Title: Establishment of MELD-lactate clearance scoring system in predicting death risk of
critically ill cirrhotic patients
Version: 1 Date: 18 Feb 2022
Reviewer's report:
Xin Li et al. conducted a retrospective study using a large intensive care database to develop the
MELD-lactate scoring system for predicting mortality in patients with liver cirrhosis treated on
an intensive care unit. The manuscript deals with an important and timely topic. However, there
are some issues that need to be addressed. Please find my comments below:
- It is recommended that TRIPOD guidelines be followed for the development of scores
for outcome prediction.  Please detail in your methods section whether you followed these
guidelines.
- As described in your results section on page 10:” MELD-#LA score was generated by the
optimal cutoff point for each factor determined via the Youden index. MELD-#LA score was
composed of creatinine, bilirubin, INR, lactate first, #LA and the using history of vasopressors,
and detailed information was given in Table 4.” This is a rather unusual procedure for this
reviewer. Typically, scores for predicting outcomes are generated based on a formula of a
regression model. Therefore, I am not convinced that this score is the ideal prediction model for
this cohort. Please compare the performance of your score with a score based on the formula of
a regression model with metric variables.
- How many patients received a liver transplantation? If this number is high, it is not correct
to use Cox regression models. Liver transplantation is a competing event for death. There are
two possibilities to handle this issue: 1) treat patients who received a liver transplantation as
complete cases (=death) or 2) use Fine and Gray models and treat liver transplantation as a
competing event.
- Please tone the conclusions of your abstract down. The predictive accuracy of the MELD-LA
score is far from excellent with AUROCs ranging between 0.757 – 0.774.
- The discriminative abilities of MELD-#LA and CLIF-SOFA do not differ regarding 28-day,
90-day and 1-year mortality in the total cohort. So, what is the advantage of MELD-#LA and
why should we implement this score into clinical routine practice?
- Please compare your score with the CLIF-C ACLF score in patients with ACLF and with the
CLIF-C AD score in patients without ACLF.
- Please display the calibration of your score.
- There are some typos and the English should be improved.
Title: Establishment of MELD-lactate clearance scoring system in predicting death risk of
critically ill cirrhotic patients
Version: 1 Date: 18 Feb 2022
Reviewer's report:
Xin Li et al. conducted a retrospective study using a large intensive care database to develop the
MELD-lactate scoring system for predicting mortality in patients with liver cirrhosis treated on
an intensive care unit. The manuscript deals with an important and timely topic. However, there
are some issues that need to be addressed. Please find my comments below:
- It is recommended that TRIPOD guidelines be followed for the development of scores
for outcome prediction.  Please detail in your methods section whether you followed these
guidelines.
- As described in your results section on page 10:” MELD-#LA score was generated by the
optimal cutoff point for each factor determined via the Youden index. MELD-#LA score was
composed of creatinine, bilirubin, INR, lactate first, #LA and the using history of vasopressors,
and detailed information was given in Table 4.” This is a rather unusual procedure for this
reviewer. Typically, scores for predicting outcomes are generated based on a formula of a
regression model. Therefore, I am not convinced that this score is the ideal prediction model for
this cohort. Please compare the performance of your score with a score based on the formula of
a regression model with metric variables.
- How many patients received a liver transplantation? If this number is high, it is not correct
to use Cox regression models. Liver transplantation is a competing event for death. There are
two possibilities to handle this issue: 1) treat patients who received a liver transplantation as
complete cases (=death) or 2) use Fine and Gray models and treat liver transplantation as a
competing event.
- Please tone the conclusions of your abstract down. The predictive accuracy of the MELD-LA
score is far from excellent with AUROCs ranging between 0.757 – 0.774.
- The discriminative abilities of MELD-#LA and CLIF-SOFA do not differ regarding 28-day,
90-day and 1-year mortality in the total cohort. So, what is the advantage of MELD-#LA and
why should we implement this score into clinical routine practice?
- Please compare your score with the CLIF-C ACLF score in patients with ACLF and with the
CLIF-C AD score in patients without ACLF.
- Please display the calibration of your score.
- There are some typos and the English should be improved.
Please review this paper.
user
Title: Establishment of MELD-lactate clearance scoring system in predicting death risk of
critically ill cirrhotic patients
Version: 1 Date: 18 Feb 2022
Reviewer's report:
Xin Li et al. conducted a retrospective study using a large intensive care database to develop the
MELD-lactate scoring system for predicting mortality in patients with liver cirrhosis treated on
an intensive care unit. The manuscript deals with an important and timely topic. However, there
are some issues that need to be addressed. Please find my comments below:
- It is recommended that TRIPOD guidelines be followed for the development of scores
for outcome prediction.  Please detail in your methods section whether you followed these
guidelines.
- As described in your results section on page 10:” MELD-#LA score was generated by the
optimal cutoff point for each factor determined via the Youden index. MELD-#LA score was
composed of creatinine, bilirubin, INR, lactate first, #LA and the using history of vasopressors,
and detailed information was given in Table 4.” This is a rather unusual procedure for this
reviewer. Typically, scores for predicting outcomes are generated based on a formula of a
regression model. Therefore, I am not convinced that this score is the ideal prediction model for
this cohort. Please compare the performance of your score with a score based on the formula of
a regression model with metric variables.
- How many patients received a liver transplantation? If this number is high, it is not correct
to use Cox regression models. Liver transplantation is a competing event for death. There are
two possibilities to handle this issue: 1) treat patients who received a liver transplantation as
complete cases (=death) or 2) use Fine and Gray models and treat liver transplantation as a
competing event.
- Please tone the conclusions of your abstract down. The predictive accuracy of the MELD-LA
score is far from excellent with AUROCs ranging between 0.757 – 0.774.
- The discriminative abilities of MELD-#LA and CLIF-SOFA do not differ regarding 28-day,
90-day and 1-year mortality in the total cohort. So, what is the advantage of MELD-#LA and
why should we implement this score into clinical routine practice?
- Please compare your score with the CLIF-C ACLF score in patients with ACLF and with the
CLIF-C AD score in patients without ACLF.
- Please display the calibration of your score.
- There are some typos and the English should be improved.
assistant