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Qiu S, Zhao Y, Hu J, Zhang Q, Wang L, Chen R, Cao Y, Liu F, Zhao C, Zhang L, Ren W, Xin S, Chen Y, Duan Z, Han T. Predicting the 28-day prognosis of acute-on-chronic liver failure patients based on machine learning. Dig Liver Dis 2024; 56:2095-2102. [PMID: 39004553 DOI: 10.1016/j.dld.2024.06.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 06/22/2024] [Accepted: 06/27/2024] [Indexed: 07/16/2024]
Abstract
BACKGROUND We aimed to establish a prognostic predictive model based on machine learning (ML) methods to predict the 28-day mortality of acute-on-chronic liver failure (ACLF) patients, and to evaluate treatment effectiveness. METHODS ACLF patients from six tertiary hospitals were included for analysis. Features for ML models' development were selected by LASSO regression. Models' performance was evaluated by area under the curve (AUC) and accuracy. Shapley additive explanation was used to explain the ML model. RESULTS Of the 736 included patients, 587 were assigned to a training set and 149 to an external validation set. Features selected included age, hepatic encephalopathy, total bilirubin, PTA, and creatinine. The eXtreme Gradient Boosting (XGB) model outperformed other ML models in the prognostic prediction of ACLF patients, with the highest AUC and accuracy. Delong's test demonstrated that the XGB model outperformed Child-Pugh score, MELD score, CLIF-SOFA, CLIF-C OF, and CLIF-C ACLF. Sequential assessments at baseline, day 3, day 7, and day 14 improved the predictive performance of the XGB-ML model and can help clinicians evaluate the effectiveness of medical treatment. CONCLUSIONS We established an XGB-ML model to predict the 28-day mortality of ACLF patients as well as to evaluate the treatment effectiveness.
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Affiliation(s)
- Shaotian Qiu
- The School of Medicine, Nankai University, Tianjin 300071, China; Department of Gastroenterology and Hepatology, Tianjin Union Medical Center Affiliated to Nankai University, Tianjin 300121, China
| | - Yumeng Zhao
- The School of Medicine, Nankai University, Tianjin 300071, China
| | - Jiaxuan Hu
- The School of Medicine, Nankai University, Tianjin 300071, China; Department of Gastroenterology and Hepatology, Tianjin Union Medical Center Affiliated to Nankai University, Tianjin 300121, China
| | - Qian Zhang
- Department of Gastroenterology and Hepatology, Tianjin Union Medical Center, Tianjin 300121, China; Tianjin Medical University, Tianjin 300070, China
| | - Lewei Wang
- Department of Gastroenterology and Hepatology, Tianjin Union Medical Center of Tianjin Medical University, Tianjin 300121, China
| | - Rui Chen
- Department of Gastroenterology and Hepatology, Tianjin Union Medical Center of Tianjin Medical University, Tianjin 300121, China
| | - Yingying Cao
- Department of Hepatology and Gastroenterology, The Third Central Clinical College of Tianjin Medical University, Tianjin 300170, China
| | - Fang Liu
- Department of Hepatology and Gastroenterology, The Third Central Clinical College of Tianjin Medical University, Tianjin 300170, China
| | - Caiyan Zhao
- Department of Infectious Disease, the Third Hospital of Hebei Medical University, Shijiazhuang 050051, China
| | - Liaoyun Zhang
- Department of Infection Disease, First Hospital of Shanxi Medical University, Taiyuan 030001, China
| | - Wanhua Ren
- Infectious Department of Shandong First Medical University Affiliated Shandong Provincial Hospital, Jinan 250021, China
| | - Shaojie Xin
- Liver Failure Treatment and Research Center, The Fifth Medical Center of Chinese PLA General Hospital, Beijing 100039, China
| | - Yu Chen
- Liver Disease Center (Difficult & Complicated Liver Diseases and Artificial Liver Center), Beijing You'an Hospital Affiliated to Capital Medical University, Beijing 100069, China
| | - Zhongping Duan
- Liver Disease Center (Difficult & Complicated Liver Diseases and Artificial Liver Center), Beijing You'an Hospital Affiliated to Capital Medical University, Beijing 100069, China
| | - Tao Han
- The School of Medicine, Nankai University, Tianjin 300071, China; Department of Gastroenterology and Hepatology, Tianjin Union Medical Center Affiliated to Nankai University, Tianjin 300121, China; Department of Gastroenterology and Hepatology, Tianjin Union Medical Center, Tianjin 300121, China; Tianjin Medical University, Tianjin 300070, China; Department of Gastroenterology and Hepatology, Tianjin Union Medical Center of Tianjin Medical University, Tianjin 300121, China; Department of Hepatology and Gastroenterology, The Third Central Clinical College of Tianjin Medical University, Tianjin 300170, China.
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Lv P, Cao Z, Zhu Z, Xu X, Zhao Z. Laboratory variables-based artificial neural network models for predicting fatty liver disease: A retrospective study. Open Med (Wars) 2024; 19:20241031. [PMID: 39291279 PMCID: PMC11406433 DOI: 10.1515/med-2024-1031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 08/07/2024] [Accepted: 08/12/2024] [Indexed: 09/19/2024] Open
Abstract
Background The efficacy of artificial neural network (ANN) models employing laboratory variables for predicting fatty liver disease (FLD) remains inadequately established. The study aimed to develop ANN models to precisely predict FLD. Methods Of 12,058 participants undergoing the initial FLD screening, 7,990 eligible participants were included. A total of 6,309 participants were divided randomly into the training (4,415 participants, 70%) and validation (1,894 participants, 30%) sets for developing prediction models. The performance of ANNs was additionally tested in the testing set (1,681 participants). The area under the receiver operating characteristic curve (AUROC) was employed to assess the models' performance. Results The 18-variable, 11-variable, 3-variable, and 2-variable models each achieved robust FLD prediction performance, with AUROCs over 0.92, 0.91, and 0.89 in the training, validation, and testing, respectively. Although slightly inferior to the other three models in performance (AUROC ranges: 0.89-0.92 vs 0.91-0.95), the 2-variable model showed 80.3% accuracy and 89.7% positive predictive value in the testing. Incorporating age and gender increased the AUROCs of the resulting 20-variable, 13-variable, 5-variable, and 4-variable models each to over 0.93, 0.92, and 0.91 in the training, validation, and testing, respectively. Conclusions Implementation of the ANN models could effectively predict FLD, with enhanced predictive performance via the inclusion of age and gender.
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Affiliation(s)
- Panpan Lv
- Department of Clinical Laboratory, Minhang Hospital, Fudan University, Shanghai, China
| | - Zhen Cao
- Department of Clinical Laboratory, Minhang Hospital, Fudan University, Shanghai, China
| | - Zhengqi Zhu
- Department of Clinical Laboratory, Minhang Hospital, Fudan University, Shanghai, China
| | - Xiaoqin Xu
- Department of Clinical Laboratory, Minhang Hospital, Fudan University, Shanghai, China
| | - Zhen Zhao
- Department of Clinical Laboratory, Minhang Hospital, Fudan University, Shanghai, China
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Zhang Q, Peng Y, Lei S, Xiong T, Zhang L, Peng H, Luo X, Wang R. A nutrition-based radiomics–clinical model to predict the prognosis of patients with acute-on-chronic liver failure. DISPLAYS 2024; 84:102750. [DOI: 10.1016/j.displa.2024.102750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
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Zeng F, Jiang W, Chang X, Yang F, Luo X, Liu R, Lei Y, Li J, Pan C, Huang X, Sun H, Lan Y. Sarcopenia is associated with short- and long-term mortality in patients with acute-on-chronic liver failure. J Cachexia Sarcopenia Muscle 2024; 15:1473-1482. [PMID: 38965993 PMCID: PMC11294047 DOI: 10.1002/jcsm.13501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 03/21/2024] [Accepted: 04/08/2024] [Indexed: 07/06/2024] Open
Abstract
BACKGROUND While sarcopenia is recognized as a predictor of mortality in cirrhosis, its influence on acute-on-chronic liver failure (ACLF) remains uncertain. Despite multiple studies examining the impact of sarcopenia on short-term mortality in patients with ACLF, the sample size of these studies was limited, and their outcomes were inconsistent. Therefore, this study aimed to explore the impact of sarcopenia on both short- and long-term mortality in patients with ACLF. METHODS This retrospective cohort study included 414 patients with ACLF that were treated between January 2016 and September 2022. Sarcopenia was diagnosed based on the measurement of the skeletal muscle index at the third lumbar vertebra (L3-SMI). Subsequently, the patients were divided into sarcopenia and non-sarcopenia groups. We analysed the basic clinical data of the two groups. Multivariate Cox proportional analysis was used to analyse short-term (28 days) and long-term (1 year and overall) mortality rates. RESULTS A total of 414 patients were included, with a mean age of 52.88 ± 13.41 years. Among them, 318 (76.8%) were male, and 239 (57.7%) had sarcopenia. A total of 280 (67.6%) patients died during the study period. Among them, 153 patients died within 28 days (37%) and 209 patients died within 1 year (50.5%). We found that the 28-day, 1-year and overall mortality rates in the sarcopenia group were significantly higher than those in the non-sarcopenia group (37% vs. 22.3%, P < 0.01; 50.5% vs. 34.9%, P < 0.01; and 67.6% vs. 53.1%, P < 0.01, respectively). Multivariate Cox regression analysis revealed that sarcopenia was significantly associated with increased mortality. The hazard ratios for sarcopenia were 2.05 (95% confidence interval [CI] 1.41-3.00, P < 0.01) for 28-day mortality, 1.81 (95% CI 1.29-2.54, P < 0.01) for 1-year mortality and 1.82 (95% CI 1.30-2.55, P < 0.01) for overall mortality. In addition, muscle density and international normalized ratio were associated with short- and long-term mortality. CONCLUSIONS Sarcopenia is associated with both short- and long-term mortality in patients with ACLF. Therefore, regular monitoring for sarcopenia is important for these patients.
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Affiliation(s)
- Fan Zeng
- Department of Intensive Care UnitSichuan Academy of Medical Sciences and Sichuan Provincial People's HospitalChengduChina
| | - Wei Jiang
- Department of Intensive Care UnitSichuan Academy of Medical Sciences and Sichuan Provincial People's HospitalChengduChina
- Clinical Medicine School of Chengdu University of Traditional Chinese MedicineChengduChina
| | - Xiujun Chang
- Department of Intensive Care UnitSichuan Academy of Medical Sciences and Sichuan Provincial People's HospitalChengduChina
- Clinical Medicine School of Chengdu University of Traditional Chinese MedicineChengduChina
| | - Fuxun Yang
- Department of Intensive Care UnitSichuan Academy of Medical Sciences and Sichuan Provincial People's HospitalChengduChina
| | - Xiaoxiu Luo
- Department of Intensive Care UnitSichuan Academy of Medical Sciences and Sichuan Provincial People's HospitalChengduChina
| | - Rongan Liu
- Department of Intensive Care UnitSichuan Academy of Medical Sciences and Sichuan Provincial People's HospitalChengduChina
| | - Yu Lei
- Department of Intensive Care UnitSichuan Academy of Medical Sciences and Sichuan Provincial People's HospitalChengduChina
| | - Jiajia Li
- Department of Intensive Care UnitSichuan Academy of Medical Sciences and Sichuan Provincial People's HospitalChengduChina
| | - Chun Pan
- Department of Intensive Care UnitSichuan Academy of Medical Sciences and Sichuan Provincial People's HospitalChengduChina
| | - Xiaobo Huang
- Department of Intensive Care UnitSichuan Academy of Medical Sciences and Sichuan Provincial People's HospitalChengduChina
| | - Huaiqiang Sun
- Huaxi MR Research Center (HMRRC), Department of RadiologyWest China Hospital of Sichuan UniversityChengduChina
| | - Yunping Lan
- Department of Intensive Care UnitSichuan Academy of Medical Sciences and Sichuan Provincial People's HospitalChengduChina
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Ranjan R, Ken-Dror G, Sharma P. Prediction of Long-Term Poor Clinical Outcomes in Cerebral Venous Thrombosis Using Neural Networks Model: The BEAST Study. Int J Gen Med 2024; 17:2919-2930. [PMID: 38978712 PMCID: PMC11228426 DOI: 10.2147/ijgm.s468433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 06/25/2024] [Indexed: 07/10/2024] Open
Abstract
Introduction Risk prediction models are commonly performed with logistic regression analysis but are limited by skewed datasets. We utilised neural networks (NNs) model to identify independent predictors of poor outcomes in cerebral venous thrombosis (CVT) due to the limitations of logistic regression (LR) analysis with complex datasets. Methods We evaluated 1309 adult CVT patients from the prospective BEAST (Biorepository to Establish the Aetiology of Sinovenous Thrombosis) study. The area under the receiver operating characteristic (AUROC) curve confirmed the goodness-of-fit of prediction models. The normalised importance (NI) of the NNs determines the significance of independent predictors. Results The stepwise logistic regression model found thrombolysis (OR 32.1; 95% CI 3.6-287.0; P=0.002), craniotomy (OR 6.9; 95% CI 1.3-36.8; P=0.02), and cerebral haemorrhage (OR 4.5; 95% CI 1.3-15.4; P=0.01) as predictors of poor clinical outcome with the AUROC of 0.71. Conversely, the NNs model identified major independent predictors of long-term poor clinical outcomes as cerebral haemorrhage (NI 100%) and thrombolysis (NI 98%), as well as trivial predictors of age (NI 2.8%) and altered mental status (NI 3.5%). The accuracy of the NNs model was 95.1% and 94.1% for self-learned randomly selected training and testing samples with an AUROC of 0.82. Positive and negative predictive values for poor outcomes were 13.2% and 97.1% for the LR model, compared with the NNs model of 18.8% and 98.7%, respectively. Conclusion Cerebral haemorrhage and thrombolysis was a strong independent predictor, whereas age merely impacts the long-term poor clinical outcome in adult CVT. Integrating unorthodox neural networks risk prediction model can improve decision-making as it outperforms conventional logistic regression with complex datasets.
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Affiliation(s)
- Redoy Ranjan
- Department of Biological Sciences, Institute of Cardiovascular Research, Royal Holloway University of London (ICR2UL), Egham, Greater London, UK
| | - Gie Ken-Dror
- Department of Biological Sciences, Institute of Cardiovascular Research, Royal Holloway University of London (ICR2UL), Egham, Greater London, UK
| | - Pankaj Sharma
- Department of Biological Sciences, Institute of Cardiovascular Research, Royal Holloway University of London (ICR2UL), Egham, Greater London, UK
- Department of Clinical Neuroscience, Imperial College Healthcare NHS Trust, London, UK
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Chiesa‐Estomba CM, González‐García JA, Larruscain E, Sistiaga Suarez JA, Quer M, León X, Martínez‐Ruiz de Apodaca P, López‐Mollá C, Mayo‐Yanez M, Medela A. Facial nerve palsy following parotid gland surgery: A machine learning prediction outcome approach. World J Otorhinolaryngol Head Neck Surg 2023; 9:271-279. [PMID: 38059137 PMCID: PMC10696266 DOI: 10.1002/wjo2.94] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 11/27/2022] [Accepted: 12/16/2022] [Indexed: 04/03/2023] Open
Abstract
Introduction Machine learning (ML)-based facial nerve injury (FNI) forecasting grounded on multicentric data has not been released up to now. Three distinct ML models, random forest (RF), K-nearest neighbor, and artificial neural network (ANN), for the prediction of FNI were evaluated in this mode. Methods A retrospective, longitudinal, multicentric study was performed, including patients who went through parotid gland surgery for benign tumors at three different university hospitals. Results Seven hundred and thirty-six patients were included. The most compelling aspects related to risk escalation of FNI were as follows: (1) location, in the mid-portion of the gland, near to or above the main trunk of the facial nerve and at the top part, over the frontal or the orbital branch of the facial nerve; (2) tumor volume in the anteroposterior axis; (3) the necessity to simultaneously dissect more than one level; and (4) the requirement of an extended resection compared to a lesser extended resection. By contrast, in accordance with the ML analysis, the size of the tumor (>3 cm), as well as gender and age did not result in a determining favor in relation to the risk of FNI. Discussion The findings of this research conclude that ML models such as RF and ANN may serve evidence-based predictions from multicentric data regarding the risk of FNI. Conclusion Along with the advent of ML technology, an improvement of the information regarding the potential risks of FNI associated with patients before each procedure may be achieved with the implementation of clinical, radiological, histological, and/or cytological data.
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Affiliation(s)
- Carlos M. Chiesa‐Estomba
- Department of Otorhinolaryngology—Head and Neck SurgeryDonostia University HospitalDonosti‐San SebastiánSpain
- Head & Neck Study Group of Young‐Otolaryngologists of the International Federations of Oto‐rhino‐laryngological Societies (YO‐IFOS)ParisFrance
- Biodonostia Health Research InstituteSan SebastiánSpain
| | - Jose A. González‐García
- Department of Otorhinolaryngology—Head and Neck SurgeryDonostia University HospitalDonosti‐San SebastiánSpain
| | - Ekhiñe Larruscain
- Department of Otorhinolaryngology—Head and Neck SurgeryDonostia University HospitalDonosti‐San SebastiánSpain
| | - Jon A. Sistiaga Suarez
- Department of Otorhinolaryngology—Head and Neck SurgeryDonostia University HospitalDonosti‐San SebastiánSpain
| | - Miquel Quer
- Department of Otorhinolaryngology, Hospital Santa Creu I Sant PauUniversitat Autònoma de BarcelonaBarcelonaSpain
| | - Xavier León
- Department of Otorhinolaryngology, Hospital Santa Creu I Sant PauUniversitat Autònoma de BarcelonaBarcelonaSpain
| | - Paula Martínez‐Ruiz de Apodaca
- Head & Neck Study Group of Young‐Otolaryngologists of the International Federations of Oto‐rhino‐laryngological Societies (YO‐IFOS)ParisFrance
- Department of OtorhinolaryngologyDoctor Peset University HospitalValenciaSpain
| | - Celia López‐Mollá
- Department of OtorhinolaryngologyDoctor Peset University HospitalValenciaSpain
| | - Miguel Mayo‐Yanez
- Head & Neck Study Group of Young‐Otolaryngologists of the International Federations of Oto‐rhino‐laryngological Societies (YO‐IFOS)ParisFrance
- Otorhinolaryngology—Head and Neck Surgery DepartmentComplexo Hospitalario Universitario A Coruña (CHUAC)A CoruñaGaliciaSpain
- Clinical Research in Medicine, International Center for Doctorate and Advanced Studies (CIEDUS), Universidade de Santiago de, Compostela (USC)Santiago de CompostelaGaliciaSpain
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Rashidi-Alavijeh J, Nuruzade N, Frey A, Huessler EM, Hörster A, Zeller AC, Schütte A, Schmidt H, Willuweit K, Lange CM. Implications of anaemia and response to anaemia treatment on outcomes in patients with cirrhosis. JHEP Rep 2023; 5:100688. [PMID: 36926273 PMCID: PMC10011825 DOI: 10.1016/j.jhepr.2023.100688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 12/29/2022] [Accepted: 01/12/2023] [Indexed: 01/30/2023] Open
Abstract
Background & Aims Anaemia is frequently observed in patients with cirrhosis and was identified as a predictor of adverse outcomes, such as increased mortality and occurrence of acute-on-chronic liver failure. To date, the possible effects of iron supplementation on these adverse outcomes are not well described. We therefore aimed to assess the role of iron supplementation in patients with cirrhosis and its capability to improve prognosis. Methods Laboratory diagnostics were performed in consecutive outpatients with cirrhosis admitted between July 2018 and December 2019 to the University Hospital Essen. Associations with transplant-free survival were assessed in regression models. Results A total of 317 outpatients with cirrhosis were included, of whom 61 received a liver transplant (n = 19) or died (n = 42). In multivariate Cox regression analysis, male sex (hazard ratio [HR] = 3.33, 95% CI [1.59, 6.99], p = 0.001), model for end-stage liver disease score (HR = 1.19, 95% CI [1.11, 1.27], p <0.001) and the increase of haemoglobin levels within 6 months (ΔHb6) (HR = 0.72, 95% CI [0.63, 0.83], p <0.001) were associated with transplant-free survival. Regarding the prediction of haemoglobin increase, intake of rifaximin (beta = 0.50, SD beta = 0.19, p = 0.007) and iron supplementation (beta = 0.79, SD beta = 0.26, p = 0.003) were significant predictors in multivariate analysis. Conclusions An increase of haemoglobin levels is associated with improvement of transplant-free survival in patients with cirrhosis. Because the prediction of haemoglobin increase significantly depends on rifaximin and iron supplementation, application of these two medications can have an important impact on the outcome of these patients. Impact and implications Anaemia is very common in patients with cirrhosis and is known to be a predictor of negative outcomes, but little is known about the effect of iron substitution in these individuals. In our cohort, increase of haemoglobin levels improved transplant-free survival of patients with cirrhosis. The increase of haemoglobin levels was mainly induced by iron supplementation and was even stronger in the case of concomitant use of iron and rifaximin. Clinical trial registration UME-ID-10042.
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Key Words
- ACLF, acute-on-chronic liver failure
- AIH, autoimmune hepatitis
- ALT, alanine aminotransferase
- AP, alkaline phosphatase
- AST, aspartate aminotransferase
- CRP, C-reactive protein
- Haemoglobin
- INR, international normalised ratio
- Iron deficiency
- Iron supplementation
- LT, liver transplantation
- Liver transplantation
- MELD, model for end-stage liver disease
- NASH, non-alcoholic steatohepatitis
- NSBBs, non-selective beta blockers
- PBC, primary biliary cholangitis
- PSC, primary sclerosing cholangitis
- Rifaximin
- SSC, secondary sclerosing cholangitis
- TIPS, transjugular intrahepatic portosystemic shunt
- aPTT, activated partial thromboplastin time
- ΔHb3, difference of haemoglobin levels after 3 months
- ΔHb6, difference of haemoglobin levels after 6 months
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Affiliation(s)
- Jassin Rashidi-Alavijeh
- Department of Gastroenterology, Hepatology and Transplant Medicine, Medical Faculty, University of Duisburg-Essen, Duisburg, Germany
| | - Nargiz Nuruzade
- Department of Gastroenterology, Hepatology and Transplant Medicine, Medical Faculty, University of Duisburg-Essen, Duisburg, Germany
| | - Alexandra Frey
- Department of Gastroenterology, Hepatology and Transplant Medicine, Medical Faculty, University of Duisburg-Essen, Duisburg, Germany
| | - Eva-Maria Huessler
- Institute for Medical Informatics, Biometry and Epidemiology (IMIBE), University of Duisburg-Essen, Duisburg, Germany
| | - Anne Hörster
- Department of Gastroenterology, Hepatology and Transplant Medicine, Medical Faculty, University of Duisburg-Essen, Duisburg, Germany
| | - Amos Cornelius Zeller
- Department of Gastroenterology, Hepatology and Transplant Medicine, Medical Faculty, University of Duisburg-Essen, Duisburg, Germany
| | - Andreas Schütte
- Department of Gastroenterology, Hepatology and Transplant Medicine, Medical Faculty, University of Duisburg-Essen, Duisburg, Germany
| | - Hartmut Schmidt
- Department of Gastroenterology, Hepatology and Transplant Medicine, Medical Faculty, University of Duisburg-Essen, Duisburg, Germany
| | - Katharina Willuweit
- Department of Gastroenterology, Hepatology and Transplant Medicine, Medical Faculty, University of Duisburg-Essen, Duisburg, Germany
| | - Christian Markus Lange
- Department of Gastroenterology, Hepatology and Transplant Medicine, Medical Faculty, University of Duisburg-Essen, Duisburg, Germany
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Gary PJ, Lal A, Simonetto DA, Gajic O, Gallo de Moraes A. Acute on chronic liver failure: prognostic models and artificial intelligence applications. Hepatol Commun 2023; 7:02009842-202304010-00015. [PMID: 36972378 PMCID: PMC10043584 DOI: 10.1097/hc9.0000000000000095] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 01/04/2023] [Indexed: 03/29/2023] Open
Abstract
Critically ill patients presenting with acute on chronic liver failure (ACLF) represent a particularly vulnerable population due to various considerations surrounding the syndrome definition, lack of robust prospective evaluation of outcomes, and allocation of resources such as organs for transplantation. Ninety-day mortality related to ACLF is high and patients who do leave the hospital are frequently readmitted. Artificial intelligence (AI), which encompasses various classical and modern machine learning techniques, natural language processing, and other methods of predictive, prognostic, probabilistic, and simulation modeling, has emerged as an effective tool in various areas of healthcare. These methods are now being leveraged to potentially minimize physician and provider cognitive load and impact both short-term and long-term patient outcomes. However, the enthusiasm is tempered by ethical considerations and a current lack of proven benefits. In addition to prognostic applications, AI models can likely help improve the understanding of various mechanisms of morbidity and mortality in ACLF. Their overall impact on patient-centered outcomes and countless other aspects of patient care remains unclear. In this review, we discuss various AI approaches being utilized in healthcare and discuss the recent and expected future impact of AI on patients with ACLF through prognostic modeling and AI-based approaches.
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Affiliation(s)
- Phillip J Gary
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
- Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, Minnesota, USA
| | - Amos Lal
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
- Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, Minnesota, USA
| | - Douglas A Simonetto
- Division of Gastroenterology and Hepatology, Mayo Clinic College of Medicine and Science, Rochester, Minnesota, USA
| | - Ognjen Gajic
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
- Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, Minnesota, USA
| | - Alice Gallo de Moraes
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
- Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, Minnesota, USA
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Wu L, Jin J, Zhou T, Wu Y, Li X, Li X, Zeng J, Wang J, Ren J, Chong Y, Zheng R. A Prognostic Nomogram with High Accuracy Based on 2D-SWE in Patients with Acute-on-chronic Liver Failure. J Clin Transl Hepatol 2022; 10:803-813. [PMID: 36304504 PMCID: PMC9547255 DOI: 10.14218/jcth.2021.00278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 11/22/2021] [Accepted: 11/29/2021] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND AND AIMS Acute-on-chronic liver failure (ACLF) is associated with very high mortality. Accurate prediction of prognosis is critical in navigating optimal treatment decisions to improve patient survival. This study was aimed to develop a new nomogram integrating two-dimensional shear wave elastography (2D-SWE) values with other independent prognostic factors to improve the precision of predicting ACLF patient outcomes. METHODS A total of 449 consecutive patients with ACLF were recruited and randomly allocated to a training cohort (n=315) or a test cohort (n=134). 2D-SWE values, conventional ultrasound features, laboratory tests, and other clinical characteristics were included in univariate and multivariate analysis. Factors with prognostic value were then used to construct a novel prognostic nomogram. Receiver operating curves (ROCs) were generated to evaluate and compare the performance of the novel and published models including the Model for End-Stage Liver Disease (MELD), MELD combined with sodium (MELD-Na), and Jin's model. The model was validated in a prospective cohort (n=102). RESULTS A ACLF prognostic nomogram was developed with independent prognostic factors, including 2D-SWE, age, total bilirubin (TB), neutrophils (Neu), and the international normalized ratio (INR). The area under the ROC curve (AUC) was 0.849 for the new model in the training cohort and 0.861 in the prospective validation cohort, which were significantly greater than those for MELD (0.758), MELD-Na (0.750), and Jin's model (0.777, all p <0.05). Calibration curve analysis revealed good agreement between the predicted and observed probabilities. The new nomogram had superior overall net benefit and clinical utility. CONCLUSIONS We established and validated a 2D-SWE-based noninvasive nomogram to predict the prognosis of ACLF patients that was more accurate than other prognostic models.
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Affiliation(s)
- Lili Wu
- Department of Medical Ultrasonics, Third Affiliated Hospital of Sun Yat-Sen University, Guangdong Key Laboratory of Liver Disease Research, Guangzhou, Guangdong, China
| | - Jieyang Jin
- Department of Medical Ultrasonics, Third Affiliated Hospital of Sun Yat-Sen University, Guangdong Key Laboratory of Liver Disease Research, Guangzhou, Guangdong, China
| | - Taicheng Zhou
- Department of Gastroenterological Surgery and Hernia Center, Sixth Affiliated Hospital of Sun Yat-Sen University, Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Supported by National Key Clinical Discipline, Guangzhou, Guangdong, China
| | - Yuankai Wu
- Department of Infectious Diseases, Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Xinhua Li
- Department of Infectious Diseases, Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Xiangyong Li
- Department of Infectious Diseases, Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Jie Zeng
- Department of Medical Ultrasonics, Third Affiliated Hospital of Sun Yat-Sen University, Guangdong Key Laboratory of Liver Disease Research, Guangzhou, Guangdong, China
| | - Jinfen Wang
- Department of Medical Ultrasonics, Third Affiliated Hospital of Sun Yat-Sen University, Guangdong Key Laboratory of Liver Disease Research, Guangzhou, Guangdong, China
| | - Jie Ren
- Department of Medical Ultrasonics, Third Affiliated Hospital of Sun Yat-Sen University, Guangdong Key Laboratory of Liver Disease Research, Guangzhou, Guangdong, China
| | - Yutian Chong
- Department of Infectious Diseases, Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Rongqin Zheng
- Department of Medical Ultrasonics, Third Affiliated Hospital of Sun Yat-Sen University, Guangdong Key Laboratory of Liver Disease Research, Guangzhou, Guangdong, China
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10
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Wang H, Tong J, Xu X, Chen J, Mu X, Zhai X, Liu Z, Chen J, Liu X, Su H, Hu J. Reversibility of acute-on-chronic liver failure syndrome in hepatitis B virus-infected patients with and without prior decompensation. J Viral Hepat 2022; 29:890-898. [PMID: 35793410 DOI: 10.1111/jvh.13732] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Revised: 06/16/2022] [Accepted: 06/20/2022] [Indexed: 12/09/2022]
Abstract
Acute-on-chronic liver failure (ACLF) is a severe clinical syndrome associated with high short-term mortality and reversibility. This study aimed to compare the characteristics of survival and reversibility in hepatitis B virus (HBV)-related ACLF (HBV-ACLF) patients with and without previous decompensation. Overall, 1044 patients who fulfilled the acute hepatic insult criteria of the APASL-ACLF Research Consortium (AARC) definition were enrolled from a prospectively established cohort of HBV-related liver failure patients. These patients were divided into the AARC ACLF group and the non-AARC ACLF group according to prior decompensation. Mortality, reversibility of ACLF syndrome, and predicted factors associated with reversibility were evaluated. Liver transplantation-free mortality of the AARC ACLF group was significantly lower than that of the non-AARC ACLF group (28 days: 28.2% vs. 40.3%, p = .012; 90 days: 41.7% vs. 65.4%, p < .001). The 5-year cumulative reversal rates of ACLF syndrome were 88.0% (374/425) and 66.0% (31/47) in the AARC and non-AARC ACLF groups, respectively, (p = .039). Following reversibility of ACLF syndrome, 340/374 (90.9%) and 21/31 (67.7%) patients in the AARC and non-AARC ACLF groups, respectively, maintained a stable status within 5 years. Although prior decompensation indicated poor reversibility of ACLF syndrome, HBV-infected patients with prior decompensation who fulfilled the acute hepatic insult criteria of the AARC definition showed favourable reversibility and maintained a stable status after receiving nucleoside analogues. The AARC ACLF definition identified HBV-ACLF as a distinct syndrome with good reversibility. HBV-infected patients with prior decompensation could be included in the AARC ACLF management.
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Affiliation(s)
- Hongmin Wang
- Senior Department of Hepatology, The Fifth Medical Center of PLA General Hospital, Beijing, China.,Peking University 302 Clinical Medical School, Beijing, China
| | - Jingjing Tong
- Chinese PLA Medical School, Beijing, China.,Department of Infectious Diseases, Beijing Jishuitan Hospital, Beijing, China
| | - Xiang Xu
- Laboratory of Translational Medicine, Medical Innovation Research Division of Chinese PLA General Hospital, Beijing, China
| | - Jing Chen
- Senior Department of Hepatology, The Fifth Medical Center of PLA General Hospital, Beijing, China.,Chinese PLA Medical School, Beijing, China
| | - Xiuying Mu
- Peking University 302 Clinical Medical School, Beijing, China
| | - Xingran Zhai
- Peking University 302 Clinical Medical School, Beijing, China
| | - Zifeng Liu
- Chinese PLA Medical School, Beijing, China
| | - Jing Chen
- Chinese PLA Medical School, Beijing, China
| | - Xiaoyan Liu
- Senior Department of Hepatology, The Fifth Medical Center of PLA General Hospital, Beijing, China
| | - Haibin Su
- Senior Department of Hepatology, The Fifth Medical Center of PLA General Hospital, Beijing, China
| | - Jinghua Hu
- Senior Department of Hepatology, The Fifth Medical Center of PLA General Hospital, Beijing, China.,Peking University 302 Clinical Medical School, Beijing, China.,Chinese PLA Medical School, Beijing, China
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11
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Wan S, Zhao X, Niu Z, Dong L, Wu Y, Gu S, Feng Y, Hua X. Influence of ambient air pollution on successful pregnancy with frozen embryo transfer: A machine learning prediction model. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2022; 236:113444. [PMID: 35367879 DOI: 10.1016/j.ecoenv.2022.113444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 03/18/2022] [Accepted: 03/19/2022] [Indexed: 06/14/2023]
Abstract
Numerous air pollutants have been reported to influence the outcomes of in vitro fertilization (IVF). However, whether air pollution affects implantation in frozen embryo transfer (FET) process is under debate. We aimed to find the association between ambient air pollution and implantation potential of FET and test the value of adding air pollution data to a random forest model (RFM) predicting intrauterine pregnancy. Using a retrospective study of a 4-year single-center design，we analyzed 3698 cycles of women living in Shanghai who underwent FET between 2015 and 2018. To estimate patients' individual exposure to air pollution, we computed averages of daily concentrations of six air pollutants including PM2.5, PM10, SO2, CO, NO2, and O3 measured at 9 monitoring stations in Shanghai for the exposure period (one month before FET). Moreover, A predictive model of 15 variables was established using RFM. Air pollutants levels of patients with or without intrauterine pregnancy were compared. Our results indicated that for exposure periods before FET, NO2 were negatively associated with intrauterine pregnancy (OR: 0.906, CI: 0.816-0.989). AUROC increased from 0.712 to 0.771 as air pollutants features were added. Overall, our findings demonstrate that exposure to NO2 before transfer has an adverse effect on clinical pregnancy. The performance to predict intrauterine pregnancy will improve with the use of air pollution data in RFM.
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Affiliation(s)
- Sheng Wan
- Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Xiaobo Zhao
- Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Zhihong Niu
- Reproductive Medical Center, Obstetrics and Gynecology Department, Ruijin Hospital Affiliated with the Medical School of Shanghai Jiao Tong University, Shanghai, China
| | - Lingling Dong
- Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yuelin Wu
- Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Shengyi Gu
- Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yun Feng
- Reproductive Medical Center, Obstetrics and Gynecology Department, Ruijin Hospital Affiliated with the Medical School of Shanghai Jiao Tong University, Shanghai, China.
| | - Xiaolin Hua
- Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China.
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12
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Gunasekharan A, Jiang J, Nickerson A, Jalil S, Mumtaz K. Application of artificial intelligence in non-alcoholic fatty liver disease and viral hepatitis. Artif Intell Gastroenterol 2022; 3:46-53. [DOI: 10.35712/aig.v3.i2.46] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 02/18/2022] [Accepted: 04/28/2022] [Indexed: 02/06/2023] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) and chronic viral hepatitis are among the most significant causes of liver-related mortality worldwide. It is critical to develop reliable methods of predicting progression to fibrosis, cirrhosis, and decompensated liver disease. Current screening methods such as biopsy and transient elastography are limited by invasiveness and observer variation in analysis of data. Artificial intelligence (AI) provides a unique opportunity to more accurately diagnose NAFLD and viral hepatitis, and to identify patients at high risk for disease progression. We conducted a literature review of existing evidence for AI in NAFLD and viral hepatitis. Thirteen articles on AI in NAFLD and 14 on viral hepatitis were included in our analysis. We found that machine learning algorithms were comparable in accuracy to current methods for diagnosis and fibrosis prediction (MELD-Na score, liver biopsy, FIB-4 score, and biomarkers). They also reliably predicted hepatitis C treatment failure and hepatic encephalopathy, for which there are currently no established prediction tools. These studies show that AI could be a helpful adjunct to existing techniques for diagnosing, monitoring, and treating both NAFLD and viral hepatitis.
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Affiliation(s)
| | - Joanna Jiang
- Department of Medicine, Ohio State University, Columbus, OH 43210, United States
| | - Ashley Nickerson
- Department of Medicine, Ohio State University, Columbus, OH 43210, United States
| | - Sajid Jalil
- Department of Medicine, Ohio State University, Columbus, OH 43210, United States
| | - Khalid Mumtaz
- Department of Medicine, Ohio State University, Columbus, OH 43210, United States
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13
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Ferrarese A, Sartori G, Orrù G, Frigo AC, Pelizzaro F, Burra P, Senzolo M. Machine learning in liver transplantation: a tool for some unsolved questions? Transpl Int 2021; 34:398-411. [PMID: 33428298 DOI: 10.1111/tri.13818] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 08/24/2020] [Accepted: 01/08/2021] [Indexed: 12/13/2022]
Abstract
Machine learning has recently been proposed as a useful tool in many fields of Medicine, with the aim of increasing diagnostic and prognostic accuracy. Models based on machine learning have been introduced in the setting of solid organ transplantation too, where prognosis depends on a complex, multidimensional and nonlinear relationship between variables pertaining to the donor, the recipient and the surgical procedure. In the setting of liver transplantation, machine learning models have been developed to predict pretransplant survival in patients with cirrhosis, to assess the best donor-to-recipient match during allocation processes, and to foresee postoperative complications and outcomes. This is a narrative review on the role of machine learning in the field of liver transplantation, highlighting strengths and pitfalls, and future perspectives.
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Affiliation(s)
- Alberto Ferrarese
- Multivisceral Transplant Unit, Department of Surgery, Oncology and Gastroenterology, Padua University Hospital, Padua, Italy
| | - Giuseppe Sartori
- Forensic Neuropsychology and Forensic Neuroscience, PhD Program in Mind Brain and Computer Science, Department of General Psychology, Padua University, Padua, Italy
| | - Graziella Orrù
- Department of Surgical, Medical, Molecular and Critical Area Pathology, University of Pisa, Pisa, Italy
| | - Anna Chiara Frigo
- Department of Cardiac-Thoracic-Vascular Sciences and Public Health, Biostatistics, Epidemiology and Public Health Unit, University of Padua, Padova, Veneto, Italy
| | - Filippo Pelizzaro
- Multivisceral Transplant Unit, Department of Surgery, Oncology and Gastroenterology, Padua University Hospital, Padua, Italy
| | - Patrizia Burra
- Multivisceral Transplant Unit, Department of Surgery, Oncology and Gastroenterology, Padua University Hospital, Padua, Italy
| | - Marco Senzolo
- Multivisceral Transplant Unit, Department of Surgery, Oncology and Gastroenterology, Padua University Hospital, Padua, Italy
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14
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Artificial Neural Network as a Tool to Predict Facial Nerve Palsy in Parotid Gland Surgery for Benign Tumors. Med Sci (Basel) 2020; 8:medsci8040042. [PMID: 33036481 PMCID: PMC7712376 DOI: 10.3390/medsci8040042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 09/06/2020] [Accepted: 09/21/2020] [Indexed: 11/22/2022] Open
Abstract
(1) Background: Despite the increasing use of intraoperative facial nerve monitoring during parotid gland surgery or the improvement in the preoperative radiological assessment, facial nerve injury (FNI) continues to be the most feared complication; (2) Methods: patients who underwent parotid gland surgery for benign tumors between June 2010 and June 2019 were included in this study aiming to make a proof of concept about the reliability of an artificial neural networks (AAN) algorithm for prediction of FNI and compared with a multivariate linear regression (MLR); (3) Results: Concerning prediction accuracy and performance, the ANN achieved the highest sensitivity (86.53% vs 46.23%), specificity (95.67% vs 92.59%), PPV (87.28% vs 66.94%), NPV (95.68% vs 83.37%), ROC–AUC (0.960 vs 0.769) and accuracy (93.42 vs 80.42) than MLR; and (4) Conclusions: ANN prediction models can be useful for otolaryngologists—head and neck surgeons—and patients to provide evidence-based predictions about the risk of FNI. As an advantage, the possibility to develop a calculator using clinical, radiological and histological or cytological information can improve our ability to generate patients counselling before surgery.
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15
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Hou Y, Zhang Q, Gao F, Mao D, Li J, Gong Z, Luo X, Chen G, Li Y, Yang Z, Sun K, Wang X. 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 Gastroenterol 2020; 20:75. [PMID: 32188419 PMCID: PMC7081680 DOI: 10.1186/s12876-020-01191-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 02/11/2020] [Indexed: 02/08/2023] Open
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 (10.1002/hep.30257).
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Affiliation(s)
- Yixin Hou
- Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, People's Republic of China
| | - Qianqian Zhang
- Department of Hepatology, The First Hospital Affiliated to Hunan University of Chinese Medicine, Changsha, Hunan, 410007, People's Republic of China
| | - Fangyuan Gao
- Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, People's Republic of China
| | - Dewen Mao
- Department of Hepatology, The First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning, Guangxi, 530021, People's Republic of China
| | - Jun Li
- Center of Integrative Medicine, Beijing 302 Hospital, Beijing, 100039, People's Republic of China
| | - Zuojiong Gong
- Department of Infectious Diseases, Renmin Hospital of Wuhan University, Wuhan, Hubei, 430060, People's Republic of China
| | - Xinla Luo
- Department of Hepatology, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhuan, Hubei, 430061, People's Republic of China
| | - Guoliang Chen
- Department of Hepatology, Xiamen Hospital of Traditional Chinese Medicine, Xiamen, Fujian, 361009, People's Republic of China
| | - Yong Li
- Department of Hepatology, The Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250014, People's Republic of China
| | - Zhiyun Yang
- Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, People's Republic of China.
| | - Kewei Sun
- Department of Hepatology, The First Hospital Affiliated to Hunan University of Chinese Medicine, Changsha, Hunan, 410007, People's Republic of China.
| | - Xianbo Wang
- Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, People's Republic of China.
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16
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Alabi RO, Elmusrati M, Sawazaki-Calone I, Kowalski LP, Haglund C, Coletta RD, Mäkitie AA, Salo T, Leivo I, Almangush A. Machine learning application for prediction of locoregional recurrences in early oral tongue cancer: a Web-based prognostic tool. Virchows Arch 2019; 475:489-497. [PMID: 31422502 PMCID: PMC6828835 DOI: 10.1007/s00428-019-02642-5] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 07/26/2019] [Accepted: 07/31/2019] [Indexed: 12/25/2022]
Abstract
Estimation of risk of recurrence in early-stage oral tongue squamous cell carcinoma (OTSCC) remains a challenge in the field of head and neck oncology. We examined the use of artificial neural networks (ANNs) to predict recurrences in early-stage OTSCC. A Web-based tool available for public use was also developed. A feedforward neural network was trained for prediction of locoregional recurrences in early OTSCC. The trained network was used to evaluate several prognostic parameters (age, gender, T stage, WHO histologic grade, depth of invasion, tumor budding, worst pattern of invasion, perineural invasion, and lymphocytic host response). Our neural network model identified tumor budding and depth of invasion as the most important prognosticators to predict locoregional recurrence. The accuracy of the neural network was 92.7%, which was higher than that of the logistic regression model (86.5%). Our online tool provided 88.2% accuracy, 71.2% sensitivity, and 98.9% specificity. In conclusion, ANN seems to offer a unique decision-making support predicting recurrences and thus adding value for the management of early OTSCC. To the best of our knowledge, this is the first study that applied ANN for prediction of recurrence in early OTSCC and provided a Web-based tool.
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Affiliation(s)
- Rasheed Omobolaji Alabi
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Mohammed Elmusrati
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Iris Sawazaki-Calone
- Oral Pathology and Oral Medicine, Dentistry School, Western Parana State University, Cascavel, PR, Brazil
| | - Luiz Paulo Kowalski
- Department of Head and Neck Surgery and Otorhinolaryngology, A.C. Camargo Cancer Center, São Paulo, SP, Brazil
| | - Caj Haglund
- Research Programs Unit, Translational Cancer Biology, University of Helsinki, Helsinki, Finland.,Department of Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Ricardo D Coletta
- Department of Oral Diagnosis, School of Dentistry, University of Campinas, Piracicaba, São Paulo, Brazil
| | - Antti A Mäkitie
- Department of Otorhinolaryngology - Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.,Research Programme in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Tuula Salo
- Department of Pathology, University of Helsinki, Helsinki, Finland.,Department of Oral and Maxillofacial Diseases, University of Helsinki, Helsinki, Finland.,Cancer and Translational Medicine Research Unit, Medical Research Center Oulu, University of Oulu and Oulu University Hospital, Oulu, Finland
| | - Ilmo Leivo
- Institute of Biomedicine, Pathology, University of Turku, Turku, Finland
| | - Alhadi Almangush
- Research Programme in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland. .,Department of Pathology, University of Helsinki, Helsinki, Finland. .,Institute of Biomedicine, Pathology, University of Turku, Turku, Finland. .,Faculty of Dentistry, University of Misurata, Misurata, Libya.
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17
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Zaccherini G, Baldassarre M, Bartoletti M, Tufoni M, Berardi S, Tamè M, Napoli L, Siniscalchi A, Fabbri A, Marconi L, Antognoli A, Iannone G, Domenicali M, Viale P, Trevisani F, Bernardi M, Caraceni P. Prediction of nosocomial acute-on-chronic liver failure in patients with cirrhosis admitted to hospital with acute decompensation. JHEP Rep 2019; 1:270-277. [PMID: 32039378 PMCID: PMC7001573 DOI: 10.1016/j.jhepr.2019.07.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2019] [Revised: 07/04/2019] [Accepted: 07/18/2019] [Indexed: 12/22/2022] Open
Abstract
Nosocomial acute-on-chronic liver failure (nACLF) develops in at least 10% of patients with cirrhosis hospitalized for acute decompensation (AD), greatly worsening their prognosis. In this prospective observational study, we aimed to identify rapidly obtainable predictors at admission, which allow for the early recognition and stratification of patients at risk of nACLF. Methods A total of 516 consecutive patients hospitalized for AD of cirrhosis were screened: those who did not present ACLF at admission (410) were enrolled and surveilled for the development of nACLF. Results Fifty-nine (14%) patients developed nALCF after a median of 7 (IQR 4-18) days. At admission, they presented a more severe disease and higher degrees of systemic inflammation and anemia than those (351; 86%) who remained free from nACLF. Competing risk multivariable regression analysis showed that baseline MELD score (sub-distribution hazard ratio [sHR] 1.15; 95% CI 1.10-1.21; p ≪0.001), hemoglobin level (sHR 0.81; 95% CI 0.68-0.96; p = 0.018), and leukocyte count (sHR 1.11; 95% CI 1.06-1.16; p ≪0.001) independently predicted nACLF. Their optimal cut-off points, determined by receiver-operating characteristic curve analysis, were: 13 points for MELD score, 9.8 g/dl for hemoglobin, and 5.6x109/L for leukocyte count. These thresholds were used to stratify patients according to the cumulative incidence of nACLF, being 0, 6, 21 and 59% in the presence of 0, 1, 2 or 3 risk factors (p ≪0.001). Nosocomial bacterial infections only increased the probability of developing nACLF in patients with at least 1 risk factor, rising from 3% to 29%, 16% to 50% and 52% to 83% in patients with 1, 2 or 3 risk factors, respectively. Conclusions Easily available laboratory parameters, related to disease severity, systemic inflammation, and anemia, can be used to identify, at admission, hospitalized patients with AD at increased risk of developing nACLF. Lay summary More than 10% of patients with cirrhosis hospitalized because of an acute decompensation develop acute-on-chronic liver failure, which is associated with high short-term mortality, during their hospital stay. We found that the combination of 3 easily obtainable variables (model for end-stage liver disease score, leukocyte count and hemoglobin level) help to identify and stratify patients according to their risk of developing nosocomial acute-on-chronic liver failure, from nil to 59%. Moreover, if a nosocomial bacterial infection occurs, such an incidence proportionally increases from nil to 83%. This simple approach helps to identify patients at risk of developing nosocomial acute-on-chronic liver failure at admission to hospital, enabling clinicians to put in place preventive measures.
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Affiliation(s)
- Giacomo Zaccherini
- Department of Medical and Surgical Sciences - University of Bologna, Italy
| | - Maurizio Baldassarre
- Department of Medical and Surgical Sciences - University of Bologna, Italy.,Centre for Applied Biomedical Research (CRBA), University of Bologna, Italy
| | - Michele Bartoletti
- Department of Medical and Surgical Sciences - University of Bologna, Italy
| | - Manuel Tufoni
- Department of Medical and Surgical Sciences - University of Bologna, Italy
| | - Sonia Berardi
- U.O. Internal Medicine and Organ Failure - S. Orsola-Malpighi University Hospital, Bologna, Italy
| | - Mariarosa Tamè
- U.O. Gastroenterology - S. Orsola-Malpighi University Hospital, Bologna, Italy
| | - Lucia Napoli
- Department of Medical and Surgical Sciences - University of Bologna, Italy
| | - Antonio Siniscalchi
- S.S.D. Intensive Care of Abdominal Transplantation and Liver Surgery - S. Orsola-Malpighi University Hospital, Bologna, Italy
| | - Angela Fabbri
- U.O. Internal Medicine, Infermi Hospital of Rimini, Area Vasta Romagna (AVR) Rimini, Italy
| | - Lorenzo Marconi
- Department of Medical and Surgical Sciences - University of Bologna, Italy
| | - Agnese Antognoli
- Department of Medical and Surgical Sciences - University of Bologna, Italy
| | - Giulia Iannone
- Department of Medical and Surgical Sciences - University of Bologna, Italy
| | - Marco Domenicali
- Department of Medical and Surgical Sciences - University of Bologna, Italy
| | - Pierluigi Viale
- Department of Medical and Surgical Sciences - University of Bologna, Italy
| | - Franco Trevisani
- Department of Medical and Surgical Sciences - University of Bologna, Italy
| | - Mauro Bernardi
- Department of Medical and Surgical Sciences - University of Bologna, Italy
| | - Paolo Caraceni
- Department of Medical and Surgical Sciences - University of Bologna, Italy.,Centre for Applied Biomedical Research (CRBA), University of Bologna, Italy
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18
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Liang Y, Li Q, Chen P, Xu L, Li J. Comparative Study of Back Propagation Artificial Neural Networks and Logistic Regression Model in Predicting Poor Prognosis after Acute Ischemic Stroke. Open Med (Wars) 2019; 14:324-330. [PMID: 30997395 PMCID: PMC6463818 DOI: 10.1515/med-2019-0030] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2018] [Accepted: 07/16/2018] [Indexed: 11/27/2022] Open
Abstract
Objective To investigate the predictive value of clinical variables on the poor prognosis at 90-day follow-up from acute stroke onset, and compare the diagnostic performance between back propagation artificial neural networks (BP ANNs) and Logistic regression (LR) models in predicting the prognosis. Methods We studied the association between clinical variables and the functional recovery of 435 acute ischemic stroke patients. The patients were divided into 2 groups according to modified Rankin Scale scores evaluated on the 90th day after stroke onset. Both BP ANNs and LR models were established for predicting the poor outcome and their diagnostic performance were compared by receiver operating curve. Results Age, free fatty acid, homocysteine and alkaline phosphatase were closely related with the poor outcome in acute ischemic stroke patients and finally enrolled in models. The accuracy, sensitivity and specificity of BP ANNs were 80.15%, 75.64% and 82.07% respectively. For the LR model, the accuracy, sensitivity and specificity was 70.61%, 88.46% and 63.04% respectively. The area under the ROC curve of the BP ANNs and LR model was 0.881and 0.809. Conclusions Both BP ANNs and LR model were promising for the prediction of poor outcome by combining age, free fatty acid, homocysteine and alkaline phosphatase. However, BP ANNs model showed better performance than LR model in predicting the prognosis.
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Affiliation(s)
- Yaru Liang
- Department of Laboratory Medicine, The Sixth Affiliated Hospital of Guangzhou Medical University, Guangzhou China
| | - Qiguang Li
- Department of Laboratory Medicine, The Sixth Affiliated Hospital of Guangzhou Medical University, Guangzhou China
| | - Peisong Chen
- Department of Laboratory Medicine, The Sixth Affiliated Hospital of Guangzhou Medical University, Guangzhou China
| | - Lingqing Xu
- Department of Laboratory Medicine, The Sixth Affiliated Hospital of Guangzhou Medical University, Guangzhou China
| | - Jiehua Li
- The Sixth Affiliated Hospital of Guangzhou Medical University Qingyuan, Qingyuan China
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Prognostic factors and treatment effect of standard-volume plasma exchange for acute and acute-on-chronic liver failure: A single-center retrospective study. Transfus Apher Sci 2018; 57:537-543. [PMID: 29880246 DOI: 10.1016/j.transci.2018.05.030] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Revised: 05/29/2018] [Accepted: 05/31/2018] [Indexed: 12/15/2022]
Abstract
Patients with acute liver failure (ALF) and acute-on-chronic liver failure (ACLF) have a high risk of mortality. Few studies have reported prognostic factors for patients receiving plasma exchange (PE) for liver support. We conducted a retrospective analysis using data of 55 patients with severe ACLF (n = 45) and ALF (n = 10) who received standard-volume PE (1-1.5 plasma volume) in the ICU. Hepatitis B virus infection accounts for the majority of ACLF (87%) and ALF (50%) patients. PE significantly improved the levels of total bilirubin, prothrombin time and liver enzymes (P<0.05). Thirteen ACLF patients (29%) and one ALF patient (10%) underwent liver transplantation. Two ALF patients (20%) recovered spontaneously without transplantation. The overall in-hospital survival rates for ACLF and ALF patients were 24% and 30%, and the transplant-free survival rates were 0% and 20%, respectively. For the 14 transplanted patients, the one-year survival rate was 86%. Multivariate analysis showed that pre-PE hemoglobin (P = 0.008), post-PE hemoglobin (P = 0.039), and post-PE CLIF-C ACLF scores (P = 0.061) were independent predictors of survival in ACLF. The post-PE CLIF-C ACLF scores ≥59 were a discriminator predicting the in-hospital mortality (area under the curve = 0.719, P = 0.030). Cumulative survival rates differed significantly between patients with CLIF-C ACLF scores ≤ 58 and those with CLIF-C ACLF scores ≥ 59 after PE (P< 0.05). The findings suggest that PE is mainly a bridge for liver transplantation and spontaneous recovery is exceptional even in patients treated with PE. A higher improvement in the post-PE CLIF-C ACLF score is associated with a superior in-hospital survival rate.
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Li G, Zhou X, Liu J, Chen Y, Zhang H, Chen Y, Liu J, Jiang H, Yang J, Nie S. Comparison of three data mining models for prediction of advanced schistosomiasis prognosis in the Hubei province. PLoS Negl Trop Dis 2018; 12:e0006262. [PMID: 29447165 PMCID: PMC5831639 DOI: 10.1371/journal.pntd.0006262] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Revised: 02/28/2018] [Accepted: 01/23/2018] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND In order to better assist medical professionals, this study aimed to develop and compare the performance of three models-a multivariate logistic regression (LR) model, an artificial neural network (ANN) model, and a decision tree (DT) model-to predict the prognosis of patients with advanced schistosomiasis residing in the Hubei province. METHODOLOGY/PRINCIPAL FINDINGS Schistosomiasis surveillance data were collected from a previous study based on a Hubei population sample including 4136 advanced schistosomiasis cases. The predictive models use LR, ANN, and DT methods. From each of the three groups, 70% of the cases (2896 cases) were used as training data for the predictive models. The remaining 30% of the cases (1240 cases) were used as validation groups for performance comparisons between the three models. Prediction performance was evaluated using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Univariate analysis indicated that 16 risk factors were significantly associated with a patient's outcome of prognosis. In the training group, the mean AUC was 0.8276 for LR, 0.9267 for ANN, and 0.8229 for DT. In the validation group, the mean AUC was 0.8349 for LR, 0.8318 for ANN, and 0.8148 for DT. The three models yielded similar results in terms of accuracy, sensitivity, and specificity. CONCLUSIONS/SIGNIFICANCE Predictive models for advanced schistosomiasis prognosis, respectively using LR, ANN and DT models were proved to be effective approaches based on our dataset. The ANN model outperformed the LR and DT models in terms of AUC.
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Affiliation(s)
- Guo Li
- Department of Epidemiology and Health Statistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Hubei Provincial Center for Disease Control and Prevention, Wuhan, Hubei, China
| | - Xiaorong Zhou
- Hubei Provincial Center for Disease Control and Prevention, Wuhan, Hubei, China
| | - Jianbing Liu
- Hubei Provincial Center for Disease Control and Prevention, Wuhan, Hubei, China
| | - Yuanqi Chen
- Department of Mathematics, Wuhan University, Wuhan, Hubei, China
| | - Hengtao Zhang
- Department of Mathematics, Wuhan University, Wuhan, Hubei, China
| | - Yanyan Chen
- Hubei Provincial Center for Disease Control and Prevention, Wuhan, Hubei, China
| | - Jianhua Liu
- Yichang Center for Disease Control and Prevention, Yichang, Hubei, China
| | - Hongbo Jiang
- Department of Epidemiology and Biostatistics, School of Public Health, Guangdong Pharmaceutical University, Guangzhou, China
| | - Junjing Yang
- Hubei Provincial Center for Disease Control and Prevention, Wuhan, Hubei, China
| | - Shaofa Nie
- Department of Epidemiology and Health Statistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
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Qiao CY, Li F, Teng Y, Zhao J, Hu N, Fan YC, Wang K. Aberrant GSTP1 promoter methylation predicts poor prognosis of acute-on-chronic hepatitis B pre-liver failure. Clin Exp Med 2018; 18:51-62. [PMID: 28676943 DOI: 10.1007/s10238-017-0466-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2017] [Accepted: 06/27/2017] [Indexed: 12/14/2022]
Abstract
It has been demonstrated that glutathione-S-transferase P1 (GSTP1) could protect cells from DNA damage mediated by oxidizing agents or electrophiles in hepatic inflammatory response. Our study evaluated the methylation status and the predictive value for prognosis of GSTP1 promoter region in patients with acute-on-chronic hepatitis B pre-liver failure (pre-ACHBLF). Methylation status of GSTP1 promoter in peripheral blood mononuclear cells (PBMCs) and plasma was measured in 103 patients with pre-ACHBLF, 80 patients with chronic hepatitis B (CHB) and 30 healthy controls (HCs) by methylation-specific polymerase chain reaction. The mRNA level of GSTP1 was detected by quantitative real-time polymerase chain reaction. The methylation frequency of GSTP1 promoter region in patients with pre-ACHBLF (35/103 in PBMCs and 33/103 in plasma) was significantly higher than CHB (2/80) and HCs (0/30), respectively. The mRNA level of GSTP1 in patients with pre-ACHBLF was significantly lower than CHB and HCs. Additionally, pre-ACHBLF patients with methylated GSTP1 presented strikingly higher incidence of ACHBLF than those without. Of note, GSTP1 methylation presented distinctly better performance than model for end-stage liver disease score [area under the receiver operating characteristic curves (AUCs) 0.825 in PBMCs and 0.798 in plasma VS 0.589; AUC 0.804 in PBMCs and 0.779 in plasma VS 0.622; AUC 0.767 in PBMCs and 0.744 in plasma VS 0.602, respectively] when used to predict the 1-, 2- or 3-month incidence of ACHBLF in patients with pre-ACHBLF. Aberrant methylation of GSTP1 has potential to be a prognostic biomarker for pre-ACHBLF.
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Affiliation(s)
- Chen-Yang Qiao
- Department of Hepatology, Qilu Hospital of Shandong University, Jinan, 250012, China
| | - Feng Li
- Department of Hepatology, Qilu Hospital of Shandong University, Jinan, 250012, China
| | - Yue Teng
- Department of Hepatology, Qilu Hospital of Shandong University, Jinan, 250012, China
| | - Jing Zhao
- Department of Hepatology, Qilu Hospital of Shandong University, Jinan, 250012, China
| | - Na Hu
- Department of Hepatology, Qilu Hospital of Shandong University, Jinan, 250012, China
| | - Yu-Chen Fan
- Department of Hepatology, Qilu Hospital of Shandong University, Jinan, 250012, China
- Institute of Hepatology, Shandong University, Jinan, 250012, China
| | - Kai Wang
- Department of Hepatology, Qilu Hospital of Shandong University, Jinan, 250012, China.
- Institute of Hepatology, Shandong University, Jinan, 250012, China.
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Lin S, Chen J, Wang M, Han L, Zhang H, Dong J, Zeng D, Jiang J, Zhu Y. Prognostic nomogram for acute-on-chronic hepatitis B liver failure. Oncotarget 2017; 8:109772-109782. [PMID: 29312647 PMCID: PMC5752560 DOI: 10.18632/oncotarget.21012] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2017] [Accepted: 08/28/2017] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND & AIMS To establish an effective prognostic nomogram for acute-on-chronic hepatitis B liver failure (ACHBLF). MATERIALS AND METHODS The nomogram was based on clinical data of 203 ACHBLF patients who admitted to the First Affiliated Hospital of Fujian Medical University from 2009 to 2014. The area under the receiver-operating characteristic curve (AUC) and calibration curve were carried out to verify the predictive accuracy ability of the nomogram. The result was validated in internal and external validation cohorts. Kaplan-Meier survival curve was used in survival analysis. RESULTS We developed a new prognostic nomogram to predict 3-month mortality based on risk factors selected by multivariate analysis. This nomogram consisted three independent factors: age, liver to abdominal area ratio (LAAR) and model for end-stage liver disease (MELD) score. The AUC of this nomogram for survival prediction was 0.877 (95% CI 0.831-0.923), which was higher than that of MELD score, MELD-Na and Child-Turcotte-Pugh (CTP). Good agreement of calibration plot for the probability of survival at 3-month was shown between the prediction by nomogram and actual observation. These results were supported by internal and external validation studies. CONCLUSIONS The ACHBLF nomogram could predict the short-term survival for ACHBLF patients.
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Affiliation(s)
- Su Lin
- Liver Research Center, First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Juan Chen
- Digestive System Department, Fujian Provincial Hospital, Fuzhou, Fujian, China
| | - Mingfang Wang
- Liver Research Center, First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Lifen Han
- Department of Infectious Disease, Meng Chao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Haoyang Zhang
- Division of Biostatistics, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Sha Tin, Hongkong, China
| | - Jing Dong
- Liver Research Center, First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Dawu Zeng
- Liver Research Center, First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Jiaji Jiang
- Liver Research Center, First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Yueyong Zhu
- Liver Research Center, First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
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Li N, Huang C, Yu KK, Lu Q, Shi GF, Zheng JM. Validation of prognostic scores to predict short-term mortality in patients with HBV-related acute-on-chronic liver failure: The CLIF-C OF is superior to MELD, CLIF SOFA, and CLIF-C ACLF. Medicine (Baltimore) 2017; 96:e6802. [PMID: 28445322 PMCID: PMC5413287 DOI: 10.1097/md.0000000000006802] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Acute-on-chronic liver failure (ACLF) in chronic hepatitis B (CHB) patients has a high short-term mortality. Identification of effective models to predict the short-term mortality may enable early intervention and improve patients' prognosis. We aim to assess the performance of the CLIF Consortium Organ Failure score (CLIF-C OFs), CLIF sequential organ failure assessment score (CLIF-SOFAs), CLIF Consortium ACLF score (CLIF-C ACLFs), ACLF grade, and model for end-stage liver disease score (MELDs) in predicting the short-term mortality in CHB patients with ACLF.Among the 155 consecutive adult patients with liver failure as a discharge diagnosis were screened, and all the patients were treated at the Department of Infectious Diseases, Huashan Hospital, Fudan University (Shanghai, China) from January 2010 to February 2016. The diagnosis of ACLF was based on the criteria formalized by the ACLF consensus recommendations of the Asian Pacific Association for the Study of the Liver (APASL). Diagnostic accuracy for predicting short-term (28-day) mortality was calculated for CLIF-C OFs, CLIF-SOFAs, CLIF-C ACLFs, ACLF grade, and MELDs in all patients.One hundred fifty-five consecutive adult liver failure patients were screened and 85 patients including 73 males and 12 females were enrolled. Overall, the 28-day transplant-free mortality was 32% in all patients, and 100% in those with severe early course (ACLF-3). The area under the receiver operating characteristic curve (AUROC) of CLIF-C OFs (AUROC: 0.906, P = .0306, compared with MELDs) was higher than those of CLIF-SOFAs (AUROC: 0.876), CLIF-C ACLFs (AUROC: 0.858), ACLF grade (AUROC: 0.857), and MELDs (AUROC: 0.838) for predicting short-term mortality. The cut-point for baseline CLIF-C OFs in predicting death was 8.5, with 67% sensitivity, 90% specificity, and AUROC of 0.906 (95% CI: 0.8450-0.9679).The results indicate that short-term mortality is high in patients with ACLF and CLIF Consortium Organ Failure score is superior to MELD, CLIF SOFA, and CLIF-C ACLF in predicting its short-term mortality.
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Shi Y, Shu Z, Sun W, Yang Q, Yu Y, Yang G, Wu W, Chen S, Huang W, Wang T, Yan H. Risk stratification of decompensated cirrhosis patients by Chronic Liver Failure Consortium scores: Classification and regression tree analysis. Hepatol Res 2017; 47:328-337. [PMID: 27287893 DOI: 10.1111/hepr.12751] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2016] [Revised: 05/15/2016] [Accepted: 05/26/2016] [Indexed: 12/22/2022]
Abstract
AIM Decompensated cirrhosis patients have greatly variable prognosis. The aim of the study was to carry out a risk stratification for those patients by Chronic Liver Failure (CLIF) Consortium scores. METHODS The performance of CLIF Consortium acute-on-chronic liver failure scores (CLIF-C ACLFs) and CLIF Consortium Acute Decompensation scores (CLIF-C ADs) were validated in 209 patients with ACLF and 1245 patients without ACLF at admission from the Ningbo Cohort. A classification and regression tree (CRT) analysis by CLIF-C ACLFs/CLIF-C ADs was carried out to stratify death risk among patients. RESULTS The CLIF-C ACLFs and CLIF-C ADs showed higher predictive accuracy than Model for End-stage Liver Disease (MELD) scores, MELD plus serum sodium (MELD-Na) scores, and Child-Turcotte-Pugh classification (CP) at main time points (28, 90, 180, and 365 days), determined by area under the receiver-operating characteristic curve and concordance index in ACLF and no-ACLF patients at admission. The CRT analysis categorized ACLF patients into two groups (advanced and early ACLF), and no-ACLF patients into three groups (high-, medium-, and low-risk AD) according to risk of death. However, early ACLF and high-risk AD patients had comparable mortality at the main time points. The CRT model had a higher area under the receiver-operating characteristic curve than MELDs, MELD-Nas, and CPs in predicting prognosis in all patients. CONCLUSIONS The CLIF-C ACLF and CLIF-C AD are better prognostic scores than MELD, MELD-Na, and CP in predicting mortality of ACLF and no-ACLF patients. A combined use of CLIF- Sequential Organ Failure Assessment, CLIF-C ACLFs, and CLIF-C ADs could identify cirrhosis patients at high death risk and assist clinical decisions for management.
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Affiliation(s)
- Yu Shi
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Disease, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zheyue Shu
- Division of Hepatobiliary and Pancreatic Surgery, Key Laboratory of Combined Multi-organ Transplantation, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wenjie Sun
- Department of Epidemiology and Health Statistics, Zhejiang University School of Public Health, Hangzhou, China
| | - Qiao Yang
- Department of Infectious Diseases, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Ningbo, China
| | - Ye Yu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Disease, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Gang Yang
- Department of Hepatology, Ningbo Multiple Organ Injury Research Center, Ningbo No.2 Hospital, School of Medicine, Ningbo University, Ningbo, China
| | - Wei Wu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Disease, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Si Chen
- Department of Hepatology, Ningbo Multiple Organ Injury Research Center, Ningbo No.2 Hospital, School of Medicine, Ningbo University, Ningbo, China
| | - Wei Huang
- Department of Hepatology, Ningbo Multiple Organ Injury Research Center, Ningbo No.2 Hospital, School of Medicine, Ningbo University, Ningbo, China
| | - Tingting Wang
- Department of Hepatology, Ningbo Multiple Organ Injury Research Center, Ningbo No.2 Hospital, School of Medicine, Ningbo University, Ningbo, China
| | - Huadong Yan
- Department of Hepatology, Ningbo Multiple Organ Injury Research Center, Ningbo No.2 Hospital, School of Medicine, Ningbo University, Ningbo, China
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Shi KQ, Cai YJ, Lin Z, Dong JZ, Wu JM, Wang XD, Song M, Wang YQ, Chen YP. Development and validation of a prognostic nomogram for acute-on-chronic hepatitis B liver failure. J Gastroenterol Hepatol 2017; 32:497-505. [PMID: 27490495 DOI: 10.1111/jgh.13502] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/25/2016] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND AIM Determining individual risk of short-term mortality in patients with acute-on-chronic hepatitis B liver failure (ACHBLF) is a difficult task. We aimed to develop and externally validate a prognostic nomogram for ACHBLF patients. METHODS The nomogram was built to estimate the probability of 30-day, 60-day, 90-day, and 60-month survival based on an internal cohort of 246 patients with ACHBLF. The predictive accuracy and discriminative ability of nomogram were determined by a concordance index (C-index), calibration curve, and time-dependent receiver operating characteristics (tdROC), comparing with model for end-stage liver disease (MELD) score. The results were validated using bootstrap resampling and an external cohort of 138 patients. Furthermore, we plotted decision curves to evaluate the clinical usefulness of nomogram. RESULTS Independent factors derived from multivariable Cox analysis of training cohort to predict mortality were age, total bilirubin, serum sodium, and prothrombin activity, which were all assembled into nomogram. The calibration curves for probability of survival showed optimal agreement between nomogram prediction and actual observation. The C-index of nomogram was higher than that of MELD score for predicting survival (30-day, 0.809 vs 0.717, P < 0.001; 60-day, 0.792 vs 0.685, P < 0.001; 90-day, 0.779 vs 0.678, P < 0.001; 6-month, 0.781 vs 0.677, P < 0.001). Additionally, tdROC and decision curves also showed that nomogram was superior to MELD score. The results were confirmed in validation cohort. CONCLUSIONS The prognostic nomogram provided an individualized risk estimate of short-term survival in patients with ACHBLF, offering to clinicians to improve their abilities to assess patient prognosis.
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Affiliation(s)
- Ke-Qing Shi
- Department of Liver Diseases, Liver Research Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yi-Jing Cai
- Department of Liver Diseases, Liver Research Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhuo Lin
- Department of Liver Diseases, Liver Research Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jin-Zhong Dong
- Department of Infection and Liver Diseases, Ningbo First Hospital, Ningbo, China
| | - Jian-Min Wu
- Institute of Genomic Medicine, Wenzhou Medical University, Wenzhou, China
| | - Xiao-Dong Wang
- Department of Liver Diseases, Liver Research Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Mei Song
- Department of Liver Diseases, Liver Research Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yu-Qun Wang
- Department of Liver Diseases, Liver Research Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yong-Ping Chen
- Department of Liver Diseases, Liver Research Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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Shi KQ, Zhou YY, Yan HD, Li H, Wu FL, Xie YY, Braddock M, Lin XY, Zheng MH. Classification and regression tree analysis of acute-on-chronic hepatitis B liver failure: Seeing the forest for the trees. J Viral Hepat 2017; 24:132-140. [PMID: 27686368 DOI: 10.1111/jvh.12617] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2016] [Accepted: 08/10/2016] [Indexed: 12/13/2022]
Abstract
At present, there is no ideal model for predicting the short-term outcome of patients with acute-on-chronic hepatitis B liver failure (ACHBLF). This study aimed to establish and validate a prognostic model by using the classification and regression tree (CART) analysis. A total of 1047 patients from two separate medical centres with suspected ACHBLF were screened in the study, which were recognized as derivation cohort and validation cohort, respectively. CART analysis was applied to predict the 3-month mortality of patients with ACHBLF. The accuracy of the CART model was tested using the area under the receiver operating characteristic curve, which was compared with the model for end-stage liver disease (MELD) score and a new logistic regression model. CART analysis identified four variables as prognostic factors of ACHBLF: total bilirubin, age, serum sodium and INR, and three distinct risk groups: low risk (4.2%), intermediate risk (30.2%-53.2%) and high risk (81.4%-96.9%). The new logistic regression model was constructed with four independent factors, including age, total bilirubin, serum sodium and prothrombin activity by multivariate logistic regression analysis. The performances of the CART model (0.896), similar to the logistic regression model (0.914, P=.382), exceeded that of MELD score (0.667, P<.001). The results were confirmed in the validation cohort. We have developed and validated a novel CART model superior to MELD for predicting three-month mortality of patients with ACHBLF. Thus, the CART model could facilitate medical decision-making and provide clinicians with a validated practical bedside tool for ACHBLF risk stratification.
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Affiliation(s)
- K-Q Shi
- Department of Hepatology, Liver Research Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.,Institute of Hepatology, Wenzhou Medical University, Wenzhou, China
| | - Y-Y Zhou
- Department of Cardiology, Jinhua Municipal Hospital, Jinhua, China
| | - H-D Yan
- Department of Infectious Diseases, Ningbo No. 2 Hospital, Ningbo, China
| | - H Li
- Department of Intensive Care Unit, Tianjin Infectious Disease Hospital, Tianjin, China
| | - F-L Wu
- Department of Hepatology, Liver Research Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.,Institute of Hepatology, Wenzhou Medical University, Wenzhou, China
| | - Y-Y Xie
- Department of Clinical Laboratory, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - M Braddock
- Global Medicines Development, AstraZeneca R&D, Loughborough, UK
| | - X-Y Lin
- Department of Clinical Laboratory, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - M-H Zheng
- Department of Hepatology, Liver Research Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.,Institute of Hepatology, Wenzhou Medical University, Wenzhou, China
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Zheng MH, Wu SJ, Shi KQ, Yan HD, Li H, Zhu GQ, Xie YY, Wu FL, Chen YP. Conditional survival estimate of acute-on-chronic hepatitis B liver failure: a dynamic prediction based on a multicenter cohort. Oncotarget 2016. [PMID: 26213849 PMCID: PMC4695116 DOI: 10.18632/oncotarget.4666] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Objectives Counseling patients with acute-on-chronic hepatitis B liver failure (ACHBLF) on their individual risk of short-term mortality is challenging. This study aimed to develop a conditional survival estimate (CSE) for predicting individualized mortality risk in ACHBLF patients. Methods We performed a large prospective cohort study of 278 ACHBLF patients from December 2010 to December 2013 at three participating medical centers. The Kaplan-Meier method was used to calculate the cumulative overall survival (OS). Cox proportional hazard regression models were used to analyze the risk factors associated with OS. 4-week CSE at “X” week after diagnostic established were calculated as CS4 = OS(X+4)/OS(X). Results The actual OS at 2, 4, 6, 8, 12 weeks were 80.5%, 71.8%, 69.3%, 66.0% and 63.7%, respectively. Using CSE, the probability of surviving an additional 4 weeks, given that the patient had survived for 1, 3, 5, 7, 9 weeks was 74%, 86%, 92%, 93%, 97%, respectively. Patients with worse prognostic feathers, including MELD > 25, Child grade C, age > 45, HE, INR > 2.5, demonstrated the greatest increase in CSE over time, when compared with the “favorable” one (Δ36% vs. Δ10%; Δ28% vs. Δ16%; Δ29% vs. Δ15%; Δ60% vs. Δ12%; Δ33% vs. Δ12%; all P < 0.001; respectively). Conclusions This easy-to-use CSE can accurately predict the changing probability of survival over time. It may facilitate risk communication between patients and physicians.
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Affiliation(s)
- Ming-Hua Zheng
- Department of Infection and Liver Diseases, Liver Research Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.,Institute of Hepatology, Wenzhou Medical University, Wenzhou 325000, China
| | - Sheng-Jie Wu
- Department of Cardiovascular Medicine, The Heart Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Ke-Qing Shi
- Department of Infection and Liver Diseases, Liver Research Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.,Institute of Hepatology, Wenzhou Medical University, Wenzhou 325000, China
| | - Hua-Dong Yan
- Department of Infectious Diseases, Ningbo 315010, China
| | - Hai Li
- Department of Intensive Care Unit, Tianjin Infectious Disease Hospital, Tianjin 300000, China
| | - Gui-Qi Zhu
- Department of Infection and Liver Diseases, Liver Research Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.,School of the First Clinical Medical Sciences, Wenzhou Medical University, Wenzhou 325000, China
| | - Yao-Yao Xie
- Department of Clinical Laboratory, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Fa-Ling Wu
- Department of Infection and Liver Diseases, Liver Research Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.,Institute of Hepatology, Wenzhou Medical University, Wenzhou 325000, China
| | - Yong-Ping Chen
- Department of Infection and Liver Diseases, Liver Research Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.,Institute of Hepatology, Wenzhou Medical University, Wenzhou 325000, China
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Fan YC, Wang N, Sun YY, Xiao XY, Wang K. TIPE2 mRNA Level in PBMCs Serves as a Novel Biomarker for Predicting Short-Term Mortality of Acute-on-Chronic Hepatitis B Liver Failure: A Prospective Single-Center Study. Medicine (Baltimore) 2015; 94:e1638. [PMID: 26426653 PMCID: PMC4616875 DOI: 10.1097/md.0000000000001638] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2015] [Revised: 08/25/2015] [Accepted: 08/26/2015] [Indexed: 12/14/2022] Open
Abstract
It remains difficult to accurately predicate short-term mortality of acute-on-chronic hepatitis B liver failure (ACHBLF). Tumor necrosis factor-α-induced protein 8-like 2 (TIPE2) is a novel identified negative regulator of immune response and we have previously demonstrated TIPE2 play an essential role in the pathogenesis of ACHBLF. We therefore aimed to evaluate the diagnosis value of TIPE2 mRNA in peripheral blood mononuclear cells (PBMCs) for predicting 3-month mortality of ACHBLF patients. This prospective study consisted of 108 ACHBLF patients from March 2009 to May 2013 as training cohort and 63 ACHBLF patients from June 2013 to December 2014 as validation cohort. Forty-two patients with chronic hepatitis B (CHB) and 22 healthy volunteers were also included as controls. The mRNA level of TIPE2 in PBMCs was determined using quantitative real-time polymerase chain reaction. Univariate analysis and Cox proportional hazard regression analysis were performed to identify independent risk factors to 3-month mortality. Area under the receptor operating characteristic curve (AUROC) was performed to assess diagnostic value of TIPE2 mRNA in training and validation cohort. The level of TIPE2 mRNA was significantly higher in ACHBLF patients (median (interquartile): 6.5 [3.7, 9.6]) compared with CHB (2.3 [1.6, 3.7]) and healthy controls (0.4 [0.3, 0.6]; both P < 0.05). Cox proportional hazards regression analyses showed 5 independent risk factors associated with 3-month mortality of ACHBLF: white blood cells (HR = 1.058, 95% CI: 1.023-1.095), spontaneous bacterial peritonitis (HR = 2.541, 95% CI: 1.378-4.686), hepatic encephalopathy (HR = 1.848, 95% CI: 1.028-3.321), model for end-stage liver diseases (MELD) score (HR = 1.062, 95% CI: 1.009-1.118), and TIPE2 mRNA (HR = 1.081, 95% CI: 1.009-1.159). An optimal cut-off point 6.54 of TIPE2 mRNA showed sensitivity of 74.63%, specificity of 90.24%, positive predictive value of 92.5%, and negative predictive value of 67.3% for predicting 3-month mortality in training cohort. Furthermore, TIPE2 mRNA plus MELD performed better than MELD alone for predicting 3-month mortality in training (AUROC, 0.853 vs 0.722, P < 0.05) and validation cohort (AUROC, 0.909 vs 0.717, P < 0.001). TIPE2 mRNA level might be a novel biomarker in predicting 3-month mortality of ACHBLF. Combination of TIPE2 mRNA and MELD would improve the diagnostic value of MELD alone in predicting 3-month mortality of patients with ACHBLF.
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Affiliation(s)
- Yu-Chen Fan
- From the Department of Hepatology, Qilu Hospital of Shandong University, Jinan, China (Y-CF, NW, Y-YS, KW); Institute of Hepatology, Shandong University, Jinan, China (Y-CF, KW); and Department of Nephrology, Qilu Hospital of Shandong University, Jinan, China (X-YX)
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Seasonal variation in onset and relapse of IBD and a model to predict the frequency of onset, relapse, and severity of IBD based on artificial neural network. Int J Colorectal Dis 2015; 30:1267-73. [PMID: 25976931 DOI: 10.1007/s00384-015-2250-6] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/08/2015] [Indexed: 02/04/2023]
Abstract
BACKGROUND Previous research has yielded conflicting data as to whether the natural history of inflammatory bowel disease follows a seasonal pattern. The purpose of this study was (1) to determine whether the frequency of onset and relapse of inflammatory bowel disease follows a seasonal pattern and (2) to establish a model to predict the frequency of onset, relapse, and severity of inflammatory bowel disease (IBD) with meteorological data based on artificial neural network (ANN). METHOD Patients with diagnosis of ulcerative colitis (UC) or Crohn's disease (CD) between 2003 and 2011 were investigated according to the occurrence of onset and flares of symptoms. The expected onset or relapse was calculated on a monthly basis over the study period. For artificial neural network (ANN), patients from 2003 to 2010 were assigned as training cohort and patients in 2011 were assigned as validation cohort. Mean square error (MSE) and mean absolute percentage error (MAPE) were used to evaluate the predictive accuracy. RESULTS We found no seasonal pattern of onset (P = 0.248) and relapse (P = 0.394) among UC patients. But, the onset (P = 0.015) and relapse (P = 0.004) of CD were associated with seasonal pattern, with a peak in July and August. ANN had average accuracy to predict the frequency of onset (MSE = 0.076, MAPE = 37.58%) and severity of IBD (MSE = 0.065, MAPE = 42.15%) but high accuracy in predicting the frequency of relapse of IBD (MSE = 0.009, MAPE = 17.1%). CONCLUSION The frequency of onset and relapse in IBD showed seasonality only in CD, with a peak in July and August, but not in UC. ANN may have its value in predicting the frequency of relapse among patients with IBD.
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Gao S, Sun FK, Fan YC, Shi CH, Zhang ZH, Wang LY, Wang K. Aberrant GSTP1 promoter methylation predicts short-term prognosis in acute-on-chronic hepatitis B liver failure. Aliment Pharmacol Ther 2015; 42:319-329. [PMID: 26040771 DOI: 10.1111/apt.13271] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2014] [Revised: 12/28/2014] [Accepted: 05/20/2015] [Indexed: 12/13/2022]
Abstract
BACKGROUND Glutathione-S-transferase P1 (GSTP1) methylation has been demonstrated to be associated with oxidative stress induced liver damage in acute-on-chronic hepatitis B liver failure (ACHBLF). AIM To evaluate the methylation level of GSTP1 promoter in acute-on-chronic hepatitis B liver failure and determine its predictive value for prognosis. METHODS One hundred and five patients with acute-on-chronic hepatitis B liver failure, 86 with chronic hepatitis B (CHB) and 30 healthy controls (HC) were retrospectively enrolled. GSTP1 methylation level in peripheral mononuclear cells (PBMC) was detected by MethyLight. Clinical and laboratory parameters were obtained. RESULTS GSTP1 methylation levels were significantly higher in patients with acute-on-chronic hepatitis B liver failure (median 16.84%, interquartile range 1.83-59.05%) than those with CHB (median 1.25%, interquartile range 0.48-2.47%; P < 0.01) and HC (median 0.80%, interquartile range 0.67-1.27%; P < 0.01). In acute-on-chronic hepatitis B liver failure group, nonsurvivors showed significantly higher GSTP1 methylation levels (P < 0.05) than survivors. GSTP1 methylation level was significantly correlated with total bilirubin (r = 0.29, P < 0.01), prothrombin time activity (r = -0.24, P = 0.01) and model for end-stage liver disease (MELD) score (r = 0.26, P = 0.01). When used to predict 1- or 2-month mortality of acute-on-chronic hepatitis B liver failure, GSTP1 methylation showed significantly better predictive value than MELD score [area under the receiver operating characteristic curve (AUC) 0.89 vs. 0.72, P < 0.01; AUC 0.83 vs. 0.70, P < 0.05 respectively]. Meanwhile, patients with GSTP1 methylation levels above the cut-off points showed significantly poorer survival than those below (P < 0.05). CONCLUSIONS Aberrant GSTP1 promoter methylation exists in acute-on-chronic hepatitis B liver failure and shows high predictive value for short-term mortality. It might serve as a potential prognostic marker for acute-on-chronic hepatitis B liver failure.
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Affiliation(s)
- S Gao
- Department of Hepatology, Qilu Hospital of Shandong University, Jinan, China
| | - F-K Sun
- Department of Hepatology, Qilu Hospital of Shandong University, Jinan, China
| | - Y-C Fan
- Department of Hepatology, Qilu Hospital of Shandong University, Jinan, China
- Institute of Hepatology, Shandong University, Jinan, China
| | - C-H Shi
- Department of Hepatology, Qingdao Infectious Disease Hospital, Qingdao, China
| | - Z-H Zhang
- Department of Hepatology, Jinan Infectious Disease Hospital, Jinan, China
| | - L-Y Wang
- Department of Hepatology, Qilu Hospital of Shandong University, Jinan, China
| | - K Wang
- Department of Hepatology, Qilu Hospital of Shandong University, Jinan, China
- Institute of Hepatology, Shandong University, Jinan, China
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Gao S, Huan SL, Han LY, Li F, Ji XF, Li XY, Fan YC, Wang K. Overexpression of serum sST2 is associated with poor prognosis in acute-on-chronic hepatitis B liver failure. Clin Res Hepatol Gastroenterol 2015; 39:315-323. [PMID: 25481239 DOI: 10.1016/j.clinre.2014.10.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2014] [Revised: 10/08/2014] [Accepted: 10/16/2014] [Indexed: 02/07/2023]
Abstract
BACKGROUND AND OBJECTIVE Interleukin-33 (IL-33) and soluble ST2 (sST2) have been demonstrated to be involved in liver injury. The present study aims to evaluate serum IL-33 and sST2 level in acute-on-chronic hepatitis B liver failure (ACHBLF) and determine their predictive value for prognosis. METHODS Serum IL-33 and sST2 level in patients with ACHBLF, chronic hepatitis B (CHB) and healthy controls (HCs) were determined by enzyme-linked immunosorbent assay (ELISA). Clinical and laboratory parameters were obtained. RESULTS Serum IL-33 was significantly higher in patients with ACHBLF (313.10±419.97pg/ml) than those with CHB (97.25±174.67pg/ml, P<0.01) and HCs (28.39±6.53pg/ml, P<0.01). Serum sST2 was significantly higher in patients with ACHBLF (1545.87±1135.70pg/ml) than those with CHB (152.55±93.28pg/ml, P<0.01) and HCs (149.27±104.90pg/ml, P<0.01). In all participants, serum IL-33 was significantly correlated with sST2 (r=0.43, P<0.01). In patients with ACHBLF, serum IL-33 was significantly correlated with alanine aminotransferase (ALT; r=0.26, P=0.04). Serum sST2 was significantly correlated with total bilirubin (TBIL; r=0.59, P<0.01), Log10 [HBV DNA] (r=-0.47, P<0.01) and model for end-stage liver diseases (MELD; r=0.28, P=0.03). Serum sST2 had an area under the receiver operating characteristic curve (AUC) of 0.81 in predicting 3-month mortality of ACHBLF. Patients with ACHBLF who had sST2 >1507pg/ml showed significantly poorer survival than those who had sST2 ≤1507pg/ml (P<0.01). Moreover, measurement of sST2 and MELD together significantly improved the diagnostic value of MELD alone (P<0.05). CONCLUSIONS Our study showed that serum IL-33 and sST2 were overexpressed in ACHBLF and sST2 might potentially serve as a prognostic marker for it.
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Affiliation(s)
- Shuai Gao
- Department of Hepatology, Qilu Hospital of Shandong University, Jinan 250012, Shandong, China
| | - Shu-Ling Huan
- Department of Hepatology, Qilu Hospital of Shandong University, Jinan 250012, Shandong, China
| | - Li-Yan Han
- Department of Hepatology, Qilu Hospital of Shandong University, Jinan 250012, Shandong, China; Institute of Hepatology, Shandong University, Jinan 250012, Shandong, China
| | - Feng Li
- Department of Hepatology, Qilu Hospital of Shandong University, Jinan 250012, Shandong, China
| | - Xiang-Fen Ji
- Department of Hepatology, Qilu Hospital of Shandong University, Jinan 250012, Shandong, China
| | - Xin-You Li
- Department of Hepatology, Qilu Hospital of Shandong University, Jinan 250012, Shandong, China
| | - Yu-Chen Fan
- Department of Hepatology, Qilu Hospital of Shandong University, Jinan 250012, Shandong, China; Institute of Hepatology, Shandong University, Jinan 250012, Shandong, China
| | - Kai Wang
- Department of Hepatology, Qilu Hospital of Shandong University, Jinan 250012, Shandong, China; Institute of Hepatology, Shandong University, Jinan 250012, Shandong, China.
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Jeong R, Lee YS, Sohn C, Jeon J, Ahn S, Lim KS. Model for end-stage liver disease score as a predictor of short-term outcome in patients with drug-induced liver injury. Scand J Gastroenterol 2015; 50:439-46. [PMID: 25639449 DOI: 10.3109/00365521.2014.958094] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
OBJECTIVES The purposes of this study were to investigate the clinical presentation, cause and outcome of drug-induced liver injury (DILI) and to evaluate the predictive value of the model for end-stage liver disease (MELD) score in DILI. METHODS Patients diagnosed with DILI between January 1, 2010 and December 31, 2012 in the Emergency Department at Asan Medical Center in Seoul, Korea were analyzed retrospectively. The primary end point was poor outcome, defined as liver transplantation or death within 30 days of the initial hospital visit. RESULTS Of 213 patients, 13.1% had a 30-day poor outcome. Folk remedies were the most common cause of DILI in 147 patients (69%). Univariate logistic regression analysis showed that multiple drugs (odds ratio [OR] 2.30, 95% confidence interval [CI]: 1.03-5.15), concurrent alcohol consumption (OR 3.69, 95% CI: 1.03-13.18), white blood cell (WBC) count (OR 1.17, 95% CI: 1.07-1.28), hemoglobin (Hb) (OR 0.60, 95% CI: 0.49-0.74), platelet count (OR 0.993, 95% CI: 0.987-0.998), total bilirubin (OR 1.09, 95% CI: 1.06-1.13) and MELD (OR 1.23, 95% CI: 1.15-1.32) were significantly associated with 30-day poor outcomes. Multivariate analysis showed that the MELD (OR 1.21, 95% CI: 1.12-1.30) and Hb (OR 0.77, 95% CI: 0.61-0.98) were independent predictors of poor outcome. For 30-day mortality, the c-statistics for MELD alone and for combination of MELD and Hb were 0.93 (95% CI: 0.89-0.97) and 0.94 (95% CI: 0.90-0.97), respectively. CONCLUSION The outcome of patients with DILI was poor. MELD score and Hb were reliable predictors of short-term outcome in patients with DILI.
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Affiliation(s)
- Rubi Jeong
- Department of Emergency Medicine, University of Ulsan College of Medicine, Asan Medical Center , Seoul , Korea
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Shi Y, Zheng MH, Yang Y, Wei W, Yang Q, Hu A, Hu Y, Wu Y, Yan H. Increased delayed mortality in patients with acute-on-chronic liver failure who have prior decompensation. J Gastroenterol Hepatol 2015; 30:712-8. [PMID: 25250673 DOI: 10.1111/jgh.12787] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/15/2014] [Indexed: 12/19/2022]
Abstract
BACKGROUND AND AIM Patients with acute-on-chronic liver failure (ACLF) represent a complex population with differential prognosis. The aim of the study was to categorize ACLF according to the severity of underlying chronic liver diseases. METHODS A total of 540 ACLF patients were recruited, including 127 with prior decompensated cirrhosis and 413 without prior decompensation (PD) including 193 with underlying chronic hepatitis and 220 with prior compensated cirrhosis. The clinical characteristics and prognosis of subgroups were compared. Cox proportional hazard model and multinominal logistic regression analysis were performed to identify significant prognostic parameters. RESULTS The 28-day, 3-month and 1-year survival of ACLF patients with or without PD were 58.9% versus 61.4%, 36.2 versus 52.5%, and 29.1% versus 49.6%, respectively. On multinominal logistic regression analysis or time-to-death analysis by Cox proportional hazard model, PD was significantly associated with post-28-day mortality but not within-28-day mortality. On multivariate time-to-death analysis, older age, high international normalized ratio (INR) and serum bilirubin, low levels of serum sodium and platelet count, and presence of hepatic encephalopathy (HE), upper gastrointestinal bleeding, and respiratory or circulation dysfunction were predictors of within-28-day mortality in patients without PD, whereas the risk factors in patients with PD were high INR, creatinine, presence of HE, and respiratory or circulation dysfunction. CONCLUSION ACLF patients with or without PD had comparable short-term prognosis but differential 1-year mortality. ACLF patients with PD were distinct from those without PD in age, types of acute insults, severity of hepatic damage, and distribution of complications, and the former group was characterized by increased delayed mortality.
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Affiliation(s)
- Yu Shi
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Gao S, Ji XF, Li F, Sun FK, Zhao J, Fan YC, Wang K. Aberrant DNA methylation of G-protein-coupled bile acid receptor Gpbar1 predicts prognosis of acute-on-chronic hepatitis B liver failure. J Viral Hepat 2015; 22:112-119. [PMID: 24995843 DOI: 10.1111/jvh.12277] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The G-protein-coupled bile acid receptor Gpbar1 (TGR5) has been demonstrated to be able to negatively regulate hepatic inflammatory response. In this study, we aimed to determine the methylation status of TGR5 promoter in patients with acute-on-chronic hepatitis B liver failure (ACHBLF) and its predictive value for prognosis. We enrolled 76 consecutive ACHBLF patients, 80 chronic hepatitis B (CHB) patients and 30 healthy controls (HCs). Methylation status of TGR5 promoter in peripheral mononuclear cell (PBMC) was detected by methylation-specific polymerase chain reaction (MSP). The mRNA level of TGR5 was determined by quantitative real-time polymerase chain reaction (RT-qPCR). We found that the frequency of TGR5 promoter methylation was significantly higher in ACHBLF (35/76, 46.05%) than CHB patients (5/80, 6.25%; χ(2) = 32.38, P < 0.01) and HCs (1/30, 3.33%; χ(2) = 17.50, P < 0.01). TGR5 mRNA level was significantly lower (Z = -9.12, P < 0.01) in participants with aberrant methylation than those without. TGR5 methylation showed a sensitivity of 46.05% (35/76), specificity of 93.75% (75/80), positive predictive value (PPV) of 87.5% (35/40) and negative predictive value (NPV) of 64.66% (75/116) in discriminating ACHBLF from CHB patients. ACHBLF patients with methylated TGR5 showed significantly poor survival than those without (P < 0.01). When used to predict 3-month mortality of ACHBLF, TGR5 methylation [area under the receiver operating characteristic curve (AUC) = 0.75] performed significantly better than model for end-stage liver diseases (MELD) score (AUC = 0.65; P < 0.05). Therefore, our study demonstrated that aberrant TGR5 promoter methylation occurred in ACHBLF and might be a potential prognostic marker for the disease.
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Affiliation(s)
- S Gao
- Department of Hepatology, Qilu Hospital of Shandong University, Jinan, China
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Wu FL, Shi KQ, Chen YP, Braddock M, Zou H, Zheng MH. Scoring systems predict the prognosis of acute-on-chronic hepatitis B liver failure: an evidence-based review. Expert Rev Gastroenterol Hepatol 2014; 8:623-32. [PMID: 24762209 DOI: 10.1586/17474124.2014.906899] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Acute-on-chronic hepatitis B liver failure is a devastating condition that is associated with mortality rates of over 50% and is consequent to acute exacerbation of chronic hepatitis B in patients with previously diagnosed or undiagnosed chronic liver disease. Liver transplantation is the definitive treatment to lower mortality rate, but there is a great imbalance between donation and potential recipients. An early and accurate prognostic system based on the integration of laboratory indicators, clinical events and some mathematic logistic equations is needed to optimize treatment for patients. As parts of the scoring systems, the MELD was the most common and the donor-MELD was the most innovative for patients on the waiting list for liver transplantation. This review aims to highlight the various features and prognostic capabilities of these scoring systems.
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Affiliation(s)
- Fa-Ling Wu
- Department of Infection and Liver Diseases, Liver Research Center, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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Pu Y, Baad MJ, Jiang Y, Chen Y. Application of artificial neural network and multiple linear regression models for predicting survival time of patients with non-small cell cancer using multiple prognostic factors including FDG-PET measurements. 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) 2014:225-230. [DOI: 10.1109/ijcnn.2014.6889882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
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Zheng MH, Seto WK, Shi KQ, Wong DKH, Fung J, Hung IFN, Fong DYT, Yuen JCH, Tong T, Lai CL, Yuen MF. Artificial neural network accurately predicts hepatitis B surface antigen seroclearance. PLoS One 2014; 9:e99422. [PMID: 24914537 PMCID: PMC4051672 DOI: 10.1371/journal.pone.0099422] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2014] [Accepted: 05/14/2014] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND & AIMS Hepatitis B surface antigen (HBsAg) seroclearance and seroconversion are regarded as favorable outcomes of chronic hepatitis B (CHB). This study aimed to develop artificial neural networks (ANNs) that could accurately predict HBsAg seroclearance or seroconversion on the basis of available serum variables. METHODS Data from 203 untreated, HBeAg-negative CHB patients with spontaneous HBsAg seroclearance (63 with HBsAg seroconversion), and 203 age- and sex-matched HBeAg-negative controls were analyzed. ANNs and logistic regression models (LRMs) were built and tested according to HBsAg seroclearance and seroconversion. Predictive accuracy was assessed with area under the receiver operating characteristic curve (AUROC). RESULTS Serum quantitative HBsAg (qHBsAg) and HBV DNA levels, qHBsAg and HBV DNA reduction were related to HBsAg seroclearance (P<0.001) and were used for ANN/LRM-HBsAg seroclearance building, whereas, qHBsAg reduction was not associated with ANN-HBsAg seroconversion (P = 0.197) and LRM-HBsAg seroconversion was solely based on qHBsAg (P = 0.01). For HBsAg seroclearance, AUROCs of ANN were 0.96, 0.93 and 0.95 for the training, testing and genotype B subgroups respectively. They were significantly higher than those of LRM, qHBsAg and HBV DNA (all P<0.05). Although the performance of ANN-HBsAg seroconversion (AUROC 0.757) was inferior to that for HBsAg seroclearance, it tended to be better than those of LRM, qHBsAg and HBV DNA. CONCLUSIONS ANN identifies spontaneous HBsAg seroclearance in HBeAg-negative CHB patients with better accuracy, on the basis of easily available serum data. More useful predictors for HBsAg seroconversion are still needed to be explored in the future.
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Affiliation(s)
- Ming-Hua Zheng
- Department of Infection and Liver Diseases, Liver Research Center, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
- Department of Medicine, the University of Hong Kong, Queen Mary Hospital, Hong Kong, China
| | - Wai-Kay Seto
- Department of Medicine, the University of Hong Kong, Queen Mary Hospital, Hong Kong, China
- State Key Laboratory for Liver Research, the University of Hong Kong, Queen Mary Hospital, Hong Kong, China
| | - Ke-Qing Shi
- Department of Infection and Liver Diseases, Liver Research Center, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Danny Ka-Ho Wong
- Department of Medicine, the University of Hong Kong, Queen Mary Hospital, Hong Kong, China
- State Key Laboratory for Liver Research, the University of Hong Kong, Queen Mary Hospital, Hong Kong, China
| | - James Fung
- Department of Medicine, the University of Hong Kong, Queen Mary Hospital, Hong Kong, China
- State Key Laboratory for Liver Research, the University of Hong Kong, Queen Mary Hospital, Hong Kong, China
| | - Ivan Fan-Ngai Hung
- Department of Medicine, the University of Hong Kong, Queen Mary Hospital, Hong Kong, China
| | - Daniel Yee-Tak Fong
- Department of Nursing Studies, the University of Hong Kong, Queen Mary Hospital, Hong Kong, China
| | - John Chi-Hang Yuen
- Department of Medicine, the University of Hong Kong, Queen Mary Hospital, Hong Kong, China
| | - Teresa Tong
- Department of Medicine, the University of Hong Kong, Queen Mary Hospital, Hong Kong, China
| | - Ching-Lung Lai
- Department of Medicine, the University of Hong Kong, Queen Mary Hospital, Hong Kong, China
- State Key Laboratory for Liver Research, the University of Hong Kong, Queen Mary Hospital, Hong Kong, China
| | - Man-Fung Yuen
- Department of Medicine, the University of Hong Kong, Queen Mary Hospital, Hong Kong, China
- State Key Laboratory for Liver Research, the University of Hong Kong, Queen Mary Hospital, Hong Kong, China
- * E-mail:
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Ma J, Cai J, Lin G, Chen H, Wang X, Wang X, Hu L. Development of LC–MS determination method and back-propagation ANN pharmacokinetic model of corynoxeine in rat. J Chromatogr B Analyt Technol Biomed Life Sci 2014; 959:10-5. [DOI: 10.1016/j.jchromb.2014.03.024] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2013] [Revised: 03/18/2014] [Accepted: 03/21/2014] [Indexed: 10/25/2022]
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Zhu L, Luo W, Su M, Wei H, Wei J, Zhang X, Zou C. Comparison between artificial neural network and Cox regression model in predicting the survival rate of gastric cancer patients. Biomed Rep 2013; 1:757-760. [PMID: 24649024 DOI: 10.3892/br.2013.140] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2013] [Accepted: 07/17/2013] [Indexed: 01/07/2023] Open
Abstract
The aim of this study was to determine the prognostic factors and their significance in gastric cancer (GC) patients, using the artificial neural network (ANN) and Cox regression hazard (CPH) models. A retrospective analysis was undertaken, including 289 patients with GC who had undergone gastrectomy between 2006 and 2007. According to the CPH analysis, disease stage, peritoneal dissemination, radical surgery and body mass index (BMI) were selected as the significant variables. According to the ANN model, disease stage, radical surgery, serum CA19-9 levels, peritoneal dissemination and BMI were selected as the significant variables. The true prediction of the ANN was 85.3% and of the CPH model 81.9%. In conclusion, the present study demonstrated that the ANN model is a more powerful tool in determining the significant prognostic variables for GC patients, compared to the CPH model. Therefore, this model is recommended for determining the risk factors of such patients.
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Affiliation(s)
- Lucheng Zhu
- Department of Radio-Chemotherapy Oncology, The First Affiliated Hospital of Wenzhou Medical College, Wenzhou, Zhejiang 325002, P.R. China
| | - Wenhua Luo
- Department of Radio-Chemotherapy Oncology, The First Affiliated Hospital of Wenzhou Medical College, Wenzhou, Zhejiang 325002, P.R. China
| | - Meng Su
- Department of Radio-Chemotherapy Oncology, The First Affiliated Hospital of Wenzhou Medical College, Wenzhou, Zhejiang 325002, P.R. China
| | - Hangping Wei
- Department of Radio-Chemotherapy Oncology, The First Affiliated Hospital of Wenzhou Medical College, Wenzhou, Zhejiang 325002, P.R. China
| | - Juan Wei
- Department of Radio-Chemotherapy Oncology, The First Affiliated Hospital of Wenzhou Medical College, Wenzhou, Zhejiang 325002, P.R. China
| | - Xuebang Zhang
- Department of Radio-Chemotherapy Oncology, The First Affiliated Hospital of Wenzhou Medical College, Wenzhou, Zhejiang 325002, P.R. China
| | - Changlin Zou
- Department of Radio-Chemotherapy Oncology, The First Affiliated Hospital of Wenzhou Medical College, Wenzhou, Zhejiang 325002, P.R. China
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