COVID-19 and the kidney: A retrospective analysis of 37 critically ill patients using machine learning

Anna Laura Herzog; Holger K. von Jouanne-Diedrich; Christoph Wanner; Dirk Weismann; Tobias Schlesinger; Patrick Meybohm; Jan Stumpner

doi:10.1371/journal.pone.0251932

Peer Review History

Original SubmissionMarch 16, 2021
15 Apr 2021 Decision Letter - Raffaele Serra, Editor PONE-D-21-08660 COVID-19 and the kidney: A retrospective analysis of 37 critically ill patients using machine learning PLOS ONE Dear Dr. Herzog, 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 May 24 2021 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. 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Thank you for stating the following in the Competing Interests section: Anna Laura Herzog, Holger K. von Jouanne-Diedrich, Dirk Weismann, Tobias Schlesinger, Patrick Meybohm, and Jan Stumpner have nothing to disclose. Christoph Wanner received honoraria for steering committee membership and lecturing outside the present work from AstraZeneca, Bayer, Boehringer-Ingelheim, Eli Lilly, Mundipharma, and MSD. Please confirm that this does not alter your adherence to all PLOS ONE policies on sharing data and materials, by including the following statement: "This does not alter our adherence to PLOS ONE policies on sharing data and materials.” (as detailed online in our guide for authors http://journals.plos.org/plosone/s/competing-interests). If there are restrictions on sharing of data and/or materials, please state these. Please note that we cannot proceed with consideration of your article until this information has been declared. 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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 ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes ****** 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. 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(Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: Herzog and Colleagues, with the present Research Article, addressed a very important research question namely whether proteinuria forecasts major renal endpoints (CKD, kidney failure and mortality) in critically ill patients with COVID-19. In my opinion the idea is of interest since controversial results have been reported about this topic showing in particular that overall CKD (but not the exact kidney measure) is a risk factor for mortality. Moreover, the presence of proteinuria has been recognized as a cardiovascular risk factor and data about its role in COVID-19 patients are thus expected. Notwithstanding, I have several concerns to share with Authors: - Sample size is small, reducing the generalizability of study results: please mention this point among the limitations of the manuscript - Curiously, I saw that Authors reported in the Results a copy of the syntax used to run the OneR algorithm. This is unusual in the context of an original research. I suggest to delete that part - Within the machine learning techniques, there are interesting approach which are useful in the case of small sample size and a large amount of data. These include the penalized regression such as the Lasso regression or the Ridge regression. Did you consider using some of them? - The discussion section could be implemented by generating more hypotheses on the link between kidney measured and cardiorenal risk.To this aim, please read and if possible cite the manuscript “doi: 10.3390/jcm9082506” and “doi: 10.2217/bmm-2020-0201”. ****** 6. 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Revision 1
27 Apr 2021 Author Response Dear Prof. Dr. Provenzano We thank you for the critical evaluation of our work. We are glad that our manuscript was well received (“technically sound, data support the conclusions, very important research question”) and would therefore like to reply to the issues raised by drawing the attention to following items: Is the manuscript presented in an intelligible fashion and written in standard English? “No” We apologize that our manuscript does not meet the required language standards. The manuscript was under professional editing, we will ask for a revision from the responsible editor once again Ad 1.: Sample size is small, reducing the generalizability of study results: please mention this point among the limitations of the manuscript Thank you for this valuable suggestion. We are absolutely aware that our case counts are too small for a valid statement. Our intention was to provide an overview of the methodology, its application and sources of error. Regarding the predictive power of proteinuria, our results are in line with previous reports despite the small number of patients, although even in much larger studies the exact correlation is not clear. To explain different ML methods, the small sample size was also sufficient, demonstrating sources of error such as overfitting in a simplified way. We have mentioned this again in the discussion. Ad 2. “Curiously, I saw that Authors reported in the results a copy of the syntax used to run the OneR algorithm. This is unusual in the context of an original research. I suggest to delete that part” Thank you for this comment. We have tried to explain ML methods in a simple way. The OneR package is an important tool for our approach. We have shown the part of the syntax to give an unfamiliar user an insight into the quite intuitive output of the program. We realize that this is unusual and have corrected that. Ad 3.: “Within the machine learning techniques, there are interesting approach which are useful in the case of small sample size and a large amount of data. These include the penalized regression such as the Lasso regression or the Ridge regression. Did you consider using some of them?” Thank you for this important remark. The paper is intentionally focused on machine learning methods, which closes a gap in the current literature. We considered regularization methods for linear regression (e.g. Lasso and Ridge regression), however, this more traditional approach would have gone beyond the scope of this work. A second more technical reason is that the target variable of Lasso and Ridge Regression is numerical whereas our target variables are categorical. Although Lasso and Ridge regression models can be interpreted to some degree they also suffer from some issues. Our approaches (i.e. OneR and decision trees) can be understood very easily and have a clear medical interpretation as we tried to show in the paper. Ad 4.: “The discussion section could be implemented by generating more hypotheses on the link between kidney measured and cardio-renal risk. To this aim, please read and if possible cite the manuscript “doi: 10.3390/jcm9082506” and “doi: 10.2217/bmm-2020-0201”. Thank you for this very interesting approach. In fact, we have recorded several cardiac parameters. We could also show that severe AKI in combination with cardiac involvement increases the risk of death. A very interesting approach for our future investigation would be to see if potential cardiac deterioration due to COVID-19 can also be detected early this way, we will follow up on this. We have largely implemented your valuable suggestions on methods and discussion, added more contemporary literature, structured the main text to make citations more clearly and explained or removed individual sentences. We have changed the manuscript and thank the reviewer for pointing out these important issues. Kind regards, Anna Laura Herzog On behalf of the authors Attachments Attachment Submitted filename: Response to Reviewers Covid ML.doc https://doi.org/10.1371/journal.pone.0251932.r002
6 May 2021 Decision Letter - Raffaele Serra, Editor COVID-19 and the kidney: A retrospective analysis of 37 critically ill patients using machine learning PONE-D-21-08660R1 Dear Dr. Herzog, 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, Prof. Raffaele Serra, M.D., Ph.D Academic Editor PLOS ONE Additional Editor Comments (optional): amended manuscript is acceptable Reviewers' comments: https://doi.org/10.1371/journal.pone.0251932.r003
Formally Accepted
10 May 2021 Acceptance Letter - Raffaele Serra, Editor PONE-D-21-08660R1 COVID-19 and the kidney: A retrospective analysis of 37 critically ill patients using machine learning Dear Dr. Herzog: 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 Prof. Raffaele Serra Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0251932.r004

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