中文｜English
ISSN 1001-5256 (Print)
ISSN 2097-3497 (Online)
CN 22-1108/R
Volume 38 Issue 11
Nov.  2022
Turn off MathJax
Article Contents
Article Navigation >  Journal of Clinical Hepatology  > 2022  >  38(11): 2499-2504

Establishment of a model for predicting the efficacy of third-generation cephalosporin in treatment of community-acquired spontaneous bacterial peritonitis

DOI: 10.3969/j.issn.1001-5256.2022.11.012
  • 1a.

    Department of Liver Disease, The Ninth Hospital of Nanchang, Nanchang 330002, China

  • 1b.

    Department of Information Technology, The Ninth Hospital of Nanchang, Nanchang 330002, China

  • 2.

    Department of Digestion, Children's Hospital of Jiangxi Province, Nanchang 330000, China

Research funding:

Key Research and Development Program of Jiangxi Province (20181BBG78010)

More Information
  • Corresponding author: ZHU Longchuan, zlcy1984@sina.com(ORCID: 0000-0002-4461-6195)
  • Received Date: 2022-04-05
  • Accepted Date: 2022-05-07
  • Published Date: 2022-11-20
    Abstract
  •   Objective  To investigate the factors for predicting the efficacy of third-generation cephalosporin (3rd GC) in the treatment of community-acquired spontaneous bacterial peritonitis (CASBP), and to establish and validate an efficacy predictive model for 3rd GC in the treatment of CASBP.  Methods  A retrospective analysis was performed for the clinical data of the patients with liver cirrhosis and CASBP who received 3rd GC monotherapy for initial treatment in The Ninth Hospital of Nanchang, and according to their treatment outcome, they were divided into non-response group and response group. The patients hospitalized from 2013 to 2018 were included in the modeling cohort (55 patients the non-response group and 110 in the response group), and those hospitalized from 2019 to 2020 were included in the validation cohort (17 patients in the non-response group and 43 in the response group). In the modeling cohort, the two groups were compared in terms of the indices including general information, underlying diseases, past history, clinical manifestation, and laboratory test results. Univariate analyses (the t-test was used for comparison of normally distributed continuous data between groups, and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between groups; the chi-square test or the Fisher's exact test was used for comparison of categorical data between groups) and a binary Logistic regression analysis were used to identify efficacy predictors, and an efficacy predictive model was established based on the logistic regression equation. The receiver operating characteristic (ROC) curve was plotted to perform internal and external validations of the model in the modeling cohort and the validation cohort, respectively.  Results  The study population had a mean age of 51.6±12.0 years, and male patients accounted for 80.0%; hepatitis B was the main cause of liver cirrhosis (66.7%), and 3rd GC had an overall response rate of 68.0%. In the modeling cohort, compared with the response group, the non-response group had significantly lower proportion of patients with the first onset of SBP, polymorphonuclear (PMN) count in ascites, and leukocyte count in ascites (all P < 0.05), as well as significantly higher proportion of patients with exposure to broad-spectrum antibiotic and platelet count (both P < 0.05). The Logistic regression analysis showed that the first onset of SBP (odds ratio [OR]=0.158, 95% confidence interval [CI]: 0.064-0.392, P < 0.001), ascites PMN count (OR=0.728, 95%CI: 0.530-0.998, P=0.046), exposure to broad-spectrum antibiotic (OR=9.152, 95%CI: 1.513-55.351, P=0.016), and platelet count (OR=1.012, 95%CI: 1.006-1.019, P < 0.001) were independent predictive factors for non-response to 3rd GC. The efficacy predictive model had an area under the ROC curve (AUC) of 0.840, and based on the maximum Youden index, predictive score ≥ 0.207 was the optimal cut-off value for predicting non-response, with a corresponding Youden index of 0.536, a sensitivity of 89.1%, a specificity of 63.6%, a positive predictive value of 55.1%, and a negative predictive value of 92.1%. This model had an AUC of 0.837 in the validation cohort.  Conclusion  The first onset of SBP and higher ascites PMN count are the protective factors against non-response to 3rd GC for the treatment of CASBP, and exposure to broad-spectrum antibiotic and higher platelet count are the risk factors for such non-response. The model established for predicting the efficacy of 3rd GC in the treatment of CASBP has good predictive performance and thus holds promise for clinical application.

     

    • FullText(HTML)
  • loading
    • References(15)
  • [1]
    Chinese Society of Hepatology, Chinese Medical Association. Guidelines on the management of ascites and complications in cirrhosis[J]. J Chin Hepatol, 2017, 33(10): 1847-1863. DOI: 10.3969/j.issn.1001-5256.2017.10.003.

    中华医学会肝病学分会. 肝硬化腹水及相关并发症的诊疗指南[J]. 临床肝胆病杂志, 2017, 33(10): 1847-1863. DOI: 10.3969/j.issn.1001-5256.2017.10.003.
    [2]
    BIGGINS SW, ANGELI P, GARCIA-TSAO G, et al. Diagnosis, evaluation, and management of ascites, spontaneous bacterial peritonitis and hepatorenal syndrome: 2021 practice guidance by the American Association for the Study of Liver Diseases[J]. Hepatology, 2021, 74(2): 1014-1048. DOI: 10.1002/hep.31884.
    [3]
    FIORE M, GENTILE I, MARAOLO AE, et al. Are third-generation cephalosporins still the empirical antibiotic treatment of community-acquired spontaneous bacterial peritonitis? A systematic review and meta-analysis[J]. Eur J Gastroenterol Hepatol, 2018, 30(3): 329-336. DOI: 10.1097/MEG.0000000000001057.
    [4]
    PUGH RN, MURRAY-LYON IM, DAWSON JL, et al. Transection of the oesophagus for bleeding oesophageal varices[J]. Br J Surg, 1973, 60(8): 646-649. DOI: 10.1002/bjs.1800600817.
    [5]
    KAMATH PS, WIESNER RH, MALINCHOC M, et al. A model to predict survival in patients with end-stage liver disease[J]. Hepatology, 2001, 33(2): 464-470. DOI: 10.1053/jhep.2001.22172.
    [6]
    SUNJAYA DB, LENNON RJ, SHAH VH, et al. Prevalence and predictors of third- generation cephalosporin resistance in the empirical treatment of spontaneous bacterial peritonitis[J]. Mayo Clin Proc, 2019, 94(8): 1499-1508. DOI: 10.1016/j.mayocp.2018.12.036..
    [7]
    PEDUZZI P, CONCATO J, KEMPER E, et al. A simulation study of the number of events per variable in Logistic regression analysis[J]. J Clin Epidemiol, 1996, 49(12): 1373-1379. DOI: 10.1016/s0895-4356(96)00236-3.
    [8]
    TAY P, XIAO J, TAN D, et al. An epidemiological meta-analysis on the worldwide prevalence, resistance, and outcomes of spontaneous bacterial peritonitis in cirrhosis[J]. Front Med (Lausanne), 2021, 8: 693652. DOI: 10.3389/fmed.2021.693652.
    [9]
    SARWAR S, TARIQUE S, WARIS U, et al. Cephalosporin resistance in community acquired spontaneous bacterial peritonitis[J]. Pak J Med Sci, 2019, 35(1): 4-9. DOI: 10.12669/pjms.35.1.17.
    [10]
    Ministry of Health of the People's Republic of China. Regulations for clinical application of antibacterial agents[J]. Chin J Clin Infect Dis, 2012, 5(4): 193-196. DOI:10.3760/cma.j.issn. 1674-2397.2012.04.001.

    中华人民共和国卫生部. 抗菌药物临床应用管理办法[J]. 中华临床感染病杂志, 2012, 5(4): 193-196. DOI: 10.3760/cma.j.issn.1674-2397.2012.04.001.
    [11]
    ZHU LC, GAN DK, HU QQ, et al. Predictive factors for third-generation cephalosporin- resistant spontaneous bacterial peritonitis[J]. J Chin Hepatol, 2019, 35(7): 1501-1504. DOI: 10.3969/j.issn.1001-5256.2019.07.016.

    朱龙川, 甘达凯, 胡青青, 等. 第三代头孢菌素耐药的自发性细菌性腹膜炎发生的预测因素[J]. 临床肝胆病杂志, 2019, 35(7): 1501-1504. DOI: 10.3969/j.issn.1001-5256.2019.07.016.
    [12]
    ARIZA X, LORA-TAMAYO J, CASTELLOTE J, et al. Polymorphonuclear counts in ascitic fluid and microorganisms producing spontaneous bacterial peritonitis: an under-recognized relationship[J]. Scand J Gastroenterol, 2013, 48(10): 1213-1221. DOI: 10.3109/00365521.2013.832367.
    [13]
    ZHANG GY, TIAN CY, HU H. Recent progress in immune dysfunction of neutrophils in patients with cirrhosis[J]. Chin Gen Pract, 2021, 24(21): 2734-2743. DOI: 10.12114/j.issn. 1007-9572.2021.00.457.

    张国远, 田彩云, 胡晗. 肝硬化患者中性粒细胞免疫功能障碍的研究进展[J]. 中国全科医学, 2021, 24(21): 2734-2743. DOI: 10.12114/j.issn.1007-9572.2021.00.457.
    [14]
    MISHRA D, DAS AK, CHAPAGAIN RH, et al. Thrombocytosis as a predictor of serious bacterial infection in febrile infants[J]. J Nepal Health Res Counc, 2019, 16(41): 401-404. DOI: 10.33314/jnhrc.v16i41.1673.
    [15]
    YUAN SN, ZHAO JJ, HAN XY, et al. Influence of bacterial and fungal infections on platelets[J]. Chin J Nosocomiol, 2016, 26(22): 5083-5086. DOI: 10.11816/cn.ni.2016-154312.

    袁胜男, 赵俊暕, 韩晓燕, 等. 细菌与真菌感染对血小板的影响研究[J]. 中华医院感染学杂志, 2016, 26(22): 5083-5086. DOI: 10.11816/cn.ni.2016-154312.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(2)  / Tables(3)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (262) PDF downloads(40) Cited by()
    Proportional views
    Related
        

    The first journal specializing in hepatobiliary and pancreatic diseases in China

    Supervisor：Ministry of Education of the People's Republic of China

    Sponsor：Jilin University

    Academic Support: Chinese Society of Hepatology，Chinese Medical Association

    Address: 461 Xinjiang Road, Changchun

    Submit：0431-88782044

    Peer review：0431-88783542

    Email：lcgdb@vip.163.com

    About Journal

    Submission Guideline

    Journal Online

    Hepatobiliary College

    Guidelines

    YesterdayIP[]YesterdayPV[]TodayIP[]TodayPV[]Online[]

    Website Design © 2020 Editorial Board of Journal of Clinical Hepatology 吉ICP备10000617号-1 Supported by: Beijing Renhe Information Technology Co. Ltd  

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return