https://orcid.org/0000-0001-5995-0543
Peer Review History
| Original SubmissionSeptember 24, 2020 |
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PONE-D-20-30127 Probabilistic analysis of COVID-19 patients’ individual length of stay in Swiss intensive care units PLOS ONE Dear Dr. Ziegel, 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. The paper is based on an interesting topic, the statistical analysis has been performed appropriately, but there many concerns authors should correct and some concepts clarified to make the manuscript suitable for publication. This paper could be very difficult to be interpreted by a physician without mathematical experience. Authors should simplify the interpretations of results.
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All PLOS journals require that the minimal data set be made fully available. For more information about our data policy, please see http://journals.plos.org/plosone/s/data-availability. Upon re-submitting your revised manuscript, please upload your study’s minimal underlying data set as either Supporting Information files or to a stable, public repository and include the relevant URLs, DOIs, or accession numbers within your revised cover letter. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. Any potentially identifying patient information must be fully anonymized. Important: If there are ethical or legal restrictions to sharing your data publicly, please explain these restrictions in detail. Please see our guidelines for more information on what we consider unacceptable restrictions to publicly sharing data: http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. Note that it is not acceptable for the authors to be the sole named individuals responsible for ensuring data access. We will update your Data Availability statement to reflect the information you provide in your cover letter. [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: Partly Reviewer #2: Partly Reviewer #3: Partly ********** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: I Don't Know Reviewer #2: I Don't Know Reviewer #3: 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. 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: No Reviewer #3: 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: Yes Reviewer #3: Yes ********** 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 attempts to provide a way to estimate LoS for individual patients, based on characteristics available within the first 24 hours of admission. I think that this is a very valuable contribution, as it can help with health care planning and provide more accurate estimates for when hospital capacity may be exceeded. Although I think this is a nice paper in principal, I don't think enough information has been provided for me to adequately review the methods and results. As it stands I don't think the methods are clear, and the interpretation of the results is difficult to follow. I have tried to highlight below areas that I think could be made clearer: Line 13, you talk about probabilistic predictions, but even with your definition it is not clear what this means. How is the uncertainty of the LoS quantified? This ties in with Figure 1, which I also don't think is clear. There is very little information provided in the figure legend or text about figure 1, and it is difficult to interpret. As I understand, each vertical line indicates how long someone actually stays in hospital, so patient 4 stayed in for 40 days? And then patient 3 was released from ICU on day ±1, but had a very low possibility of doing so (±0.05)? I think more of an explanation is the figure legend and text is required to adequately explain this figure, maybe even just proving a small example as I have done above would help. I think it's a nice figure which if adequately explained in the legend and text would help to clarify the aim/ methods of the paper. At the moment I don't think your overall aim is clear. It is also not made clear in the introduction how having the CDF would be used. Who are you expecting to use your results, and how? It says in the abstract that individuals with long LoS could be discovered early, indicating that maybe this is for hospital planning purposes? Also, going back to figure 1, patient 4 has the longest LoS, but it is not clear precisely what indicates that patient 4 is going to have the longest LoS from the Forecast CDF. So difficult to see how the CDF is going to indicate long los. Methods: I understand that you are not able to release your data, however, there is no reason that the code could not be released, along with a brief description of the datasets you have available (could even consider creating some dummy datasets). This would allow others to understand your methods more clearly, and be able to repeat your analysis. From your methods, it's not adequately explained what the difference is between ECDF and CDF. In addition, in the results you discuss ECDF, and show plots comparing them to CDF, but I think providing a brief summary at the beginning of the results would aid interpretation. Results: You talk about how this is done on an individual level, and yet in Figures 2-4 your provide figures for one overall CDF. So is this a CDF for the whole dataset? How have you combined them? Line 138 you talk about panel c of figure 2, but there is no panel c. Overall, I think this is an interesting idea and concept, but in it's current format I don't think their methods are reproducible and their results are not easy to follow. I think they need to be clear who is there target audience, is it for mathematical modellers or people with a more clinical background? Clinicians or anyone with a non-technical background would struggle to know what to do with this information. However, I do think it is highly relevant, so I hope that the authors are able to revise their manuscript to make things clearer. I think being able to predict who is going to spend a long time on ICU is of great value. Best of luck with the submission. Reviewer #2: In this paper the authors developed a new semi parametric distributional index model that should provide a probabilistic prediction of ICU length of stay 24 hours after admission for COVID-19 patients. The model is based on 4 covariates: age, gender, SAPS II and NEMS. According to the authors these parameters were the only possible choice. I wonder if and how covariates dependance affects the model. Particularly: - age is included in SAPS II - both SAPS II and NEMS are expression of the severity of patients status, is this model performing better than a simpler model including just SAPS II? On the opposite, I wonder if including more specific variables, such as the coagulation status would provide better predictions. For the quality and soundness of the statistical analysis, I do not have the skills to judge, and an expert’s opinion is needed. Minor comments: * Minor language revision is needed, as the manuscript contains several typos Reviewer #3: The authors proposed a new semi-parametric model to predict individual ICU LOS even though prediction of LOS at the patient level is difficult and none of the available models were reliable. The model was fully demonstrated in two of method papers showing that it provides more information than classical models. In Figure 1, predicted CDFs of LOS and the corresponding true LOS were depicted. In reality, patient 3 had the shortest LOS and patients 1 and 2 had longer LOS. However, the forecasted CDF for those three patients were very close to each other. It looks like the proposed model doesn’t have the ability to provide satisfactory forecasts at the patient level. The authors mentioned LOS of a patient in ICU also depends on the characteristics and policies of the ICU. The COVID-19 pandemic placed a significant burden on healthcare systems. It induced unprecedented strain on ICU resources. It brought systematic error of prediction by using patients who were diagnosed of ARDS in 6 concurrent years as the training cohort. Because both measured and unmeasured confounders wound be unbalanced between training and COVID-19 cohorts. It may be helpful to present comparisons of other measured confounders and patients’ dispositions between cohorts. Patients who were admitted to ICU had two dispositions, discharged or dead. It looks like the authors included both discharged and dead patients in analysis. Was patients’ disposition considered differently in model? If not, can the authors clarify? In line 153, the authors reported patients who were greater than 80 years of age have much shorter LOS. Is it because in-ICU mortality rate was higher for this age-subgroup? The authors used cubic spline for continuous variables age, SAPS II, and NEMS in regression. Can the authors justify their decision to use cubic spline and how the assumptions of such a model were considered? Did the authors conducted tests for curvature and tests for significance of each curve? Figure 1: The color and pattern of lines confused me. Why did you use green lines for patients 1, 2, and 3 and red lines for patient 4? The paragraph of censoring of LOS in Appendix should be reported as a limitation in the discussion section. ********** 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: No Reviewer #2: No Reviewer #3: 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. |
| Revision 1 |
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PONE-D-20-30127R1 Probabilistic analysis of COVID-19 patients’ individual length of stay in Swiss intensive care units PLOS ONE Dear Dr. Ziegel, 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. ACADEMIC EDITOR: Most of comments have been addressed. I've asked a check to the statistician, see conclusions below. Please modify the manuscript as suggested to make it definitely suitable for publication. Please submit your revised manuscript by Feb 15th. 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:
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: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols We look forward to receiving your revised manuscript. Kind regards, Martina Crivellari 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 #3: (No Response) ********** 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 #3: Yes ********** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #3: 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 #3: 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 #3: 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 #3: The authors have addressed most of my comments and concerns. The reviewer still worry about prediction accuracy. In figure 1, patient 3 had the shortest LoS but the forecast CDF curve was in the middle. The order of realized LoS from the shortest to the longest were (patient 3 < patient 2 < patient 1 < patient 4). But the predicted probabilities were in different order. In the legend of figure 1, you said if a patient left on day t, the predictive CDF would jump from 0 to 1 at t. However, the CDFs in figure 1 didn’t do so. Patient left on day 1, but the corresponding CDF didn’t jump to 1. The reviewer suggested to draw a 2-D scatter plot to indicate prediction accuracy. The x-axis is the observed values of the LoS and the y-axis is the probability that the respective patient would discharged on the realized day. Each dot represents each patient. For example, patient 1 was discharged/dead on day 20 and the probability that he/she would discharged on day 20 was about 80%. So the dot of patient 1 should be located at (x = 20, y = 80%). ********** 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 #3: 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. |
| Revision 2 |
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Probabilistic analysis of COVID-19 patients’ individual length of stay in Swiss intensive care units PONE-D-20-30127R2 Dear Dr. Ziegel, 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, Martina Crivellari Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: |
| Formally Accepted |
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PONE-D-20-30127R2 Probabilistic analysis of COVID-19 patients' individual length of stay in Swiss intensive care units Dear Dr. Ziegel: 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. Martina Crivellari Academic Editor PLOS ONE |
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