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: 22 Jul 2023
Reviewer's report:
The main goal of this study was to appraise effective predictors for infection in patients with
decompensated cirrhosis (DC) by using XGBoost algorithm in a retrospective case-control
study. The results showed that the simple-tree XGBoost predictive model developed based on
a minimal amount of clinical data available to DC patients with restricted medical resources
could help primary healthcare practitioners promptly identify potential infection. The whole
methodology and research framework were complete and specific. But certain aspects are still
worthy of further discussion.
The following advices for amendments were listed below.
1. This study uses the correlation analysis based on the cases with confirmed diagnoses. It is
a good choice to analyze the confirmed diagnosis groups, but there may be selection errors.
This result only shows that the six indicators obtained have excellent predictions, but it does not
explain these values. Clinically, they may go on different time axes, and there will be different
data. The authors did not explain that the figures he chose were obtained during the period of
the case, so this study may have an overly idealized analysis and induction.
2. Relevant research results can only prove that the XGBoost method used may be better than
the current linear regression analysis. In clinical practice, there may be more verification of big
data and do not use it for verification on selected cases. Maybe better analysis results can be
obtained. At present, it can only be regarded as a pilot study.
3. This study does not address the level of hospital chosen and whether they compare differently
in terms of diagnostic agreement and prognosis for this disease.
4. In the discussion part of this study, there is no comparison of relevant clinical indicators
and multiple related indicators presented in many literatures. It only shows the difference
between different analysis methods. In this way, in many simulation studies, relevant analysis
and comparison can be carried out. Therefore, the overall practical value of this study is not high
at present. However, if you really use large community screening in the future, if you can get
reliable data, you may consider using it.
5. Please explains the limitations of this study in the discussion. and the future work.
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: 22 Jul 2023
Reviewer's report:
The main goal of this study was to appraise effective predictors for infection in patients with
decompensated cirrhosis (DC) by using XGBoost algorithm in a retrospective case-control
study. The results showed that the simple-tree XGBoost predictive model developed based on
a minimal amount of clinical data available to DC patients with restricted medical resources
could help primary healthcare practitioners promptly identify potential infection. The whole
methodology and research framework were complete and specific. But certain aspects are still
worthy of further discussion.
The following advices for amendments were listed below.
1. This study uses the correlation analysis based on the cases with confirmed diagnoses. It is
a good choice to analyze the confirmed diagnosis groups, but there may be selection errors.
This result only shows that the six indicators obtained have excellent predictions, but it does not
explain these values. Clinically, they may go on different time axes, and there will be different
data. The authors did not explain that the figures he chose were obtained during the period of
the case, so this study may have an overly idealized analysis and induction.
2. Relevant research results can only prove that the XGBoost method used may be better than
the current linear regression analysis. In clinical practice, there may be more verification of big
data and do not use it for verification on selected cases. Maybe better analysis results can be
obtained. At present, it can only be regarded as a pilot study.
3. This study does not address the level of hospital chosen and whether they compare differently
in terms of diagnostic agreement and prognosis for this disease.
4. In the discussion part of this study, there is no comparison of relevant clinical indicators
and multiple related indicators presented in many literatures. It only shows the difference
between different analysis methods. In this way, in many simulation studies, relevant analysis
and comparison can be carried out. Therefore, the overall practical value of this study is not high
at present. However, if you really use large community screening in the future, if you can get
reliable data, you may consider using it.
5. Please explains the limitations of this study in the discussion. and the future work.
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: 22 Jul 2023
Reviewer's report:
The main goal of this study was to appraise effective predictors for infection in patients with
decompensated cirrhosis (DC) by using XGBoost algorithm in a retrospective case-control
study. The results showed that the simple-tree XGBoost predictive model developed based on
a minimal amount of clinical data available to DC patients with restricted medical resources
could help primary healthcare practitioners promptly identify potential infection. The whole
methodology and research framework were complete and specific. But certain aspects are still
worthy of further discussion.
The following advices for amendments were listed below.
1. This study uses the correlation analysis based on the cases with confirmed diagnoses. It is
a good choice to analyze the confirmed diagnosis groups, but there may be selection errors.
This result only shows that the six indicators obtained have excellent predictions, but it does not
explain these values. Clinically, they may go on different time axes, and there will be different
data. The authors did not explain that the figures he chose were obtained during the period of
the case, so this study may have an overly idealized analysis and induction.
2. Relevant research results can only prove that the XGBoost method used may be better than
the current linear regression analysis. In clinical practice, there may be more verification of big
data and do not use it for verification on selected cases. Maybe better analysis results can be
obtained. At present, it can only be regarded as a pilot study.
3. This study does not address the level of hospital chosen and whether they compare differently
in terms of diagnostic agreement and prognosis for this disease.
4. In the discussion part of this study, there is no comparison of relevant clinical indicators
and multiple related indicators presented in many literatures. It only shows the difference
between different analysis methods. In this way, in many simulation studies, relevant analysis
and comparison can be carried out. Therefore, the overall practical value of this study is not high
at present. However, if you really use large community screening in the future, if you can get
reliable data, you may consider using it.
5. Please explains the limitations of this study in the discussion. and the future work.
assistant