Title: Serum Soluble T-Cell Immunoglobulin Mucin Domain-3 as an Early Predictive Marker
for Severity of Acute Pancreatitis This study was a retrospective analysis
Version: 3 Date: 30 Mar 2022
Reviewer's report:
The authors performed an analysis of sTIM-3 on the patients with acute pancreatitis. Some
comments about the study:
1. The title “Serum Soluble T-Cell Immunoglobulin Mucin Domain-3 as an Early Predictive
Marker for Severity of Acute Pancreatitis” was supposed to investigate the effect of serum
soluble T-cell immunoglobulin (X or the independent variable) on the result of the severity of
acute pancreatitis (Y or the dependent variable). However, the model used the sTIM-3 as the Y
and AP severity as the X, and readers could not evaluate how sTIM-3 affects the AP severity in
the regression model. Therefore, the authors may consider regressing the sTIM-3 level (with a
cut-off) on the presence of SAP (binary outcome) in logistic regression.
2. To determine the performance of a biomarker, we usually need to know the area under the
curve (AUC) in the receiver operating curve (ROC) of the sTIM-3 for important outcomes (e.g.
SAP or 30-day mortality). For biomarkers to be helpful in a clinical scenario, we can determine a
cut-off on the ROC. Once we have the cut-off level, you can evaluate this biomarker’s sensitivity
and specificity. We can also put the biomarker as a binary covariate together with APACHE#or
other covariates in the regression model to predict SAP or mortality in logistic regression(1).
3. In the abstract on page 2, line 9, “After adjustments for potential mediators,…”. The term
“mediators” needs to be revised. In the regression model, we usually adjust confounders or
covariates. Adjustment of “mediators” should be cautious and usually are avoided because the
inappropriate adjustment could eliminate the actual effects of interested factors and result in
misinterpretation.
4. In the discussion section, the authors will be more persuasive to mention the most significant
finding of this study in the initial paragraph.
The major limitation of this study is the lack of detailed performance of the biomarker
and reasonable multivariate regression. The authors did however has a nice material for the
retrospective analysis. The manuscript can be re-evaluated if the above comments are addressed.
1. Li YM, Li Y, Yan L, et al. Assessment of serum Tim-3 and Gal-9 levels in predicting the risk
of infection after kidney transplantation. International Immunopharmacology 2019;75:105803.
Title: Serum Soluble T-Cell Immunoglobulin Mucin Domain-3 as an Early Predictive Marker
for Severity of Acute Pancreatitis This study was a retrospective analysis
Version: 3 Date: 30 Mar 2022
Reviewer's report:
The authors performed an analysis of sTIM-3 on the patients with acute pancreatitis. Some
comments about the study:
1. The title “Serum Soluble T-Cell Immunoglobulin Mucin Domain-3 as an Early Predictive
Marker for Severity of Acute Pancreatitis” was supposed to investigate the effect of serum
soluble T-cell immunoglobulin (X or the independent variable) on the result of the severity of
acute pancreatitis (Y or the dependent variable). However, the model used the sTIM-3 as the Y
and AP severity as the X, and readers could not evaluate how sTIM-3 affects the AP severity in
the regression model. Therefore, the authors may consider regressing the sTIM-3 level (with a
cut-off) on the presence of SAP (binary outcome) in logistic regression.
2. To determine the performance of a biomarker, we usually need to know the area under the
curve (AUC) in the receiver operating curve (ROC) of the sTIM-3 for important outcomes (e.g.
SAP or 30-day mortality). For biomarkers to be helpful in a clinical scenario, we can determine a
cut-off on the ROC. Once we have the cut-off level, you can evaluate this biomarker’s sensitivity
and specificity. We can also put the biomarker as a binary covariate together with APACHE#or
other covariates in the regression model to predict SAP or mortality in logistic regression(1).
3. In the abstract on page 2, line 9, “After adjustments for potential mediators,…”. The term
“mediators” needs to be revised. In the regression model, we usually adjust confounders or
covariates. Adjustment of “mediators” should be cautious and usually are avoided because the
inappropriate adjustment could eliminate the actual effects of interested factors and result in
misinterpretation.
4. In the discussion section, the authors will be more persuasive to mention the most significant
finding of this study in the initial paragraph.
The major limitation of this study is the lack of detailed performance of the biomarker
and reasonable multivariate regression. The authors did however has a nice material for the
retrospective analysis. The manuscript can be re-evaluated if the above comments are addressed.
1. Li YM, Li Y, Yan L, et al. Assessment of serum Tim-3 and Gal-9 levels in predicting the risk
of infection after kidney transplantation. International Immunopharmacology 2019;75:105803.
Please review this paper.
user
Title: Serum Soluble T-Cell Immunoglobulin Mucin Domain-3 as an Early Predictive Marker
for Severity of Acute Pancreatitis This study was a retrospective analysis
Version: 3 Date: 30 Mar 2022
Reviewer's report:
The authors performed an analysis of sTIM-3 on the patients with acute pancreatitis. Some
comments about the study:
1. The title “Serum Soluble T-Cell Immunoglobulin Mucin Domain-3 as an Early Predictive
Marker for Severity of Acute Pancreatitis” was supposed to investigate the effect of serum
soluble T-cell immunoglobulin (X or the independent variable) on the result of the severity of
acute pancreatitis (Y or the dependent variable). However, the model used the sTIM-3 as the Y
and AP severity as the X, and readers could not evaluate how sTIM-3 affects the AP severity in
the regression model. Therefore, the authors may consider regressing the sTIM-3 level (with a
cut-off) on the presence of SAP (binary outcome) in logistic regression.
2. To determine the performance of a biomarker, we usually need to know the area under the
curve (AUC) in the receiver operating curve (ROC) of the sTIM-3 for important outcomes (e.g.
SAP or 30-day mortality). For biomarkers to be helpful in a clinical scenario, we can determine a
cut-off on the ROC. Once we have the cut-off level, you can evaluate this biomarker’s sensitivity
and specificity. We can also put the biomarker as a binary covariate together with APACHE#or
other covariates in the regression model to predict SAP or mortality in logistic regression(1).
3. In the abstract on page 2, line 9, “After adjustments for potential mediators,…”. The term
“mediators” needs to be revised. In the regression model, we usually adjust confounders or
covariates. Adjustment of “mediators” should be cautious and usually are avoided because the
inappropriate adjustment could eliminate the actual effects of interested factors and result in
misinterpretation.
4. In the discussion section, the authors will be more persuasive to mention the most significant
finding of this study in the initial paragraph.
The major limitation of this study is the lack of detailed performance of the biomarker
and reasonable multivariate regression. The authors did however has a nice material for the
retrospective analysis. The manuscript can be re-evaluated if the above comments are addressed.
1. Li YM, Li Y, Yan L, et al. Assessment of serum Tim-3 and Gal-9 levels in predicting the risk
of infection after kidney transplantation. International Immunopharmacology 2019;75:105803.
assistant