Title: Models to predict the short-term survival of acute-on-chronic liver failure patients
following liver transplantation
Version: 1 Date: 30 Dec 2021
Reviewer's report:
Yang et al provide the analysis of short-term survival risk factors of a single-center cohort of
132 patients transplanted for ACLF  then develop a machine-learning approach to identify the
best predictive model using easy to obtain clinical and biological data.
The subject is trendy and the cohort is significant. However, many weaknesses limit the scope
of the study.
MAJOR issues:
-APASL ACLF criteria was chosen however most studies recently published about this subject
use CLIF-ACLF criteria so the conclusions are difficult to transpose.
-CLIF-ACLF grade (1/2/3) is not explicited.
-Regarding the biological data: Which value was computed? The last before LT or the worst?
It has been shown that the evolution of ACLF in the first 72h after ICU admission are crucial
and that outcomes are very different betxeen "improvers" vs "worseners" in this timeframe.
-Major clinical data missing: How many patients were on ICU vs conventional hospitalization?
How many patients were on renal replacement therapy? And mechanical ventilation? An d
the cause for mechanical ventilation?  Pre-LT arterial lactate levels?  Recent infection from
multidrug resistant organisms? (previously shown as risk-factors for mortality after LT for
ACLF)
-Mortality rate is rather high, probably due to the large inclusion period: was the mortality rate
influenced by the timeframe? Less mortality in more recent years? (after 2016 for example).
-No data on the donor (age/donor risk index/steatosis/cold ischemia time).
Minor issues:
-English editing needed, mostly for the Discussion section.
-Some citations missing: Artru et al J Hepatol 2017, Belli  et al J Hepatol 2021.
-Data on other etiologies for LT (besides HBV infection is needed)
-Flowchart is misguiding: patients "excluded" are actually patient snot meeting inclusion
criteria.
-Some graphs were generated with GraphPad Prism, however this software is not mentioned in
the methods section.
Title: Models to predict the short-term survival of acute-on-chronic liver failure patients
following liver transplantation
Version: 1 Date: 30 Dec 2021
Reviewer's report:
Yang et al provide the analysis of short-term survival risk factors of a single-center cohort of
132 patients transplanted for ACLF  then develop a machine-learning approach to identify the
best predictive model using easy to obtain clinical and biological data.
The subject is trendy and the cohort is significant. However, many weaknesses limit the scope
of the study.
MAJOR issues:
-APASL ACLF criteria was chosen however most studies recently published about this subject
use CLIF-ACLF criteria so the conclusions are difficult to transpose.
-CLIF-ACLF grade (1/2/3) is not explicited.
-Regarding the biological data: Which value was computed? The last before LT or the worst?
It has been shown that the evolution of ACLF in the first 72h after ICU admission are crucial
and that outcomes are very different betxeen "improvers" vs "worseners" in this timeframe.
-Major clinical data missing: How many patients were on ICU vs conventional hospitalization?
How many patients were on renal replacement therapy? And mechanical ventilation? An d
the cause for mechanical ventilation?  Pre-LT arterial lactate levels?  Recent infection from
multidrug resistant organisms? (previously shown as risk-factors for mortality after LT for
ACLF)
-Mortality rate is rather high, probably due to the large inclusion period: was the mortality rate
influenced by the timeframe? Less mortality in more recent years? (after 2016 for example).
-No data on the donor (age/donor risk index/steatosis/cold ischemia time).
Minor issues:
-English editing needed, mostly for the Discussion section.
-Some citations missing: Artru et al J Hepatol 2017, Belli  et al J Hepatol 2021.
-Data on other etiologies for LT (besides HBV infection is needed)
-Flowchart is misguiding: patients "excluded" are actually patient snot meeting inclusion
criteria.
-Some graphs were generated with GraphPad Prism, however this software is not mentioned in
the methods section.
Please review this paper.
user
Title: Models to predict the short-term survival of acute-on-chronic liver failure patients
following liver transplantation
Version: 1 Date: 30 Dec 2021
Reviewer's report:
Yang et al provide the analysis of short-term survival risk factors of a single-center cohort of
132 patients transplanted for ACLF  then develop a machine-learning approach to identify the
best predictive model using easy to obtain clinical and biological data.
The subject is trendy and the cohort is significant. However, many weaknesses limit the scope
of the study.
MAJOR issues:
-APASL ACLF criteria was chosen however most studies recently published about this subject
use CLIF-ACLF criteria so the conclusions are difficult to transpose.
-CLIF-ACLF grade (1/2/3) is not explicited.
-Regarding the biological data: Which value was computed? The last before LT or the worst?
It has been shown that the evolution of ACLF in the first 72h after ICU admission are crucial
and that outcomes are very different betxeen "improvers" vs "worseners" in this timeframe.
-Major clinical data missing: How many patients were on ICU vs conventional hospitalization?
How many patients were on renal replacement therapy? And mechanical ventilation? An d
the cause for mechanical ventilation?  Pre-LT arterial lactate levels?  Recent infection from
multidrug resistant organisms? (previously shown as risk-factors for mortality after LT for
ACLF)
-Mortality rate is rather high, probably due to the large inclusion period: was the mortality rate
influenced by the timeframe? Less mortality in more recent years? (after 2016 for example).
-No data on the donor (age/donor risk index/steatosis/cold ischemia time).
Minor issues:
-English editing needed, mostly for the Discussion section.
-Some citations missing: Artru et al J Hepatol 2017, Belli  et al J Hepatol 2021.
-Data on other etiologies for LT (besides HBV infection is needed)
-Flowchart is misguiding: patients "excluded" are actually patient snot meeting inclusion
criteria.
-Some graphs were generated with GraphPad Prism, however this software is not mentioned in
the methods section.
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