PONE-D-21-02959

Profiles and outcomes in patients with COVID-19 admitted to wards of a French oncohematological hospital: a clustering approach

PLOS ONE

Dear Dr. Anne Bergeron,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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ACADEMIC EDITOR: The reviewers have raised a number of points which we believe major modifications are necessary to improve the manuscript, taking into account the reviewers' remarks. Please consider and address each of the comments raised by the reviewers before resubmitting the manuscript. This letter should not be construed as implying acceptance, as a revised version will be subject to re-review.

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Kind regards,

Wisit Cheungpasitporn, MD

Academic Editor

PLOS ONE

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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: Partly

Reviewer #2: Partly

Reviewer #3: Partly

Reviewer #4: Partly

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

Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: No

Reviewer #4: 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

Reviewer #3: No

Reviewer #4: 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

Reviewer #3: Yes

Reviewer #4: 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: In this case, I recognise the potential interest of your findings for the COVID specialists perhaps in the future. However, I regret that I cannot conclude that the paper offers the sort of advance in fundamental scientific understanding or technological capability that would be likely to excite the immediate interest of the broader readership. It does not offer any scientific advancement in a reviewer's mind on how this work is going to solve anything in such a global crisis.

As a manuscript it has very poor quality representation with only three/four figures which does not add much to the story. It's hard to accept that simple clustering is going to solve the problem of overcrowded hospital wards around the world with so many dying covid patients!

Reviewer #2: 1. Mathematical discussion on each algorithm is essential, it will make the article easy for the

reader

Reviewer #3: The paper presents a statistical analysis of 220 patients admitted to wards in a French hospital from Feb to Apr 2020. The study was carried out in a single location and not validated somewhere else.

Data used in the study are not publicly available during the time of this review. The instructions are to email somebody to request the data.

For the analysis, the authors collect a profile for every patient accepted to a ward with respiratory symptoms. A principal components analysis is applied over the profiles to identify patterns in the data. In the analysis, the authors identified 3 clusters of patients, which are then characterised in this paper.

**Main comments**

PCA and clustering:

- The main objective of this work is to classify patients according to their probability to survive COVID-19 or not. The probabilities come from an unsupervised clustering. The clustering done is very shallow and based on a "blackbox" tool, which is not described properly. It's not clear why 3 is the best number of clusters or what parameters where used during the hyper-parameter search.

- It is not clear what features where used or if any transformation was used over the raw profiles. Labels or outcomes are also not clearly explained. Did you collect this information for all patients? Where cases where no answer could be obtained about the patient?

- How do you deal with missing values in the data? For example, not all patients have an active malignant disease. How does your clustering approach deals with that?

- Data normalisation is a must when applying PCA. What normalisation was applied to the data?

- While describing the clustering used, i.e., K-means, it is not clear how many components were computed and how many were used.

- In the description of the results, it's not clear what causes of mortality are associated with each of the clusters. This brings me to think that K-means may not be the best approach to inform practitioners. Could be more useful in this case to consider a hierarchical clustering where you can identify subgroups similarity?

Impact and usability of the results:

- I agree with the authors that the results presented here are somehow expected considering the selection criteria of the cohort

- My expectation of a paper like this is to present materials and methods along with the reproducible results that can be adopted in another institution. However, to that end, there are missing details to help others to use your results.

- How can we test the reproducibility of your results? What should another hospital do to adopt your approach? What considerations should be taken? (e.g., this only works for patients above 48 years old)

Questions that require an answer:

- How does the admission criteria to wards look like? Is there any standard followed?

- Are patients with lost of the sense of smell considered in your analysis? This is based on various cases of asymptomatic patients that may not be considered in your study

- The outcome results are extracted from the very early cases considered in the "first wave". I think it would be interesting to know if this has changed since then in the following waves (second, third, etc.)

- Why the results were not compared against supervised models? I ask this because it should be very simple to perform this comparison.

- [Line 270] "the poor outcome of patients with malignancies is worthy of attention" To me this is what deserves more attention and what could bring new insights into this study.

**Minor comments**

- [Line 84] First mention of SARS-CoV-2, previously you refer only to COVID-19

- [Line 125] Typo, "Firs" → "First"

- [Line 125] "nonsupervised" → "non-supervised" Use hyphen

- [Line 133] Missing reference to the “NbClust” package

- [Line 134] Which are the best hyper-parameters found by the package?

- [Line 138] "nonparametric" → "non-parametric"

- [Line 144] p-values are denoted in different ways across the text, e.g., uppercase, lowercase, italic. Please chose one and stick to it.

- [Line 202] "Base line" → "Baseline"

- [Line 208] "mechanically" → "mechanical"

- [Line 215] "iQR" → "IQR" all in uppercase

- [Figure 1] It was not mentioned earlier in the text that patients whose stay was less than 2 days were left out of the analysis

- [Figure 3] What's the unit of time here?

Reviewer #4: In this paper, the authors analyze influence factors regarding the prognosis of a COVID-19 infection.

In general, the paper is well written and highly relevant to the current situation. However, a few points should be clarified.

- For me it is unclear how exactly the clustering is performed. In lines 131-132 you write that an iterative partitioning k-means method is used. This contradicts

lines 133-135 where you say that the best clustering result is used (obtained by trying all combinations of the number of clusters,

distance measures and clustering methods).

- In the PCA that your are doing, I think that you reduce the data-dimensions to two, this is not exactly described. What percentage of data is described by these two

components?

- In line 156: Why exactly is 65 used as the age limit here? Can you present a histogram of the age distribution here?

- The discussion of the results is rather brief and much is left to the reader to infer. Please expand the discussion of the results further.

More Points

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- Line 125: Firs_t_, we use ....

Recommendations

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- I would like to suggest that autoencoder is used for dimensionality reduction, because it also finds nonlinear relationships in the data.

- If you are looking for clusters that have high mortality, I would recommend the subgroup discovery method.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes: Emir Munoz

Reviewer #4: No

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Profiles and outcomes in patients with COVID-19 admitted to wards of a French oncohematological hospital: a clustering approach

PONE-D-21-02959R1

Dear Dr. Anne Bergeron,

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.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Wisit Cheungpasitporn, MD, FACP

Academic Editor

PLOS ONE

Additional Editor Comments:

I reviewed the revised manuscript and the response to reviewers' comments. Revised Manuscript is well written. All comments have been addressed and thus accepted for publication.

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 #2: All comments have been addressed

Reviewer #3: All comments have been addressed

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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 #2: Yes

Reviewer #3: Partly

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

Reviewer #2: Yes

Reviewer #3: Yes

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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 #2: Yes

Reviewer #3: No

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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 #2: Yes

Reviewer #3: Yes

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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 #2: Looks good to me.

Reviewer #3: I would like to thank the authors for addressing all my comments from the first round. This revision, with the additional details on the methods, is clearer. I still believe there is much more research to be done with such type of data and using profiles such as the ones introduced here is a good place to start.

Because of that, I'd like to recommend this article for publication. Though I still have some minor comments, which I think they can be made while preparing the final submission.

Minor comments

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- The quality of the images provided should be improved with a higher resolution

- The paper has a few different styles of writing. I'd recommend to proof read the document and ensure, for example, that you use either British or American english through the whole document and not a mix

- [Line 98] "from February 2020, to April 2020" → no comma needed

- [Line 119] "and the time to death was recorded" → replace "to" by "of"

- [Line 151] Missing reference for the Forgy method

- [Line 202] "likely to present with dyspnea" → remove "with"

- [Line 244] In terms of style, avoid parentheses within parentheses. You can use brackets inside the parentheses instead

- [Table 3] "DDimers" → "D-dimers"

- [Line 325] Missing reference for subgroup discovery

- In the conclusion section, there is a mention to a Spanish cohort, but there is no reference to the publication of that other analysis. Please add the missing reference.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

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 #2: No

Reviewer #3: Yes: Emir Munoz