-->PONE-D-25-63990-->-->The Prognostic Value of the Early Neutrophil-to-Lymphocyte Ratio for 28-Day Mortality in Sepsis Patients: A Machine Learning-Based Investigation of the MIMIC Database-->-->PLOS One

Dear Dr. Lai,

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 Mar 26 2026 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 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,

Siddharth Gosavi, MBBS, MD Internal Medicine,DNB Internal Medicine

Academic Editor

PLOS One

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1.Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Please include your tables as part of your main manuscript and remove the individual files. Please note that supplementary tables (should remain/ be uploaded) as separate "supporting information" files.

3. Please note that funding information should not appear in any section or other areas of your manuscript. We will only publish funding information present in the Funding Statement section of the online submission form. Please remove any funding-related text from the manuscript.

4. Thank you for stating the following financial disclosure:

“This project was supported by the grants of High-level Medical Team Project in Baoan (No.202405) and Medical and Health Scientific Research Project of Shenzhen Bao 'an District in 2023 (No.2023JD118).”

Please state what role the funders took in the study.  If the funders had no role, please state: "The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript."

If this statement is not correct you must amend it as needed.

Please include this amended Role of Funder statement in your cover letter; we will change the online submission form on your behalf.

5. In the online submission form you indicate that your data is not available for proprietary reasons and have provided a contact point for accessing this data. Please note that your current contact point is a co-author on this manuscript. According to our Data Policy, the contact point must not be an author on the manuscript and must be an institutional contact, ideally not an individual. Please revise your data statement to a non-author institutional point of contact, such as a data access or ethics committee, and send this to us via return email. Please also include contact information for the third party organization, and please include the full citation of where the data can be found.

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 move it to the Methods section and delete it from any other section. Please ensure that your ethics statement is included in your manuscript, as the ethics statement entered into the online submission form will not be published alongside your manuscript.

7. Please include a separate caption for each figure in your manuscript.

8. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information.

9. If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

10. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

This is a well-conducted retrospective study that leverages a large public database and advanced machine learning techniques to investigate the prognostic value of the neutrophil-to-lymphocyte ratio (NLR) in sepsis. The manuscript is generally well-written, and the methodological approach, including the two-stage feature selection process using Boruta and XGBoost, is a strength. However, several important issues need to be addressed to strengthen the manuscript's claims and contextualize its findings within the existing literature. Firstly, the novelty statement should be moderated. While the combination of methods is robust, claiming this as the "first large-scale retrospective study employing machine learning-based feature selection to evaluate the ability of the NLR" may be too absolute. Recent research has increasingly applied ML techniques to similar prognostic questions in sepsis using the MIMIC database. For instance, a 2024 study in Scientific Reports used the MIMIC-IV database to explore the triglyceride-glucose (TyG) index, another accessible biomarker, and its association with mortality in septic patients, employing similar analytical techniques including restricted cubic splines and subgroup analysis (DOI: 10.1038/s41598-024-75050-8). Reframing the contribution to highlight the specific, rigorous feature selection pipeline and the detailed exploration of NLR's biphasic predictive pattern across clinical subgroups would provide a more accurate and compelling narrative.

Several methodological aspects require further clarification. The decision to use only the first NLR value within six hours of ICU admission is pragmatic but represents a significant limitation, as a single static measurement may not capture the dynamic inflammatory state critical in sepsis. The discussion should more thoroughly acknowledge that this "early" snapshot might miss peak values or trends that carry greater prognostic weight. Furthermore, while the use of the missForest package for imputation is appropriate, the manuscript would benefit from a brief validation statement or supplementary table comparing the distribution of key variables before and after imputation to assure readers of the integrity of the imputed dataset. The model performance is commendable, but the discussion would be enriched by a more direct comparison with other contemporary biomarker-based models developed on similar cohorts. For example, studies have examined composite indices like the creatinine-to-albumin ratio (CAR) for sepsis-associated acute kidney injury, finding non-linear associations with mortality, which echoes your findings on NLR's biphasic pattern (DOI: 10.3389/fcimb.2025.1602921). Situating your XGBoost model's performance relative to these emerging indicators would better define its relative clinical utility.

The most substantial limitation is the lack of external validation, which is rightly mentioned but deserves greater emphasis in the discussion. The reliance solely on the MIMIC database, while common, introduces risks related to selection bias and protocol homogeneity from a single healthcare system. The conclusions about NLR's predictive value, particularly its diminished utility in severe organ failure, must be framed as hypothesis-generating and require validation in independent, prospective cohorts. Additionally, please ensure all figures and tables are correctly numbered and embedded in the main text for review, and standardize the formatting of abbreviations throughout (e.g., SAPS II vs. SAPSII). In the results, when stating survivors had better vital signs, please specify which parameters showed significant differences. The statistical methods section should explicitly state which p-values were adjusted using the Benjamini-Hochberg procedure.

The discussion effectively interprets the biphasic nature of the NLR's predictive value. To further strengthen it, consider integrating the concept that the predictive weight of inflammatory markers like NLR may be context-dependent, overshadowed by organ dysfunction in advanced disease. This resonates with research on other composite markers that combine inflammatory and nutritional/renal dimensions, such as the ratio of neutrophil counts to the prognostic nutritional index (NPNR), which has shown J- or V-shaped associations with mortality in septic patients, highlighting the complex interplay between inflammation and host reserve (DOI: 10.3389/fcimb.2025.1603104; DOI: 10.3389/fnut.2025.1602016). Citing such work would help build a more nuanced theoretical framework for why NLR's utility plateaus in critical illness. Finally, the reference list should be formatted consistently according to the journal's style, and including some of the more recent, relevant studies suggested here would update the scholarly context.

In conclusion, this study provides valuable insights into the conditional prognostic value of the NLR in sepsis. By addressing the points above—particularly tempering the novelty claim, elaborating on methodological limitations, and engaging with contemporary literature on sepsis biomarkers—the manuscript can be significantly enhanced. I recommend a minor revision to allow the authors to refine these aspects and produce a more robust and contextualized final paper.

[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

**********

-->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. 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

**********

-->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

**********

-->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 is a well-conducted retrospective study that leverages a large public database and advanced machine learning techniques to investigate the prognostic value of the neutrophil-to-lymphocyte ratio (NLR) in sepsis. The manuscript is generally well-written, and the methodological approach, including the two-stage feature selection process using Boruta and XGBoost, is a strength. However, several important issues need to be addressed to strengthen the manuscript's claims and contextualize its findings within the existing literature. Firstly, the novelty statement should be moderated. While the combination of methods is robust, claiming this as the "first large-scale retrospective study employing machine learning-based feature selection to evaluate the ability of the NLR" may be too absolute. Recent research has increasingly applied ML techniques to similar prognostic questions in sepsis using the MIMIC database. For instance, a 2024 study in Scientific Reports used the MIMIC-IV database to explore the triglyceride-glucose (TyG) index, another accessible biomarker, and its association with mortality in septic patients, employing similar analytical techniques including restricted cubic splines and subgroup analysis (DOI: 10.1038/s41598-024-75050-8). Reframing the contribution to highlight the specific, rigorous feature selection pipeline and the detailed exploration of NLR's biphasic predictive pattern across clinical subgroups would provide a more accurate and compelling narrative.

Several methodological aspects require further clarification. The decision to use only the first NLR value within six hours of ICU admission is pragmatic but represents a significant limitation, as a single static measurement may not capture the dynamic inflammatory state critical in sepsis. The discussion should more thoroughly acknowledge that this "early" snapshot might miss peak values or trends that carry greater prognostic weight. Furthermore, while the use of the missForest package for imputation is appropriate, the manuscript would benefit from a brief validation statement or supplementary table comparing the distribution of key variables before and after imputation to assure readers of the integrity of the imputed dataset. The model performance is commendable, but the discussion would be enriched by a more direct comparison with other contemporary biomarker-based models developed on similar cohorts. For example, studies have examined composite indices like the creatinine-to-albumin ratio (CAR) for sepsis-associated acute kidney injury, finding non-linear associations with mortality, which echoes your findings on NLR's biphasic pattern (DOI: 10.3389/fcimb.2025.1602921). Situating your XGBoost model's performance relative to these emerging indicators would better define its relative clinical utility.

The most substantial limitation is the lack of external validation, which is rightly mentioned but deserves greater emphasis in the discussion. The reliance solely on the MIMIC database, while common, introduces risks related to selection bias and protocol homogeneity from a single healthcare system. The conclusions about NLR's predictive value, particularly its diminished utility in severe organ failure, must be framed as hypothesis-generating and require validation in independent, prospective cohorts. Additionally, please ensure all figures and tables are correctly numbered and embedded in the main text for review, and standardize the formatting of abbreviations throughout (e.g., SAPS II vs. SAPSII). In the results, when stating survivors had better vital signs, please specify which parameters showed significant differences. The statistical methods section should explicitly state which p-values were adjusted using the Benjamini-Hochberg procedure.

The discussion effectively interprets the biphasic nature of the NLR's predictive value. To further strengthen it, consider integrating the concept that the predictive weight of inflammatory markers like NLR may be context-dependent, overshadowed by organ dysfunction in advanced disease. This resonates with research on other composite markers that combine inflammatory and nutritional/renal dimensions, such as the ratio of neutrophil counts to the prognostic nutritional index (NPNR), which has shown J- or V-shaped associations with mortality in septic patients, highlighting the complex interplay between inflammation and host reserve (DOI: 10.3389/fcimb.2025.1603104; DOI: 10.3389/fnut.2025.1602016). Citing such work would help build a more nuanced theoretical framework for why NLR's utility plateaus in critical illness. Finally, the reference list should be formatted consistently according to the journal's style, and including some of the more recent, relevant studies suggested here would update the scholarly context.

In conclusion, this study provides valuable insights into the conditional prognostic value of the NLR in sepsis. By addressing the points above—particularly tempering the novelty claim, elaborating on methodological limitations, and engaging with contemporary literature on sepsis biomarkers—the manuscript can be significantly enhanced. I recommend a minor revision to allow the authors to refine these aspects and produce a more robust and contextualized final paper.

**********

-->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

**********

[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.]

To ensure your figures meet our technical requirements, please review our figure guidelines: https://journals.plos.org/plosone/s/figures

You may also use PLOS’s free figure tool, NAAS, to help you prepare publication quality figures: https://journals.plos.org/plosone/s/figures#loc-tools-for-figure-preparation.

NAAS will assess whether your figures meet our technical requirements by comparing each figure against our figure specifications.

-->

The Prognostic Value of the Early Neutrophil-to-Lymphocyte Ratio for 28-Day Mortality in Sepsis Patients: A Machine Learning-Based Investigation of the MIMIC Database

PONE-D-25-63990R1

Dear Dr. Lai,

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 will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. For questions related to billing, please contact billing support.

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,

Chiara Lazzeri

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 #1: All comments have been addressed

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: Partly

**********

-->3. Has the statistical analysis been performed appropriately and rigorously? -->

Reviewer #1: Yes

Reviewer #2: I Don't Know

**********

-->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: The authors have thoroughly and satisfactorily addressed all of the concerns raised in the previous review round. The revised manuscript is substantially improved in terms of methodological transparency, contextualization within the existing literature, and appropriate framing of conclusions. The study makes a valuable contribution to the field of sepsis prognostication by applying a rigorous machine learning pipeline to re-evaluate the prognostic role of the neutrophil-to-lymphocyte ratio (NLR).

Minor Suggestions:

Consider briefly mentioning in the Limitations that the optimal NLR cutoffs (7.3 and 27.3) were derived from a single dataset and may require calibration before clinical use in other settings.

The abbreviation "SAPS II" is correct, but in Table 2 and a few places, it appears as "SAPS II" – ensure consistency (looks fine; just a quick double-check).

In the Discussion, the phrase "infection point" appears twice (e.g., "first infection point"). It should be "inflection point". Please correct.

These do not affect the overall quality or validity of the work. The manuscript is well revised and suitable for publication. I recommend acceptance without further revision.

Reviewer #2: Overall Assessment: Minor Revision

Summary: This study uses MIMIC-IV database and machine learning (XGBoost) to evaluate the prognostic value of early NLR for 28-day mortality in sepsis patients (N=4,376). The authors found that NLR was independently associated with mortality (OR=1.16) and identified a biphasic predictive pattern where NLR's utility was pronounced in patients with SOFA≤4 but lost significance in SOFA≥9.

Strengths:

1.Large sample size with rigorous feature selection (Boruta + XGBoost)

2.Novel finding of biphasic, phenotype-specific predictive pattern

3.Thorough discussion of limitations

Weaknesses & Suggestions:

1.Only the first NLR measurement was used; dynamic changes may carry greater prognostic weight. Please discuss.

2.Tables 1 and 2 are overly dense. Consider moving less critical variables to supplementary materials.

3.In the abstract, PO2 value (221.07 ± 115.04 mmHg) appears unusually high for arterial blood. Please verify.

4.While the ML approach is robust, the clinical utility of NLR alone remains limited (SHAP rank #14). This should be more explicitly stated in the conclusion.

Recommendation: Minor revision.

**********

-->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: No

**********