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: 28 Mar 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-While the role of inflammatory cytokines is mentioned, a more detailed exploration of how
these cytokines contribute specifically to infection risk in liver failure patients would strengthen
the theoretical foundation of the study. Understanding the precise mechanisms by which these
markers influence infection susceptibility could provide deeper insights.
-Although the study includes a broad range of variables, some potential confounding factors
(e.g., concurrent use of medications, immunosuppressive therapy, or comorbid conditions like
diabetes) may not be sufficiently controlled for. Addressing these factors in the analysis could
further refine the predictive model.
-The PNI calculation is based on serum albumin levels and peripheral lymphocyte count, which
is a straightforward and widely used method. However, given that liver failure often involves
complex alterations in protein metabolism and immune function, it would be valuable to include
additional biomarkers or other clinical factors that may better capture the nutritional and immune
status of these patients.
-There is a potential risk of overfitting the model given the number of variables included in
the multivariate analysis. While the study performs collinearity analysis to address this issue,
additional steps, such as cross-validation, could help reduce the risk of overfitting and improve
the model's generalizability.
-While the nomogram is a useful tool, its clinical applicability could be further discussed. It
would be important to explain how easily clinicians could use the nomogram in routine practice.
For instance, incorporating a web-based or mobile application version could enhance its usability
for healthcare providers.
-The study focuses on infection as the main outcome, but it would be helpful to clarify how
the infection diagnoses were standardized across the cohort. The study also does not provide
external validation or testing on a separate cohort, which would be important to establish the
model’s performance beyond the internal dataset.
-While the discussion links CRP, sTREM-1, and SIRI to infection risk, there is still room
to delve deeper into the specific mechanisms by which these markers influence infection
susceptibility in liver failure patients. While the role of sTREM-1 and CRP in immune activation
is addressed, further exploration of how these markers interact with liver function and immune
cells, particularly in liver failure, would strengthen the argument.
-The discussion emphasizes the importance of the APACHE II score as a predictor of infection
risk. While this is important, the discussion could have placed more focus on the novel
contributions of the study, particularly the combination of PNI and inflammatory markers. The
APACHE II score is already widely recognized in the clinical setting, so further exploration
of how the PNI and cytokine markers provide additional predictive value would make the
discussion more unique and focused on the study's innovations.
-While the study does a good job of linking predictors with infection risk, the potential
confounding factors that might influence infection outcomes (e.g., comorbidities like diabetes,
use of immunosuppressive drugs, and concurrent infections) could be more explicitly discussed.
Addressing how these factors were controlled for or their potential impact on the results would
provide a clearer understanding of the robustness of the findings.
-The discussion mentions that the model was not externally validated, which is a key limitation.
While the authors suggest the need for further research, they could have also explored potential
methods for external validation or described the steps that would be taken to ensure that the
predictive model holds up in different clinical settings.
-The discussion briefly mentions that the study's generalizability is limited by its single-center
and small sample design. While this is a valid concern, more attention could be given to the
potential strategies for overcoming this limitation. For instance, how can the model be adapted
for diverse populations, or how can future multicenter studies address the issues of patient
heterogeneity and sample bias?
-Although the discussion introduces the nomogram as a clinical tool, it would benefit from more
practical advice on how clinicians can implement this predictive model in real-world settings.
For example, details on how the nomogram can be integrated into routine clinical workflows,
such as through electronic health records, would enhance its applicability.
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: 28 Mar 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-While the role of inflammatory cytokines is mentioned, a more detailed exploration of how
these cytokines contribute specifically to infection risk in liver failure patients would strengthen
the theoretical foundation of the study. Understanding the precise mechanisms by which these
markers influence infection susceptibility could provide deeper insights.
-Although the study includes a broad range of variables, some potential confounding factors
(e.g., concurrent use of medications, immunosuppressive therapy, or comorbid conditions like
diabetes) may not be sufficiently controlled for. Addressing these factors in the analysis could
further refine the predictive model.
-The PNI calculation is based on serum albumin levels and peripheral lymphocyte count, which
is a straightforward and widely used method. However, given that liver failure often involves
complex alterations in protein metabolism and immune function, it would be valuable to include
additional biomarkers or other clinical factors that may better capture the nutritional and immune
status of these patients.
-There is a potential risk of overfitting the model given the number of variables included in
the multivariate analysis. While the study performs collinearity analysis to address this issue,
additional steps, such as cross-validation, could help reduce the risk of overfitting and improve
the model's generalizability.
-While the nomogram is a useful tool, its clinical applicability could be further discussed. It
would be important to explain how easily clinicians could use the nomogram in routine practice.
For instance, incorporating a web-based or mobile application version could enhance its usability
for healthcare providers.
-The study focuses on infection as the main outcome, but it would be helpful to clarify how
the infection diagnoses were standardized across the cohort. The study also does not provide
external validation or testing on a separate cohort, which would be important to establish the
model’s performance beyond the internal dataset.
-While the discussion links CRP, sTREM-1, and SIRI to infection risk, there is still room
to delve deeper into the specific mechanisms by which these markers influence infection
susceptibility in liver failure patients. While the role of sTREM-1 and CRP in immune activation
is addressed, further exploration of how these markers interact with liver function and immune
cells, particularly in liver failure, would strengthen the argument.
-The discussion emphasizes the importance of the APACHE II score as a predictor of infection
risk. While this is important, the discussion could have placed more focus on the novel
contributions of the study, particularly the combination of PNI and inflammatory markers. The
APACHE II score is already widely recognized in the clinical setting, so further exploration
of how the PNI and cytokine markers provide additional predictive value would make the
discussion more unique and focused on the study's innovations.
-While the study does a good job of linking predictors with infection risk, the potential
confounding factors that might influence infection outcomes (e.g., comorbidities like diabetes,
use of immunosuppressive drugs, and concurrent infections) could be more explicitly discussed.
Addressing how these factors were controlled for or their potential impact on the results would
provide a clearer understanding of the robustness of the findings.
-The discussion mentions that the model was not externally validated, which is a key limitation.
While the authors suggest the need for further research, they could have also explored potential
methods for external validation or described the steps that would be taken to ensure that the
predictive model holds up in different clinical settings.
-The discussion briefly mentions that the study's generalizability is limited by its single-center
and small sample design. While this is a valid concern, more attention could be given to the
potential strategies for overcoming this limitation. For instance, how can the model be adapted
for diverse populations, or how can future multicenter studies address the issues of patient
heterogeneity and sample bias?
-Although the discussion introduces the nomogram as a clinical tool, it would benefit from more
practical advice on how clinicians can implement this predictive model in real-world settings.
For example, details on how the nomogram can be integrated into routine clinical workflows,
such as through electronic health records, would enhance its applicability.
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: 28 Mar 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-While the role of inflammatory cytokines is mentioned, a more detailed exploration of how
these cytokines contribute specifically to infection risk in liver failure patients would strengthen
the theoretical foundation of the study. Understanding the precise mechanisms by which these
markers influence infection susceptibility could provide deeper insights.
-Although the study includes a broad range of variables, some potential confounding factors
(e.g., concurrent use of medications, immunosuppressive therapy, or comorbid conditions like
diabetes) may not be sufficiently controlled for. Addressing these factors in the analysis could
further refine the predictive model.
-The PNI calculation is based on serum albumin levels and peripheral lymphocyte count, which
is a straightforward and widely used method. However, given that liver failure often involves
complex alterations in protein metabolism and immune function, it would be valuable to include
additional biomarkers or other clinical factors that may better capture the nutritional and immune
status of these patients.
-There is a potential risk of overfitting the model given the number of variables included in
the multivariate analysis. While the study performs collinearity analysis to address this issue,
additional steps, such as cross-validation, could help reduce the risk of overfitting and improve
the model's generalizability.
-While the nomogram is a useful tool, its clinical applicability could be further discussed. It
would be important to explain how easily clinicians could use the nomogram in routine practice.
For instance, incorporating a web-based or mobile application version could enhance its usability
for healthcare providers.
-The study focuses on infection as the main outcome, but it would be helpful to clarify how
the infection diagnoses were standardized across the cohort. The study also does not provide
external validation or testing on a separate cohort, which would be important to establish the
model’s performance beyond the internal dataset.
-While the discussion links CRP, sTREM-1, and SIRI to infection risk, there is still room
to delve deeper into the specific mechanisms by which these markers influence infection
susceptibility in liver failure patients. While the role of sTREM-1 and CRP in immune activation
is addressed, further exploration of how these markers interact with liver function and immune
cells, particularly in liver failure, would strengthen the argument.
-The discussion emphasizes the importance of the APACHE II score as a predictor of infection
risk. While this is important, the discussion could have placed more focus on the novel
contributions of the study, particularly the combination of PNI and inflammatory markers. The
APACHE II score is already widely recognized in the clinical setting, so further exploration
of how the PNI and cytokine markers provide additional predictive value would make the
discussion more unique and focused on the study's innovations.
-While the study does a good job of linking predictors with infection risk, the potential
confounding factors that might influence infection outcomes (e.g., comorbidities like diabetes,
use of immunosuppressive drugs, and concurrent infections) could be more explicitly discussed.
Addressing how these factors were controlled for or their potential impact on the results would
provide a clearer understanding of the robustness of the findings.
-The discussion mentions that the model was not externally validated, which is a key limitation.
While the authors suggest the need for further research, they could have also explored potential
methods for external validation or described the steps that would be taken to ensure that the
predictive model holds up in different clinical settings.
-The discussion briefly mentions that the study's generalizability is limited by its single-center
and small sample design. While this is a valid concern, more attention could be given to the
potential strategies for overcoming this limitation. For instance, how can the model be adapted
for diverse populations, or how can future multicenter studies address the issues of patient
heterogeneity and sample bias?
-Although the discussion introduces the nomogram as a clinical tool, it would benefit from more
practical advice on how clinicians can implement this predictive model in real-world settings.
For example, details on how the nomogram can be integrated into routine clinical workflows,
such as through electronic health records, would enhance its applicability.
assistant