Review History


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Summary

  • The initial submission of this article was received on May 27th, 2024 and was peer-reviewed by 3 reviewers and the Academic Editor.
  • The Academic Editor made their initial decision on August 28th, 2024.
  • The first revision was submitted on September 22nd, 2024 and was reviewed by 3 reviewers and the Academic Editor.
  • A further revision was submitted on October 30th, 2024 and was reviewed by the Academic Editor.
  • A further revision was submitted on November 11th, 2024 and was reviewed by 1 reviewer and the Academic Editor.
  • The article was Accepted by the Academic Editor on December 23rd, 2024.

Version 0.4 (accepted)

· Dec 23, 2024 · Academic Editor

Accept

Dear authors, congratulations! I am now accepting your manuscript for publication.

[# PeerJ Staff Note - this decision was reviewed and approved by Konstantinos Kormas, a PeerJ Section Editor covering this Section #]

Reviewer 3 ·

Basic reporting

no comment

Experimental design

no comment

Validity of the findings

no comment

Additional comments

no comment

Version 0.3

· Oct 31, 2024 · Academic Editor

Minor Revisions

Dear authors, i think that your response to authors appears to miss the reviewer's point namely when it comes to the significance levels. Although you correctly state that your analysis involves linear multiple regression rather than ANOVA, this does NOT necessarily negate the need for adjusting significance levels when multiple statistical tests are performed. In multiple regression, if the model includes several predictors and each predictor is tested individually (e.g., testing each coefficient's significance), then multiple hypothesis tests are indeed conducted. In such cases, multiple testing adjustments (e.g., Bonferroni correction, Holm-Bonferroni) are commonly applied to avoid inflating the risk of Type I errors (false positives) due to multiple comparisons. And i do think that that was the reviewer's concern. I think that there is a need for clarification on whether multiple tests were performed on individual predictors and, if so, whether you applied any adjustments to address potential inflation of Type I error - which I think you did not. But why? This needs to be explicitly clarified.
And may be correlated with the other doubt reported by the other reviewer about the Kaplan-Meier Curves:
statistical analysis section contain some methodological ambiguities and potential misunderstandings that could benefit from further clarification. The authors’ addition of the purpose, “to evaluate the influence of different risk factors on the survival of patients,” is an improvement but lacks detail. To address the reviewer’s concern more thoroughly, the authors could specify: (1) Which risk factors they analyzed in the KM curves, and (2) How these risk factors were stratified (e.g., grouping patients by diabetes status or other risk factors of interest).
Adding such details would clarify the clinical relevance of the KM curves and the specific influence of each factor on survival.
So, since linear regression was applied to “identify factors associated with death,” the authors need to clarify whether they performed multiple tests on each predictor variable in the model. This would typically necessitate adjusting the p-value thresholds to control for Type I error due to multiple comparisons (e.g., Bonferroni or Holm adjustments).
Some additional notes that may help to clarify in your statistical analysis section:
(1) the description is unclear on whether univariate or multivariate linear regression was used. If multiple independent variables were included simultaneously (i.e., multivariate regression), this should be specified. Also, shouldn't the authors have used logistic regression for binary outcomes (like death) instead of linear regression, which is generally inappropriate for categorical outcomes?
(2) ROC curves - it should be clarified whether this was conducted as a standalone analysis or as part of validating the regression model.
In conclusion, we need you to specify the stratification factors in the Kaplan-Meier curves; why did you use than linear regression over logistic regression for binary outcomes like mortality?; and address the need for adjustment if multiple independent tests were conducted.
This feedback will improve the manuscript’s statistical clarity and reduce potential misinterpretations by readers.

Version 0.2

· Oct 22, 2024 · Academic Editor

Minor Revisions

Dear authors, please revise your methodology, thoroughly namely the statistical analysis.

Reviewer 1 ·

Basic reporting

No comments

Experimental design

Line 132 "The Kaplan-Meier survival curves were constructed accordingly." The purpose of KM curve is not described.

Validity of the findings

Citations needed for line 69-82.

Additional comments

No comments

·

Basic reporting

No comment.

Experimental design

No comment.

Validity of the findings

No comment.

Additional comments

Thanks for addressing my concerns.

Reviewer 3 ·

Basic reporting

"no comment"

Experimental design

Significance levels seem to be unadjusted for multiple comparisons.

Validity of the findings

"no comment"

Version 0.1 (original submission)

· Aug 28, 2024 · Academic Editor

Major Revisions

Dear authors,

Thank you for submitting your manuscript to PeerJ. We have received thorough reviews from three experts in the field. After careful consideration, I believe the manuscript has potential, but significant revisions are necessary before it can be considered for publication.

While PeerJ does not consider novelty a key element for publications acceptance, i suggest a clear justification of your study, how does it contribute to the scientific literature, particularly in light of the substantial amount of existing research on COVID-19 and diabetes in China. Your introduction should clearly outline the specific research gaps your study addresses and emphasize how your findings contribute new insights to the field.

Importantly, several methodological issues require clarification, in particular, cohort selection and confounding and statistical analysis.

Data presentation also requires some polishing, namely p-values origin/presentation and any sex differences.

In general aspects: Review the manuscript for consistent verb tense, technical terminology, and accuracy in reporting percentages and statistical results & ensure all citations are correctly formatted and referenced in the text.

Many thanks.

Reviewer 1 ·

Basic reporting

Line 70 - please specify what exactly is "our hospital" and the uniqueness of "our hospital"

Experimental design

1. In cohort selection, how do you know if the hospitalization is due to COVID or not?
2. Kaplan Meier curve was drawn but not described in Methods section
3. ROC curve does not seem to be the best way to identify risk factors.
4. How is confounding controlled? All analyses included seem to be unadjusted.

Validity of the findings

Given that there have been plenty of research articles published regarding COVID and diabetes in China, the novelty of this article should be further addressed.

·

Basic reporting

1. Please maintain consistent verb tense throughout the manuscript.
2. Line 68: The statement that studies on the association between COVID-19 and diabetes are rare in China is not accurate. One of the earliest studies on this topic originated in China, and numerous subsequent studies have followed. Given that the findings of this study align with well-established research, a stronger rationale and emphasis on novelty are needed in the introduction to justify the objectives of this research.
3. The introduction should summarize existing knowledge regarding the association between COVID-19 and diabetes. It should then clearly identify the specific research gaps this study aims to address, highlighting the unique contributions it will make to the existing body of knowledge.

Experimental design

1. Please explain whether the 500 participants were consecutively enrolled or randomly selected from the total number of patients admitted to the center between December 2022 and February 2023.
2. Please report the number of patients excluded from the study and provide specific reasons for exclusion. A flowchart for this would be nice.
3. Please cite a reference for the definition of diabetes.
4. What does the gestational period mean?
5. Please specify whether the data were collected from electronic health records.
6. Please explain why regression analyses were not used to examine the association between COVID-19, diabetes, and mortality, especially when investigating mortality differences between those with and without diabetes.

Validity of the findings

The findings of this study have been well-established by previous studies. It is not clear what is new.

Reviewer 3 ·

Basic reporting

This is an original study, that investigates the impact of diabetes on mortality and adverse outcomes in patients admitted to a central hospital with COVID-19. It is a retrospective cohort study which included 500 patients, 214 of them with diabetes.
The authors compare the characteristics and outcomes of COVID -19 survivors and non-survivors; of patients with and without diabetes; and of survivor and non-survivor patients with diabetes. Kaplan-Meier analysis for death of patients with diabetes in the 6 months after COVID-19 infection is also presented.
Results revealed a higher mortality rate among patients with diabetes at discharge and after discharge. Several parameters were analyzed and revealed to be significantly different in patients with diabetes, and between survivors and non-survivors. Patients with diabetes and COVID-19 infection had more adverse outcomes and higher mortality rate. Also, in patients with diabetes, poor glycemic control and diabetes complications were significantly related to higher mortality rates.

The article is clear and unambiguous. Adequate structure and context is provided, along with relevant results. Technically correct English is used. Only some aspects are mentioned that the authors may improve.

In the discussion section, the authors could comment on the 6 months’ after COVID-19 infection survival and the temporal evolution observed for that survival analysis.

Experimental design

Line 84
“(…) subjects with HbA1c ≥ 6.5% were considered to have diabetes”. It is a plausible decision. Although those hyperglycemias may also relate to outcomes, it is possible that some of the patients did not have previous diabetes, but transient hyperglycemia related to the COVID-19 infection, or its treatment and it might be considered in the interpretation of the results.

Line 86
The authors mention that “500 patients were included in this study.” Was it the exact number of patients that fulfilled the inclusion criteria? Or did the authors select the first 500 patients?

Validity of the findings

An extensive set of variables are used. Statistical correction factors due to the fact that multiple comparisons are used may be important to confirm statistically significant differences. Are the p results corrected for multiple comparisons (for instance in table 1, 2 and 3)? If so, please refer it in the methodology.

Line 166
The proportion of female was higher in non-survivors. However, this could be related to older age in female patients with diabetes. Was mean age different between genders? The authors could test if the higher proportion of female patients in non-survivors, was independent of age. If so, please state it.
Eventually, test some differences between genders.

Table 3
The percentage of males that survived and did not survive is shown. But the percentage of females that survived or not is not in the table.

Lines 180-183
In the chapter comparing characteristics and outcomes of survivors and non-survivors of COVID-19 patients, there is the following sentence: “Besides, we found that the mortality rate of patients who received intensive care treatment (72.7%) and patients who needed mechanical ventilation treatment (66.7%) were much higher than that in patients without intensive care treatment (25.1%) or mechanical ventilation treatment (24.0%) (p < 0.01).”
Please confirm the percentages, as they do not match those in the tables. Also, it is not clear if the sentence refers only to patients with diabetes or all the patients.

Additional comments

Line 107
Instead of “receiver’s work characteristic (ROC) curve”, maybe the authors mean “Receiver Operating Characteristics”.

Line 154
Maybe the authors could add the value in dollars, and/or another monetary unit so that the paper could be more clearly interpreted all around the world.

Please review the text for small typos, the introduction of references along the paper, and the meaning of abbreviations the first time they are used. Examples:
Line 151 “COVID-19”
Line 185 “asscociations”
Line 228 “(…) (F et al., 2021; A et al., 2022a).”
Line 246 “(…) (W, A & A, 2021).”
Line 258 “Oudit et al. found that (…)”
Line 264 “LTB4”
Line 300-301 “diabetes can causes ketoacidosis”

Please review references list regarding its alphabetical order.

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