Early biochemical analysis of COVID-19 patients helps severity prediction

Andrés Roncancio-Clavijo; Miriam Gorostidi-Aicua; Ainhoa Alberro; Andrea Iribarren-Lopez; Ray Butler; Raúl Lopez; Jose Antonio Iribarren; Diego Clemente; Jose María Marimon; Javier Basterrechea; Bruno Martinez; Alvaro Prada; David Otaegui

doi:10.1371/journal.pone.0283469

Peer Review History

Original SubmissionJune 20, 2022
17 Aug 2022 Decision Letter - Kovy Arteaga-Livias, Editor PONE-D-22-16300Early biochemical analysis of COVID-19 patients helps severity predictionPLOS ONE Dear Dr. Otaegui, 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. We have seen citation suggestions in the reviews. Remind you that this is not mandatory and that the inclusion or not of citations will have no influence on the decision made about your manuscript. Please submit your revised manuscript by Oct 01 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. 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We note that you have stated that you will provide repository information for your data at acceptance. Should your manuscript be accepted for publication, we will hold it until you provide the relevant accession numbers or DOIs necessary to access your data. If you wish to make changes to your Data Availability statement, please describe these changes in your cover letter and we will update your Data Availability statement to reflect the information you provide. 5. Please ensure that you include a title page within your main document. You should list all authors and all affiliations as per our author instructions and clearly indicate the corresponding author. 6. Your ethics statement should only appear in the Methods section of your manuscript. If your ethics statement is written in any section besides the Methods, please delete it from any other section. [Note: HTML markup is below. Please do not edit.] 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: Yes Reviewer #2: Partly ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: No ****** 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: Yes Reviewer #2: Yes ****** 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: No ****** 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: Overall, the study was very well conducted. I start by congratulating the authors. Below are my comments and I hope they are useful to improve your study. 1. Line 63, “Continuous variables have been described by the mean, minimum and maximum data”. Clinical data have almost always been shown not to have a normal distribution, my suggestion is that these results be presented in the form of median and interquartile range. 2. Line 73, “Statistical significance was defined as follows: p<0.001”. Why are the levels of significance different? Was there any sampling procedure that was done to get to this point? 3.On the lines 99-100, "The Model Training step is 100 described in Figure 1". I saw to figure 1, it's actually just a graph of the age distribution as a function of severity. I understood nothing. Are you sure it's figure 1? 4.Lines 100-101, “Finally, a Cross-Validation 102 method is applied to decrease overfitting impact”. What type of cross-validation was used? Venetian blind?, leave-one-out? 5.I encourage the authors to insert the formulas for calculating the metrics for evaluating the performance of the models: sensitivity, specificity, accuracy(material and methods). Still on the evaluation of the performance of the models, I ask that the authors discuss the following scientific articles: PMID: 35751188, PMID: 34091385. 6.Still on the evaluation of the performance of the models, I ask that authors present a table of confusion matrix with: true positives, true negatives, false positives, false negatives, sensitivity, specificity and accuracy. This must be shown for all classifiers used. For more understanding, see the PMID paper table: 35751188. 7.What types of preprocessing are used for machine learning data? I encourage authors to clarify, in the material and methods section and in the Results section. Reviewer #2: The article needs revision and re-writing with good language and grammar. 1. What is the novelty of this research? 2.What did the authors find new ? 3. The data is old and dates back to the year 2020, and much research has been published on this topic. 4. Why did the authors not focus on albumin levels, despite the fact that albumin was found to decrease in much of the research 5.Why did the authors not focus on LDH levels, despite the fact that albumin was found to increase in much of the research. 6.Most of the discussion is repetition of the result. Please delete and explore the result through analysis of the result In addition to some minor revisions. Line 25, Please revise "COVID-19 is an infectious disease" as "Coronavirus Disease 2019 (COVID-19) is zoonotic infectious disease" Please revise COVID-19 disease with "COVID-19" infection. Line 8, delete "our," , because it globally effects the capacity protocol of hospitals. On Line 9, add " AUC" to Intensive Care Units between brackets. Lines 25-29, please add references. "Coronavirus disease 2019 (COVID-19) Situation Report – 57, WHO, 2020, 10 AM CET 17 March 2020" ******* 6. 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 #1: Yes: Alexandre de Fátima Cobre Reviewer #2: No ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0283469.r001
Revision 1
31 Oct 2022 Author Response Dear Dr. Arteaga-Livias Thanks for the opportunity to answer the reviewer’s comments. We are grateful for their carefully reading of our work and for their helpful advices and comments. We answer point-by-point each comment ad rewrite the manuscript incorporating these changes. We make an effort in explain better the algorithm and the used methodology an also the novelty and the focus of this work REVIEWER 1: Reviewer #1: Overall, the study was very well conducted. I start by congratulating the authors. We thank the reviewer for their comment and their help to improve the manuscript Below are my comments and I hope they are useful to improve your study. 1. Line 63, “Continuous variables have been described by the mean, minimum and maximum data”. Clinical data have almost always been shown not to have a normal distribution, my suggestion is that these results be presented in the form of median and interquartile range. We totally agree with the reviewer´s suggestion, data presentation has been changed in the new manuscript version. 2. Line 73, “Statistical significance was defined as follows: p<0.001**”. Why are the levels of significance different? Was there any sampling procedure that was done to get to this point? Our impression is that there is some misunderstanding. The type I level of significance has been set at 0.05, however in the charts, one star means that the p-value is below 0.05, two stars mean that is below 0.01, and three stars that is below 0.001. We will rewrite it in the new manuscript version. 3.On the lines 99-100, "The Model Training step is 100 described in Figure 1". I saw to figure 1, it's actually just a graph of the age distribution as a function of severity. I understood nothing. Are you sure it's figure 1? Sorry about this mistake, as you notice there is an error in the order of the uploaded figures. Figure 1 is indeed a schematic description of the model. 4.Lines 100-101, “Finally, a Cross-Validation 102 method is applied to decrease overfitting impact”. What type of cross-validation was used? Venetian blind?, leave-one-out? We use k-fold as a cross-validation method. This procedure has been used with a k =10 meaning a 10-fold cross validation. This information has been added to the new version of the manuscript. 5.I encourage the authors to insert the formulas for calculating the metrics for evaluating the performance of the models: sensitivity, specificity, accuracy (material and methods). Still on the evaluation of the performance of the models, I ask that the authors discuss the following scientific articles: PMID: 35751188, PMID: 34091385. The formulas used are the ones by default in the used approach (SciPy): - Sensitivity: True positives /(True positives+ False Negatives) - Specificity: True Negative / (True negative + False Positives) - accuracy: (True positive + True Negative) / (True Positive + True Negative + False Positive + False Negative) We include in the discussion the proposed references. 6.Still on the evaluation of the performance of the models, I ask that authors present a table of confusion matrix with: true positives, true negatives, false positives, false negatives, sensitivity, specificity and accuracy. This must be shown for all classifiers used. For more understanding, see the PMID paper table: 35751188. A new supplementary figure including the Table with the values for each model has been generated and added as Supplementary information. 7.What types of preprocessing are used for machine learning data? I encourage authors to clarify, in the material and methods section and in the Results section. The dataset was constructed with the 42 features of 1082 patients, from these 37 variables were chosen avoiding redundancy. Aassociation and correlation between the variables were analyzed (AutoDiscovery Strength Score) to avoid model overfitting and to identify the patient's features that were more associated with the predictive goal. In this specific case, the association of the variables with the severity variable was evaluated and one of the correlated pair of variables was removed, if any. To do that, AutoDiscovery selected the proper numerical method based on the data type and distribution of the variables assessed. Numerical tests applied in each case are Spearman’s Rank Correlation, Variance Analysis (ANOVA one-way, U Mann-Whitney, and Kruskal-Wallis; normality was tested in these cases with D’Agostino/Pearson methods), and Cramer’s V Contingency Index. This massive data exploration process was carried out systematically for each patient group (stratum), considering only those correlations evaluated in a data subset with n≥5, two-tailed p < 0.05 and Spearman’s rank correlation coefficient > 0.8 to be relevant. Given the nature of this multiple testing method, a high significance threshold was calculated based on Benjamini-Hochberg method (False-Discovery Rate) to classify the rest associations. The Feature Selection process, where, based on Recursive Feature Elimination (RFE) and the AutoDiscovery Strength Score, age decade and the Lymphocytes to Immature Granulocyte ratio were added as variables for the model. This information has been added to the manuscript. REVIEWER 2: The article needs revision and re-writing with good language and grammar. Thanks to the reviewer for his/her comments that help us to improve the manuscript 1. What is the novelty of this research? Although research in COVID19 moves very fast and the needs are changing, our work present6 an algorithm based in data that are obtained in all Hospitals routinely and no need for any extra analysis. That is novel and helpful for all the hospitals in the different countries to take fast decisions. Moreover our work underline the importance of the immature granulocites status in the gravity of the evolution. 2.What did the authors find new ? We present an algorithm based in routinely parameters obtained in the clinical routine. Therefore decisions can be made in the first days of hospitalization without any additional analysis, just based in the regular data obtained the first day of hospitalization. We also highlight the importance of immature granulocytes in the evolution of the disease. 3. The data is old and dates back to the year 2020, and much research has been published on this topic. We partially agree with the reviewer. We try to share our data as quickly as we could (and for that reason manuscript was located at BioRiv repository), however although the data are from the 2020, this is the most naïve data (without vaccinated people) and the ones that give us a more accurate idea about how the disease work. That could be useful for a new coronavirus pandemic and also to understand better the new cases that re arriving to the hospitals. 4. Why did the authors not focus on albumin levels, despite the fact that albumin was found to decrease in much of the research. The aim of the work was not to select the biomarkers but to use the ones that are routinely produced in the regular clinical practice. In fact we recover the data from the clinical records with no interaction (at that moment) with the clinicians, meaning that the choice of variables was based in the clinical needs and in the regular protocols. Is an undirected approach with the aim to find an easy and cheap way to make decisions at the hospital. 5.Why did the authors not focus on LDH levels, despite the fact that albumin was found to increase in much of the research. Please, see response to answer 4. 6.Most of the discussion is repetition of the result. Please delete and explore the result through analysis of the result Thanks to the reviewer for his/her suggestions. We rewrite the discussion focusing in the analysis of the results. Attachments Attachment Submitted filename: response to reviewers_PLos One v 3.docx* https://doi.org/10.1371/journal.pone.0283469.r002
24 Nov 2022 Decision Letter - Kovy Arteaga-Livias, Editor PONE-D-22-16300R1Early biochemical analysis of COVID-19 patients helps severity predictionPLOS ONE Dear Dr. Otaegui, 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. Please submit your revised manuscript by Jan 08 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. Please include the following items when submitting your revised manuscript: A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols. We look forward to receiving your revised manuscript. Kind regards, Kovy Arteaga-Livias Academic Editor PLOS ONE [Note: HTML markup is below. Please do not edit.] 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: (No Response) Reviewer #2: All comments have been addressed ******** 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: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: No Reviewer #2: Yes ****** 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: Yes ****** 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: Yes ****** 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: Dear Editor, I would like to thank you for the opportunity to review the article “Early biochemical analysis of COVID-19 patients helps severity prediction”, and I would like to take this opportunity to apologize for the delay in completing the review, I had unforeseen personal problems. Second, I would like to congratulate the authors for the article. Overall I enjoyed the post however some improvements need to be made. My observations follow. 1.In the “Abstract” section, the authors describe “…The test has been done by standardized cost-effective technique available in all the hospitals”. If the method is being used in clinical practice, it means that the model has been deployed. If so, I strongly recommend that you provide the access link for your Machine Learning App. I am just saying that the authors need to be realistic, because if the App has not been developed for the developed models, I recommend that they remove the phrase “…The test has been done by standardized cost-effective technique available in all the hospitals” in the manuscript. 2.In the material and methods section, “Continuous variables have been described by the mean, minimum and maximum data…”. I suggest initially testing the normality of the data. If normality is proven, the results must be presented as mean and standard deviation, otherwise the results must be presented as median and interquartile range. 3.The data used in this study are unbalanced: severe cases 449 (41.5%); ICU admission 60 (5.55%); deceased 140 (12.94%). It is important to point out that for imbalance problems, the AUC is not a better metric to evaluate the performance of the classifiers. The most reliable metric is the “F1 Score”. I strongly recommend that authors report these data in the article, including citing them in the abstract. 4.Still in material and methods, the authors describe “…Finally, a Cross-Validation 102 method is applied to decrease overfitting impact”. It needs to be clear which type of cross-validation was adopted. 5.The figures are very blurred. I ask you to improve the resolution of the figures. I also ask you to increase the size of the X and Y axes of the figures. Reviewer #2: The author response to all reviewer comment , and the manuscript developed to the level of publication ****** 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 #1: Yes: Alexandre de Fátima Cobre Reviewer #2: No ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0283469.r003
Revision 2
12 Dec 2022 Author Response Dear Dr. Arteaga-Livias Thanks for the opportunity to answer this second round of reviewer’s comments. We are grateful for their carefully reading of our work and for their helpful advices and comments. We answer point-by-point each comment ad rewrite the manuscript incorporating these changes. REVIEWER 1: Dear Editor, I would like to thank you for the opportunity to review the article “Early biochemical analysis of COVID-19 patients helps severity prediction”, and I would like to take this opportunity to apologize for the delay in completing the review, I had unforeseen personal problems. Second, I would like to congratulate the authors for the article. Overall I enjoyed the post however some improvements need to be made. My observations follow. 1.In the “Abstract” section, the authors describe “…The test has been done by standardized cost-effective technique available in all the hospitals”. If the method is being used in clinical practice, it means that the model has been deployed. If so, I strongly recommend that you provide the access link for your Machine Learning App. I am just saying that the authors need to be realistic, because if the App has not been developed for the developed models, I recommend that they remove the phrase “…The test has been done by standardized cost-effective technique available in all the hospitals” in the manuscript. Sorry for the misunderstanding. The model has been developed from biochemical data obtained in a routine assay. This sentence wants to emphasize that this kind of data could be easily obtained in the vast majority of hospitals and therefore the methodology proposed in our work can be easily transferred to clinical practice. We change this phrase in the abstract to clarify the point. 2.In the material and methods section, “Continuous variables have been described by the mean, minimum and maximum data…”. I suggest initially testing the normality of the data. If normality is proven, the results must be presented as mean and standard deviation, otherwise the results must be presented as median and interquartile range. Thanks for the suggestion. As requested by a reviewer we change the presentation of these data to mean, minimum and maximum because, according to the reviewer, is the way in which clinical data should be presented. Moreover we perform a Normality test (Shapiro- Wilk) to confirm that our the variables should be presented in this way. 3.The data used in this study are unbalanced: severe cases 449 (41.5%); ICU admission 60 (5.55%); deceased 140 (12.94%). It is important to point out that for imbalance problems, the AUC is not a better metric to evaluate the performance of the classifiers. The most reliable metric is the “F1 Score”. I strongly recommend that authors report these data in the article, including citing them in the abstract. Thanks for the comment. In this work we do not design the groups but use the data from the “real world”, recovering the data from a real situation from our hospital during a period of time. Following your suggestion we add the F1-score value to the manuscript. 4.Still in material and methods, the authors describe “…Finally, a Cross-Validation 102 method is applied to decrease overfitting impact”. It needs to be clear which type of cross-validation was adopted. We use k-fold as a cross-validation method. This procedure has been used with a k =10 meaning a 10-fold cross validation. This information has been added to the new version of the manuscript. 5.The figures are very blurred. I ask you to improve the resolution of the figures. I also ask you to increase the size of the X and Y axes of the figures. Quality of the figures and Axes have been improved REVIEWER 2: The author response to all reviewer comment , and the manuscript developed to the level of publication Attachments Attachment Submitted filename: response to reviewers 2nd round.docx https://doi.org/10.1371/journal.pone.0283469.r004
9 Mar 2023 Decision Letter - Kovy Arteaga-Livias, Editor Early biochemical analysis of COVID-19 patients helps severity prediction PONE-D-22-16300R2 Dear Dr. Otaegui, 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, Kovy Arteaga-Livias 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 #2: All comments have been addressed ******** 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 ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #2: Yes ****** 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 ****** 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 ****** 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: All comments has been addressed . The manuscript improved to the level of publication....................... ****** 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 ******** https://doi.org/10.1371/journal.pone.0283469.r005
Formally Accepted
10 May 2023 Acceptance Letter - Kovy Arteaga-Livias, Editor PONE-D-22-16300R2 Early biochemical analysis of COVID-19 patients helps severity prediction Dear Dr. Otaegui: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. 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. If we can help with anything else, please email us at plosone@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Kovy Arteaga-Livias Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0283469.r006

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