Title: Association Between Albumin-Corrected Anion Gap and Delirium in Acute Pancreatitis:
Insights from the MIMIC-IV Database
Version: 1 Date: 20 Jun 2025
Reviewer's report:
The authors Ge et al. present a study that aims to investigate the association between delirium
and albumin-corrected anion gap based on the MIMIC-IV dataset. The manuscript is well written
but misses several aspects that are crucial to follow the content:
Major Comments
- Since you apply machine learning, please give the reader an overview
about related work and cite a few references that show that delirium ML
models were applied for septic patients, also with MIMIC-IV data already or
in a perioperative context https://www.nature.com/articles/s41598-023-38650-4, https://
journals.plos.org/plosone/article?id=10.1371/journal.pone.0319297, https://doi.org/10.1371/
journal.pdig.0000414, https://www.nature.com/articles/s41598-024-61627-w
- In Methods, further describe how you defined Clinical Outcomes and especially delirium
(CAM-ICU, Nu-DESC) etc. The usage of statistical tests like MWU test should be backed up
with references. You should explain how you retrieved 95% CI on the basis of your cox model.
Please also motivate more why you used cubic splines, is there evidence that your data follows
these? “The dataset was randomly split into a training set (75%) and a validation set (25%).”
Were the data spitted by patient ids or stays? Elaborate more what you exactly predict and on
what kind of bases also including the exact time of delirium assessment and your prediction
horizon. Justify why you did not apply multiple testing correction and why you picked SHAP
values for explanations. You fitted a COX model where the coefficients are relatively straight
forward to interpret.
- In your Results, you should always cite an effect size alongside your statistical p-values.
- “Boruta Algorithm” this section is “floating” in the text and the reader cannot follow 1) how
this algorithm works 2) why you have chosen this for feature selection 3) why the algorithm has
picked your presented features
- You should add further explanations and performance metrics for your ML algorithms like
AUPRC curves alongside AUROC and critically discuss the application thereof. How do you
intend to apply these models?
- In the Discussion, you should critically explain the role of missing data like missing ACAG
potentially leading to skewed distributions, at least arguing why no imputation was used
-
Minor Comments
- “Intensive Care Units at Beth Israel Deaconess Medical Center in Boston, Massachusetts.”,
add that the data is from the US to be precise
- “As a valuable resource for clinical research, particularly in intensive care, epidemiology, and
outcomes analysis, the MIMIC-IV database is structured to support large-scale data analysis
and has been widely employed in studies on critical illnesses, predictive modeling, and the
development of decision-support tools.“ shorten the sentence and consecutive ones , these are
not 100% relevant for your study
-
Title: Association Between Albumin-Corrected Anion Gap and Delirium in Acute Pancreatitis:
Insights from the MIMIC-IV Database
Version: 1 Date: 20 Jun 2025
Reviewer's report:
The authors Ge et al. present a study that aims to investigate the association between delirium
and albumin-corrected anion gap based on the MIMIC-IV dataset. The manuscript is well written
but misses several aspects that are crucial to follow the content:
Major Comments
- Since you apply machine learning, please give the reader an overview
about related work and cite a few references that show that delirium ML
models were applied for septic patients, also with MIMIC-IV data already or
in a perioperative context https://www.nature.com/articles/s41598-023-38650-4, https://
journals.plos.org/plosone/article?id=10.1371/journal.pone.0319297, https://doi.org/10.1371/
journal.pdig.0000414, https://www.nature.com/articles/s41598-024-61627-w
- In Methods, further describe how you defined Clinical Outcomes and especially delirium
(CAM-ICU, Nu-DESC) etc. The usage of statistical tests like MWU test should be backed up
with references. You should explain how you retrieved 95% CI on the basis of your cox model.
Please also motivate more why you used cubic splines, is there evidence that your data follows
these? “The dataset was randomly split into a training set (75%) and a validation set (25%).”
Were the data spitted by patient ids or stays? Elaborate more what you exactly predict and on
what kind of bases also including the exact time of delirium assessment and your prediction
horizon. Justify why you did not apply multiple testing correction and why you picked SHAP
values for explanations. You fitted a COX model where the coefficients are relatively straight
forward to interpret.
- In your Results, you should always cite an effect size alongside your statistical p-values.
- “Boruta Algorithm” this section is “floating” in the text and the reader cannot follow 1) how
this algorithm works 2) why you have chosen this for feature selection 3) why the algorithm has
picked your presented features
- You should add further explanations and performance metrics for your ML algorithms like
AUPRC curves alongside AUROC and critically discuss the application thereof. How do you
intend to apply these models?
- In the Discussion, you should critically explain the role of missing data like missing ACAG
potentially leading to skewed distributions, at least arguing why no imputation was used
-
Minor Comments
- “Intensive Care Units at Beth Israel Deaconess Medical Center in Boston, Massachusetts.”,
add that the data is from the US to be precise
- “As a valuable resource for clinical research, particularly in intensive care, epidemiology, and
outcomes analysis, the MIMIC-IV database is structured to support large-scale data analysis
and has been widely employed in studies on critical illnesses, predictive modeling, and the
development of decision-support tools.“ shorten the sentence and consecutive ones , these are
not 100% relevant for your study
-
Please review this paper.
user
Title: Association Between Albumin-Corrected Anion Gap and Delirium in Acute Pancreatitis:
Insights from the MIMIC-IV Database
Version: 1 Date: 20 Jun 2025
Reviewer's report:
The authors Ge et al. present a study that aims to investigate the association between delirium
and albumin-corrected anion gap based on the MIMIC-IV dataset. The manuscript is well written
but misses several aspects that are crucial to follow the content:
Major Comments
- Since you apply machine learning, please give the reader an overview
about related work and cite a few references that show that delirium ML
models were applied for septic patients, also with MIMIC-IV data already or
in a perioperative context https://www.nature.com/articles/s41598-023-38650-4, https://
journals.plos.org/plosone/article?id=10.1371/journal.pone.0319297, https://doi.org/10.1371/
journal.pdig.0000414, https://www.nature.com/articles/s41598-024-61627-w
- In Methods, further describe how you defined Clinical Outcomes and especially delirium
(CAM-ICU, Nu-DESC) etc. The usage of statistical tests like MWU test should be backed up
with references. You should explain how you retrieved 95% CI on the basis of your cox model.
Please also motivate more why you used cubic splines, is there evidence that your data follows
these? “The dataset was randomly split into a training set (75%) and a validation set (25%).”
Were the data spitted by patient ids or stays? Elaborate more what you exactly predict and on
what kind of bases also including the exact time of delirium assessment and your prediction
horizon. Justify why you did not apply multiple testing correction and why you picked SHAP
values for explanations. You fitted a COX model where the coefficients are relatively straight
forward to interpret.
- In your Results, you should always cite an effect size alongside your statistical p-values.
- “Boruta Algorithm” this section is “floating” in the text and the reader cannot follow 1) how
this algorithm works 2) why you have chosen this for feature selection 3) why the algorithm has
picked your presented features
- You should add further explanations and performance metrics for your ML algorithms like
AUPRC curves alongside AUROC and critically discuss the application thereof. How do you
intend to apply these models?
- In the Discussion, you should critically explain the role of missing data like missing ACAG
potentially leading to skewed distributions, at least arguing why no imputation was used
-
Minor Comments
- “Intensive Care Units at Beth Israel Deaconess Medical Center in Boston, Massachusetts.”,
add that the data is from the US to be precise
- “As a valuable resource for clinical research, particularly in intensive care, epidemiology, and
outcomes analysis, the MIMIC-IV database is structured to support large-scale data analysis
and has been widely employed in studies on critical illnesses, predictive modeling, and the
development of decision-support tools.“ shorten the sentence and consecutive ones , these are
not 100% relevant for your study
-
assistant