Reviewer ’s report  
 
Title: Development and validation of three machine -learning models for predicting multiple 
organ  failure in moderately severe and severe acute pancreatitis  
 
Version: 0 Date: 19 D ec 2018  
 
Reviewer: David C. Whitcomb  
 
Reviewer's report:  
 
The manuscript by Qui et al describes a retrospective study in which patient previously admitted 
with acute pancreatitis were used in a nested case -control study based on mild or severe acute 
pancreatitis.  The study assessed the accuracy of three machine -learning models for predicting 
multi -organ failure.  The paper is interesting, and addresses an important topic.  
 
The major findings are that, desp ite use of machine learning, the accuracy of the test is no better 
than existing methods such as APACHE II.  No other test, such as BISAP, or even SIRS was 
evaluated.  The conclusion is that, although the machine learning methods were no better than 
APACHE  II, the could obtain similar accuracy with fewer parameters.  
 
 
Comments:  
 
1) one of the most well defined indicators of poor outcome is hemoconcentration (high 
hematocrit, HCT).   I this study the patients with more sever disease had significantly lower HC T 
(37.00 in mild vs 31.6 in severe).   Likewise, the patients with more severe disease had lower 
white blood cell counts (13.47 mild vs 12.97 severe).  These data are counterintuitive, suggesting 
a methodological problem.   
 
2) Of note, the Methods (page 5 line 28) states that the patients received standard treatment 
including "early aggressive hydration".  Did all patients receive the same fluids?  And what was 
the time between the initial evaluation of the patient and the da ta set used to measure severity?  
Were some of the patients transferred after initial treatment?  The manuscript does not state 
exactly when the data that was used in the calculations was collected relative to presentation and 
initial assessment.  
 
3) The s tudy did not evaluate subsets of patients based on (1) etiology, or (2) BMI, which are 
know to affect the severity and clinical course.  
Are the methods appropriate and well described?  
If not, please specify what is required in your comments to the au thors.  
 
No 
 
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.  
 
No 
 
Are you able to assess any statistics in the manuscript or would you recommend an  
additional statistical review?  
If an additional statistical review is recommended, please specify 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 language corrections before being published  
 
Declaration of competing interests  
Please complete a declaration of competing interests, considering the following questi ons: 
 
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 financially from the publication of this  
manuscript, either now or in the future?  
 
2. Do you hold any stocks or s hares in an organisation that may in any way gain or lose  
financially from the publication of 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 applied 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 inter ests in relation to this paper?  
If you can answer no to all of the above, write 'I declare that I have no competing interests' 
below.  If your reply is yes to any, please give details below.  
 
no 
 
I agree to the open  peer review policy of the journal. I understand that my name will be included  
on my report to the authors and, if the manuscript is accepted for publication, my named report  
including any attachments I upload will be posted on the website along with the a uthors'  
responses. I agree for my report to be made available under an Open Access Creative Commons  
CC-BY license ( http://creativecommons.org/licenses/by/4.0/ ). I understand that any comments  
which I do not wish to be included in my named report can be inc luded as confidential comments  
to the editors, which will not be published.  
 
I agree to the open pe er review policy of the journal  
Reviewer ’s report  
 
Title: Development and validation of three machine -learning models for predicting multiple 
organ  failure in moderately severe and severe acute pancreatitis  
 
Version: 0 Date: 19 D ec 2018  
 
Reviewer: David C. Whitcomb  
 
Reviewer's report:  
 
The manuscript by Qui et al describes a retrospective study in which patient previously admitted 
with acute pancreatitis were used in a nested case -control study based on mild or severe acute 
pancreatitis.  The study assessed the accuracy of three machine -learning models for predicting 
multi -organ failure.  The paper is interesting, and addresses an important topic.  
 
The major findings are that, desp ite use of machine learning, the accuracy of the test is no better 
than existing methods such as APACHE II.  No other test, such as BISAP, or even SIRS was 
evaluated.  The conclusion is that, although the machine learning methods were no better than 
APACHE  II, the could obtain similar accuracy with fewer parameters.  
 
 
Comments:  
 
1) one of the most well defined indicators of poor outcome is hemoconcentration (high 
hematocrit, HCT).   I this study the patients with more sever disease had significantly lower HC T 
(37.00 in mild vs 31.6 in severe).   Likewise, the patients with more severe disease had lower 
white blood cell counts (13.47 mild vs 12.97 severe).  These data are counterintuitive, suggesting 
a methodological problem.   
 
2) Of note, the Methods (page 5 line 28) states that the patients received standard treatment 
including "early aggressive hydration".  Did all patients receive the same fluids?  And what was 
the time between the initial evaluation of the patient and the da ta set used to measure severity?  
Were some of the patients transferred after initial treatment?  The manuscript does not state 
exactly when the data that was used in the calculations was collected relative to presentation and 
initial assessment.  
 
3) The s tudy did not evaluate subsets of patients based on (1) etiology, or (2) BMI, which are 
know to affect the severity and clinical course.  
Are the methods appropriate and well described?  
If not, please specify what is required in your comments to the au thors.  
 
No 
 
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.  
 
No 
 
Are you able to assess any statistics in the manuscript or would you recommend an  
additional statistical review?  
If an additional statistical review is recommended, please specify 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 language corrections before being published  
 
Declaration of competing interests  
Please complete a declaration of competing interests, considering the following questi ons: 
 
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 financially from the publication of this  
manuscript, either now or in the future?  
 
2. Do you hold any stocks or s hares in an organisation that may in any way gain or lose  
financially from the publication of 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 applied 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 inter ests in relation to this paper?  
If you can answer no to all of the above, write 'I declare that I have no competing interests' 
below.  If your reply is yes to any, please give details below.  
 
no 
 
I agree to the open  peer review policy of the journal. I understand that my name will be included  
on my report to the authors and, if the manuscript is accepted for publication, my named report  
including any attachments I upload will be posted on the website along with the a uthors'  
responses. I agree for my report to be made available under an Open Access Creative Commons  
CC-BY license ( http://creativecommons.org/licenses/by/4.0/ ). I understand that any comments  
which I do not wish to be included in my named report can be inc luded as confidential comments  
to the editors, which will not be published.  
 
I agree to the open pe er review policy of the journal  
Please review this paper.
user
Reviewer ’s report  
 
Title: Development and validation of three machine -learning models for predicting multiple 
organ  failure in moderately severe and severe acute pancreatitis  
 
Version: 0 Date: 19 D ec 2018  
 
Reviewer: David C. Whitcomb  
 
Reviewer's report:  
 
The manuscript by Qui et al describes a retrospective study in which patient previously admitted 
with acute pancreatitis were used in a nested case -control study based on mild or severe acute 
pancreatitis.  The study assessed the accuracy of three machine -learning models for predicting 
multi -organ failure.  The paper is interesting, and addresses an important topic.  
 
The major findings are that, desp ite use of machine learning, the accuracy of the test is no better 
than existing methods such as APACHE II.  No other test, such as BISAP, or even SIRS was 
evaluated.  The conclusion is that, although the machine learning methods were no better than 
APACHE  II, the could obtain similar accuracy with fewer parameters.  
 
 
Comments:  
 
1) one of the most well defined indicators of poor outcome is hemoconcentration (high 
hematocrit, HCT).   I this study the patients with more sever disease had significantly lower HC T 
(37.00 in mild vs 31.6 in severe).   Likewise, the patients with more severe disease had lower 
white blood cell counts (13.47 mild vs 12.97 severe).  These data are counterintuitive, suggesting 
a methodological problem.   
 
2) Of note, the Methods (page 5 line 28) states that the patients received standard treatment 
including "early aggressive hydration".  Did all patients receive the same fluids?  And what was 
the time between the initial evaluation of the patient and the da ta set used to measure severity?  
Were some of the patients transferred after initial treatment?  The manuscript does not state 
exactly when the data that was used in the calculations was collected relative to presentation and 
initial assessment.  
 
3) The s tudy did not evaluate subsets of patients based on (1) etiology, or (2) BMI, which are 
know to affect the severity and clinical course.  
Are the methods appropriate and well described?  
If not, please specify what is required in your comments to the au thors.  
 
No 
 
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.  
 
No 
 
Are you able to assess any statistics in the manuscript or would you recommend an  
additional statistical review?  
If an additional statistical review is recommended, please specify 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 language corrections before being published  
 
Declaration of competing interests  
Please complete a declaration of competing interests, considering the following questi ons: 
 
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 financially from the publication of this  
manuscript, either now or in the future?  
 
2. Do you hold any stocks or s hares in an organisation that may in any way gain or lose  
financially from the publication of 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 applied 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 inter ests in relation to this paper?  
If you can answer no to all of the above, write 'I declare that I have no competing interests' 
below.  If your reply is yes to any, please give details below.  
 
no 
 
I agree to the open  peer review policy of the journal. I understand that my name will be included  
on my report to the authors and, if the manuscript is accepted for publication, my named report  
including any attachments I upload will be posted on the website along with the a uthors'  
responses. I agree for my report to be made available under an Open Access Creative Commons  
CC-BY license ( http://creativecommons.org/licenses/by/4.0/ ). I understand that any comments  
which I do not wish to be included in my named report can be inc luded as confidential comments  
to the editors, which will not be published.  
 
I agree to the open pe er review policy of the journal  
assistant