Dear Dr. Cobler-Lichter,

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Laila Cure

Academic Editor

PLOS ONE

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I have read the journal's policy and the authors of this manuscript have the following competing interests:

JPM: research support and travel/lodging support from CSL Behring and Takeda Pharmaceuticals; grant support from the U.S. Department of Defense/AMRMC (W81XWH-15-9-0001); speaking honoraria from Cerus Corporation and Memorial Healthcare System; scientific advisory board for Haemonetics, Inc.; editorial board for Trauma Surgery and Acute Care Open and Journal of Surgical Research.

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Additional Editor Comments:

In addition to the comments from the reviewers, please address the following that will help clarify the value of your machine learning exercise using data from the trauma quality improvement program. I think this manuscript can be improved so that the value of the research is clear to readers with and without trauma decision making background.

1. The introduction should describe how the MT decision is currently made and what criteria emergency physicians use to order MT or UMT. Is this decision made upon arrival to happen 4 hours later? Or once it is decided then it happens almost instantaneously? This will be useful in evaluating your model with respect to the current protocols.

2. You are only including patients that received MT and UMT in your analysis. You are excluding patients that did not receive it. So, your models have no information on what happens to a patient that should have received MT but didn’t (we assume they do not survive?). This also implies that the decision or prediction is related patients who would have already received the MT, so how would you save blood resources if your assumption is that these patients had already received transfusion? Otherwise, how would you know they will receive MT with data upon arrival? This ties to the lack of clarity on the decision implied by the prediction (this should be clear so that we know which predictors and the timing of their measurement are appropriate to include in the model).

3. At the end of your introduction, you are stating that predictions can be “improved further with the incorporation of more clinical information that becomes available as the resuscitation progresses.”, which again, sounds like what physicians already do. However, emergency physicians can explain the logic of their decision making. How would these models (or their insight) fit into the workflow of an emergency care team during resuscitation?

4. When I read the description of the research in the introduction and the methods, it appears that your model is not really for prediction, but for description of the mortality associated with patients already receiving MT and UMT (even though you are using predictive modeling techniques, these can be used for descriptive analyses). You are identifying factors that influence mortality in patients that already received these types of transfusion, most likely following some protocol. If this is the case, then please revise the manuscript to reflect this analysis and avoid the prediction/decision-support confusion.

5. In the model evaluation section, the first sentence should be revised for grammar.

6. In the methods and results section, we need model interpretation for the best performing model in terms of current transfusion protocols. Is there something that your models are suggesting that emergency physicians are not already supposed to look at?

7. How do your models compare to the protocols in practice? Can you tell if the protocol was not followed with the patients based on the dataset?

8. I echo the comments from the reviewers related to the need for being more specific in how the decision-makers would use this information to support their decision. The way the manuscript is written, the decision appears to be whether to withhold transfusion. Please specify who you see as the decision maker. How do you envision this model being used? You mentioned embedded in the medical record (again, your methods imply that this information would appear after the MT or UMT has been implemented).

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: This study, utilizing the American College of Surgeons Trauma Quality Improvement Program (TQIP) database, developed a predictive model with high accuracy for 6-hour mortality in trauma patients requiring massive transfusion (MT) or ultra-massive transfusion (UMT). While demonstrating clinical value for assessing post-transfusion risks, the manuscript establishes a prediction model without explicitly providing the predictive formula.

There are some Comments:

1. The authors emphasize the model’s utility in optimizing blood resource allocation and clinical decision-making. However, we wish to highlight a potential ethical consideration: If the model suggests limited therapeutic benefit of transfusion for certain trauma patients, could the authors please discuss how clinicians should balance resource allocation with individualized care? Might withholding transfusion inadvertently exacerbate mortality risks?

2. Please clarify the reasons for selecting the 4-hour and 6-hour timepoints. e.g., from admission time, cumulative transfusion duration, or continuous infusion time and provide a detailed methodological justification for these choices.

3. In the Methods section: The exclusion of variables based on high missing rates may introduce selection bias. Please perform sensitivity analyses to evaluate how varying missingness thresholds impact model robustness.

4. The study does not address potential collinearity among variables, which could affect model stability or parameter interpretation. We suggest adding variance inflation factor (VIF) analyses or similar methods to validate variable independence.

5. In the Results section, weight is identified as a significant predictor. Could the authors please address the practical challenges of obtaining accurate weight measurements in emergency trauma settings?

6. In the Discussion section, please add the mechanistic links between key predictors and transfusion-related outcomes.

Reviewer #2: 1.Thank you for giving me the opportunity to review that manuscript. The given article tackles the highly relevant topic of trauma triage decision-making. The results are certainly relevant to practice and can lead to a change in current practice

The main text of the article is technically complex, especially for readers from the area of clinical care. Therefore, we propose that in the chapter on material and methods, as well as in the secondary objectives, it is structured, as far as possible, in an approximate way based on the following scheme, based on domains that is recommended for Health Technology Assessment, based on domains that range from the description of the health problem to monitoring and post-implementation:

Health Problem and Target Population

A clear description of the health problem and target population is required for intervention.

Definition of the health problem, including prevalence and incidence.

Information on the target population, such as mean age and risk factors.

Details on the standard therapeutic approach.

Technology Description

A detailed description of the technology and its context should be provided.

Main characteristics of the technology and the needs it covers.

Intent to use, including intended and unintended use.

Technical evaluation and validation of the technology.

Technical Aspects of Technology

The technical evaluation focuses on the effectiveness and performance of the technology.

Training data used and their representativeness.

Model evaluation metrics and their clinical justification.

Robustness of the model and its transferability capacity.

Traceability and records management to ensure transparency.

Technology Security

The risks associated with the use of technology are identified and assessed.

Clinical safety and risk assessment for patients and professionals.

Technical security, including privacy and information quality.

Clinical Efficacy and Effectiveness

The clinical benefits of the technology under controlled and uncontrolled conditions are evaluated.

Evidence of effectiveness to support expected benefits.

Analysis of outcome variables according to the intended purpose.

Economic Aspects of Technology

The cost and cost-effectiveness of the technology are analyzed.

Comparison of acquisition, maintenance and use costs.

Evaluation of efficiency and use of resources compared to alternatives.

Of course, it is not necessary to complete all the domains, as some of them would be the subject of one or more subsequent studies.

2.Finally, regarding the discussion and conclusions, we would like to add the following comments that could honestly be the subject of future publications and continuation of such extraordinary work:

Artificial Intelligence (AI) is presented as a tool with enormous potential in the field of medical emergency management due to its characteristics. AI is part of the field of computer science that develops both systems and machines capable of performing tasks that require human intelligence, including Generative Artificial Intelligence and especially LLMs (Large Language Models), that is, that require actions such as learning, reasoning, decision-making, pattern recognition, language comprehension and problem solving. These systems are not governed by strict rules, but are able to learn from data and experiences to improve their performance.

All these applications, such as the one discussed in this study, illustrate how AI, in addition to complementing human capabilities, contributes to improving the effectiveness and efficiency of current management systems in Critical Care. However, the perception of the usefulness and usability of AI in the management of critical medical situations is a fundamental aspect to understand its effectiveness and adoption by responders in emergency situations. Although AI offers great potential, its successful implementation depends largely on how professionals responsible for critical patient care perceive its value and ease of use. In this sense, it is essential to understand in the first instance how users value the capabilities of AI in this context and thus allow the optimization of these systems and guarantee their accessibility and effectiveness.

To this end, three guidelines were proposed on the assessment of usability in this type of tool: visibility, cognitive load and trust. These guidelines were evaluated for the development of the Health Information Technology Usability Assessment Scale (Health-ITUES), a validated instrument that serves as a guide for evaluating technologies applied to health and allows harmonization between different studies. References:

Tan ML, Prasanna R, Stock K, Doyle EEH, Leonard G, Johnston D. Understanding end-users’ perspectives: Towards developing usability guidelines for disaster apps. Progress in Disaster Science. 2020 Oct;7:100118.

Schnall R, Cho H, Liu J. Health Information Technology Usability Evaluation Scale (Health-ITUES) for Usability Assessment of Mobile Health Technology: Validation Study. JMIR Mhealth Uhealth. 2018 Jan 5; 6(1):e4.

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Reviewer #1: Yes:  Muhan Lü

Reviewer #2: No

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Dear Dr. Cobler-Lichter,

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 Nov 12 2025 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.

• A rebuttal 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 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,

Laila Cure

Academic Editor

PLOS ONE

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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. 

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:

Page 4 line 87 replace "achieved" with "achieve".

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #2: Yes

**********

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

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #2: Yes

**********

Reviewer #2: (No Response)

**********

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 #2: No

**********

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MIASurviveMTP: Machine learning for immediate assessment and survival prediction after Massive Transfusion Protocol

PONE-D-25-20654R2

Dear Dr. Cobler-Lichter,

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Kind regards,

Laila Cure

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments: