Title: Construction of a Predictive Model for Concurrent Infection in Liver Failure Patients
Based on Prognostic Nutritional Index and Inflammatory Cytokine Analysis
Version: 1 Date: 21 Mar 2025
Reviewer:  Rafael Suarez del Villar
Reviewer's report:
Dear Corresponding Author,
Thank you for submitting your manuscript. Your study presents a clinically relevant approach
by integrating inflammatory and nutritional markers to predict infection in liver failure patients.
The use of a nomogram based on CRP, sTREM-1, SIRI, PNI, and APACHE II is well justified
and statistically robust.
Below are several suggestions aimed at enhancing the clarity, reproducibility, and clinical value
of the manuscript.
> Major Revisions
1. Model Validation and Overfitting Risk:
The internal validation via ROC analysis shows excellent predictive power (AUC = 0.960).
However, there is no external validation or cross-validation method applied.
Recommendation: Please elaborate on whether any cross-validation was performed (e.g., k-fold)
to assess model robustness. If not, acknowledge this limitation more explicitly and suggest future
validation strategies.
2. Handling of Missing Data:
The retrospective nature of the study raises concerns about potential missing or incomplete data.
Recommendation: Clarify whether there were missing values in any of the predictors and how
they were handled statistically (e.g., complete case analysis, imputation).
3. Definition of Infections and Diagnostic Consistency:
The infection criteria include both culture-based and empirical (response-to-treatment)
diagnoses. This may introduce subjectivity.
Recommendation: Consider discussing the potential for misclassification bias and how it might
affect model specificity.
4. Collinearity Analysis and Predictor Selection:
Although VIF and tolerance were reported, the manuscript would benefit from a clearer
explanation of how predictors were selected and whether any stepwise or penalized regression
methods were considered.
Recommendation: Briefly discuss the predictor selection strategy and its potential implications
on model generalizability.
> Minor Revisions
1. Terminology Consistency:
Ensure consistent use of acronyms (e.g., PNI, SIRI) across the manuscript. At times, full terms
and abbreviations are interchanged inconsistently.
2. Language and Grammar:
While generally clear, the manuscript would benefit from a light English-language edit to correct
some awkward phrasing and improve flow, especially in the introduction and discussion.
3. Figures and Tables:
Improve figure resolution and ensure that axis labels and scoring interpretation on the nomogram
are clearly readable and intuitive.
4. References and Context:
Several key references are provided, but it would help to briefly contrast your model with similar
predictive tools used in liver failure or infectious complications to position your work in the
broader literature.
Best regards,
Title: Construction of a Predictive Model for Concurrent Infection in Liver Failure Patients
Based on Prognostic Nutritional Index and Inflammatory Cytokine Analysis
Version: 1 Date: 21 Mar 2025
Reviewer:  Rafael Suarez del Villar
Reviewer's report:
Dear Corresponding Author,
Thank you for submitting your manuscript. Your study presents a clinically relevant approach
by integrating inflammatory and nutritional markers to predict infection in liver failure patients.
The use of a nomogram based on CRP, sTREM-1, SIRI, PNI, and APACHE II is well justified
and statistically robust.
Below are several suggestions aimed at enhancing the clarity, reproducibility, and clinical value
of the manuscript.
> Major Revisions
1. Model Validation and Overfitting Risk:
The internal validation via ROC analysis shows excellent predictive power (AUC = 0.960).
However, there is no external validation or cross-validation method applied.
Recommendation: Please elaborate on whether any cross-validation was performed (e.g., k-fold)
to assess model robustness. If not, acknowledge this limitation more explicitly and suggest future
validation strategies.
2. Handling of Missing Data:
The retrospective nature of the study raises concerns about potential missing or incomplete data.
Recommendation: Clarify whether there were missing values in any of the predictors and how
they were handled statistically (e.g., complete case analysis, imputation).
3. Definition of Infections and Diagnostic Consistency:
The infection criteria include both culture-based and empirical (response-to-treatment)
diagnoses. This may introduce subjectivity.
Recommendation: Consider discussing the potential for misclassification bias and how it might
affect model specificity.
4. Collinearity Analysis and Predictor Selection:
Although VIF and tolerance were reported, the manuscript would benefit from a clearer
explanation of how predictors were selected and whether any stepwise or penalized regression
methods were considered.
Recommendation: Briefly discuss the predictor selection strategy and its potential implications
on model generalizability.
> Minor Revisions
1. Terminology Consistency:
Ensure consistent use of acronyms (e.g., PNI, SIRI) across the manuscript. At times, full terms
and abbreviations are interchanged inconsistently.
2. Language and Grammar:
While generally clear, the manuscript would benefit from a light English-language edit to correct
some awkward phrasing and improve flow, especially in the introduction and discussion.
3. Figures and Tables:
Improve figure resolution and ensure that axis labels and scoring interpretation on the nomogram
are clearly readable and intuitive.
4. References and Context:
Several key references are provided, but it would help to briefly contrast your model with similar
predictive tools used in liver failure or infectious complications to position your work in the
broader literature.
Best regards,
Please review this paper.
user
Title: Construction of a Predictive Model for Concurrent Infection in Liver Failure Patients
Based on Prognostic Nutritional Index and Inflammatory Cytokine Analysis
Version: 1 Date: 21 Mar 2025
Reviewer:  Rafael Suarez del Villar
Reviewer's report:
Dear Corresponding Author,
Thank you for submitting your manuscript. Your study presents a clinically relevant approach
by integrating inflammatory and nutritional markers to predict infection in liver failure patients.
The use of a nomogram based on CRP, sTREM-1, SIRI, PNI, and APACHE II is well justified
and statistically robust.
Below are several suggestions aimed at enhancing the clarity, reproducibility, and clinical value
of the manuscript.
> Major Revisions
1. Model Validation and Overfitting Risk:
The internal validation via ROC analysis shows excellent predictive power (AUC = 0.960).
However, there is no external validation or cross-validation method applied.
Recommendation: Please elaborate on whether any cross-validation was performed (e.g., k-fold)
to assess model robustness. If not, acknowledge this limitation more explicitly and suggest future
validation strategies.
2. Handling of Missing Data:
The retrospective nature of the study raises concerns about potential missing or incomplete data.
Recommendation: Clarify whether there were missing values in any of the predictors and how
they were handled statistically (e.g., complete case analysis, imputation).
3. Definition of Infections and Diagnostic Consistency:
The infection criteria include both culture-based and empirical (response-to-treatment)
diagnoses. This may introduce subjectivity.
Recommendation: Consider discussing the potential for misclassification bias and how it might
affect model specificity.
4. Collinearity Analysis and Predictor Selection:
Although VIF and tolerance were reported, the manuscript would benefit from a clearer
explanation of how predictors were selected and whether any stepwise or penalized regression
methods were considered.
Recommendation: Briefly discuss the predictor selection strategy and its potential implications
on model generalizability.
> Minor Revisions
1. Terminology Consistency:
Ensure consistent use of acronyms (e.g., PNI, SIRI) across the manuscript. At times, full terms
and abbreviations are interchanged inconsistently.
2. Language and Grammar:
While generally clear, the manuscript would benefit from a light English-language edit to correct
some awkward phrasing and improve flow, especially in the introduction and discussion.
3. Figures and Tables:
Improve figure resolution and ensure that axis labels and scoring interpretation on the nomogram
are clearly readable and intuitive.
4. References and Context:
Several key references are provided, but it would help to briefly contrast your model with similar
predictive tools used in liver failure or infectious complications to position your work in the
broader literature.
Best regards,
assistant