Artificial neural network-based models used for predicting 28- and 90-day mortality of patients with hepatitis B-associated acute-on-chronic liver failure
Abstract Background This study aimed to develop prognostic models for predicting 28- and 90-day mortality rates of hepatitis B virus (HBV)-associated acute-on-chronic liver failure (HBV-ACLF) through artificial neural network (ANN) systems. Methods Six hundred and eight-four cases of consecutive HBV...
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doaj-e25eae0833da4493934466daa2772e2d2020-11-25T03:51:07ZengBMCBMC Gastroenterology1471-230X2020-03-0120111210.1186/s12876-020-01191-5Artificial neural network-based models used for predicting 28- and 90-day mortality of patients with hepatitis B-associated acute-on-chronic liver failureYixin Hou0Qianqian Zhang1Fangyuan Gao2Dewen Mao3Jun Li4Zuojiong Gong5Xinla Luo6Guoliang Chen7Yong Li8Zhiyun Yang9Kewei Sun10Xianbo Wang11Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical UniversityDepartment of Hepatology, The First Hospital Affiliated to Hunan University of Chinese MedicineCenter of Integrative Medicine, Beijing Ditan Hospital, Capital Medical UniversityDepartment of Hepatology, The First Affiliated Hospital of Guangxi University of Chinese MedicineCenter of Integrative MedicineDepartment of Infectious Diseases, Renmin Hospital of Wuhan UniversityDepartment of Hepatology, Hubei Provincial Hospital of Traditional Chinese MedicineDepartment of Hepatology, Xiamen Hospital of Traditional Chinese MedicineDepartment of Hepatology, The Affiliated Hospital of Shandong University of Traditional Chinese MedicineCenter of Integrative Medicine, Beijing Ditan Hospital, Capital Medical UniversityDepartment of Hepatology, The First Hospital Affiliated to Hunan University of Chinese MedicineCenter of Integrative Medicine, Beijing Ditan Hospital, Capital Medical UniversityAbstract Background This study aimed to develop prognostic models for predicting 28- and 90-day mortality rates of hepatitis B virus (HBV)-associated acute-on-chronic liver failure (HBV-ACLF) through artificial neural network (ANN) systems. Methods Six hundred and eight-four cases of consecutive HBV-ACLF patients were retrospectively reviewed. Four hundred and twenty-three cases were used for training and constructing ANN models, and the remaining 261 cases were for validating the established models. Predictors associated with mortality were determined by univariate analysis and were then included in ANN models for predicting prognosis of mortality. The receiver operating characteristic curve analysis was used to evaluate the predictive performance of the ANN models in comparison with various current prognostic models. Results Variables with statistically significant difference or important clinical characteristics were input in the ANN training process, and eight independent risk factors, including age, hepatic encephalopathy, serum sodium, prothrombin activity, γ-glutamyltransferase, hepatitis B e antigen, alkaline phosphatase and total bilirubin, were eventually used to establish ANN models. For 28-day mortality in the training cohort, the model’s predictive accuracy (AUR 0.948, 95% CI 0.925–0.970) was significantly higher than that of the Model for End-stage Liver Disease (MELD), MELD-sodium (MELD-Na), Chronic Liver Failure-ACLF (CLIF-ACLF), and Child-Turcotte-Pugh (CTP) (all p < 0.001). In the validation cohorts the predictive accuracy of ANN model (AUR 0.748, 95% CI: 0.673–0.822) was significantly higher than that of MELD (p = 0.0099) and insignificantly higher than that of MELD-Na, CTP and CLIF-ACLF (p > 0.05). For 90-day mortality in the training cohort, the model’s predictive accuracy (AUR 0.913, 95% CI 0.887–0.938) was significantly higher than that of MELD, MELD-Na, CTP and CLIF-ACLF (all p < 0.001). In the validation cohorts, the prediction accuracy of the ANN model (AUR 0.754, 95% CI: 0.697–0.812 was significantly higher than that of MELD (p = 0.019) and insignificantly higher than MELD-Na, CTP and CLIF-ACLF (p > 0.05). Conclusions The established ANN models can more accurately predict short-term mortality risk in patients with HBV- ACLF. The main content has been postered as an abstract at the AASLD Hepatology Conference ( https://doi.org/10.1002/hep.30257 ).http://link.springer.com/article/10.1186/s12876-020-01191-5Hepatitis B virusAcute-on-chronic liver failureShort-termMortalityPrognosisArtificial neural network |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yixin Hou Qianqian Zhang Fangyuan Gao Dewen Mao Jun Li Zuojiong Gong Xinla Luo Guoliang Chen Yong Li Zhiyun Yang Kewei Sun Xianbo Wang |
spellingShingle |
Yixin Hou Qianqian Zhang Fangyuan Gao Dewen Mao Jun Li Zuojiong Gong Xinla Luo Guoliang Chen Yong Li Zhiyun Yang Kewei Sun Xianbo Wang Artificial neural network-based models used for predicting 28- and 90-day mortality of patients with hepatitis B-associated acute-on-chronic liver failure BMC Gastroenterology Hepatitis B virus Acute-on-chronic liver failure Short-term Mortality Prognosis Artificial neural network |
author_facet |
Yixin Hou Qianqian Zhang Fangyuan Gao Dewen Mao Jun Li Zuojiong Gong Xinla Luo Guoliang Chen Yong Li Zhiyun Yang Kewei Sun Xianbo Wang |
author_sort |
Yixin Hou |
title |
Artificial neural network-based models used for predicting 28- and 90-day mortality of patients with hepatitis B-associated acute-on-chronic liver failure |
title_short |
Artificial neural network-based models used for predicting 28- and 90-day mortality of patients with hepatitis B-associated acute-on-chronic liver failure |
title_full |
Artificial neural network-based models used for predicting 28- and 90-day mortality of patients with hepatitis B-associated acute-on-chronic liver failure |
title_fullStr |
Artificial neural network-based models used for predicting 28- and 90-day mortality of patients with hepatitis B-associated acute-on-chronic liver failure |
title_full_unstemmed |
Artificial neural network-based models used for predicting 28- and 90-day mortality of patients with hepatitis B-associated acute-on-chronic liver failure |
title_sort |
artificial neural network-based models used for predicting 28- and 90-day mortality of patients with hepatitis b-associated acute-on-chronic liver failure |
publisher |
BMC |
series |
BMC Gastroenterology |
issn |
1471-230X |
publishDate |
2020-03-01 |
description |
Abstract Background This study aimed to develop prognostic models for predicting 28- and 90-day mortality rates of hepatitis B virus (HBV)-associated acute-on-chronic liver failure (HBV-ACLF) through artificial neural network (ANN) systems. Methods Six hundred and eight-four cases of consecutive HBV-ACLF patients were retrospectively reviewed. Four hundred and twenty-three cases were used for training and constructing ANN models, and the remaining 261 cases were for validating the established models. Predictors associated with mortality were determined by univariate analysis and were then included in ANN models for predicting prognosis of mortality. The receiver operating characteristic curve analysis was used to evaluate the predictive performance of the ANN models in comparison with various current prognostic models. Results Variables with statistically significant difference or important clinical characteristics were input in the ANN training process, and eight independent risk factors, including age, hepatic encephalopathy, serum sodium, prothrombin activity, γ-glutamyltransferase, hepatitis B e antigen, alkaline phosphatase and total bilirubin, were eventually used to establish ANN models. For 28-day mortality in the training cohort, the model’s predictive accuracy (AUR 0.948, 95% CI 0.925–0.970) was significantly higher than that of the Model for End-stage Liver Disease (MELD), MELD-sodium (MELD-Na), Chronic Liver Failure-ACLF (CLIF-ACLF), and Child-Turcotte-Pugh (CTP) (all p < 0.001). In the validation cohorts the predictive accuracy of ANN model (AUR 0.748, 95% CI: 0.673–0.822) was significantly higher than that of MELD (p = 0.0099) and insignificantly higher than that of MELD-Na, CTP and CLIF-ACLF (p > 0.05). For 90-day mortality in the training cohort, the model’s predictive accuracy (AUR 0.913, 95% CI 0.887–0.938) was significantly higher than that of MELD, MELD-Na, CTP and CLIF-ACLF (all p < 0.001). In the validation cohorts, the prediction accuracy of the ANN model (AUR 0.754, 95% CI: 0.697–0.812 was significantly higher than that of MELD (p = 0.019) and insignificantly higher than MELD-Na, CTP and CLIF-ACLF (p > 0.05). Conclusions The established ANN models can more accurately predict short-term mortality risk in patients with HBV- ACLF. The main content has been postered as an abstract at the AASLD Hepatology Conference ( https://doi.org/10.1002/hep.30257 ). |
topic |
Hepatitis B virus Acute-on-chronic liver failure Short-term Mortality Prognosis Artificial neural network |
url |
http://link.springer.com/article/10.1186/s12876-020-01191-5 |
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