Review for 10.1186/s12876-020-01191-5 (2020)

Reviewer ’s report  
 
Title: Artificial neural network -based models used for predict ing 28 - and 90 -day mortality of 
patients with hepatitis B -associated acute -on-chronic liver failure  
 
Version: 0 Date: 09 Nov 2019  
 
Reviewer: Jian-Ming Zheng  
 
Reviewer's report:  
 
Comments to the Author (BMGE -D-19-00933)  
 
The study tried to develop prognostic models for predicting 28 - and 90 -day mo rtality of hepatitis 
B virus (HBV) -associated acute -on-chronic liver failure (HBV -ACLF) through artificial neural 
network (ANN) systems. In this retrospective controlled study, 684 cases of consecutive patients 
with HBV -ACLF 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. The similar researches were performed before.  
 
 
MAJOR COMMENTS  
 
1. Did all patients have cirrhosis?  Cirrhosis was defined in the part of diagnosis. Whether the 
prognosis of patients with cirrhosis or without, will be different?  
 
2. The article showed that eight independent risk factors, including age, hepatic encephalopathy, 
serum sodium, prothrombin ac tivity, γ -glutamyltransferase, hepatitis B e antigen, alkaline 
phosphatase and total bilirubin, were eventually used to establish ANN models. Multivariate 
analysis should be done to avoid that these factors are not independent risk factors, but just the 
confounding factors.  
 
3. Why not use INR instead of PTA? INR, not PTA, is used as a coagulation function assessment 
in AASLD, EASL or APASL guideline. This article will be published in international 
magazines, and it is not suitable to use parameters only use d in local areas.  
 
4. Why there were not enrolled white blood cell, INR, or serum creatinine, some important 
parameters in CLIF -ACLF or MELD scores, in construction of ANN models, which Power was 
also below 0.05?  
 
5. Why the sodium and HBeAg positivity rate  were negatively associated with 28 -day mortality 
but were positively associated with 90 -day mortality?  
 
6. The conclusions of this study is that the established ANN models can more accurately predict 
short -term mortality risk in patients with HBV - ACLF. Th e article showed that since the ANN 
models in this study were based on more subjects and more (8) variables than those in Zheng et 
al.'s study, it is not surprising that the prediction efficacy of this study is superior. It's not 
appropriate. Zheng et al o nly found six variables, age (P &lt; 0.001), prothrombin activity (P &lt; 
0.001), serum sodium (P &lt; 0.001), total bilirubin (P = 0.015), hepatitis B e antigen positivity 
rate (P &lt; 0.001) and haemoglobin (P &lt; 0.001) were significantly related to th e prognosis of 
ACHBLF and were selected to build the ANN. These variables are similar to those in this article. 
The more parameters do not always mean the better prediction, especially some parameters may 
not be the independent risk factors. How to appropr iately explain these different results in 
different studies?  
 
Are the methods appropriate and well describ ed? 
If not, please specify what is required in your comments to the authors.  
 
Yes 
 
Does the work include the necessary controls?  
If not, please specify which controls are required in your comments to the authors.  
 
Yes 
 
Are the conclusions drawn adequately supported by the data shown?  
If not, please explain in your comments to the authors.  
 
Yes 
 
Are you able to assess any statistics in the manus cript or would you recommend an 
additional statistical review?  
If an additional statistical review is recommended, please speci fy what aspects require further 
assessment in your comments to the editors.  
 
I am able to assess the statistics  
 
Quality of written English  
Please indicate the quality of language in the manuscript:  
 
Needs some l anguage corrections before being published  
 
Declaration of competing interests  
Please complete a declaration of competing interests, considering the following questions:  
 
1.  Have you in the past five years received reimbursements, fees, funding, or salary 
from an organisation that may in any way gain or lose financial ly from the 
publication of this manuscript, either now or in the future?  
 
2.  Do you hold any stocks or shares in an organisation t hat may in any way gain or 
lose financially from the publication o f this manuscript, either now or in the 
future?  
3.  Do you hold or are you currently applying for any patents  relating to the content 
of the manuscript?  
 
4.  Have you received reimbursements, fees, funding, or salary from an organization 
that holds or has app lied for patents relating to the content of the manuscript?  
 
5.  Do you have any other financial competing interests?  
 
6.  Do you have any non -financial competing interests in relation to this paper?  
 
If you can answer no to all of the above, write 'I declare t hat I have  no competing interests' 
below. If your reply is yes to any, please give details below.  
 
I declare that I have no competing interests' below.  
 
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