Prognosis of acute-on-chronic liver failure patients treated with artificial liver support system
AIM: To establish a new model for predicting survival in acute-on-chronic liver failure(ACLF) patients treated with an artificial liver support system. METHODS: One hundred and eighty-one ACLF patients who were admitted to the hospital from January 1, 2012 to December 31, 2014 and were treated with...
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Published in | World journal of gastroenterology : WJG Vol. 21; no. 32; pp. 9614 - 9622 |
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28.08.2015
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Abstract | AIM: To establish a new model for predicting survival in acute-on-chronic liver failure(ACLF) patients treated with an artificial liver support system. METHODS: One hundred and eighty-one ACLF patients who were admitted to the hospital from January 1, 2012 to December 31, 2014 and were treated with an artificial liver support system were enrolled in this retrospective study, including a derivation cohort(n = 113) and a validation cohort(n = 68). Laboratory parameters at baseline were analyzed and correlatedwith clinical outcome. In addition to standard medical therapy, ACLF patients underwent plasma exchange(PE) or plasma bilirubin adsorption(PBA) combined with plasma exchange. For the derivation cohort, KaplanMeier methods were used to estimate survival curves, and Cox regression was used in survival analysis to generate a prognostic model. The performance of the new model was tested in the validation cohort using a receiver-operator curve.RESULTS: The mean overall survival for the derivation cohort was 441 d(95%CI: 379-504 d), and the 90- and 270-d survival probabilities were 70.3% and 58.3%, respectively. The mean survival times of patients treated with PBA plus PE and patients treated with PE were 531 d(95%CI: 455-605 d) and 343 d(95%CI: 254-432 d), respectively, which were significantly different(P = 0.012). When variables with bivariate significance were selected for inclusion into the multivariate Cox regression model, number of complications, age, scores of the model for end-stage liver disease(MELD) and type of artificial liver support system were defined as independent risk factors for survival in ACLF patients. This new prognostic model could accurately discriminate the outcome of patients with different scores in this cohort(P < 0.001). The model also had the ability to assign a predicted survival probability for individual patients. In the validation cohort, the new model remained better than the MELD.CONCLUSION: A novel model was constructed to predict prognosis and accurately discriminate survival in ACLF patients treated with an artificial liver support system. |
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AbstractList | AIM: To establish a new model for predicting survival in acute-on-chronic liver failure(ACLF) patients treated with an artificial liver support system. METHODS: One hundred and eighty-one ACLF patients who were admitted to the hospital from January 1, 2012 to December 31, 2014 and were treated with an artificial liver support system were enrolled in this retrospective study, including a derivation cohort(n = 113) and a validation cohort(n = 68). Laboratory parameters at baseline were analyzed and correlatedwith clinical outcome. In addition to standard medical therapy, ACLF patients underwent plasma exchange(PE) or plasma bilirubin adsorption(PBA) combined with plasma exchange. For the derivation cohort, KaplanMeier methods were used to estimate survival curves, and Cox regression was used in survival analysis to generate a prognostic model. The performance of the new model was tested in the validation cohort using a receiver-operator curve.RESULTS: The mean overall survival for the derivation cohort was 441 d(95%CI: 379-504 d), and the 90- and 270-d survival probabilities were 70.3% and 58.3%, respectively. The mean survival times of patients treated with PBA plus PE and patients treated with PE were 531 d(95%CI: 455-605 d) and 343 d(95%CI: 254-432 d), respectively, which were significantly different(P = 0.012). When variables with bivariate significance were selected for inclusion into the multivariate Cox regression model, number of complications, age, scores of the model for end-stage liver disease(MELD) and type of artificial liver support system were defined as independent risk factors for survival in ACLF patients. This new prognostic model could accurately discriminate the outcome of patients with different scores in this cohort(P < 0.001). The model also had the ability to assign a predicted survival probability for individual patients. In the validation cohort, the new model remained better than the MELD.CONCLUSION: A novel model was constructed to predict prognosis and accurately discriminate survival in ACLF patients treated with an artificial liver support system. To establish a new model for predicting survival in acute-on-chronic liver failure (ACLF) patients treated with an artificial liver support system. One hundred and eighty-one ACLF patients who were admitted to the hospital from January 1, 2012 to December 31, 2014 and were treated with an artificial liver support system were enrolled in this retrospective study, including a derivation cohort (n = 113) and a validation cohort (n = 68). Laboratory parameters at baseline were analyzed and correlated with clinical outcome. In addition to standard medical therapy, ACLF patients underwent plasma exchange (PE) or plasma bilirubin adsorption (PBA) combined with plasma exchange. For the derivation cohort, Kaplan-Meier methods were used to estimate survival curves, and Cox regression was used in survival analysis to generate a prognostic model. The performance of the new model was tested in the validation cohort using a receiver-operator curve. The mean overall survival for the derivation cohort was 441 d (95%CI: 379-504 d), and the 90- and 270-d survival probabilities were 70.3% and 58.3%, respectively. The mean survival times of patients treated with PBA plus PE and patients treated with PE were 531 d (95%CI: 455-605 d) and 343 d (95%CI: 254-432 d), respectively, which were significantly different (P = 0.012). When variables with bivariate significance were selected for inclusion into the multivariate Cox regression model, number of complications, age, scores of the model for end-stage liver disease (MELD) and type of artificial liver support system were defined as independent risk factors for survival in ACLF patients. This new prognostic model could accurately discriminate the outcome of patients with different scores in this cohort (P < 0.001). The model also had the ability to assign a predicted survival probability for individual patients. In the validation cohort, the new model remained better than the MELD. A novel model was constructed to predict prognosis and accurately discriminate survival in ACLF patients treated with an artificial liver support system. AIMTo establish a new model for predicting survival in acute-on-chronic liver failure (ACLF) patients treated with an artificial liver support system.METHODSOne hundred and eighty-one ACLF patients who were admitted to the hospital from January 1, 2012 to December 31, 2014 and were treated with an artificial liver support system were enrolled in this retrospective study, including a derivation cohort (n = 113) and a validation cohort (n = 68). Laboratory parameters at baseline were analyzed and correlated with clinical outcome. In addition to standard medical therapy, ACLF patients underwent plasma exchange (PE) or plasma bilirubin adsorption (PBA) combined with plasma exchange. For the derivation cohort, Kaplan-Meier methods were used to estimate survival curves, and Cox regression was used in survival analysis to generate a prognostic model. The performance of the new model was tested in the validation cohort using a receiver-operator curve.RESULTSThe mean overall survival for the derivation cohort was 441 d (95%CI: 379-504 d), and the 90- and 270-d survival probabilities were 70.3% and 58.3%, respectively. The mean survival times of patients treated with PBA plus PE and patients treated with PE were 531 d (95%CI: 455-605 d) and 343 d (95%CI: 254-432 d), respectively, which were significantly different (P = 0.012). When variables with bivariate significance were selected for inclusion into the multivariate Cox regression model, number of complications, age, scores of the model for end-stage liver disease (MELD) and type of artificial liver support system were defined as independent risk factors for survival in ACLF patients. This new prognostic model could accurately discriminate the outcome of patients with different scores in this cohort (P < 0.001). The model also had the ability to assign a predicted survival probability for individual patients. In the validation cohort, the new model remained better than the MELD.CONCLUSIONA novel model was constructed to predict prognosis and accurately discriminate survival in ACLF patients treated with an artificial liver support system. AIM: To establish a new model for predicting survival in acute-on-chronic liver failure (ACLF) patients treated with an artificial liver support system. METHODS: One hundred and eighty-one ACLF patients who were admitted to the hospital from January 1, 2012 to December 31, 2014 and were treated with an artificial liver support system were enrolled in this retrospective study, including a derivation cohort ( n = 113) and a validation cohort ( n = 68). Laboratory parameters at baseline were analyzed and correlated with clinical outcome. In addition to standard medical therapy, ACLF patients underwent plasma exchange (PE) or plasma bilirubin adsorption (PBA) combined with plasma exchange. For the derivation cohort, Kaplan-Meier methods were used to estimate survival curves, and Cox regression was used in survival analysis to generate a prognostic model. The performance of the new model was tested in the validation cohort using a receiver-operator curve. RESULTS: The mean overall survival for the derivation cohort was 441 d (95%CI: 379-504 d), and the 90- and 270-d survival probabilities were 70.3% and 58.3%, respectively. The mean survival times of patients treated with PBA plus PE and patients treated with PE were 531 d (95%CI: 455-605 d) and 343 d (95%CI: 254-432 d), respectively, which were significantly different ( P = 0.012). When variables with bivariate significance were selected for inclusion into the multivariate Cox regression model, number of complications, age, scores of the model for end-stage liver disease (MELD) and type of artificial liver support system were defined as independent risk factors for survival in ACLF patients. This new prognostic model could accurately discriminate the outcome of patients with different scores in this cohort ( P < 0.001). The model also had the ability to assign a predicted survival probability for individual patients. In the validation cohort, the new model remained better than the MELD. CONCLUSION: A novel model was constructed to predict prognosis and accurately discriminate survival in ACLF patients treated with an artificial liver support system. |
Author | Pi-Qi Zhou Shao-Ping Zheng Min Yu Sheng-Song He Zhi-Hong Weng |
AuthorAffiliation | Department of Integrated Traditional and Chinese Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology Department of Ultrasonography, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology Department of Internal Medicine, Wuhan Eleventh Hospital Department of Infectious Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology |
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Cites_doi | 10.1016/S1665-2681(19)30925-1 10.1136/gut.2004.062208 10.1053/jhep.2002.34856 10.1177/039139880202501003 10.1136/gut.2009.189639 10.1177/039139889201501110 10.1177/039139889201500107 10.1097/00003246-199805000-00021 10.1177/039139880402700810 10.3892/etm.2013.1241 10.1053/jhep.2002.31250 10.1159/000047017 10.1016/j.transproceed.2005.11.044 10.1111/j.1525-1594.1994.tb02214.x 10.1111/j.1525-1594.1993.tb00416.x 10.1186/1756-0500-5-297 10.1002/hep.25680 10.1111/j.1525-1594.1989.tb01556.x 10.4161/org.3.1.3635 10.1111/j.1365-2893.2008.01046.x 10.1111/j.1365-2133.2006.07451.x 10.1016/S0140-6736(08)60383-9 10.1136/gut.2006.107789 10.3748/wjg.v10.i20.2984 10.1016/j.suc.2013.10.004 10.1159/000014407 10.1007/s12072-008-9106-x 10.1016/0016-5085(88)90011-X 10.1002/(SICI)1097-0258(20000229)19:4<453::AID-SIM350>3.0.CO;2-5 10.1016/s0140-6736(73)91220-8 10.1111/j.1440-1746.2004.03188.x 10.1016/j.jhep.2004.02.010 10.1016/j.cgh.2010.12.027 10.1053/gast.2003.50016 10.1016/S0140-6736(77)90001-0 10.1111/j.1478-3231.2012.02790.x |
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Keywords | Artificial liver support system Plasma exchange Acute-on-chronic liver failure Model for end-stage liver disease Plasma bilirubin adsorption |
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Notes | AIM: To establish a new model for predicting survival in acute-on-chronic liver failure(ACLF) patients treated with an artificial liver support system. METHODS: One hundred and eighty-one ACLF patients who were admitted to the hospital from January 1, 2012 to December 31, 2014 and were treated with an artificial liver support system were enrolled in this retrospective study, including a derivation cohort(n = 113) and a validation cohort(n = 68). Laboratory parameters at baseline were analyzed and correlatedwith clinical outcome. In addition to standard medical therapy, ACLF patients underwent plasma exchange(PE) or plasma bilirubin adsorption(PBA) combined with plasma exchange. For the derivation cohort, KaplanMeier methods were used to estimate survival curves, and Cox regression was used in survival analysis to generate a prognostic model. The performance of the new model was tested in the validation cohort using a receiver-operator curve.RESULTS: The mean overall survival for the derivation cohort was 441 d(95%CI: 379-504 d), and the 90- and 270-d survival probabilities were 70.3% and 58.3%, respectively. The mean survival times of patients treated with PBA plus PE and patients treated with PE were 531 d(95%CI: 455-605 d) and 343 d(95%CI: 254-432 d), respectively, which were significantly different(P = 0.012). When variables with bivariate significance were selected for inclusion into the multivariate Cox regression model, number of complications, age, scores of the model for end-stage liver disease(MELD) and type of artificial liver support system were defined as independent risk factors for survival in ACLF patients. This new prognostic model could accurately discriminate the outcome of patients with different scores in this cohort(P < 0.001). The model also had the ability to assign a predicted survival probability for individual patients. In the validation cohort, the new model remained better than the MELD.CONCLUSION: A novel model was constructed to predict prognosis and accurately discriminate survival in ACLF patients treated with an artificial liver support system. Acute-on-chronic liver failure;Artificial liver su Pi-Qi Zhou;Shao-Ping Zheng;Min Yu;Sheng-Song He;Zhi-Hong Weng;Department of Integrated Traditional and Chinese Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology;Department of Ultrasonography, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology;Department of Internal Medicine, Wuhan Eleventh Hospital;Department of Infectious Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Correspondence to: Zhi-Hong Weng, Associate Professor, Department of Infectious Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Avenue, Wuhan 430022, Hubei Province, China. wzh941@126.com Telephone: +86-27-85726783 Fax: +86-27-85356369 Author contributions: Zhou PQ and Zheng SP contributed equally to this work; Weng ZH proposed the concept and designed the study; Zhou PQ and Yu M performed the study; Zheng SP and He SS analysed and interpreted the data; Zhou PQ and Zheng SP drafted the article and revised it critically for important intellectual content; and Weng ZH approved the paper to be submitted. |
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SubjectTerms | Acute-on-chronic Acute-On-Chronic Liver Failure - blood Acute-On-Chronic Liver Failure - diagnosis Acute-On-Chronic Liver Failure - mortality Acute-On-Chronic Liver Failure - therapy Adult Aged Aged, 80 and over Area Under Curve Bilirubin - blood Decision Support Techniques failure;Artificial Female Humans Kaplan-Meier Estimate liver Liver, Artificial Male Middle Aged Multivariate Analysis Plasma Exchange Predictive Value of Tests Proportional Hazards Models Reproducibility of Results Retrospective Studies Retrospective Study ROC Curve Time Factors Treatment Outcome Young Adult |
Title | Prognosis of acute-on-chronic liver failure patients treated with artificial liver support system |
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