PONE-D-22-28067Extracting relevant predictive variables for COVID-19 severity prognosis: An exhaustive comparison of feature selection techniquesPLOS ONE

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Universitari i Polit`ecnic La Fe de Valencia, Galdakao-Usansoloko Unibertsitate

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: No

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This paper by Hayet-Otero et al. proposes a complex mathematical model for a fully data driven approach, assessing the relevant predictors of severity in Covid-19 disease. Although this topic looks interesting, the method used to fill biofactors and clinical data into the mathematical model could result questionable, in that it did not consider some previous reports on prognostic predictors of both community acquired pneumonia (CAP) and Covid-19 disease.

Specific Comments

1. Neutrophil-to-Lymphocyte Ratio was shown to predict intra-hospital mortality in patients with CAP (Cataudella et al., J Am Ger Soc 2017), performing better than PSI and CURB-65. This evidence further strenghtened that combined, but not separate, evaluation of neutrophils and lymphocytes should be considered mandatory to improve prognosis prediction. This is due to the fact that in patients with worse prognosis neutrophilia runs in parallel with lymphopenia.It does not seem that Authors follow this strategy, since they used absolute values of both cells separately.

2. A poorer prognosis of Covid-19 patients strictly depends on an early fall in PaO2/FiO2, that was recently shown to be inversely related to NLR (Regolo et al., J Clin Med 2022). It looks as if Authors filled in the model only O2 levels. What about PaO2/FiO2?

3. Systemic Inflammation Index (SII), a combined index using neutropohils, platelets and lymphocytes, was shown to be another important predictor of intubation in Covid-19 patients (Muhammed et al., Pathogens 2021). Did Authors consideri t?

4. MECOR is an emerging score to recognize a possible viral etiology of CAP, providing a prompt Covid-19 triage (Sambataro et al., Diagnostics 2020), helping to prevent complications.

5. So far, Covid 19 disease is considered the result of a derangement between innate and adaptive immunity (for review, see Buonacera et al., Int J Mol Sci 2022). This is a furher reason for paying attention to the strategy used in filling biofactors and clinical data into the mathematical model (see points 1,2,3)

Reviewer #2: 1.Introduction section needs to be re-written to improve its quality and readability.

2.The literature has to be strongly updated with some relevant and recent papers focused on the fields dealt with in the manuscript.

3. Clinical data can be summarized in a table for better understanding.

4. Explain in detail what type of hyperpaprameter selection methodology is used for selecting best parameters for each ML algorithms.

5. Explain why the current method was selected for the study, its importance and compare with traditional methods.

6.Authors are suggested to include more discussion on the results and also include some explanation regarding the justification to support why the proposed method is better in comparison towards other methods

7.Does this kind of study have never attempted before? Justify this statement and give an appropriate explanation to do so in this paper.

8. There are several machine learning algorithms what is the reason for using onlt LR, Ridge and KNN?.

9. Mentioning number of features before and after using FS algorithms in table can be a simplified version of summarizing.

10. Overall the presentation and provided results are satifactory.

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Reviewer #1: No

Reviewer #2: Yes: Usha Moorthy

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Extracting relevant predictive variables for COVID-19 severity prognosis: An exhaustive comparison of feature selection techniques

PONE-D-22-28067R1

Dear Dr. García-García,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Sathishkumar V E

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: (No Response)

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: (No Response)

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: (No Response)

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: (No Response)

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This Reviewer has no further concern, since all comments were fulfilled. This paper has been improved.

Reviewer #2: (No Response)

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Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: Usha Moorthy

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