Title: Clinical Data Based XGBoost Algorithm for Infection Risk Prediction of Patients
with Decompensated Cirrhosis: A 10-year (2012-2021) Multicenter Retrospective Case-control
Study
Version: 2 Date: 20 Jul 2023
Reviewer's report:
1. Without page numbers, the reviewer's comments are difficult to indicate.
2. On page 8, “In training set, the simple-tree XGBoost model with 6 selected features revealed
superior performance compared to the LR with all 11 features (AUROC: 0.971 vs 0.869,
P<0.001) or 6 features (AUROC: 0.971 vs 0.864, P<0.001) (Figure 4)." The results suggest that
5 out of 11 features do not add more predictive ability since the AUROC is 0.971 in both models.
The authors did not discuss in detail the five useless features."
3. In Table 3: the sensitivity of logistic-6 is higher than logistic-11. Why do more predictors
reduce sensitivity?
4. What is the simple-tree XGBoost? What does it look like? I did not see a reference for such
method. This particular name is rare since XGBoosting machine has numerous trees, not just
one.
5. It is a retrospective study, and the study lasts ten years. What is the time between the
predictors measured in Table 1? Do all patients use these measures simultaneously and then
predict infection outcomes after ten years? This study did reveal such temporal information.
Thus, the analyses and results raise concerns regarding validity.
6. The reference for the XGBoosting machine is “21. Hou N, Li M, He L, Xie B, Wang L,
Zhang R, et al. Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine
learning approach using XGboost. Journal of Translational Medicine. 2020;18(1):462. It is not
a common way to cite the XGBoosting machine. The authors are not familiar with machine
learning models.
7. Do patients present identical characteristics and the distribution of the outcome? The authors
did not examine the center by predictor interactions. In addition, the cluster effect due to different
centers is likely and should be appropriately handled.
8. What does the p-value stand for in Table 2? Does it mean the comparison between the three
columns or just the last two columns?
9. The 11 features are GIB, HF, TP, TB, hemoglobin, Na, ALB, PTA, BUN, WBC count, and
NLR (Figure 1). However, the sex variable was excluded from the list. It raises critical concerns
regarding the analysis and results since sex is a highly significant predictor for infection in Table
2.
Title: Clinical Data Based XGBoost Algorithm for Infection Risk Prediction of Patients
with Decompensated Cirrhosis: A 10-year (2012-2021) Multicenter Retrospective Case-control
Study
Version: 2 Date: 20 Jul 2023
Reviewer's report:
1. Without page numbers, the reviewer's comments are difficult to indicate.
2. On page 8, “In training set, the simple-tree XGBoost model with 6 selected features revealed
superior performance compared to the LR with all 11 features (AUROC: 0.971 vs 0.869,
P<0.001) or 6 features (AUROC: 0.971 vs 0.864, P<0.001) (Figure 4)." The results suggest that
5 out of 11 features do not add more predictive ability since the AUROC is 0.971 in both models.
The authors did not discuss in detail the five useless features."
3. In Table 3: the sensitivity of logistic-6 is higher than logistic-11. Why do more predictors
reduce sensitivity?
4. What is the simple-tree XGBoost? What does it look like? I did not see a reference for such
method. This particular name is rare since XGBoosting machine has numerous trees, not just
one.
5. It is a retrospective study, and the study lasts ten years. What is the time between the
predictors measured in Table 1? Do all patients use these measures simultaneously and then
predict infection outcomes after ten years? This study did reveal such temporal information.
Thus, the analyses and results raise concerns regarding validity.
6. The reference for the XGBoosting machine is “21. Hou N, Li M, He L, Xie B, Wang L,
Zhang R, et al. Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine
learning approach using XGboost. Journal of Translational Medicine. 2020;18(1):462. It is not
a common way to cite the XGBoosting machine. The authors are not familiar with machine
learning models.
7. Do patients present identical characteristics and the distribution of the outcome? The authors
did not examine the center by predictor interactions. In addition, the cluster effect due to different
centers is likely and should be appropriately handled.
8. What does the p-value stand for in Table 2? Does it mean the comparison between the three
columns or just the last two columns?
9. The 11 features are GIB, HF, TP, TB, hemoglobin, Na, ALB, PTA, BUN, WBC count, and
NLR (Figure 1). However, the sex variable was excluded from the list. It raises critical concerns
regarding the analysis and results since sex is a highly significant predictor for infection in Table
2.
Please review this paper.
user
Title: Clinical Data Based XGBoost Algorithm for Infection Risk Prediction of Patients
with Decompensated Cirrhosis: A 10-year (2012-2021) Multicenter Retrospective Case-control
Study
Version: 2 Date: 20 Jul 2023
Reviewer's report:
1. Without page numbers, the reviewer's comments are difficult to indicate.
2. On page 8, “In training set, the simple-tree XGBoost model with 6 selected features revealed
superior performance compared to the LR with all 11 features (AUROC: 0.971 vs 0.869,
P<0.001) or 6 features (AUROC: 0.971 vs 0.864, P<0.001) (Figure 4)." The results suggest that
5 out of 11 features do not add more predictive ability since the AUROC is 0.971 in both models.
The authors did not discuss in detail the five useless features."
3. In Table 3: the sensitivity of logistic-6 is higher than logistic-11. Why do more predictors
reduce sensitivity?
4. What is the simple-tree XGBoost? What does it look like? I did not see a reference for such
method. This particular name is rare since XGBoosting machine has numerous trees, not just
one.
5. It is a retrospective study, and the study lasts ten years. What is the time between the
predictors measured in Table 1? Do all patients use these measures simultaneously and then
predict infection outcomes after ten years? This study did reveal such temporal information.
Thus, the analyses and results raise concerns regarding validity.
6. The reference for the XGBoosting machine is “21. Hou N, Li M, He L, Xie B, Wang L,
Zhang R, et al. Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine
learning approach using XGboost. Journal of Translational Medicine. 2020;18(1):462. It is not
a common way to cite the XGBoosting machine. The authors are not familiar with machine
learning models.
7. Do patients present identical characteristics and the distribution of the outcome? The authors
did not examine the center by predictor interactions. In addition, the cluster effect due to different
centers is likely and should be appropriately handled.
8. What does the p-value stand for in Table 2? Does it mean the comparison between the three
columns or just the last two columns?
9. The 11 features are GIB, HF, TP, TB, hemoglobin, Na, ALB, PTA, BUN, WBC count, and
NLR (Figure 1). However, the sex variable was excluded from the list. It raises critical concerns
regarding the analysis and results since sex is a highly significant predictor for infection in Table
2.
assistant