Peer Review History
Original SubmissionNovember 18, 2020 |
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PONE-D-20-36121 A clinical prediction model to identify children at risk for revisits with serious illness to the emergency department: a prospective multicentre observational study PLOS ONE Dear Dr. Nijman, Your manuscript "A clinical prediction model to identify children at risk for revisits with serious illness to the emergency department: a prospective multicentre observational study" has been assessed by two reviewers. Based on these reports and my assessment as Editor, I am pleased to inform you that it is potentially acceptable for publication in PLoS One Journal once you have carried out some essential revisions suggested by the reviewers. Their reports, together with many other comments, are below. Thank you for providing the Tripod checklist. When completing the list, please use section and paragraph numbers rather than page numbers. Did your study have a prospective protocol or analysis plan? Please state this (either way) early in the Methods section. If a prospective analysis plan was used in designing the study, please include the relevant prospectively written document with your revised manuscript as a Supporting information file to be published alongside your study, and cite it in the Methods Section. If no such document exists, please ensure that the Methods section transparently describes when analyses were planned and when/why any data-driven changes to analyses took place. Best wishes, Jose Moreira Please submit your revised manuscript by May 22 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. Please include the following items when submitting your revised manuscript:
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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: Yes Reviewer #2: Yes ********** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: 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: 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: 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 article is interesting, original and relevant when proposing a prediction model that helps in the pediatric emergency care. It aims to identify children who are more likely probability to revisit ED with serious illness. The article is well written, and the analysis was well conducted. Several points were already answered previously by authors for the other reviewers. However, some explanations about the statistical models are not completely clear yet. Major comments 1. I do not understand the sentence “Using the 10 cases : 1 predictor rule of thumb, our cohort provided enough power to evaluate approximately 100 predictor variables….(21)” (page 8). I understand that the authors use the “rule” of 10 observations (cases) per predictor variable, but it is controversial, and it is not sufficient to justify a study power. The ideal would be calculate the sample size or power, but I understand the obstacles to calculate it without parameters. I suggest that authors exclude this sentence. 2. The additional analysis with comorbidities in three hospitals seems misplaced or it is not linked to objective and the other analysis. I suggest the authors that consider the need of this inclusion. It is not clear why only the effect of comorbidities is shown in the table S3, while several effects are shown in the table 2. Moreover, the sentence in the results section “Comorbidity”….(S3 appendix 3, page 10) does not highlighted the differences between the values. 3. The term “stepwise approach” (page 8) can cause misunderstandings. It was clarified by the authors in the answers for other reviewer, but this sentence in the text remains unclear. I only suggest that authors excluded the name of the approach (e.g. Secondly, we developed two clinical prediction models using logistic regression: clinical model and extended model. The first model…..). 4. The authors show the explicative (Table 2) and predictive (other tables) models. The role of the explicative model (Table 2) is not clear in the text, I think that it was used to select the factors to the predictive model. 5. It is not clear the reason to not include the goodness-of-fit measures of the logistic models (such as, Hosmer and Lemeshow test and/or residual analysis). 6. Some prediction variables can be correlated (multicollinearity) and the use of VIF measure could be important. 7. It is suggested that the authors include the interpretation of AUC in the methods section. 8. The crude OR would be useful to compare with adjusted OR and to detect modification effects. 9. Why are not the medication effect explained in the paragraph “Characteristics…serious illness” (page 10)? 10. It is important highlighted that the multicovariate models are joint adjusted by all variables, so the interpretation of effects should be clear about this adjustment. For example, the paragraph “Characteristics of the index…serious illness” (page 10) only mention about the significant effects. 11. The guidelines often ask to avoid repeating the values from tables and graphs (ORs, AUCs). It is suggested that the authors try to reduce the use of the values in the text. 12. Although the explanation about discrimination and calibration in the paragraph “Overall, discrimination (summary AUC)…(table3)” (page 11) can be understood, the citations of tables and figures are grouped all together and it is hard to check them. One suggestion is split these citations along the sentences and maybe compare the clinical and extended models in distinct sentences, one about the discrimination and another about the calibration. 13. Some confidence intervals have the same values for the lower and upper limits (e.g. 0.97-0.97, page 11 and in some tables). 14. There is an increase critical to use of p-values in big data, hence some p-values and effects (ORs) can show significance. It is suggested to provide this limitation in the discussion section. 15. The likelihood ratio test was used to interpret p-values of all models, but it is not clear in the methods section. 16. It was not finding the same probabilities (3.1% and 2.2%) with the parameters provided in the example (figure 3). The digital calculator could have some explanations about the variable names and the clinical and extended definitions. 17. It does not seem adequate to interpret the effects and outcome as “risk”, so the authors used logistic models and ORs. 18. A short explanation about the different health systems across sites could be useful in the methods section and/or in the limitations, stressing the type of care in the sites (e.g. universal). 19. Two points could be mentioned in the limitations, the external validity to other countries (eg. not-developed countries, outside Europe and different health systems) and the statistical limitations (use of pvalue to build the regression model). Minor comments 1. The name of design study (cohort study?), as well the hospitals, would be useful in the “Design, participants and setting” subsection. The abbreviations about hospital are in the page 7, but they are not defined before. 2. Number and name of hospitals could be defined in the “Design, participants and setting” (page 6). 3. Are there some references for the sentence “Data were extracted from the hospital’s electronic systems and checked for completeness, validity and outliers” (page 6)? 4. Some abbreviations are not defined in the first citation in the text (e.g. PICU-Abstract section, AVPU-methods section, EMC, SMH and MUW- page 7). 5. What are the variables specified in the sentence “Only variables with data available in all settings were used in the multivariable regression analysis” (page 6)? The tables do only seem show variables which were included in the modelling. I think this sentence is not necessary. The same happens with sentence “with high number of missings were not considered…” (page 8). 6. It was not cited the use of odds ratio and confidence intervals in the methods section. 7. The term “cross validation” could be used in the methods section to clarify the explanation about “leave-one-out“ approach. 8. Some ORa values are incorrect if we compare with table 2 (page 10). 9. The sentence “First, we developed…cohort once.” (page 11) is more appropriate in the methods section. 10. The sentence “Intercept coefficients ranged…calibration-in-the large” (page 11) seems misplaced. I suggest that it is moved for the beginning or the end of the paragraph. 11. It is suggested to check the guidelines for formatting tables (e.g. lines, definitions of abreviations, etc). For example, it was not defined the clinical and extended model in the table 3. 12. The use of term “multivariable” can bring some misunderstands. Although many publications in epidemiology use this term to refer the adjustment for several covariables, in the statistical this term refers to a multivariate outcome. I recommend change this term, such as multi-covariate logistic model. 13. The term MICE refers to the package number, but is also refers to name of the statistical method, so I recommend that the complete name is included (eg. Imputation was performed using the Multivariate imputation by chained equations in the MICE package….). 14. It is suggested to include the version of R software, not only, the version of the mice package. 15. The figure 2 seems not necessary since the authors already include the information in the table and the text. 16. It is suggested to include labels (eg. a,b,c) for subfigures, which can facilitate the citation in the text. Reviewer #2: Thank you for the opportunity to review the manuscript. The authors have developed a clinical prediction model to identify children at risk for revisits with serious illness to the emergency department. Although I am not medically qualified to judge the clinical aspects of the modelling, my overall impression is that the authors should be congratulated for undertaking this important research that will likely have a clinical implication. However, I have few methodological questions/suggestions to the authors, clarification of which might further help the readers of the manuscript. 1. Adequacy of sample size for model development Page 8, lines 13-14: Using the 10 cases: 1 predictor rule of thumb, our cohort provided enough power to evaluate approximately 100 predictor variables. The “rule of thumb” of 10 events for predictor as used by the authors is subject to criticism in many methodological literature. For example, please see the following article: https://pubmed.ncbi.nlm.nih.gov/29966490/ I would also like to direct the authors to the recent methods proposed by Riley et al. that provides a much more robust framework to assess the adequacy of the sample size the authors have used and will allow them to assess the minimum number of “parameters” (not predictors”) can the authors consider for development of a new prognostic model https://pubmed.ncbi.nlm.nih.gov/30357870/ https://www.bmj.com/content/368/bmj.m441 2. Adequacy of sample size for validation Page 8, lines 13-14: “Similarly, each cohort had >100 cases to allow for sufficient power for the validation studies.” Again, If possible, please consider assessing if the validation cohort still meets the new criterion presented in a recent methodological publication: https://onlinelibrary.wiley.com/doi/full/10.1002/sim.8766 I understand that it might be an extreme request at this stage of revision, as this newly suggested rule didn’t exist at the time the authors began their model development. However, it may be worthwhile to flag this up in the discussion.
3. Imputation model Page 8-9: “Missing data for vital signs (table 2) were imputed using a multiple imputation model including a hospital variable, age, available vital signs, triage urgency, presenting problem, and discharge destination” From their statement, it appears that the “outcome” (i.e. serious illness defined as hospital admission or PICU admission or death ED after an unplanned revisit within 7 days of the index visit) from the analysis model is not included in the imputation model. It is known that an imputation model that doesn’t include the outcome will lead to biased estimates. https://pubmed.ncbi.nlm.nih.gov/16980150/ https://pubmed.ncbi.nlm.nih.gov/21225900/ Please clarify if that was the case or not (“a hospital variable” was included and it is not clear if that was the outcome of the target analysis). If outcome was not included, then please consider justifying the reasons for such non-exclusion. 4. Table 3: Performance measures Are these performance measures optimism-corrected? If it was please clearly indicated or perhaps consider referring these measures as Optimism corrected performance measures in the table of the title. 5. Table 4 and S7 Appendix Table Are these coefficients adjusted for optimism/shrinkage? Apologies for asking these if these were corrected for optimism as it was not evident that these methods had been applied from the methods and the results 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 [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 |
A clinical prediction model to identify children at risk for revisits with serious illness to the emergency department: a prospective multicentre observational study PONE-D-20-36121R1 Dear Dr. Nijman, Your manuscript has now been formally accepted for publication in PLoS One. Please see important details concerning the publication process below. Your efforts during the process of revision are acknowledged and I hope you also are pleased with the final result. We appreciate being able to publish your work and look forward to seeing your paper online as soon as possible. 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, José Moreira Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: |
Formally Accepted |
PONE-D-20-36121R1 A clinical prediction model to identify children at risk for revisits with serious illness to the emergency department: a prospective multicentre observational study Dear Dr. Nijman: 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. José Moreira Academic Editor PLOS ONE |
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