Development and external validation of a prognostic tool for COVID-19 critical disease

Daniel S. Chow; Justin Glavis-Bloom; Jennifer E. Soun; Brent Weinberg; Theresa Berens Loveless; Xiaohui Xie; Simukayi Mutasa; Edwin Monuki; Jung In Park; Daniela Bota; Jie Wu; Leslie Thompson; Bernadette Boden-Albala; Saahir Khan; Alpesh N. Amin; Peter D. Chang

doi:10.1371/journal.pone.0242953

Peer Review History

Original SubmissionMay 15, 2020
30 Jul 2020 Decision Letter - Itamar Ashkenazi, Editor Transfer Alert This paper was transferred from another journal. As a result, its full editorial history (including decision letters, peer reviews and author responses) may not be present. PONE-D-20-14464 Development and External Validation of a Prognostic Tool for COVID-19 Critical Disease PLOS ONE Dear Dr. Chow, 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. Two reviewers commented on your study. Their comments were not supportive in favor of accepting your study for publication. Their specific comments are attached below and should be addressed if a revision is submitted. I want to add one more comment that should be addressed. This study was performed 4 months ago. If your tool was used prospectively since then, this should be commented on, providing the observed reliability of the tool in identifying patients who will develop critical disease. If this request exceeds what was authorized by the institution's research ethics committee, please use this letter when approaching the IRB to authorize a change in the protocol. Please submit your revised manuscript by Sep 13 2020 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). 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Please see http://www.bmj.com/content/340/bmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. We will update your Data Availability statement on your behalf to reflect the information you provide. [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 ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: I Don't Know ****** 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: No ****** 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 ****** 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: The concept of developing a risk score for which Covid 19 patients will require critical care is laudable. Prediction of which patients will come to need such care is very moprtant for planning and allocation of resources. The paper is written in clear and standard English. The methodology is sound, however this reviewer would be grateful fi the authors could address the following: The very limited sample size - 87 in the drivation cohort of which 21 wre critical and 40 in the validation cohort of which 26 were critical. Why the derivation and validation cohorts were drawn from different insitutions, particulalry where the laboratory sampling in the validation group did not include important elements of the derivation group data. What effect the retrospective data collection might have had on study results (this is briefly alluded to in the "limitations" section). Reviewer #2: The authors made a prediction model for critical disease (defined as ICU admission, MV or death) in 87 COVID-19 patients and found a high C-statistic of 0.95 and 0.94 in the validation cohort and concluded that the model performed well. I have some questions. Effective sample size is 21 (out of 87), is it allowed to put 13 variables in the model (the full model consisted even more variables) while one variable (total number of co-morbidities consists another 8 variables)? If it is allowed, for clinical practice it is not useful. A practical clinical prediction model consists of 3-4 variables that are ready available at the bedside. Of course does accuracy increase with more variables, but consider reducing the amount of variables. Out of 299 patients, 212 were excluded due to missing variables. The fact that data are missing in these patient may not be random. Furthermore, even in de 87 eligible patients data was not complete (mean missing’s was 1.33(range 1-3) for every patient. Therefore imputation was performed. What were the results when missing’s considered as missing’s? What is the necessity and effect of normalizing the data? What were the criteria for including a variable in the model? I miss a Table 1 with all baseline characteristics (and difference between yes/no critical disease) and perhaps a table 2 with univariate logistic regression analyses of these parameters. ****** 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: PV van Heerden Reviewer #2: Yes:** Walter m. van den Bergh [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.0242953.r001
Revision 1
18 Sep 2020 Author Response Ref: PONE-D-20-14464 Dr. Ashkenazi and PLOS ONE Reviewers, Thank you for your review of our manuscript. We sincerely appreciate the comments provided. We have made edits to our manuscript based on these comments, addressing all of the issues that have been raised. In addition, we have responded directly to each of the reviewer remarks below. Please do not hesitate to contact us with additional comments or suggestions. Reviewer #1: 1. The concept of developing a risk score for which Covid 19 patients will require critical care is laudable. Prediction of which patients will come to need such care is very moprtant for planning and allocation of resources. The paper is written in clear and standard English. Thank you for reviewing our manuscript and for your comments and suggestions. 2. The methodology is sound, however this reviewer would be grateful fi the authors could address the following: a. The very limited sample size - 87 in the drivation cohort of which 21 wre critical and 40 in the validation cohort of which 26 were critical. Why the derivation and validation cohorts were drawn from different insitutions, particulalry where the laboratory sampling in the validation group did not include important elements of the derivation group data. Early in the COVID-19 pandemic there were a limited number of patients from which to derive and validate our predictive model. Utilizing a cohort of patients from an outside institution was necessary, as we lacked sufficient numbers of patients at our institution to perform both derivation and validation. As laboratory variables were collected retrospectively, there were differences in clinical practice patterns which affected data availability. Additionally, the use of a completely external validation group increases the confidence in generalizability of the results. Though the external validation group lab values are not as comprehensive as the training set, the relatively robust performance suggests that the algorithm will nonetheless maintain accuracy even with missing data. b. What effect the retrospective data collection might have had on study results (this is briefly alluded to in the "limitations" section). The principal limitation is data availability. To overcome this limitation, we suggested the development of a panel of COVID-19 laboratory tests, which can help standardize clinical practice, and which we have subsequently implemented at our institution. Reviewer #2: 3. The authors made a prediction model for critical disease (defined as ICU admission, MV or death) in 87 COVID-19 patients and found a high C-statistic of 0.95 and 0.94 in the validation cohort and concluded that the model performed well. Thank you for reviewing our manuscript and for your comments and suggestions. 4. Effective sample size is 21 (out of 87), is it allowed to put 13 variables in the model (the full model consisted even more variables) while one variable (total number of co-morbidities consists another 8 variables)? Thank you for this suggestion. We agree that the use of many model parameters may increase the risk of model overfitting. Using a recursive feature selection process, one feature was added to the model at a time and the performance of the new model was assessed via a cross-validation technique. Despite the use of thirteen features, no significant overfitting was observed across each of the cross-validation folds during training. Furthermore, the high performance of the model on the external test set helps to validate this approach and give confidence to the use of all thirteen variables in the final predictive algorithm. Regarding the composite variable for comorbidity: a composite variable that captures the total number of 8 comorbidities demonstrated strong predictive value (Wald score 0.491). We hypothesize that this is because COVID-19 is a multiorgan/multisystem disease and that overall patient health status, as captured by the total number of comorbidities, is more important than a specific comorbid condition. 5. If it is allowed, for clinical practice it is not useful. A practical clinical prediction model consists of 3-4 variables that are ready available at the bedside. Of course does accuracy increase with more variables, but consider reducing the amount of variables. Thank you for this observation. While the full model can use up to 13 different variables, in fact there is no requirement that all variables must be present for either training of prediction. During training, only 30/87 patients had all thirteen lab values present. The remaining patients had at least one missing variable, the distribution of which is shown here: Number of variables Total patient count 7 2 8 37 9 2 10 5 11 5 12 6 13 30 During the prediction process, all missing data is accounted for using median imputation from population statistics from the training data. While imputation is an imperfect approximation to true lab value data, the high performance on an external data set with missing data suggests that the approach is reasonable. Additionally, as a surrogate for clinical utility, the clinical prediction model has been integrated into the clinical workflow at our hospital and is furthermore available as a public website at http://covidrisk.hs.uci.edu (Figure 3). This table and added has been added to the results of the manuscript as well as the discussion in the limitation section. Out of 299 patients, 212 were excluded due to missing variables. The fact that data are missing in these patient may not be random. Furthermore, even in de 87 eligible patients data was not complete (mean missing’s was 1.33(range 1-3) for every patient. Therefore imputation was performed. What were the results when missing’s considered as missing’s? The distribution of data availability per patient is shown in the table above. Patients missing more than 50% of data (e.g. having 6 or less variables) were excluded from analysis. In our cohort, no patient requiring ICU admission had less than 6 lab values. This discussion has been added to the methods and results where appropriate. We agree that these patients may not be random. In general, such patients have a less severe clinical presentation and have been determined by a medical expert to require less laboratory testing. By contrast this tool is designed to identify patients at high risk for decompensation. As in the discussion above, median imputation is used during inference based on population statistics. This approach allows for patients with less than 6 lab values to be analyzed by the tool, with the acknowledgement that the prediction may be less accurate than patients with additional lab testing. 6. What is the necessity and effect of normalizing the data? Normalization of the data to similar scales facilitates numeric stability during the algorithm training process and ensures that all variables are initialized with relatively equal contribution to the prediction. This has been added to the methods. 7. What were the criteria for including a variable in the model? Using a recursive feature selection technique, univariate statistical testing was applied to the cohort to identify the variables with greatest differences in distribution when stratified based on outcomes. Starting with the variable calculated to have the largest differences, additional variables are added to the model, one-by-one, in order of significance based on univariate testing, until the model performance plateaus. In this study, the optimal number of variables based on this technique was chosen to be 13. It should be noted that model performance did not degrade with additional variables (e.g. instead overall performance reached a plateau); thus the total number of variables used in this study represents the minimum amount needed to approximate the performance models using arbitrarily large number of covariates. This has been added to the methods. 8. I miss a Table 1 with all baseline characteristics (and difference between yes/no critical disease) and perhaps a table 2 with univariate logistic regression analyses of these parameters. Thank you for this comment. We have added Table 1 as well as the f-test data for table 2. The ranked f-tests are below: 'Resp': 27.739717, 'LDH': 23.125122, 'Comorbidities': 16.79656, 'BMI': 13.908264, 'Ferritin': 12.375533, 'WBC': 7.8667326, 'Procalcitonin': 7.5904236, 'Lymph': 7.47715, 'Troponin': 7.263967, 'CRP': 6.589011, 'Creatinine': 5.7942467, 'Age': 1.0775236, 'Gender': 0.5919638 Attachments Attachment Submitted filename: Revision Letter.docx https://doi.org/10.1371/journal.pone.0242953.r002
13 Nov 2020 Decision Letter - Itamar Ashkenazi, Editor Development and External Validation of a Prognostic Tool for COVID-19 Critical Disease PONE-D-20-14464R1 Dear Dr. Chow, 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, Itamar Ashkenazi Academic Editor PLOS ONE Additional Editor Comments (optional): The three reviewers differed in their opinion regarding this study. The main problem discussed with the reviewers was that this forecast model was constructed based on many variables and this model was validated on a rather small cohort. Another problem is that many of the parameters included in the forecast model rely on tests that are not in common use in the set up of acute care. I believe a note on these issues should be posted with the manuscript to be published. The authors could then reply. Whether more data has been accumulated (since this study was sent for publication) on this score in the authors' daily practice, presentation of these will be appreciated. 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: All comments have been addressed 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 #1: Yes Reviewer #2: Partly Reviewer #3: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: I Don't Know Reviewer #3: No ****** 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 Reviewer #3: No ****** 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 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 #1: (No Response) Reviewer #2: (No Response) Reviewer #3: An important factor was ignored in the study—race. Disparity of COVID-19 between ethnic groups in US is profound. ****** 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: No Reviewer #2: Yes:** Walter M. van den Bergh Reviewer #3: No https://doi.org/10.1371/journal.pone.0242953.r003
Formally Accepted
20 Nov 2020 Acceptance Letter - Itamar Ashkenazi, Editor PONE-D-20-14464R1 Development and External Validation of a Prognostic Tool for COVID-19 Critical Disease Dear Dr. Chow: 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. Itamar Ashkenazi Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0242953.r004

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