales species except for Klebsiella,22.5% and 23.6% of which were resistant to imipenem and meropenem,respectively .Most Enterobacterales isolates were highly susceptible to tigecycline,colistin and polymyxin B,with resistance rates ranging from 0.6% to 10.0%.The resistance rate to imipenem and meropenem was 21.9% and 17.4% for Pseudomonas aeruginosa,respectively,and 67.5% and 68.1% for Acinetobacter baumannii,respectively.Conclusions Increasing resistance to the commonly used antimicrobial agents is still observed in clinical bacterial isolates.However,the prevalence of important crabapenem-resistant organisms such as crabapenem-resistant K.pneumoniae,P.aeruginosa,and A.baumannii showed a slightly decreasing trend.This finding suggests that strengthening bacterial resistance surveillance and multidisciplinary linkage are important for preventing the occurrence and development of bacterial resistance.
7.Incidence and related factors of antiviral drug resistance in HIV-infected pregnant and postpartum women in some areas of three western provinces of China from 2017 to 2019.
He SUN ; Ai Ling WANG ; Jun YAO ; Jia Rui ZHENG ; Qing Hua QIN ; Wu Li SHA ; Xiao Yan WANG ; Ya GAO ; Zhen LI ; Dong Xu HUANG ; Qian WANG
Chinese Journal of Preventive Medicine 2023;57(11):1788-1793
Objective: To analyze the incidence and related factors of drug resistance in HIV-infected pregnant and postpartum women in some areas of three western provinces of China from 2017 to 2019. Methods: From April 2017 to April 2019, face-to-face questionnaires and blood sample testing were conducted in all health care institutions providing maternal and perinatal care and midwifery-assisted services in 7 prevention of mother-to-child transmissi project areas in Xinjiang, Yunnan and Guangxi provinces/autonomous regions. Information was collected during the perinatal period and viral load, CD4+T lymphocytes and drug resistance genes were detected at the same time. The multivariate logistic regression model was used to analyze the relationship between different factors and drug resistance in HIV-infected pregnant and postpartum women. Results: A total of 655 HIV-infected pregnant and postpartum women were included in this study. The incidence of drug resistance was 3.4% (22/655), all of whom were cross-drug resistant. The rate of low, moderate and high drug resistance was 2.1% (14/655), 1.2% (8/655) and 0.8% (5/655), respectively. The drug resistance rate in the people who had previously used antiviral drugs was 1.9% (8/418), and the drug resistance rate in the people who had not used drugs was 5.9% (14/237). The NNRTI drug resistance accounted for 2.8% (18/655) and the NRTI drug resistance rate was 2.5% (16/655). The multivariate logistic regression model showed that the risk of HIV resistance was lower in pregnant women who had previously used antiviral drugs (OR=0.32, 95%CI: 0.11-0.76). Conclusion: Strengthening the management of antiviral drug use and focusing on pregnant and postpartum women who have not previously used antiviral drugs can help reduce the occurrence of drug-resistant mutations. Personalized antiviral therapy should be considered to achieve viral inhibition effects in clinical practice.
Female
;
Humans
;
Pregnancy
;
HIV Infections/drug therapy*
;
Incidence
;
China/epidemiology*
;
Infectious Disease Transmission, Vertical/prevention & control*
;
Postpartum Period
;
Drug Resistance, Viral/genetics*
;
Antiviral Agents/therapeutic use*
8.Incidence and related factors of antiviral drug resistance in HIV-infected pregnant and postpartum women in some areas of three western provinces of China from 2017 to 2019.
He SUN ; Ai Ling WANG ; Jun YAO ; Jia Rui ZHENG ; Qing Hua QIN ; Wu Li SHA ; Xiao Yan WANG ; Ya GAO ; Zhen LI ; Dong Xu HUANG ; Qian WANG
Chinese Journal of Preventive Medicine 2023;57(11):1788-1793
Objective: To analyze the incidence and related factors of drug resistance in HIV-infected pregnant and postpartum women in some areas of three western provinces of China from 2017 to 2019. Methods: From April 2017 to April 2019, face-to-face questionnaires and blood sample testing were conducted in all health care institutions providing maternal and perinatal care and midwifery-assisted services in 7 prevention of mother-to-child transmissi project areas in Xinjiang, Yunnan and Guangxi provinces/autonomous regions. Information was collected during the perinatal period and viral load, CD4+T lymphocytes and drug resistance genes were detected at the same time. The multivariate logistic regression model was used to analyze the relationship between different factors and drug resistance in HIV-infected pregnant and postpartum women. Results: A total of 655 HIV-infected pregnant and postpartum women were included in this study. The incidence of drug resistance was 3.4% (22/655), all of whom were cross-drug resistant. The rate of low, moderate and high drug resistance was 2.1% (14/655), 1.2% (8/655) and 0.8% (5/655), respectively. The drug resistance rate in the people who had previously used antiviral drugs was 1.9% (8/418), and the drug resistance rate in the people who had not used drugs was 5.9% (14/237). The NNRTI drug resistance accounted for 2.8% (18/655) and the NRTI drug resistance rate was 2.5% (16/655). The multivariate logistic regression model showed that the risk of HIV resistance was lower in pregnant women who had previously used antiviral drugs (OR=0.32, 95%CI: 0.11-0.76). Conclusion: Strengthening the management of antiviral drug use and focusing on pregnant and postpartum women who have not previously used antiviral drugs can help reduce the occurrence of drug-resistant mutations. Personalized antiviral therapy should be considered to achieve viral inhibition effects in clinical practice.
Female
;
Humans
;
Pregnancy
;
HIV Infections/drug therapy*
;
Incidence
;
China/epidemiology*
;
Infectious Disease Transmission, Vertical/prevention & control*
;
Postpartum Period
;
Drug Resistance, Viral/genetics*
;
Antiviral Agents/therapeutic use*
10.Analysis of prognosis and influencing factors of No. 253 lymph node metastasis in descending colon, sigmoid colon, and rectal cancer: a multicenter study.
Fu Qiang ZHAO ; Lei ZHOU ; Xiao Hui DU ; Ai Wen WU ; Hua YANG ; Lai XU ; Xin Zhi LIU ; Shi Dong HU ; Yi XIAO ; Qian LIU
Chinese Journal of Surgery 2023;61(9):761-768
Objectives: To analyze the influencing factors of No. 253 lymph node metastasis in descending colon cancer, sigmoid colon cancer, and rectal cancer, and to investigate the prognosis of No. 253 lymph node-positive patients by propensity score matching analysis. Methods: A retrospective analysis was performed on clinical data from patients with descending colon cancer, sigmoid colon cancer, rectosigmoid junction cancer, and rectal cancer who underwent surgery between January 2015 and December 2019 from the Cancer Hospital of the Chinese Academy of Medical Sciences, China-Japan Friendship Hospital, Peking Union Medical College Hospital, General Hospital of the Chinese People's Liberation Army, and Peking University Cancer Hospital. A total of 3 016 patients were included according to inclusion and exclusion criteria, comprising 1 848 males and 1 168 females, with 1 675 patients aged≥60 years and 1 341 patients aged<60 years. Clinical and pathological factors from single center data were subjected to univariate analysis to determine influencing factors of No. 253 lymph node metastasis, using a binary Logistic regression model. Based on the results of the multivariate analysis, a nomogram was constructed. External validation was performed using data from other multicenter sources, evaluating the effectiveness through the area under the receiver operating characteristic curve and the calibration curve. Using data from a single center, the No. 253 lymph node-positive group was matched with the negative group in a 1∶2 ratio (caliper value=0.05). Survival analysis was performed using the Kaplan-Meier method and Log-rank test. The Cox proportional hazards model was used to determine independent prognostic factors. Results: (1) The tumor diameter≥5 cm (OR=4.496,95%CI:1.344 to 15.035, P=0.015) T stage (T4 vs. T1: OR=11.284, 95%CI:7.122 to 15.646, P<0.01), N stage (N2 vs. N0: OR=60.554, 95%CI:7.813 to 469.055, P=0.043), tumor differentiation (moderate vs. well differentiated: OR=1.044, 95%CI:1.009 to 1.203, P=0.044; poor vs. well differentiated: OR=1.013, 95%CI:1.002 to 1.081, P=0.013), tumor location (sigmoid colon vs. descending colon: OR=9.307, 95%CI:2.236 to 38.740, P=0.002), pathological type (mucinous adenocarcinoma vs. adenocarcinoma: OR=79.923, 95%CI:15.113 to 422.654, P<0.01; signet ring cell carcinoma vs. adenocarcinoma: OR=27.309, 95%CI:4.191 to 177.944, P<0.01), and positive vascular invasion (OR=3.490, 95%CI:1.033 to 11.793, P=0.044) were independent influencing factors of No. 253 lymph node metastasis. (2) The area under the curve of the nomogram prediction model was 0.912 (95%CI: 0.869 to 0.955) for the training set and 0.921 (95%CI: 0.903 to 0.937) for the external validation set. The calibration curve demonstrated good consistency between the predicted outcomes and the actual observations. (3) After propensity score matching, the No. 253 lymph node-negative group did not reach the median overall survival time, while the positive group had a median overall survival of 20 months. The 1-, 3- and 5-year overall survival rates were 83.9%, 61.3% and 51.6% in the negative group, and 63.2%, 36.8% and 15.8% in the positive group, respectively. Multivariate Cox analysis revealed that the T4 stage (HR=3.067, 95%CI: 2.357 to 3.990, P<0.01), the N2 stage (HR=1.221, 95%CI: 0.979 to 1.523, P=0.043), and No. 253 lymph node positivity (HR=2.902, 95%CI:1.987 to 4.237, P<0.01) were independent adverse prognostic factors. Conclusions: Tumor diameter ≥5 cm, T4 stage, N2 stage, tumor location in the sigmoid colon, adverse pathological type, poor differentiation, and vascular invasion are influencing factors of No. 253 lymph node metastasis. No. 253 lymph node positivity indicates a poorer prognosis. Therefore, strict dissection for No. 253 lymph node should be performed for colorectal cancer patients with these high-risk factors.

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