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 inWorld journal of gastroenterology : WJG Vol. 21; no. 32; pp. 9614 - 9622
Main Authors Zhou, Pi-Qi, Zheng, Shao-Ping, Yu, Min, He, Sheng-Song, Weng, Zhi-Hong
Format Journal Article
LanguageEnglish
Published United States Baishideng Publishing Group Inc 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.
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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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 &lt; 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
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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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Snippet AIM: To establish a new model for predicting survival in acute-on-chronic liver failure(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...
AIMTo establish a new model for predicting survival in acute-on-chronic liver failure (ACLF) patients treated with an artificial liver support...
AIM: To establish a new model for predicting survival in acute-on-chronic liver failure (ACLF) patients treated with an artificial liver support system....
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SourceType Open Access Repository
Aggregation Database
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StartPage 9614
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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https://www.ncbi.nlm.nih.gov/pubmed/26327769
https://search.proquest.com/docview/1709393057
https://pubmed.ncbi.nlm.nih.gov/PMC4548122
Volume 21
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