1.Advances in Lung Cancer Treatment: Integrating Immunotherapy and Chinese Herbal Medicines to Enhance Immune Response.
Yu-Xin XU ; Lin CHEN ; Wen-da CHEN ; Jia-Xue FAN ; Ying-Ying REN ; Meng-Jiao ZHANG ; Yi-Min CHEN ; Pu WU ; Tian XIE ; Jian-Liang ZHOU
Chinese journal of integrative medicine 2025;31(9):856-864
2.Expert consensus on the diagnosis and treatment of cemental tear.
Ye LIANG ; Hongrui LIU ; Chengjia XIE ; Yang YU ; Jinlong SHAO ; Chunxu LV ; Wenyan KANG ; Fuhua YAN ; Yaping PAN ; Faming CHEN ; Yan XU ; Zuomin WANG ; Yao SUN ; Ang LI ; Lili CHEN ; Qingxian LUAN ; Chuanjiang ZHAO ; Zhengguo CAO ; Yi LIU ; Jiang SUN ; Zhongchen SONG ; Lei ZHAO ; Li LIN ; Peihui DING ; Weilian SUN ; Jun WANG ; Jiang LIN ; Guangxun ZHU ; Qi ZHANG ; Lijun LUO ; Jiayin DENG ; Yihuai PAN ; Jin ZHAO ; Aimei SONG ; Hongmei GUO ; Jin ZHANG ; Pingping CUI ; Song GE ; Rui ZHANG ; Xiuyun REN ; Shengbin HUANG ; Xi WEI ; Lihong QIU ; Jing DENG ; Keqing PAN ; Dandan MA ; Hongyu ZHAO ; Dong CHEN ; Liangjun ZHONG ; Gang DING ; Wu CHEN ; Quanchen XU ; Xiaoyu SUN ; Lingqian DU ; Ling LI ; Yijia WANG ; Xiaoyuan LI ; Qiang CHEN ; Hui WANG ; Zheng ZHANG ; Mengmeng LIU ; Chengfei ZHANG ; Xuedong ZHOU ; Shaohua GE
International Journal of Oral Science 2025;17(1):61-61
Cemental tear is a rare and indetectable condition unless obvious clinical signs present with the involvement of surrounding periodontal and periapical tissues. Due to its clinical manifestations similar to common dental issues, such as vertical root fracture, primary endodontic diseases, and periodontal diseases, as well as the low awareness of cemental tear for clinicians, misdiagnosis often occurs. The critical principle for cemental tear treatment is to remove torn fragments, and overlooking fragments leads to futile therapy, which could deteriorate the conditions of the affected teeth. Therefore, accurate diagnosis and subsequent appropriate interventions are vital for managing cemental tear. Novel diagnostic tools, including cone-beam computed tomography (CBCT), microscopes, and enamel matrix derivatives, have improved early detection and management, enhancing tooth retention. The implementation of standardized diagnostic criteria and treatment protocols, combined with improved clinical awareness among dental professionals, serves to mitigate risks of diagnostic errors and suboptimal therapeutic interventions. This expert consensus reviewed the epidemiology, pathogenesis, potential predisposing factors, clinical manifestations, diagnosis, differential diagnosis, treatment, and prognosis of cemental tear, aiming to provide a clinical guideline and facilitate clinicians to have a better understanding of cemental tear.
Humans
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Dental Cementum/injuries*
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Consensus
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Diagnosis, Differential
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Cone-Beam Computed Tomography
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Tooth Fractures/therapy*
3.Develop and assessment of a predictive model for the first-course efficacy of acute myeloid leukemia
Feng ZHU ; Yile ZHOU ; Yi ZHANG ; Liping MAO ; De ZHOU ; Liya MA ; Chunmei YANG ; Wenjuan YU ; Xingnong YE ; Juying WEI ; Haitao MENG ; Min YANG ; Wenyuan MAI ; Jiejing QIAN ; Yanling REN ; Yinjun LOU ; Jian HUANG ; Gaixiang XU ; Wanzhuo XIE ; Hongyan TONG ; Huafeng WANG ; Jie JIN
Chinese Journal of Hematology 2025;46(4):336-342
Objective:To identify the relevant factors for the first-course remission of acute myeloid leukemia (AML) and to develop a predictive model as well as assess its predictive capability.Methods:Clinical data of 749 patients newly diagnosed with AML admitted to the Department of Hematology, the First Affiliated Hospital, Zhejiang University, School of Medicine from January 1, 2019, to April 30, 2023, were collected and randomly divided into training and validation sets. Multivariate logistic regression analysis was conducted to determine variables associated with complete remission in the first course of induction therapy, and a predictive model was established based on these variables. The receiver operating characteristic (ROC) curve of the predictive model was plotted, and the area under the curve (AUC) was calculated.Results:The indicators predicting the first remission course included peripheral blood white blood cell count during onset, CBF::MYH11 fusion gene, CEBPA bZIP region mutation, myelodysplastic syndrome-related gene mutation, and induction chemotherapy regimen selection as independent factors for the first remission course. The model’s area under the training and validation curves was 0.738 (95% CI: 0.696-0.780) and 0.726 (95% CI: 0.650-0.801), respectively. The Hosmer-Lemeshow test results yielded P-values of 0.993 and 0.335, respectively. Conclusion:In this study, the developed model demonstrates a strong predictive capability for the efficacy of the first course of patients with AML, providing valuable guidance to clinicians in assessing patient prognosis and selecting appropriate treatment strategies.
4.Development and validation of nomogram models for poor short-term response to recombinant human growth hormone treatment in children with short stature
Xuyang GONG ; Mengxing PAN ; Qianshuai LI ; Shuai ZHU ; Xinjing LIU ; Tianfang WANG ; Xulong LI ; Yanshuang CUI ; Yijing XIE ; Yi SONG ; Linlin ZHAO ; Jinqin WANG ; Yawei ZHANG ; Na XU ; Qiao REN ; Linqi DIAO ; Guijun QIN ; Yanyan ZHAO
Chinese Journal of Endocrinology and Metabolism 2025;41(6):467-475
Objective:To develop and validate clinical predictive models for identifying poor short-term response to recombinant human growth hormone(rhGH) treatment in children with short stature.Methods:A retrospective analysis was conducted on 118 children diagnosed with growth hormone deficiency or idiopathic short stature who were treated at the First Affiliated Hospital of Zhengzhou University and two other hospitals between January 1, 2020, and January 1, 2024. A poor response to rhGH was defined as a height increase of less than 0.2 standard deviation score(SDS) after 6 months of rhGH treatment. LASSO regression was used to identify predictive variables from baseline and follow-up data. Two logistic regression models were conducted: Model A(incorporating baseline variables only) and model B(incorporating both baseline and follow-up variables), and nomograms were created for visualization. External data and internal resampling were used for dual validation of the models, and their performance was compared.Results:A total of 118 children with short stature were included. Six baseline predictive variables(diagnosis, initial height SDS, bone age, bone age-chronological age difference, rhGH dose, and gender) and one follow-up variable(height SDS after 3 months of rhGH treatment) were identified. Area under the curve values for Model A and Model B were 0.753(95% CI 0.696-0.811) and 0.930(95% CI 0.891-0.975), respectively. Calibration curves, decision curve analysis, and other evaluation metrics demonstrated good discrimination and clinical utility for both models. Model B, incorporating the 3-month follow-up variable, showed superior predictive performance compared to Model A. Conclusions:The clinical prediction models developed in this study(Model A and Model B) are practical and reliable tools for quantitatively, conveniently, and intuitively identifying children with short stature at risk of poor response to rhGH treatment.
5.Develop and assessment of a predictive model for the first-course efficacy of acute myeloid leukemia
Feng ZHU ; Yile ZHOU ; Yi ZHANG ; Liping MAO ; De ZHOU ; Liya MA ; Chunmei YANG ; Wenjuan YU ; Xingnong YE ; Juying WEI ; Haitao MENG ; Min YANG ; Wenyuan MAI ; Jiejing QIAN ; Yanling REN ; Yinjun LOU ; Jian HUANG ; Gaixiang XU ; Wanzhuo XIE ; Hongyan TONG ; Huafeng WANG ; Jie JIN
Chinese Journal of Hematology 2025;46(4):336-342
Objective:To identify the relevant factors for the first-course remission of acute myeloid leukemia (AML) and to develop a predictive model as well as assess its predictive capability.Methods:Clinical data of 749 patients newly diagnosed with AML admitted to the Department of Hematology, the First Affiliated Hospital, Zhejiang University, School of Medicine from January 1, 2019, to April 30, 2023, were collected and randomly divided into training and validation sets. Multivariate logistic regression analysis was conducted to determine variables associated with complete remission in the first course of induction therapy, and a predictive model was established based on these variables. The receiver operating characteristic (ROC) curve of the predictive model was plotted, and the area under the curve (AUC) was calculated.Results:The indicators predicting the first remission course included peripheral blood white blood cell count during onset, CBF::MYH11 fusion gene, CEBPA bZIP region mutation, myelodysplastic syndrome-related gene mutation, and induction chemotherapy regimen selection as independent factors for the first remission course. The model’s area under the training and validation curves was 0.738 (95% CI: 0.696-0.780) and 0.726 (95% CI: 0.650-0.801), respectively. The Hosmer-Lemeshow test results yielded P-values of 0.993 and 0.335, respectively. Conclusion:In this study, the developed model demonstrates a strong predictive capability for the efficacy of the first course of patients with AML, providing valuable guidance to clinicians in assessing patient prognosis and selecting appropriate treatment strategies.
6.Development and validation of nomogram models for poor short-term response to recombinant human growth hormone treatment in children with short stature
Xuyang GONG ; Mengxing PAN ; Qianshuai LI ; Shuai ZHU ; Xinjing LIU ; Tianfang WANG ; Xulong LI ; Yanshuang CUI ; Yijing XIE ; Yi SONG ; Linlin ZHAO ; Jinqin WANG ; Yawei ZHANG ; Na XU ; Qiao REN ; Linqi DIAO ; Guijun QIN ; Yanyan ZHAO
Chinese Journal of Endocrinology and Metabolism 2025;41(6):467-475
Objective:To develop and validate clinical predictive models for identifying poor short-term response to recombinant human growth hormone(rhGH) treatment in children with short stature.Methods:A retrospective analysis was conducted on 118 children diagnosed with growth hormone deficiency or idiopathic short stature who were treated at the First Affiliated Hospital of Zhengzhou University and two other hospitals between January 1, 2020, and January 1, 2024. A poor response to rhGH was defined as a height increase of less than 0.2 standard deviation score(SDS) after 6 months of rhGH treatment. LASSO regression was used to identify predictive variables from baseline and follow-up data. Two logistic regression models were conducted: Model A(incorporating baseline variables only) and model B(incorporating both baseline and follow-up variables), and nomograms were created for visualization. External data and internal resampling were used for dual validation of the models, and their performance was compared.Results:A total of 118 children with short stature were included. Six baseline predictive variables(diagnosis, initial height SDS, bone age, bone age-chronological age difference, rhGH dose, and gender) and one follow-up variable(height SDS after 3 months of rhGH treatment) were identified. Area under the curve values for Model A and Model B were 0.753(95% CI 0.696-0.811) and 0.930(95% CI 0.891-0.975), respectively. Calibration curves, decision curve analysis, and other evaluation metrics demonstrated good discrimination and clinical utility for both models. Model B, incorporating the 3-month follow-up variable, showed superior predictive performance compared to Model A. Conclusions:The clinical prediction models developed in this study(Model A and Model B) are practical and reliable tools for quantitatively, conveniently, and intuitively identifying children with short stature at risk of poor response to rhGH treatment.
7.The enlightenment of the design of original drug medical insurance payment standard system in typical countries and regions for China
Lei REN ; Jin-Ping XIE ; Yi-Fan YANG ; Rong SHAO
Chinese Journal of Health Policy 2024;17(8):65-71
Objective:To provide reference for the management of drug medical insurance payment standard under the background of the national drug centralized procurement policy.Method:Research on the management experience of drug and medical insurance payment standard in typical countries and regions.Result:There is a relatively complete drug medical insurance payment standard management system in the typical countries and regions of studied,refer to multiple factors such as the actual trading price in the market,and there are differences in drug medical insurance payment standard between generic and original drug.Recommendation:It is recommended to improve relevant policy documents,and establish a scientific management system for drug medical insurance payment standards,establish a market-oriented mechanism for forming drug medical insurance payment standards through drug market price transactions,and refine classification management based on different attributes of different drugs such as generic drugs and original drugs.At the same time,under the current policy of implementing the same drug medical insurance payment standard for drugs with the same generic name of national drug centralized procurement,it is recommended to continue promoting and optimizing the consistency evaluation of generic drugs,achieving consistency in clinical efficacy between generic drugs and original drugs,in line with the current policy orientation of implementing the same drug medical insurance payment standard for generic drugs and original drugs.
8.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.
9.Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients (version 2024)
Yao LU ; Yang LI ; Leiying ZHANG ; Hao TANG ; Huidan JING ; Yaoli WANG ; Xiangzhi JIA ; Li BA ; Maohong BIAN ; Dan CAI ; Hui CAI ; Xiaohong CAI ; Zhanshan ZHA ; Bingyu CHEN ; Daqing CHEN ; Feng CHEN ; Guoan CHEN ; Haiming CHEN ; Jing CHEN ; Min CHEN ; Qing CHEN ; Shu CHEN ; Xi CHEN ; Jinfeng CHENG ; Xiaoling CHU ; Hongwang CUI ; Xin CUI ; Zhen DA ; Ying DAI ; Surong DENG ; Weiqun DONG ; Weimin FAN ; Ke FENG ; Danhui FU ; Yongshui FU ; Qi FU ; Xuemei FU ; Jia GAN ; Xinyu GAN ; Wei GAO ; Huaizheng GONG ; Rong GUI ; Geng GUO ; Ning HAN ; Yiwen HAO ; Wubing HE ; Qiang HONG ; Ruiqin HOU ; Wei HOU ; Jie HU ; Peiyang HU ; Xi HU ; Xiaoyu HU ; Guangbin HUANG ; Jie HUANG ; Xiangyan HUANG ; Yuanshuai HUANG ; Shouyong HUN ; Xuebing JIANG ; Ping JIN ; Dong LAI ; Aiping LE ; Hongmei LI ; Bijuan LI ; Cuiying LI ; Daihong LI ; Haihong LI ; He LI ; Hui LI ; Jianping LI ; Ning LI ; Xiying LI ; Xiangmin LI ; Xiaofei LI ; Xiaojuan LI ; Zhiqiang LI ; Zhongjun LI ; Zunyan LI ; Huaqin LIANG ; Xiaohua LIANG ; Dongfa LIAO ; Qun LIAO ; Yan LIAO ; Jiajin LIN ; Chunxia LIU ; Fenghua LIU ; Peixian LIU ; Tiemei LIU ; Xiaoxin LIU ; Zhiwei LIU ; Zhongdi LIU ; Hua LU ; Jianfeng LUAN ; Jianjun LUO ; Qun LUO ; Dingfeng LYU ; Qi LYU ; Xianping LYU ; Aijun MA ; Liqiang MA ; Shuxuan MA ; Xainjun MA ; Xiaogang MA ; Xiaoli MA ; Guoqing MAO ; Shijie MU ; Shaolin NIE ; Shujuan OUYANG ; Xilin OUYANG ; Chunqiu PAN ; Jian PAN ; Xiaohua PAN ; Lei PENG ; Tao PENG ; Baohua QIAN ; Shu QIAO ; Li QIN ; Ying REN ; Zhaoqi REN ; Ruiming RONG ; Changshan SU ; Mingwei SUN ; Wenwu SUN ; Zhenwei SUN ; Haiping TANG ; Xiaofeng TANG ; Changjiu TANG ; Cuihua TAO ; Zhibin TIAN ; Juan WANG ; Baoyan WANG ; Chunyan WANG ; Gefei WANG ; Haiyan WANG ; Hongjie WANG ; Peng WANG ; Pengli WANG ; Qiushi WANG ; Xiaoning WANG ; Xinhua WANG ; Xuefeng WANG ; Yong WANG ; Yongjun WANG ; Yuanjie WANG ; Zhihua WANG ; Shaojun WEI ; Yaming WEI ; Jianbo WEN ; Jun WEN ; Jiang WU ; Jufeng WU ; Aijun XIA ; Fei XIA ; Rong XIA ; Jue XIE ; Yanchao XING ; Yan XIONG ; Feng XU ; Yongzhu XU ; Yongan XU ; Yonghe YAN ; Beizhan YAN ; Jiang YANG ; Jiangcun YANG ; Jun YANG ; Xinwen YANG ; Yongyi YANG ; Chunyan YAO ; Mingliang YE ; Changlin YIN ; Ming YIN ; Wen YIN ; Lianling YU ; Shuhong YU ; Zebo YU ; Yigang YU ; Anyong YU ; Hong YUAN ; Yi YUAN ; Chan ZHANG ; Jinjun ZHANG ; Jun ZHANG ; Kai ZHANG ; Leibing ZHANG ; Quan ZHANG ; Rongjiang ZHANG ; Sanming ZHANG ; Shengji ZHANG ; Shuo ZHANG ; Wei ZHANG ; Weidong ZHANG ; Xi ZHANG ; Xingwen ZHANG ; Guixi ZHANG ; Xiaojun ZHANG ; Guoqing ZHAO ; Jianpeng ZHAO ; Shuming ZHAO ; Beibei ZHENG ; Shangen ZHENG ; Huayou ZHOU ; Jicheng ZHOU ; Lihong ZHOU ; Mou ZHOU ; Xiaoyu ZHOU ; Xuelian ZHOU ; Yuan ZHOU ; Zheng ZHOU ; Zuhuang ZHOU ; Haiyan ZHU ; Peiyuan ZHU ; Changju ZHU ; Lili ZHU ; Zhengguo WANG ; Jianxin JIANG ; Deqing WANG ; Jiongcai LAN ; Quanli WANG ; Yang YU ; Lianyang ZHANG ; Aiqing WEN
Chinese Journal of Trauma 2024;40(10):865-881
Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.
10.Multicenter evaluation of the diagnostic efficacy of jaundice color card for neonatal hyperbilirubinemia
Guochang XUE ; Huali ZHANG ; Xuexing DING ; Fu XIONG ; Yanhong LIU ; Hui PENG ; Changlin WANG ; Yi ZHAO ; Huili YAN ; Mingxing REN ; Chaoying MA ; Hanming LU ; Yanli LI ; Ruifeng MENG ; Lingjun XIE ; Na CHEN ; Xiufang CHENG ; Jiaojiao WANG ; Xiaohong XIN ; Ruifen WANG ; Qi JIANG ; Yong ZHANG ; Guijuan LIANG ; Yuanzheng LI ; Jianing KANG ; Huimin ZHANG ; Yinying ZHANG ; Yuan YUAN ; Yawen LI ; Yinglin SU ; Junping LIU ; Shengjie DUAN ; Qingsheng LIU ; Jing WEI
Chinese Journal of Pediatrics 2024;62(6):535-541
Objective:To evaluate the diagnostic efficacy and practicality of the Jaundice color card (JCard) as a screening tool for neonatal jaundice.Methods:Following the standards for reporting of diagnostic accuracy studies (STARD) statement, a multicenter prospective study was conducted in 9 hospitals in China from October 2019 to September 2021. A total of 845 newborns who were admitted to the hospital or outpatient department for liver function testing due to their own diseases. The inclusion criteria were a gestational age of ≥35 weeks, a birth weight of ≥2 000 g, and an age of ≤28 days. The neonate′s parents used the JCard to measure jaundice at the neonate′s cheek. Within 2 hours of the JCard measurement, transcutaneous bilirubin (TcB) was measured with a JH20-1B device and total serum bilirubin (TSB) was detected. The Pearson′s correlation analysis, Bland-Altman plots and the receiver operating characteristic (ROC) curve were used for statistic analysis.Results:Out of the 854 newborns, 445 were male and 409 were female; 46 were born at 35-36 weeks of gestational age and 808 were born at ≥37 weeks of gestational age. Additionally, 432 cases were aged 0-3 days, 236 cases were aged 4-7 days, and 186 cases were aged 8-28 days. The TSB level was (227.4±89.6) μmol/L, with a range of 23.7-717.0 μmol/L. The JCard level was (221.4±77.0) μmol/L and the TcB level was (252.5±76.0) μmol/L. Both the JCard and TcB values showed good correlation ( r=0.77 and 0.80, respectively) and agreements (96.0% (820/854) and 95.2% (813/854) of samples fell within the 95% limits of agreement, respectively) with TSB. The JCard value of 12 had a sensitivity of 0.93 and specificity of 0.75 for identifying a TSB ≥205.2?μmol/L, and a sensitivity of 1.00 and specificity of 0.35 for identifying a TSB ≥342.0?μmol/L. The TcB value of 205.2?μmol/L had a sensitivity of 0.97 and specificity of 0.60 for identifying TSB levels of 205.2 μmol/L, and a sensitivity of 1.00 and specificity of 0.26 for identifying TSB levels of 342.0 μmol/L. The areas under the ROC curve (AUC) of JCard for identifying TSB levels of 153.9, 205.2, 256.5, and 342.0 μmol/L were 0.96, 0.92, 0.83, and 0.83, respectively. The AUC of TcB were 0.94, 0.91, 0.86, and 0.87, respectively. There were both no significant differences between the AUC of JCard and TcB in identifying TSB levels of 153.9 and 205.2 μmol/L (both P>0.05). However, the AUC of JCard were both lower than those of TcB in identifying TSB levels of 256.5 and 342.0 μmol/L (both P<0.05). Conclusions:JCard can be used to classify different levels of bilirubin, but its diagnostic efficacy decreases with increasing bilirubin levels. When TSB level are ≤205.2 μmol/L, its diagnostic efficacy is equivalent to that of the JH20-1B. To prevent the misdiagnosis of severe jaundice, it is recommended that parents use a low JCard score, such as 12, to identify severe hyperbilirubinemia (TSB ≥342.0 μmol/L).

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